Research Paper

Student’s Name

Institution

Date

Statistics Research Paper

Research Question: how do you monitor teens’ internet usage?

Dataset: General Social Questionnaire, 2017

Dependent Variable: PMONWEB – how does R’s family income compare to average? (ordinal)

Independent Variable 1:r(Suicide)

Independent Variable 2: Internet usage (categorical)

The internet has changed the way people interact and it affects the lives of majority of teenagers. It is therefore, important to ensure that teenagers’ internet usage is efficient. Monitoring teen’s internet usage is an important for reducing the spread of PMONWEB. Studies have indicated that teens learn a lot of bad behavior through internet. Some of them become suicidal due to influence from the internet. The different between teens whose internet usage is being monitored by their parents is evident because internet influences behavior of many kids. Survey has also indicated that teens suicidal and other negative behavior have increased of late and it is because of the things most kids learn from the internet. The data used for this study was obtained using quantitative research questions and analyzed using descriptive statistic to test the two variables. The variable tested using quantitative research method was whether controlling teens’ internet usage is directly related to PMONWEB. It is analyses the relationship between the PMONWEB and the behavior of teens based on the number of times teens are access and uses internet.

Descriptive statistic and variable coding

The variable of the study are categorized and therefore, each variable will be studied based on its relationship with the topic. The table will be 1 and 3 and to ensure that the variables are properly presented in the stable. The variable suicidal was created to reflect the impact of using internet a lot by teenagers. In order to determine the effect of PMONWEB the data was analyzed based on the research questions and the derived variables. However, the three variables used to complete this study are PMONWEB, suicidal, and internet usage among the teens. It is also important to point that the data analysis was done using descriptive statistic using software excel and for advance analysis the SPSS was used. The SD, mode, mean and frequency of the data was also obtained using the same data.

Relationship between variables

The variable of the study was categorical and therefore, the t-test was used to study the relationship o the variables and their significant to the study. Table 1 indicates the relationship between teens’ internet usage and increased suicide rate in the society. It is therefore, evident that internet usage among teenager is directly related to suicide and therefore, the more internet teenager is allowed to access the higher the chances of suicide. The hypothesis was also tested to determine the relationship between internet usage and the topic. In the process the hypothesis was null since variables are almost unrelated. To determine the relationship between variables, the variance was also established in order to calculate the variables of the study. It is also important to point that the study was drawn based the data.

However, the mean standard deviation (N Mean Std. Deviation) of the study was 3370 6.882 55.245. It is also indicated that the mean for parents usually monitor teens interne usage was 861 25.5. It means that majority of participants monitor their teens when using the internet. The mean for usually watching teens was 736 21.8 and rarely monitor teens is 253 7.5, never watched 229 6.8 and the participants who do not have internet were 667 19.8777. However, the mean of parents who do not know 12 0.4888, which means that they participated in the study but do not know where it teens should be monitored when using the internet.

The study indicates that majority of participants monitor their teens internet usages. And therefore, they ensure that what their teen’s access over the internet is properly reviewed and acceptable. It is also shows that few people refused to take part on the study and the number is very insignificant. However, there are also numbers of people who never monitor their teens and rarely monitor their teens. It is also important to state that teens that have never monitored when using the internet are more likely to be suicidal and therefore, there is direct relationship between monitoring teen’s internet usage and suicide.

The 54% of participants indicated that suicide is directly related to internet usage among teenagers. It is also established that teenagers who are not monitored are more likely to commit suicide compared to teenagers who are being monitored by their parents. The p-value is 0.000, which below the significant level and therefore, it indicates that there are high chances that there is a direct relationship between monitoring teens and the number of suicide in the suicide. It is therefore, important to ensure that teens are efficiently monitored to limit the number of suicide in the society. It is also indicated that children who have never been monitored are more likely to access sides which are bad and therefore, they are more psychologically affected compared to the other teens that are being monitored.

Sampling



Sampling

Jason

School or Institution Name (University at Place or Town, State)

Main Post:

The shopping store has every single item for daily use which ranges from food to gardening items such as pots for plants, which are rarely bought. Working as the Store Manager in one of the franchises for one of the most popular stores in the U.S. has broadened my experience related to public dealing by looking after almost 300 customers on a daily basis. I was asked by the head of the franchise to gather data which tells us the customer's preference related to the most popular item of the store. With our store dealing with shopping items of every type whether it is food or other shopping items of daily use, it was difficult for us to conduct a survey which would allow us to get a single item from almost 5,000 items we sold. So, we decided to be generic and use simple random sampling, asking the customers as they were about to check out from the store about their favorite item from the store. And note the name of that item with their name so we could choose a sample without replacement. Sampling without replacement allows us to get more than one opinion about a particular subject ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"b9IaUAYD","properties":{"formattedCitation":"(Bavarov & Belyaev, 1961)","plainCitation":"(Bavarov & Belyaev, 1961)","noteIndex":0},"citationItems":[{"id":482,"uris":["http://zotero.org/users/local/tjMYMZX7/items/T3R4B35T"],"uri":["http://zotero.org/users/local/tjMYMZX7/items/T3R4B35T"],"itemData":{"id":482,"type":"article-journal","title":"On Testing the Randomness of Sampling without Replacement","container-title":"Theory of Probability and its Applications; Philadelphia","page":"4","volume":"6","issue":"4","source":"ProQuest","abstract":"The limit distribution of a $\\chi ^2 $-test statistic for the case of sampling without replacement is studied. The results obtained are used for checking the randomness of sampling.","DOI":"http://dx.doi.org/10.1137/1106055","ISSN":"0040585X","language":"English","author":[{"family":"Bavarov","given":"E. A."},{"family":"Belyaev","given":"P. F."}],"issued":{"date-parts":[["1961"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Bavarov & Belyaev, 1961). The sample set would be the first 500 customers checking out from the store, following the idea of systematic sampling. The question would be general, as to what is the best one item sold by the store according to them. The sample of the survey, though it would focus on teenagers and adults, would also consider the opinion of children traveling with adults. The view of the children, if any, would be other than the 500 adults and teenagers who were made part of the survey.

Follow Up Post# 1:

The best way to take a survey would be to survey with sampling without replacement and choosing the cluster sample. With population comprising different age group visiting the store, what can be done is to manage the survey in such a way that we divide the community based on the age group, i.e., 1-12 years old as kids, 13-19 years old as teenagers and from 20 to onwards years old as adults. Then we number each age group and choose 500 adults and teenagers from these clusters who approach the counter for checking out first. Kids are an exception as they are not very common to be found at the store, but their opinion is also taken into consideration.

Follow Up Post# 2:

Another way of surveying our case would be to take a stratified sample. This will be the worst form of the survey if all the products on the store are divided into different groups, i.e. carrot, tomatoes, potatoes, etc. are put in the category of vegetables while frozen fish, frozen French fries are placed in the category of frozen food items. This would make the survey very complicated as the users would have to choose from a long list of shopping items which would have to be brought to them in the form of a paper on which the list would be printed. This would make the survey distasteful for the customers by having to choose from a long list of items, which would take a lot of their time. The successful and effective polls tend to be short and concise, making a stratified sample in this scenario the worst form of a survey.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Bavarov, E. A., & Belyaev, P. F. (1961). On Testing the Randomness of Sampling without Replacement. Theory of Probability and Its Applications; Philadelphia, 6(4), 4. http://dx.doi.org/10.1137/1106055

Simple Inferential Statistics

Hypothesis testing and estimation for proportions

[Name of the Writer]

[Name of the Institution]

Answer 1:

Hypothesis:

H0:  most people in B.C. does not think their local police are doing a good or very good job at treating people fairly

H1:  most people in B.C. think their local police are doing a good or very good job at treating people fairly

Significance level:

The significance level for this study is set at 0.05. This shows that we have a 5% chance of rejecting a null hypothesis when it is really true. This is also refered to as type 1 error. The type 2 error is the failure to reject the null hypothesis when it is really false. In this example the probability of committing type2 error is accounted for by selecting a reasonably large sample size for the analysis.

Test Statistic:

Z = (P’- p)/ (p.q)/n

Where

P’ = 1916/2400

P= 0.5

q = 1-p

= 0.5

Calculation:

Putting values in the above formula

Z = (0.798-0.5)/ (0.5*0.5)/2400

= 0.298/0.0102

= 29.21

Tabulated value of Z:

The tabulated value of Z is 1.645. This is because the test is one tailed. The tail of the test is decided by the way in which hypothesis are developed. If the hypothesis are developed in terms of = for H0 and ≠ for H1 then it will be considered a two tailed test. However in the case under consideration, the hypothesis are stated in terms of more than and less than. This way of stating the hypothesis leads us to one tailed test. In calculation, there is slight difference between the tabulated values of Z when we work with two tailed or one tailed tests.

Conclusion:

The calculated value of Z is greater than the tabulated value so we reject H0. This means that we could not gather enough evidence to accept the null hypothesis. It means that  most people in B.C. think their local police are doing a good or very good job at treating people fairly The hypothesis are developed in terms of the population proportion i.e. P. Then we have used the sample data for calculation purposes. In this analysis, we have stated the hypothesis theoretically. If we convert it to numeric form, we will have P≤ 0.5 and P> 0.5. It is easier to state the hypothesis in numerical terms and it is also easier to comprehend for the readers.

Part 2

Confidence coefficient for proportions:

A confidence interval is used to show that the value of a certain parameter lies within the specified limits. A parameter is a value calculated form a population. The figures used for the calculations undertaken are the statistics i.e. taken from the sample. I will choose a 99% confidence interval because the higher confidence interval would mean lesser error. The error term is 100-CI which comes to 1 % in this case. The other name of this figure is the level of significance.

Calculating the proportion:

This method is used when we need to know how many times does a certain attribute occur within a given set of data. In this case the attribute is the response of people with good or very good option. We have proportioned those people who think that the police is doing a good job at ensuring the safety of the people. The desired criteria in the given excel sheet shows that there are 2365 occurrences of good or very good. Thus the proportion would be 2365/2400 = 0.98. This is refered to as p^. This is also named as sample proportion. The symbol P is used to show the term population proportion.

Margin of error:

The margin of error is calculated as Z* P’(1-P’)

= 2.58* (2365/2400)(1-2365/2400)

= 2.58* (0.98)(0.02).

= 0.3612

Formula for confidence interval:

P’-margin of error & P’+ margin of error

= 0.98-0.3612, 0.98+0.3612

= 0.6188, 1.3412

Part 3:

In the first part we have enough evidence to reject H0. We could not have enough evidence to accept the null hypothesis. Thus we can say that the police is doing a good job in treating the people fairly. The statistical analysis done shows a clear tilt of the people towards the performance of the police department. We have accounted for both types of errors. Type 1 and type 2 errors.

In the second part we are 99% sure that a given value will lie between the given interval. There is only 1% chance that any given data point containing good or very good criterion will fall outside this limit. Also the limits are not very wide which shows that data are condensed. That is beacause we have used 99 % confidence interval for our analysis. If we represent it on a diagram, we will have a large majority of the values falling within a specified limit. We have estimated the limits within which we are 99% confident that the population proportion will fall.

STA 322

Basic Statistic

Student’s Name

Instructor

Date

Analysis of Statement

Mean is the average of the total data number of data. It is obtained by adding the data and then divides by the number of data. For instance the mean of clients’ age, wait time and satisfactory is obtained by adding the total and then divides by the number of participants or clients. The mean of clients’ age is 34.65, wait time is 59.05 and satisfy scale is 2.8. However, the median is described as the number, which appears in the middle of the list of numbers. In data set the median in the list of clients’ age is 35, wait time is 19 and satisfactory is 2.5. The mode of the data is a value that appears more often or frequent. It is the number or the value, which is more likely to be sampled. The mode of clients’ age is 50, wait time is 60 and the satisfactory is 1.

The standard deviation is the quantity, which expressed how a member of group is different from the mean value for the group. It measures how a number is spread out in the data set. Standard deviation is measured as the square root of variation. The standard deviation of clients’ age 19.59128431, wait time standard deviation is 132.0524118 and the satisfactory standard deviation is 1.609184167.

Figure 1: Column Chart for all 3 data sets

The graph indicates that Kelly has the highest wait time of 600 and satisfactory level of less than 100 and wait time. However, Leah and Stephen have the lowest level of satisfactory, wait time and client age. The data therefore, shows that the clients experience different age, satisfactory level and wait time. The chart therefore, reveals a unique trend in customers’ satisfactory level and the wait time. It means that wait time affect the customer satisfactory level. And based on the graph most clients who wait for a longer time are not satisfied with the services being offered by the company.

Range

The range is the area of variation which exists between the upper and lower limits on a certain scale. The range of clients’ age is 65; wait time is 595 and satisfactory is 4. The descriptive statistics below therefore, shows the range, mean, median, mode and standard deviation.

Client Age

Wait Time

Satisfactory

Mean

34.65

Mean

59.05

Mean

2.8

Standard Error

4.380744349

Standard Error

29.52781695

Standard Error

0.359824519

Median

35

Median

19

Median

2.5

Mode

50

Mode

60

Mode

1

Standard Deviation

19.59128431

Standard Deviation

132.0524118

Standard Deviation

1.609184167

Sample Variance

383.8184211

Sample Variance

17437.83947

Sample Variance

2.589473684

Kurtosis

-0.91665052

Kurtosis

16.8427622

Kurtosis

-1.536404568

Skewness

-0.18864775

Skewness

4.013436705

Skewness

0.276192405

Range

65

Range

595

Range

4

Minimum

2

Minimum

5

Minimum

1

Maximum

67

Maximum

600

Maximum

5

Sum

693

Sum

1181

Sum

56

Count

20

Count

20

Count

20

Figure 2: Descriptive Statistic

The mid range is the mean of the smallest and the largest values in the group of data. The mid range is therefore, 2.8 +59.05= 30.92. The variation of the data set for client age is 383.818421, wait time is 17437.8395 and satisfactory level is 2.58947368.

Anova: Single Factor

SUMMARY

Groups

Count

Sum

Average

Variance

Client age

20

693

34.65

383.818421

Wait time

20

1181

59.05

17437.8395

Satisfactory

20

56

2.8

2.58947368

ANOVA

Source of Variation

SS

df

MS

F

P-value

F crit

Between Groups

31825.63

2

15912.82

2.678287

0.07732

3.158843

Within Groups

338660.7

57

5941.416

Total

370486.3

59

Figure 3: Anova: Single Factor.

Coefficient of variation

It is the ratio of standard deviation to the mean. Therefore, the standard deviation to the mean of clients’ age is 19.59128431. The coefficient of variation is zero (0).

Analysis of the data

It is evident that the wait time is correlated to the satisfactory level of the clients. The data shows that most clients who wait for a longer time have lower satisfactory level. The data indicates that clients who wait for longer time have satisfactory level of 1 and clients who wait for a short time have high satisfactory level. This means that it is important for the company to provide efficient services to customers. Offering services efficiently would help in avoiding long waiting and therefore, most clients would be satisfied with services being provided by the company. The data also indicates that the age of clients is directly related to the wait time and the satisfactory level of clients. The data set indicates that older clients have low satisfactory level and therefore, it means that majority of them are unsatisfied with the service being offered.

In conclusion, it would be important for the company to improve services to reduce the wait time and this would help in satisfactory level of customers. It is also important for the company to look into the age factor when dealing with clients. The data shows that majority of clients who are unsatisfied with services being provided are senior citizens and this can be avoided by ensuring that older clients are served first. The company should develop a system which monitors service delivery because this affect the performance of the company based on the data.

Standard Normal Distribution

The probability of less than 2.02 is 0.0217 and the probability of 1.45 is 0.0735 and the probability of 1.8 is 0.7764 and greater than 2.08 are 0.6826.

Statistical Concepts

Your Name

Instructor Name

Course Number

Date

Essay: Statistical Concepts

Engineers analyze and solve numerous problems involving statistical models and data, on a daily basis which proves the great value of Statistics in the domain of Engineering.

Practical Uses

Statistics has multiple and wide ranges of applications in the fields of Science and Engineering. The sources explained below supports their usefulness to solve complex engineering problems, by particularly connecting the discussion to some common measurements, like measurement of the volume of three-dimensional shapes.

Measurements of Vibration Levels

These measurements provide the students with an opportunity to explore the numerous applications of statistics and mathematics within the industry of electrical and mechanical engineering, as the vibration levels measured are of Steam Turbines. This process requires the use of different statistical models for setting an alarm to detect any diversions in the vibration level from the optimal/normal value. The purpose of this alarm is to avoid any kind of failure and loss in revenue, by immediately indicating the problem. This allows corrective action to be taken, as soon as the problem arises. However, this particular activity demands the calculation of the probability and the total number of false alarms. The students can successfully accomplish this task by:

understanding the basic concepts of mathematical modeling

understanding numerous functions and their graphs mathematically

understanding the use of probability as a measurement of possibility

defining some basic engineering problems in the mathematical context

Recording Engineers' Data

This process requires students to use the application of statistics and mathematics to record the total volume of registered engineers. The students will use different graphs and Statistical calculations to describe the variations and differences in the total number of memberships, over time. Following tasks need to be accomplished by the students to complete this activity:

Calculate the mean and median of the provided set of data

Use different software to plot graphs

Statistical Write Up

Statistical Analysis

[Enter name of Student here]

[Enter the name of institution here]

The second example will include implementation of multiple regression analysis techniques to test a hypothesis in a cross-section study. Mediation means to what extent a variable interferes in a relationship between two variables. In other words, does a variable facilitates or hinders the relationship between any two variables. The current scenario has three variables namely cognitive reappraisal, attachment anxiety and social anxiety. The mediating variable is cognitive reappraisal while the other two variables are attachment anxiety and social anxiety.

Social anxiety is defined as a situation that comes out as a result of some social situation. The major reason for this situation is the fear that some person will evaluate others in some negative ways. Literature suggests that people who experience social anxiety also experience attachment anxiety. This is a situation in which a person thinks negatively of himself and tries to get closer to others. Both these variables are associated with a lower level of cognitive reappraisal. This concept is defined as implementing the appropriate emotion management strategies to minimize the tension by changing the effect of factors generating emotions. Following will be the hypothesis for this study.

There is no impact of cognitive reappraisal on the relationship between attachment anxiety and social anxiety.

In order to test the above hypothesis, a combination of effect size and null hypothesis significance testing. The effect size is a technique to show the strength of relationship between two variables on a numeric scale. In our example, we will take the difference between values of social and attachment anxiety with and without cognitive reappraisal and the resulting figure will be the effect size.

Participants (Sample)

The sample consisted of a total of 253 participants out of which 47 were males and 202 were females and 4 people did not show their gender. The ages of participants were distributed from 18 to 74 years. These participants were selected using the various face book groups and online platform of the University of New England. The psychology students who participated in this study were rewarded with a course credit.

Measures

The social anxiety was measured by using the interpersonal situations discomfort scale which is used to measure the discomfort level experienced by the people under various situations CITATION RVa99 \l 1033 (Dam-Baggen & Kraaimaat, 1999). This scale includes 35 items which show 35 specific social situations. The responses included from 1 none to 5 always. A higher score of a person means that he or she is at a higher level of discomfort under these specific situations.

Attachment anxiety was measured by using a mean score on the attachment anxiety subscale of the experience in close relationships CITATION RCh00 \l 1033 (Fraley, Waller, & A.Brennan, 2000). There is a seven-point scale from 1 strongly disagree to 7 strongly agree regarding their feelings in close relationships with higher scores showing that the person is more anxious about the attachment. This subscale comprised of 18 items.

Cognitive reappraisal was assessed by using the Cognitive Reappraisal subscale of the Emotion regulation questionnaire. There were six items on the scale which were judged on a seven-point likert scale from 1 strongly disagree to 7 strongly agree. Higher average was shown as a higher cognitive reappraisal.

Statistical Analysis

Analyses

There have been a number of statistical analyses run in this project using the SPSS software. The major statistical concept used is the regression analysis between the various variables. The histograms have been presented to check whether the data gathered in various variables is normally distributed or not. Another important consideration in this section was the presence of multicollinearity among the independent variables of a model. This means that the relationships between the independent variables will affect the relationship between independent and dependent variable. There have been considerable diagnostics applied to the data so that this multicollinearity issue can be resolved.

Results

The first variable considered is the attachment anxiety which is shown to be right or positively skewed in the histogram. The variable cognitive reappraisal has been found to be negatively skewed in the relevant histogram. The variable social anxiety is found to be approximately normally distributed by the histogram.

The first regression model studies the relationship by putting social anxiety as the dependent variable while attachment anxiety and cognitive reappraisal have been used as the independent variables. The overall model is weak as shown by the value of r squared which shows that only 17.6 percent variation in the dependent variable has been shown by the independent variables. The value of Durbin-Watson test in the last column shows the positive auto correlation between the variables. In the coefficients table, we see that attachment anxiety has a stronger impact on the social anxiety than cognitive reappraisal shown by a higher coefficient.

When the relationship between social anxiety and attachment anxiety has been studied without mediation, it is observed that the independent variable defines 32.6 percent variation in the dependent variable.

There is a negative relationship found between attachment anxiety as an independent variable and cognitive reappraisal as a dependent variable. This model is also weak as the explained variation is 10.2 %. In order to make a better model, some more explanatory variables should be added to the model.

When social anxiety has been kept as dependent variable while attachment anxiety and cognitive reappraisal have been considered as independent variables, there is a negative relationship between social anxiety and cognitive reappraisal. There is a stronger relationship between social anxiety and cognitive reappraisal as compared to social anxiety and attachment anxiety.

The mediation effect has been analyzed by using the boot strap process. This process is used to obtain robust estimates of standard errors and confidence intervals for values such as mean, median and other similar values. There have been negative relationships found between cognitive reappraisal and attachment anxiety and social anxiety and cognitive reappraisal. There has been a positive total relationship between social anxiety and attachment anxiety. The indirect effects have been analyzed by the bootstrap technique. This technique makes a large number of samples to test the indirect effects of mediation. There have been 5000 such samples made in this example. The direct effects are significant at 0.05 level of significance as shown by the p value in the regression model output. If we see the indirect effects of mediation, there are three different effects to be studied, firstly the cognitive reappraisal effect on the social anxiety, the effect variable showing the indirect effect is the difference between the total and direct effects. The partially standardized indirect effects show that the effects have increased. The completely standardized indirect effects show that the effect has increased further.

This analysis shows the relationships between mediator and both the variables under consideration. The last model shows the mediation effect in the whole model. As shown by the higher effects, we can conclude that there is a significant mediation effect of mediator on the relationship of dependent and independent variable. Thus, we will reject the null hypothesis that there is no mediating effect of cognitive reappraisal on the relationship between social and attachment anxiety. The regression standardized residuals have a normal distribution as shown by the histogram showing social anxiety. This also shows that there is a significant impact of mediator variable on the original relationship of variables.

References

BIBLIOGRAPHY Dam-Baggen, R., & Kraaimaat, F. (1999). Assessing social anxiety: The inventory of Interpersonal Situations. European Journal of Psychological Assessment, 25-38.

Fraley, R., Waller, G., & A.Brennan, K. (2000). An item response theory analysis of self report measures of adult attachment. Journal of Personality and Social Psychology, 350-365.

Statistics

Chart-1

Table-1

Mean of Demographic data and Anthropometric Measurements of Students in Three Programs

Program

Number

Mean ± Standard deviation

Age

Weight (lb)

Height

BMI

WC

WHR

Fat%

DC

20

26.30±6.89

151.42±28.03

5.44±0.39

24.79 ± 5.15

32.45 ± 4.65

0.78 ± 0.065

25.10 ± 6.70

Nutrition

20

28.70±6.70

129.54±22.54

5.26±0.34

23.17 ± 3.29

30.32 ± 3.53

0.78 ± 0.07

26.19 ± 6.14

Non- Health related majors

20

21.20 ± 4.10

143.85± 25.67

5.45±0.23

24.22 ± 3.98

29.73 ± 3.80

0.76 ± 0.07

25.10 ± 6.10

Total

60

25.40±6.64

141.60±26.69

5.39±0.33

24.06 ± 4.19

30.83 ± 4.12

0.78 ± 0.07

25.45 ± 6.22

DC: Doctor of Chiropractic, BMI: Body Mass Index, WC: Waist Circumference, WHR: Waist-Hip Ratio.

Table 1 shows the mean of Demographic data and anthropometric measurements of participants among 60 students in the three deferent programs (Doctor of Chiropractic (DC), Nutrition and Non Health Related Programs). Female participants Age, Weight in pounds, Height in feet and inches, BMI, WC in inches, WHR, Fat% mean and standard deviations were found 25.40±6.64, 141.60±26.69, 5.39±0.33, 24.06±4.19, 30.83±4.12, 0.78±0.07, and 25.45±6.22. Among 20 students in DC program, the mean and standard deviations of age was 26.30±6.89, weight in pounds 151.42±28.03, height in feet and inches 5.44±0.39 BMI 24.79±5.15, WC 32.45±4.65, WHR 0.78±0.065, and Fat% 25.10± 6.70. Student from Nutrition obtained a mean of age of 28.70±6.70, weight in pounds 129.54±22.54, height in feet and inches 5.26±0.34, BMI 23.17±3.29, WC 30.32±3.53, WHR 0.78±0.07, and fat% 26.19±6.14. The mean age of students from non- health related programs was 21.20±4.10, weight 143.85±25.67, height 5.45±0.23, BMI 24.22 ± 3.98, WC 29.73 ± 3.80, WHR 0.76 ± 0.07, and fat% 25.10 ± 6.10.

Table-2

Comparison of Programs

Variable

N

Mean

Std. Deviation

Std. Error

P-Value

EAT-26 Score

DC

20

7.25

8.783

1.964

NS(0.962)

Nutrition

20

6.85

9.241

2.066

Non Health Program

20

7.55

5.186

1.160

Total

60

7.22

7.816

1.009

TDS

DC

20

32.40

3.393

0.759

NS(0.144)

Nutrition

20

31.50

3.069

0.686

Non Health Program

20

30.30

3.496

0.782

Total

60

31.40

3.381

0.436

BMI of Student

DC

20

24.7850

5.15050

1.15169

NS(0.472)

Nutrition

20

23.1660

3.28575

.73472

Non Health Program

20

24.2150

3.97999

.88995

Total

60

24.0553

4.19260

.54126

WC of Student

DC

20

32.4500

4.65069

1.03993

NS(0.088)

Nutrition

20

30.3200

3.52534

.78829

Non Health Program

20

29.7300

3.79974

.84965

Total

60

30.8333

4.12371

.53237

WHR of Student

DC

20

0.7785

.06548

.01464

NS(0.716)

Nutrition

20

0.7810

.07297

.01632

Non Health Program

20

0.7640

.07358

.01645

Total

60

0.7745

.06997

.00903

Fat %

DC

20

25.0700

6.69588

1.49724

NS(0.816)

Nutrition

20

26.1900

6.14414

1.37387

Non Health Program

20

25.1000

6.07012

1.35732

Total

60

25.4533

6.22391

.80350

Table 2 shows the comparison of EAT-26, TDS and body composition measurements between the three programs. Nutrition students had a little lower mean of EAT-26 score (6.85) and a lower mean of BMI (23.1660), and a little higher mean of fat mass percentage (26.1900) than DC and non-health program students. DC students had a little higher mean of TDS than nutrition and non-health program students, but non-health program students showed a little lower mean of WC (29.7300) and WHR (0.7640). However, there were no significant differences between students of the three groups of degrees and the EAT total score (p = 0.864), TDS (p=0.144) BMI (p=0.472), WC (p=0.088), WHR(p=0.716), and fat mass percentage (p=0.816).

Chart-2

Comparison the mean of Eating Attitude Test-26 (EAT-26) scores.

Chart-3

Comparison the mean of Tendency to Diet Scale (TDS) scores.

Table-3

Prevalence of eating disorder (EDs)

EAT-26 Score

DC

%(n)

Nutrition

%(n)

Non Health Programs

%(n)

Total

%(n)

P-value

EAT < 19 (Normal)

95%(19)

90%(18)

100%(20)

95%(57)

0.349(NS)

EAT ≥ 20 (Eating disorder)

5.0%(1)

10%(2)

0.0%(0)

5.0%(3)

Total

100%(20)

100%(20)

100%(20)

100%(60)

Table 3 shows the prevalence of EDs among students that randomly selected from three different majors. Although there were no significant differences between students of the three groups of degrees and the EAT total score (p = 0.349), 5% of students were identified with EDs from the three programs. Five percent were identified with EDs in DC students, 10% in nutrition students indicated with EDs, while no students were identified with EDs in non-health related majors.

Table-4

Comparison of EAT-26 and TDS scores

Program

SCORE TYPE

N

Mean

Std. Deviation

P-Value

Score

EAT26

60

7.2167

7.81587

HS(0.000)

TDS

60

31.4000

3.38090

DC

EAT26

20

7.2500

8.7821

HS(0.000)

TDS

20

32.4000

3.3945

Nutrition

EAT26

20

6.8500

9.2411

HS(0.000)

TDS

20

31.5000

3.0693

Non-health

EAT26

20

7.5000

5.1858

HS(0.000)

TDS

20

30.3000

3.4959

*HS(Highly significant)

Table 4 shows a Comparison of EAT-26 and TDS scores between the three groups of different majors. There was a significant association between EAT-26 and TDS, statically highly significant (p=0.000), which is ˂0.01. The results are indicating that students in all groups were a greater tendency to diet.

Table – 5

Comparison of EAT-26 and TDS between students in different years

Program

Variable

Year

N

Mean

Std. Deviation

P-Value

DC

EAT-26 Score

Year 1 and 2

12

9.08

10.833

S(0.038)

Year 3 and 4

8

4.50

3.251

TDS Score

Year 1 and 2

12

32.00

3.742

NS(0.863)

Year 3 and 4

8

33.00

2.928

Nutrition

EAT-26 Score

Undergraduate

9

1.89

1.691

HS(0.002)

Graduate

11

10.91

10.940

TDS Score

Undergraduate

9

30.00

3.464

NS(0.414)

Graduate

11

32.73

2.149

Non-health

EAT-26 Score

Year 1 and 2

16

7.19

5.205

NS(0.0660)

Year 3 and 4

4

9.00

5.598

TDS Score

Year 1 and 2

16

30.88

3.462

NS(0.514)

Year 3 and 4

4

28.00

2.944

Table 5 shows a Comparison of EAT-26 and TDS between students in different years of the study. The mean of EAT-26 score in the first and second year DC students (9.08) was significantly higher than third and fourth-year students (P=0.038). However, the mean of TDS scores in the DC student in the different years was not significant (P=0.863). In the nutrition students, the mean of graduate students was highly significant than undergraduate students (p=0.002), while the TDS did not show significant differences between the nutrition students (p=0.414). The mean of EAT-26 and TDS scores in non-health related major students did not show significant differences (p=0.0660, 0.514 respectively). The results indicated that graduate nutrition and the first and second year DC students are at risk of EDs.

Chart-4

Comparison the mean of Eating Attitude Test-26 (EAT-26) scores between DC students.

Chart-5

Comparison the mean of Tendency to Diet Scale (TDS) scores between DC students

Chart-6

Comparison the mean of Eating Attitude Test-26 (EAT-26) scores between undergraduate and graduate nutrition students.

Chart-7

Comparison the mean of Tendency to Diet Scale (TDS) scores between undergraduate and graduate nutrition students.

Chart-8

Comparison the mean of Eating Attitude Test-26 (EAT-26) scores between non –health related major students.

Chart-9

Comparison the mean of Tendency to Diet Scale (TDS) scores between non –health related major students.

Table-6 Classification and comparison of BMI.

BMI Category

DC

%(n)

Nutrition

%(n)

Non Health Programs

%(n)

Total

%(n)

P-value

Normal

65%(13)

75%(15)

65%(13)

68.3%(41)

0.501(NS)

Overweight

20%(4)

25%(5)

20%(4)

21.7%(13)

Obesity

15%(3)

0%

15%(3)

10%(6)

Total

100%(20)

100%(20)

100%(20)

100%(60)

Table 6 shows the comparison of BMI categories between the three majors; DC, Nutrition and non-health related majors. Nutrition students had 75% of normal BMI and no obesity was identified compared to 65% of normal BMI and 15% obesity in DC students and non-health related programs. However, there was no statically significant found between the groups (P= 0.501).

Chart-10

Total of BMI Classification of Students in the Three Majors

Table 7 Waistcircumfrence classification and comparison.

Waistcircumfrence

DC

%(n)

Nutrition

%(n)

Non Health Programs

%(n)

Total

%(n)

P-value

No Risk

60.0%(12)

65%(13)

65%(13)

63.3%(38)

0.931(NS)

Risk

40.0%(8)

35.0%(7)

35.0%(7)

36.7%(22)

Total

100%(20)

100%(20)

100%(20)

100%(60)

Table 7 shows the classification and comparison of Waist circumference among 60 students between the three majors. The majority of students (63.3%) were not at the risk of central obesity that associated with chronic diseases. Among the groups, 65% of nutrition and non-health related programs, and 60% of DC students were not at the risk of central obesity. However, the results showed no statically significant between the groups (P=0.931).

Chart-11

Total of WC Classification of Students in the Three Majors

Table 8 Waist Hip Ratio classification and comparisons

Waist Hip Ratio

DC

%(n)

Nutrition

%(n)

Non- Health Programs

%(n)

Total

%(n)

P-value

Low

60%(12)

65.0%(13)

70%(14)

65%(39)

0.908(NS)

Moderate

30%(6)

20%(4)

20%(4)

23.3%(14)

High

10%(2)

15.0%(3)

10.0%(2)

11.7%(7)

Total

100%(20)

100%(20)

100%(20)

100%(60)

Table 8 shows the classifications and comparisons of the waist-hip ratio among the three majors. The results show that there were no statically significant was found between the groups (P= 0.908). The majority of students (65%) in the groups were in the low category of WHR. Non-health related program students were the majority that had the lowest category (70%) followed by nutrition students were 65% and DC students 60%. Fifteen percent of nutrition students had high WHR compared to 10% of students from the DC and non-health related majors.

Chart-12

Total of WHR Classification of Students in the Three Majors

Table 9 Classification and comparison of Fat Mass percentages

Fat%

DC

%(n)

Nutrition

%(n)

Non -Health Programs

%(n)

Total

%(n)

P-value

≤24%

55%(11)

50%(10)

50%(10)

51.7%(31)

0. 890 (NS)

25-31%

30%(6)

35%(7)

25%(5)

30%(18)

≥32%

15%(3)

15%(3)

25%(5)

18.3%(11)

Total

100%(20)

100%(20)

100%(20)

100%(60)

Table 9 shows a comparison of fat mass percentage between the three programs. Around half of the students in the three programs had ≤24% of fat mass, which indicated that 51.7% students were fitness participants. Among the groups, 50% of nutrition and non-health related major students had ≤24% of fat mass compared to 55% of DC students. Obese students that had ≥32% of fat mass were observed more in non-health related major students (25%) in contrast to DC and nutrition students were 15%. No significant differences were found in fat mass percentage among the three groups, p = 0.890.

Chart-13

Total of Fat% Classification of Students in the Three Majors

Chart-14

Obesity Students in the Three Programs

Table- 10 Relation between body composition and EAT-26.

Model

Standardized Coefficients

Beta

T

Sig.

EAT-26

(Constant)

-.465

.644

BMI of Student

.202

.675

.502

WC of Student

-.170

-.570

.571

WHR of Student

.205

1.019

.313

Fat %

-.109

-.463

.645

Table9 shows the relation between body composition and EAT-26 between the groups. The results found no significance association between the response of EAT-26 scores and the body composition measurements, BMI (p=0.502), WC (p=0.571), WHR (p=0.313) and fat % (p=0.645).

Table 11 Relation between body composition and TDS

Model

Standardized Coefficients

Beta

t

Sig.

TDS

(Constant)

4.616

.000

BMI of Student

.218

.759

.451

WC of Student

.253

.885

.380

WHR of Student

-.076

-.0396

.694

Fat %

-.132

-.586

.560

Table 11 shows the relation between body composition and TDS between the groups. The results found no significance association between the response of TDS scores and the body composition measurements, BMI (p=0.451), WC (p=0.380), WHR (p=0.694) and fat % (p=0.560).

Table 12 Correlation between body composition measurements, EAT-26, and TDS scores

EAT-26

P-value

TDS

P-value

BMI

0.13

NS (0.924)

0.287

S(0.026)

WC

0.32

NS (0.811)

0.286

S(0.027)

WHR

0.118

NS (0.367)

0.085

NS (0.520)

Fat %

-0.028

NS (0.834)

0.199

NS (0.127)

Table 12 shows the correlation between body composition measurement, EAT-26, and TDS scores between the participants in the three groups. Although there was no significant correlation between EAT-26 and body composition measurements, there were little correlations were found in BMI (0.13; p=0.924), WC (32; p=0.811), and WHR (0.118 p=0.367). The results showed a significance correlation between TDS and BMI (0.287;p=0.026), and in WC (0.286; p=0.027). TDS showed a little correlation in fat mass percentage (0.199), but the correlation was not significance (p=0.127)

Table 13 comparison of body composition between DC students in different years.

DC

Year

N

Mean

Std. Deviation

P-Value

BMI of Student

Year 1 and 2

12

26.4250

5.77410

S(0.049)

Year 3 and4

8

22.3250

2.84341

WC of Student

Year 1 and 2

12

33.6667

5.22813

S(0.05)

Year 3 and 4

8

30.6250

3.06769

WHR of Student

Year 1 and 2

12

.7975

.07238

NS(0.187)

Year 3 and 4

8

.7500

.04309

Fat %

Year 1 and 2

12

26.6750

7.43115

NS(0.303)

Year 3 and 4

8

22.6625

4.88641

Table 13 shows a comparison of body composition between DC students in different years. First and second-year group and third and fourth years group of DC students had a significant association in BMI (p=0.049), and in WC (p=0.05). The BMI of third and fourth year DC students were normal (=22.3) and lower in WC (=30.6) compared to first and second year of DC students were overweight (=26.4) and higher in WC (=33.6).

Table 14 comparison of body composition between undergraduate and graduate nutrition students.

Nutrition

Program levels

N

Mean

Std. Deviation

P-Value

BMI of Student

Undergraduate

9

24.4778

3.59088

NS(0.373)

Graduate

11

22.0927

2.71687

WC of Student

Undergraduate

9

31.5556

4.30439

NS(0.204)

Graduate

11

29.3091

2.50857

WHR of Student

Undergraduate

9

.7678

.08772

NS(0.479)

Graduate

11

.7918

.06063

Fat %

Undergraduate

9

28.5556

7.20141

NS(0.361)

Graduate

11

24.2545

4.59138

Table 14 shows comparison of body composition between undergraduate and graduate nutrition students. The graduate students had a lower mean of BMI (22), WC (29), and fat% (24) than undergraduate students. However, the results of nutrition students did not show any significant association in body composition measurements between undergraduate and graduate students.

Table 15 comparison of body composition between non-health related majors student in different years.

Non-health

Program levels

N

Mean

Std. Deviation

P-Value

BMI of Student

Year 1 and 2

16

24.4688

4.36482

S(0.033)

Year 3 and4

4

23.2000

1.82939

WC of Student

Year 1 and 2

16

29.4125

4.10087

NS(0.112)

Year 3 and 4

4

31.0000

2.16025

WHR of Student

Year 1 and 2

16

0.7513

.07544

NS(0.362)

Year 3 and 4

4

0.8150

.03873

Fat %

Year 1 and 2

16

25.6250

6.23490

NS(0.764)

Year 3 and 4

4

23.0000

5.62494

Table 15 comparison of body composition between non-health related majors student in different years. There was a significant association between BMI, and the first and second year and third and fourth-year students from non-health related major (p=0.033). The third and fourth-year students from non-health related major students had a lower mean of BMI (=23.2), fat (=23%) than the first and second-year students. Although there no statically significant was found, first and second-year students had a lower mean of WC (=29.4), WHR (=0.75) than third and fourth-year students in non-health related major.

Table 16 ANOVA about the Relation between EAT-26 and TDS Scores

ANOVA

Sum of Squares

df

Mean Square

F

Sig.

TDSQ1

Between Groups

1.881

3

.627

.430

.732

Within Groups

84.506

58

1.457

Total

86.387

61

TDSQ2

Between Groups

7.824

3

2.608

3.394

.024

Within Groups

44.563

58

.768

Total

52.387

61

TDSQ3

Between Groups

3.041

3

1.014

1.491

.227

Within Groups

39.427

58

.680

Total

42.468

61

TDSQ4

Between Groups

2.822

3

.941

1.683

.181

Within Groups

32.420

58

.559

Total

35.242

61

TDSQ5

Between Groups

.852

3

.284

2.186

.099

Within Groups

7.535

58

.130

Total

8.387

61

TDSQ6

Between Groups

.046

3

.015

.956

.420

Within Groups

.938

58

.016

Total

.984

61

TDSQ7

Between Groups

.906

3

.302

.524

.667

Within Groups

33.432

58

.576

Total

34.339

61

TDSQ8

Between Groups

.233

3

.078

.258

.855

Within Groups

17.461

58

.301

Total

17.694

61

TDSQ9

Between Groups

2.912

3

.971

1.619

.195

Within Groups

34.766

58

.599

Total

37.677

61

TDSQ10

Between Groups

2.368

3

.789

1.101

.356

Within Groups

41.567

58

.717

Total

43.935

61

TDSQ11

Between Groups

1.531

3

.510

.621

.604

Within Groups

47.646

58

.821

Total

49.177

61

TDSQ12

Between Groups

1.417

3

.472

.730

.538

Within Groups

37.503

58

.647

Total

38.919

61

TDSQ13

Between Groups

.774

3

.258

.261

.853

Within Groups

57.419

58

.990

Total

58.194

61

TDSQ14

Between Groups

.959

3

.320

.469

.705

Within Groups

39.509

58

.681

Total

40.468

61

TDSQ15

Between Groups

5.764

3

1.921

2.476

.070

Within Groups

45.011

58

.776

Total

50.774

61

Table 16 based on the results of the ANOVA test that defines about the association between the variables of EAT-26 and all the TDS scores used in the model. The approach of one-way ANOVA is applied in the test as there are only two variables. The output of the ANOVA table explains about the existence of statistically difference between different group means. The results of the ANOVA test explain that there are two values of TDS 2 and TDS 15 that referred as statistically significantly when it comes to the p values in table. The p-values for these scores are 0.024 and 0.07 respectively which is below the standard of 0.05 ultimately referred as the existence of the significant mean difference exist between these groups.

The test of ANOVA based on the proper consideration of Analysis of Variance (ANOVA) that is used to identify the existing difference between means of groups to find out the prevailing difference ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"BNq9W8BQ","properties":{"formattedCitation":"(Vik, 2013)","plainCitation":"(Vik, 2013)","noteIndex":0},"citationItems":[{"id":606,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/RPBQ6UJ7"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/RPBQ6UJ7"],"itemData":{"id":606,"type":"book","title":"Regression, ANOVA, and the General Linear Model: A Statistics Primer","publisher":"SAGE Publications","URL":"https://books.google.com.pk/books?id=CbMgAQAAQBAJ","ISBN":"978-1-4833-1601-7","author":[{"family":"Vik","given":"P."}],"issued":{"date-parts":[["2013"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Vik, 2013).

Table 17 F test about the Association between EAT-26 and TDS Scores

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

13.465

15

.898

1.102

.381b

Residual

37.455

46

.814

Total

50.919

61

a. Dependent Variable: EATQ26

b. Predictors: (Constant), TDSQ15, TDSQ8, TDSQ2, TDSQ5, TDSQ14, TDSQ3, TDSQ11, TDSQ6, TDSQ10, TDSQ7, TDSQ9, TDSQ1, TDSQ4, TDSQ13, TDSQ12

Table 17 is the representation of the results of the F test that is used to determine about the overall fitness of the model. It explains about the overall significance of the regression model. The result of f test in the table explains about the variance of the group means. The value for the F test in 1.102 which is close to 1 that’s why it positively explains about the overall significance of the association. The significant value of p-value with the value of .381 ultimately helps to determine about the value of f test for the model. The overall f-test clearly explains about the overall strength of the relationship between the variables of EAT-26 and all the scores of TDS.

Table 18 Correlation between EAT-26 and TDS Scores

Correlations

EATQ26

TDSQ1

TDSQ2

TDSQ3

TDSQ4

TDSQ5

EATQ26

Pearson Correlation

1

-.135

-.175

.059

-.281*

-.192

Sig. (2-tailed)

.295

.174

.651

.027

.135

N

62

62

62

62

62

62

TDSQ1

Pearson Correlation

-.135

1

-.392**

-.005

.568**

.237

Sig. (2-tailed)

.295

.002

.971

.000

.063

N

62

62

62

62

62

62

TDSQ2

Pearson Correlation

-.175

-.392**

1

.261*

-.218

-.066

Sig. (2-tailed)

.174

.002

.041

.089

.609

N

62

62

62

62

62

62

TDSQ3

Pearson Correlation

.059

-.005

.261*

1

.000

-.015

Sig. (2-tailed)

.651

.971

.041

.997

.906

N

62

62

62

62

62

62

TDSQ4

Pearson Correlation

-.281*

.568**

-.218

.000

1

.311*

Sig. (2-tailed)

.027

.000

.089

.997

.014

N

62

62

62

62

62

62

TDSQ5

Pearson Correlation

-.192

.237

-.066

-.015

.311*

1

Sig. (2-tailed)

.135

.063

.609

.906

.014

N

62

62

62

62

62

62

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Table 18 explains about the existing correlation between EAT-26 and the particular scores of TDS referred as TDS 1, TDS 2, TDS 3, TDS 4, and TDS 5. The statistical aspect in the form of correlation helps to determine about the existing strength of relationship between the variables. The following table indicate about the existing association between EAT-26 and TDS scores. The significance of p-value for the test determine on 2-tailed with the sample of 62. The value of -.135 determine about the significant association between EAT-26 and TDS 1 as the p-value is below 0.05. The value of -.175 determine about the significant association between EAT-26 and TDS 2 as the p-value is below 0.05. The p-value for TDS 3 is .059 that refer it as insignificant information to reject the null hypothesis. The value of -.281 determine about the significant association between EAT-26 and TDS 4 as the p-value is below 0.05. The results of TDS 5 also explain its strong linear relationship with EAT-26. The value of -.192 determine about the significant association between EAT-26 and TDS 5 as the p-value is below 0.05.

Table 19 Correlation between EAT-26 and TDS Scores

Correlations

EATQ26

TDSQ6

TDSQ7

TDSQ8

TDSQ9

TDSQ10

EATQ26

Pearson Correlation

1

.057

.068

.112

.004

-.207

Sig. (2-tailed)

.660

.598

.385

.977

.106

N

62

62

62

62

62

62

TDSQ6

Pearson Correlation

.057

1

-.219

.274*

.196

.005

Sig. (2-tailed)

.660

.087

.031

.127

.970

N

62

62

62

62

62

62

TDSQ7

Pearson Correlation

.068

-.219

1

-.224

-.020

.066

Sig. (2-tailed)

.598

.087

.079

.879

.612

N

62

62

62

62

62

62

TDSQ8

Pearson Correlation

.112

.274*

-.224

1

.087

-.154

Sig. (2-tailed)

.385

.031

.079

.499

.232

N

62

62

62

62

62

62

TDSQ9

Pearson Correlation

.004

.196

-.020

.087

1

.089

Sig. (2-tailed)

.977

.127

.879

.499

.493

N

62

62

62

62

62

62

TDSQ10

Pearson Correlation

-.207

.005

.066

-.154

.089

1

Sig. (2-tailed)

.106

.970

.612

.232

.493

N

62

62

62

62

62

62

*. Correlation is significant at the 0.05 level (2-tailed).

Table 19 is the explanation of the outcomes of correlation between the variables of EAT-26 and scores of TDS in the form of TDS 6, TDS 7, TDS 8. TDS 9, and TDS 10. The p value for the relationship between EAT-26 and TDS 6 identify as .057 that refer the insignificant association between these two elements. The p-value for TDS 7 is .068 that refer it as insignificant information to reject the null hypothesis. The p-value for TDS 8 is .112 that refer it as insignificant information to reject the null hypothesis. The value of .004 determine about the significant association between EAT-26 and TDS 9 as the p-value is below 0.05. The value of -.207 determine about the significant association between EAT-26 and TDS 10 as the p-value is below 0.05.

Table 20 Correlation between EAT-26 and TDS Scores

Correlations

EATQ26

TDSQ11

TDSQ12

TDSQ13

TDSQ14

TDSQ15

EATQ26

Pearson Correlation

1

-.007

-.024

.071

.082

-.143

Sig. (2-tailed)

.958

.851

.583

.526

.268

N

62

62

62

62

62

62

TDSQ11

Pearson Correlation

-.007

1

.678**

.622**

.427**

-.130

Sig. (2-tailed)

.958

.000

.000

.001

.315

N

62

62

62

62

62

62

TDSQ12

Pearson Correlation

-.024

.678**

1

.607**

.673**

-.298*

Sig. (2-tailed)

.851

.000

.000

.000

.019

N

62

62

62

62

62

62

TDSQ13

Pearson Correlation

.071

.622**

.607**

1

.704**

-.177

Sig. (2-tailed)

.583

.000

.000

.000

.169

N

62

62

62

62

62

62

TDSQ14

Pearson Correlation

.082

.427**

.673**

.704**

1

-.236

Sig. (2-tailed)

.526

.001

.000

.000

.065

N

62

62

62

62

62

62

TDSQ15

Pearson Correlation

-.143

-.130

-.298*

-.177

-.236

1

Sig. (2-tailed)

.268

.315

.019

.169

.065

N

62

62

62

62

62

62

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 20 is the explanation of the outcomes of correlation between the variables of EAT-26 and scores of TDS in the form of TDS 11, TDS 12, TDS 13, TDS 14, and TDS 15. The p value for the relationship between EAT-26 and TDS 11 identify as -.007 that refer the significant association between these two elements. The p-value for TDS 12 is -.024 that refer it as significant information to identify the existing relationship between EAT-26 and TDS 12. The p-value for TDS 13 is .071 that refer it as insignificant information to reject the null hypothesis. The value of .082 determine the insignificant association between EAT-26 and TDS 14 as the p-value is below 0.05. The value of -.143 determine about the significant association between EAT-26 and TDS 15 as the p-value is below 0.05.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Vik, P. (2013). Regression, ANOVA, and the General Linear Model: A Statistics Primer. SAGE Publications. Retrieved from https://books.google.com/books?id=CbMgAQAAQBAJ

Appendix

Statistics Assignment

Statistics Assignment

[Author Name(s), First M. Last, Omit Titles and Degrees]

[Institutional Affiliation(s)]

The news article entitled, “Student Bullying Is Down Significantly” is chosen for this assignment. There are fewer cases of bullying, but some of them are crueler, violent, and serious. This news article tells the how the issue of student bullying increase or decrease in the schools of US. Experts attribute this decrease to the reduction of the most superficial events because they are detected earlier thanks to greater social awareness (Camera, 2019). But in almost half of the cases attended, the frequency and intensity of the harassment increased over time. The victims take more than a year to tell what happens to them. About to statistics data, 90% of those affected suffer psychological disorders, such as anxiety or depressive symptoms. They live with permanent fear.

To what populations(s) does the question apply?

This news article is applying on the students who faced the issue of bullying at the schools. The study on school bullying and cyberbullying according to those affected has been prepared based on the analysis of the cases reported. Last year there were 16,350 fewer than in 2016, something that the report's developers attribute to the reduction of less serious cases, thanks to an earlier detection that allows these situations to be tackled beforehand. But 96.7% of the cases treated were of medium or high severity, not only because of the violence of the attacks, but because of the isolation to which the victims are subjected. The third part of children who are harassed does not tell the problem to their parents or their teachers. The rest takes between 13 and 15 months to do it.

One in four victims suffer harassment in social networks (the so-called cyberbullying), especially through WhatsApp, where the number of insults (from 52.1% to 67.9%) and threatening messages (from the 22.3% to 35.7%) that they receive. Therefore, the report developers recommend delaying as much as possible the age at which a mobile phone with Internet access is given to children. Regarding bullying, in more than half of the cases it lasts for more than a year (52.9%), a figure that in cyberbullying drops to 40.6%. In three out of four cases, harassment at school is daily. 64.4% of cyberbullying victims are also harassed every day.

Is the study experimental or observational?

This study is observational and recorded of the data from different sources. The average age of the victim is 10.9 years in the case of bullying (46.8% of girls vs. 53.2% of boys) and 13.5 years in the case of cyberbullying (65%)., 6% of girls versus 34.4% of boys). The aggressors have an average of 11.3 years in the case of bullying, and 13.9 years in cyberbullying. The different ones are attacked. In social networks, highlights the increase in aggression between two and five people, which have gone from 36.7% in 2016 to 55.5% in 2017. It is positive that it has decreased by more than eight points the harassment through social networks that practically perpetrates the whole class to a single victim. As he says, there is "more sensitivity" to this situation and classmates tend to support children who are harassed more. More than half of the victims show attitudes of defense or confrontation with the aggressor.

The report's authors request that children who are victims of violence be protected by law. "We ask for a protocol at the state level that allows families to demand and know how to act," says the program manager of the ANAR Foundation. And remember that the association has a free phone number, which is staffed by psychologists, as well as a chat for minors that can be accessed through their website.

Around 1/3 scholars, 32%, has been intimidated by their aristocrats at college no less than once in the previous month and some ratio has suffered physical fierceness, as said by a new account by the UN Educational Organization.

The article entitled, “Behind the numbers: End school violence and bullying”, was published 2019 World Education Environment in London gathers data from 144 countries. The region of the world with the most children suffering from intimidation is sub-Saharan Africa (around 50 of children), followed by North Africa (43.8%) and the Middle East (41.1%). Intimidation is less frequent in Europe (25%), the Caribbean (25%) and Central America (22.8%). The Caribbean is the second region in the world with the highest rates of physical violence. 38% of Caribbean students have been involved in a fight and almost 34% have suffered an attack.

The LGTBI students are at the highest risk of becoming victims of violence and intimidation, with those who do not meet gender stereotypes, such as "effeminate" boys or the "masculine" girls. For example, in New Zealand, lesbian, gay, and bisexual students are three periods more likely to be bullied and transsexuals five times more likely than their heterosexual partners. The form of intimidation also depends on the gender. Children suffer more physical violence, while girls are victims of psychological violence. In addition, harassment is also increasing online and by mobile phone, the report says.

It is important not to confuse this phenomenon with sporadic aggressions among the students or other violent manifestations that do not suppose inferiority of one of the participants in the event. Bullying presents the following characteristics: Imbalance of power: there is an inequality of physical, psychological and social power that generates an inequity of forces in social relationships.

Intentionality / replication: intentionality is uttered in a violent act that is recurrent over period and that generates in the target the anticipation of being the target of upcoming attacks.

Helplessness is the impartial of abuse is typically a single student or student, who is positioned in this way in a condition of helplessness.

Harassment tends to have a collective or group component, since usually there is not only one aggressor but several and because the situation is usually known by other comrades, passive observers who do not contribute enough to stop the aggression.

For an experimental study, what are the treatments and outcomes?

The present investigation is based on a quantitative approach. Quantitative approaches use data collection to test hypotheses, based on numerical measurement and statistical analysis, to establish patterns of behavior and to test theories. The level of research in this study is descriptive, "... that seeks to specify the properties, characteristics or profiles of people, groups, communities, processes, objects, or any other that is subject to analysis". Also, it was considered as a field design, because all the required information comes basically from the subjects that have a direct link with the educational institutions of the primary level.

The sample selected was 60 elementary school students of the fifth cycle (5th and 6th grade) of Basic Education of the Educational Institution, which after having applied the instruments to diagnose if there is bullying at the different public educational institutions, stayed with a sample that according to reports, are the most affected by the problem of violence. The distribution by gender was 33 females and 27 males. The age fluctuated between 10 and 12 years. Of these, the totality belongs to a low socio-economic system. All these informants are regular students of the institution. Of them we have dysfunctional families, single parent, single mother; in our case, single-parent families prevailed. In this way, 60 individual interviews and 3 focus groups were completed. In this section the results of the application of the instrument are presented, as well as a concise analysis of them.

What are the relevant variables in the study?

In this sense, the normality test was used, which is a process that is carried out to determine if the data come from a population with normal distribution or not. When presenting a normal distribution, we proceed to work with the parametric tests, otherwise the non-parametric tests will be performed. In this case, normality is operated with the Kolmogorov - Smirnov statistic, since the sample is greater than 50 people, which will yield a probability level that may be higher or lower than the level of established significance. If the level "p" (probability) is greater than the level of significance, the Ho is not rejected, however, if the level "p" is lower,

On the other hand, the use of parametric or non-parametric tests does not only depend on having normality or not, but on analyzing the variables. In the case of presenting any categorical variable of ordinal or numerical type of interval type, a nonparametric test will be automatically performed, regardless of whether it presents a normal distribution or not.

What is the size of the sample in the study?

According to the contingency table, it is evident that there is a higher percentage of the presence of bullying in the female gender. In this research, bullying and self-esteem in elementary school students; the 60 students that represent 100% of the evaluated population; 52.5% have high school harassment, 39.2% have moderate harassment and 8.3% have low harassment. Likewise, 45.8% have low self-esteem, 40.8% have a moderate self-esteem, and 13.4% have a high self-esteem.

As can be seen, after the correlation analysis, a p value of 0.009 was obtained at the level of 0.05, which finally indicates that bullying has a significant relationship with school self-esteem. The tests used in this research work on the variables school bullying and school self-esteem present validity and reliability according to the statistical analyzes performed.

After having presented and analyzed the results, some inferences are made according to the objective of this article: It is shown that there is high school bullying and that school self-esteem is low. It is important to note that, according to the results and observation during the research process, it is the girls who have the highest percentage of school bullying, with respect to children.

The results obtained indicate in this research that school bullying is directly and significantly related to school self-esteem in educational institutions of the primary level of institution. Finally, we can conclude that school bullying is a public health problem, so it becomes necessary the permanent and coordinated intervention of school authorities in coordination with the local educational management unit and the central government.

What are the parameters (means or proportions) of interest?

In virtually half of the 71 nations and territories deliberate, intimidation has decreased and in a like amount of countries, fights or physical attacks have also been reduced. These countries have in common a number of factors that have contributed to success in reducing bullying:

Commitment to promote a school climate and a harmless and optimistic classroom setting

Operational systems for commentary and observing school fierceness and intimidation.

Programs and interventions based on empirical data

Training and provision for instructors

Provision and guidance of pretentious students

Enablement and student participation

"We are very heartened that almost half of the nations for which data are available have reduced the rates of school vehemence and bullying

"All offspring and young individuals have the right to a harmless, comprehensive and effective knowledge environment".

Bullying is a characteristic and extreme form of school violence. The World Health Organization ( WHO, 2002 ) defines violence as: "The deliberate use of physical force or power, either in terms of threat or cash, in contradiction of oneself, another individual or a group or communal, causing or likely to cause damage, death, psychological damage, evolving disorders or deprivation. " Violence is an attempt to subdue the other, against his will, through force and power." From the foregoing, violence is any act that refers to the use of physical or psychological force against a fellow to hurt, abuse, humiliate, harm, dominate and harm.

The problems that arise within school environments tend to manifest themselves in different ways. Each of these forms of violence has its own particularities. Here only the delimitation between school violence and bullying is made. In that sense, school violence is any intentional act or omission that, in school, around school or after-school activities, harms or may harm third parties. These third parties can be things, such as the destruction of school furniture or damage to the other partner's property. When school violence is between people it is presented under three modalities: one is the teacher's violence against the student; the other, that of the student against the teacher; and the third modality is violence between partners, here it is necessary to highlight physical and emotional violence.

For San Martín, The problem of school violence becomes relevant when peer violence degenerates into school bullying." But, according to Serrano And Ibarra, one is in an act of bullying when at least three of the following criteria are met: The victim feels intimidated, the victim feels excluded, the victim perceives the aggressor as stronger, aggressions are increasingly intense and aggressions usually occur in private. In this regard, Olweus, makes a precision to identify the bullying of other types of aggression among school children: "But it is not called bullying when it is bothered in a friendly and playful way. Nor is bullying when two students of more or less of the same strength or power argue or fight. "

The characteristics of bullying can be evidenced if a student becomes a victim when he or she is exposed, repeatedly and for a time, to negative actions that are manifested through different forms of harassment or harassment committed in their school setting, brought to done by another student or student or several of them, being in a situation of inferiority with respect to the aggressor.

2.1. Consequences of peer abuse

For the victim: it can be translated into school failure, psychological trauma, physical risk, dissatisfaction, anxiety, unhappiness, personality problems and risk to their balanced development. For the aggressor or aggressor: can be the prelude to a future criminal behavior, an interpretation of the obtaining of power based on aggression, which can be perpetuated in adult life, and even a supravaluation of the violent act as socially acceptable and rewarded. However, for fellow observers: it can lead to a passive and complacent attitude towards injustice and a wrong modeling of personal worth.

On the other hand, self-esteem is the feeling of acceptance and appreciation towards oneself, which is linked to the feeling of competence and personal worth (Aguirre and Vauro, 2009 ). In this sense, with regard to the position of Barroso (1995), self-esteem is defined as an energy, an internal force that starts from the moment of conception, capable of organizing everything that happens giving meaning, guidance, guidance, direction and importance to life. Likewise, Satir (1981) states that self-esteem is the crucial factor of what happens both within and between people ... considering that it is the center of their being and indispensable to live freely from the first years of life in the family.

In the same order of ideas, Bednar, R. L., & Peterson, S. R. (1995) defines habitual self-esteem as an "Attitude towards oneself, the habitual way of thinking, loving, feeling and behaving towards oneself. It is the permanent description according to which one faces like ourselves " (Bednar, 1995). It is the fundamental system by which our experiences are ordered by referring to our personal "I". The term self-esteem refers to the evaluations that a person makes and commonly maintains about himself; that is, global self-esteem, is an expression of approval or disapproval that indicates the extent to which the person believes to be competent, important and dignified.

On the contrary, people with low self-esteem, leads to: Lack of credibility in itself, that is, insecurity. Attributing to internal causes the difficulties, increasing personal justifications, performance falls, the proposed goals are not reached, lack of adequate social skills to resolve conflictive situations (submissive or very aggressive people). No constructive and positive criticism is made, guilt feeling, increase of fears and social rejection, therefore; inhibition to participate actively in situations.

Self-esteem, are values, emotions and styles of behavior directed towards ourselves, towards our way of being and coping, and towards the physiognomy of our body and our profile. In short, it is the evaluative insight of our own person. The position of the psychoanalyst Eriksen, (2009) is highlighted; who explains that during adolescence a critical process of searching for identification occurs. In this stage a process of self-clarification, conflict and emotional development is presented. Thus; adults and the policies of educational institutions fulfill an important role for the socialization of the child, as well as relationships with their peers, that is, their schoolmates.

The significances of bullying are observed in three proportions of the action of the affected, in this respect with Lazo And Salazar (2011) ,:

1. The change of performance: segregation, unwillingness, reduced verbal communiqué, rebellion and inattentiveness in their homebased and / or school work, reduced or improved eating performance, tetchiness, and crying.

2. Emotional changes are striking: they go from annoyed revolt to states of grief, to unhappiness.

3. They give an account of the perception of oneself: the child expresses his philosophies about the limits of his physical volume, distinguishes his faintness or hopelessness to face difficulties, determines the need to change situations; and finally they come to self-disqualification, destroying their self-esteem, this can development by seriousness until the idea of suicide.

4. Among the characteristics presented by the victim and the perpetrator, the following are identified:

The vulnerable child tends to be submissive, fearful, uncommunicative, and of low sociability with other children. In family relationships: they come from incomplete families with members in conflict and authoritative communication; limited supervision of parents and adults, family environment with habits of alcoholism and other addictions. In the child victimizers. A great need for affection is discovered that they try to compensate by seeking social recognition through, the power of physical force, the need to disqualify the behaviors that oppose their censored or lacking features.

References

Camera, L. (2019). Student Bullying Is Down Significantly. [online] US NEWS. Available at: https://www.usnews.com/news/data-mine/articles/2018-03-15/student-bullying-is-down-significantly [Accessed 15 Mar. 2018].

Bednar, R. L., & Peterson, S. R. (1995). Self-esteem: Paradoxes and innovations in clinical theory and practice. American Psychological Association.

Eriksen, E. O. (2009). The unfinished democratization of Europe. Oxford University Press.

Lazo, H., & Salazar, M. (2011). Bullying. Revista Salud, Sexualidad y Sociedad, 3(4), 1-4.

Vauro, R., & Aguirre, C. (2009). FENPRUSS: Estudio de indicadores organizacionales.

Statistics Homework

Statistics Homework

[Enter name of student here]

[Enter name of institution here]

Answer 21.3

This pertains to quantitative data because reading scores have been recorded by researcher. A paired sample t-test will be appropriate here because subjects on which experiment is performed remain the same and experiment is performed before and after training.

Answer 21.4

Same quantitative data has been used pertaining to reading scores. An independent samples t test will be used by this researcher because students who are assigned to training condition or control condition are not the same.

Answer 21.7

This is qualitative data because it is related to creativity and astrological signs which cannot be quantified. A chi square goodness of fit test will be applied with one variable i.e. creativity.

Answer 21.8

This data will be considered qualitative because both the sexual codes and behaviors are qualitative variables. A chi squared test will be applied to this situation with two variables.

Answer 21.12

This data is qualitative in nature because operating room environment cannot be quantified as well as the state of emergency amputees. Although there are 100 amputees but the variable is dichotomous which shows that it is qualitative in nature. A chi squared test for two variables will be used to analyze the data is this scenario.

Answer 21.13

This study is quantitative in nature because the results will be shown in terms of activeness times of rats from different cages. A two way ANOVA test will be implemented in this situation because researcher wants to compare more than two groups and she also wants to see if there different genders react differently to all conditions.

Answer 21.14

The study uses quality of speech as a variable which is qualitative in nature because it is not possible to quantify this variable directly. A t test with independent samples is used because the students used for the experiment are not the same.

Answer 21.15

The study is qualitative in nature because the variable of depression is not quantifiable because of its nature. This study will use a one sample t-test which will compare sample mean to population mean.

Answer 21.16

The study is qualitative in nature because the variable of depression is not quantifiable because of its nature. This study will use the independent sample t-test because there are two distinct samples to be tested separately.

Answer 21.17

This study is quantitative in nature because test scores can easily be quantified and analyzed accordingly. One way ANOVA will be used in this scenario because the study only wants to see the differences across groups.

Answer 21.18

This study is qualitative in nature because both variables cannot be quantified directly. A chi square for two variables will be used to test the difference between these attributes.

Answer 21.19

This study is qualitative because it involves behavioral tests related to chimpanzees. A goodness of fit test for chi square will be applied in this scenario.

Answer 21.20

This study is quantitative in nature because the correct predictions made by the participants is a numerical outcome. Single sampled t-test will be applied to this situation because a standard score of 20 is given against which test score will be compared.

Answer 21.21

This study is qualitative in nature because both the variables cannot be expressed in exact numbers. A chi squared test for two variables will be used to analyze data in this scenario.

Statistics In News Article

Statistics in News Article

[Author’s name]

[Institute’s name]

Statistics in News Article

Introduction

           The great influence of different statistical concepts can be a witness in the case of different aspects of daily life. It is noteworthy to indicate that consideration of different statistical concepts such as mean, median, proportion, standard deviation, etc. ultimately helps people to better figure out different trends and developments of life ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"dbmS6YE9","properties":{"formattedCitation":"(Ahn & Fessler, 2003)","plainCitation":"(Ahn & Fessler, 2003)","noteIndex":0},"citationItems":[{"id":1731,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/L67ZPR48"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/L67ZPR48"],"itemData":{"id":1731,"type":"article-journal","container-title":"EECS Department, The University of Michigan","page":"1-2","title":"Standard errors of mean, variance, and standard deviation estimators","author":[{"family":"Ahn","given":"Sangtae"},{"family":"Fessler","given":"Jeffrey A."}],"issued":{"date-parts":[["2003"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Ahn & Fessler, 2003). An appropriate statistical analysis of various issues helps to find out possible solutions according to the need of society. Standard deviation is defined as one crucial concept of statistics that express the specific quantity by how much the members of a group diverge from the mean value for the group ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"fM2pzQra","properties":{"formattedCitation":"(Altman & Bland, 2005)","plainCitation":"(Altman & Bland, 2005)","noteIndex":0},"citationItems":[{"id":1730,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/8UUL78A9"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/8UUL78A9"],"itemData":{"id":1730,"type":"article-journal","container-title":"Bmj","issue":"7521","page":"903","title":"Standard deviations and standard errors","volume":"331","author":[{"family":"Altman","given":"Douglas G."},{"family":"Bland","given":"J. Martin"}],"issued":{"date-parts":[["2005"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Altman & Bland, 2005). A measurement of the amount of dispersion is important to condition to evaluate the idea of variation. A CNN news article, "marijuana's effects on young brains diminish 72 hours after use; research says," is considered to critically apprehends the statistical concept of standard deviation.

Discussion

Identification of the broad question of the selected article is important to understand the overall research scenario relevant to statistical consideration. The central concern that was addressed in the article is to evaluate the real effect of marijuana on the brains of young individuals. In other words, the focal point of this research work is to critically examine the potential influences of marijuana on people's activities by focusing on the functioning of their brains. The negative influences of using marijuana can mainly be observed in the forms of great damage to cognitive functioning, such as in the forms of learning, memory, and attention span.

           The statistical technique of meta-analysis is used by the researchers to examine the influence of marijuana on young people’s minds. The main question of this exploratory research work applies to the population of adolescents to determine the influence of marijuana use on their brains. The research study mentioned in this article is characterized as an observational study as it is based on critically examining 69 research studies to determine the real effects of frequent use of marijuana on the brain operations of young individuals. The useful statistical outcomes obtained by observing former research is the study that compared the 2,152 frequent marijuana users with the 6,575 non-users ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"4pyJ16N1","properties":{"formattedCitation":"(Leiber, 2018)","plainCitation":"(Leiber, 2018)","noteIndex":0},"citationItems":[{"id":1732,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/KMFKCISH"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/KMFKCISH"],"itemData":{"id":1732,"type":"webpage","container-title":"CNN","title":"Marijuana's effects on young brains diminish 72 hours after use, research says","author":[{"family":"Leiber","given":"Mark"}],"issued":{"date-parts":[["2018"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Leiber, 2018). The primary aim of this approach is to find out the influence of regular use of marijuana on the participants, who constantly use drugs as compared to non-users.

Exploration of the relevant variables in the case of this research study is also a necessary condition to examine the prevailing connection between the issue of the negative influence of marijuana on the dependent variable of brains of young individuals. The factors of learning, memory, and attention span are used as the associated variables to determine the actual effects of marijuana on young people's minds. The size of the sample for this exploratory research work is comprised of 60 former research studies to examine the marijuana' effects on young individuals considering the benchmark of 72 hours. The factor of standard deviation is used as the parameter of interest in this explanatory research study. The parameter of standard deviation helped to examine the largest influence of marijuana on adolescents' minds. It is observed that the major impact of marijuana approximately appeared as a third of a standard deviation.

Conclusion

To conclude the discussion on the statistical approach in the form of standard deviation, it is vital to indicate that regular use of marijuana has the capacity to reduce by one-third of a standard deviation. The small effect size played a crucial role in determining the worse impact of marijuana on the larger population. The results of this research work are mainly related to the worse application of drugs in case of cognitive outcomes.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Ahn, S., & Fessler, J. A. (2003). Standard errors of mean, variance, and standard deviation estimators. EECS Department, The University of Michigan, 1–2.

Altman, D. G., & Bland, J. M. (2005). Standard deviations and standard errors. Bmj, 331(7521), 903.

Leiber, M. (2018). Marijuana’s effects on young brains diminish 72 hours after use, research says. CNN. Retrieved from:

https://edition.cnn.com/2018/04/18/health/marijuana-cognitive-effects-study/index.html

Suicide 2007 And 2017

Same-Sex Marriage in the U.S.

Your Name (First M. Last)

School or Institution Name (University at Place or Town, State)

Same-Sex Marriage in the U.S.

Introduction

Marriage is both central and ubiquitous. Throughout the United States of America (USA), people observe the bond of marriage in each religion, class, ethnicity and race. Primarily, it is deemed the key to happiness and the person who does not marry is thought to be excluded from essential values of the American life cycle. If people enter the bond of marriage through religious group or Church, they are not legally accepted without seeking the marriage license from government authorities. Contrary to private actors, the state does not hold the absolute prerogative to decide and allow people to marry or not. The issue of same-sex marriage is one of the most divisive social and political concern in the United States. The expressive dimensions of same-sex marriages raise contentious and distinct questions. The acceptance or denial of same-sex marriage lies at the very heart of the public opinion which is reflected in statistics and social norms.

The practice of same-sex marriage in the United States expanded from a single state in 2004 to 50 states in 2015 via state legislation, several state court rulings, federal court rulings and direct popular votes. The states have distinct marriage always which must adhere to the provisions which identify marriage as the primary right guaranteed by the Fourteenth Amendment of the Constitution ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"kC48niaV","properties":{"formattedCitation":"(\\uc0\\u8220{}A Right to Marry?,\\uc0\\u8221{} n.d.)","plainCitation":"(“A Right to Marry?,” n.d.)","noteIndex":0},"citationItems":[{"id":438,"uris":["http://zotero.org/users/local/yvjivw9i/items/Q4TTJAHS"],"uri":["http://zotero.org/users/local/yvjivw9i/items/Q4TTJAHS"],"itemData":{"id":438,"type":"webpage","title":"A Right to Marry? Same-sex Marriage and Constitutional Law","container-title":"Dissent Magazine","abstract":"Marriage is both ubiquitous and central. All across our country, in every region, every social class, every race and ethnicity, every religion or non-religion, people get married. For many if not most people, moreover, marriage is not a trivial matter. …","URL":"https://www.dissentmagazine.org/article/a-right-to-marry-same-sex-marriage-and-constitutional-law","title-short":"A Right to Marry?","accessed":{"date-parts":[["2019",5,27]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (“A Right to Marry?,” n.d.). The staunch advocates of same-sex marriage are civil right organizations and civil and human right organizations. Religious groups confront the practice thoroughly and term it the desecration of the sacred ritual of marriage. However, it is imperative to advance the debate on the basis of evidence, public opinion, social values and above all, empirical evidence.

Pro Arguments

In 2004, the Pew Research center conducted votes where American citizens opposed same sex-marriages by a ratio of 60% to 31%. The support and advocacy of same sex-marriages have accelerated in the past fifteen years. In the contemporary era, the advocacy remains at the peak since the initiation of polling on the issue. Based on the results of 2019, a wide range of citizens honored same-sex marriages (61%) while 315 opposed it. A critical appraisal of the matter underpins the impact of religious views on the issue. People who are religiously unassociated, they are inclined toward supporting same-sex marriage. Among them, 79% are of the view that these marriages ought to be permitted without any impediments. The advocacy for same-sex marriages has remained equal among both women and men since 2017 ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KgJmPwAV","properties":{"formattedCitation":"(NW, Washington, & Inquiries, n.d.)","plainCitation":"(NW, Washington, & Inquiries, n.d.)","noteIndex":0},"citationItems":[{"id":439,"uris":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"uri":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"itemData":{"id":439,"type":"post-weblog","title":"Changing Attitudes on Same-Sex Marriage | Pew Research Center","abstract":"In Pew Research Center polling in 2001, Americans opposed same-sex marriage by a margin of 57% to 35%. Since then, support for same-sex marriage has steadily grown.","URL":"https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/","language":"en-US","author":[{"family":"NW","given":"1615 L. St"},{"family":"Washington","given":"Suite 800"},{"family":"Inquiries","given":"DC 20036 USA202-419-4300 | Main202-419-4349 | Fax202-419-4372 | Media"}],"accessed":{"date-parts":[["2019",5,27]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (NW, Washington, & Inquiries, n.d.). 575 of men and 67% of women stand for same-sex marriages. The drastic rise in the acceptance and support of these marriages among adults are prominent manifestations of the generation gap as the younger generation reflects a great acceptance of marriages.

Over the past year, research conducted by Gallup indicates approximately one-ten LGBT citizens are married to the same-sex partner. Lesbians, bisexual and transgender (LGBT) cite love as the defining passion and element for getting married. A research study of the Pew Research Center survey, in 2013, further highlighted 88% of the general public and 84% of LGBT termed love as the dominant reason for getting married. The dimensions and public opinion of same-sex marriages witnessed a revolutionary transition in 2016. The U.S. Supreme court announced a landmark ruling granting same-sex couples a fundamental constitutional right to marry. It legalized gay marriages across the nation comprising the 14 states which prohibited lesbians and gays to get married. The verdict was a critical interpretation and apprehension of the 14th Amendment wherein justices marked limiting marriages only to heterosexual people desecrates the 14th Amendment’s provision of equal protection under the law. Besides, the verdict cast a positive impact on the practice of same-sex marriages and encouraged LGBTs. Consequently, 61% of same-sex living couples are married which was 385 prior to the ruling ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KgJmPwAV","properties":{"formattedCitation":"(NW, Washington, & Inquiries, n.d.)","plainCitation":"(NW, Washington, & Inquiries, n.d.)","noteIndex":0},"citationItems":[{"id":439,"uris":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"uri":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"itemData":{"id":439,"type":"post-weblog","title":"Changing Attitudes on Same-Sex Marriage | Pew Research Center","abstract":"In Pew Research Center polling in 2001, Americans opposed same-sex marriage by a margin of 57% to 35%. Since then, support for same-sex marriage has steadily grown.","URL":"https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/","language":"en-US","author":[{"family":"NW","given":"1615 L. St"},{"family":"Washington","given":"Suite 800"},{"family":"Inquiries","given":"DC 20036 USA202-419-4300 | Main202-419-4349 | Fax202-419-4372 | Media"}],"accessed":{"date-parts":[["2019",5,27]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (NW, Washington, & Inquiries, n.d.).

The deliberated evidence and statistics is essential to supplement the argument of same-sex marriages. Irrefutably, marriage is internationally accepted the fundamental human right for all people. The human rights Charter promulgates values to allow each human to marry and establish a family. It is necessary to comprehend marriage is not merely a means of extending generations. Had it been the case, couples refraining from having children or infertile children would never marry. Marriage is a principle which transcends religious norms and influences the legal aspects.

Con Arguments

The acceptance of same-sex marriage is not the same throughout communities and politicians in the United States. A survey released by the Public Region Research Institute in 2018 indicates a wide range of evangelical Protestants (58%), conservative Republicans (58%), adults in Alabama (51%) and Mormons (53%) are against same-sex marriages. The PRRI research relied on more than 41,000 interviews across the nation. 30% of citizens confront same-sex marriage thoroughly while 39% loathe the practice. These numbers are smaller than those who advocate for same-sex marriages but these numbers have a significant impact. It comprises a crucial chunk in the Republican Party that dominates the politics across several states. The issue is primarily associated with the culture of gender orientation. The survey asked the citizens whether or not they support the business which denies services to lesbian or gay people as it may conflict with their religious beliefs.335 of citizens acceded to the idea. Moreover, the people with stA general perception indicates opposition of gay marriages has become limited to a small number of political, regional or demographic groups. However, the survey indicated opposition is strong even in those territories which appear to be staunch supporters of same-sex marriage. In Illinois, California and Maryland, a quarter of population opposes same-sex marriage in addition to 40% of conservative Democrats and 395 of blacks ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"KgJmPwAV","properties":{"formattedCitation":"(NW, Washington, & Inquiries, n.d.)","plainCitation":"(NW, Washington, & Inquiries, n.d.)","noteIndex":0},"citationItems":[{"id":439,"uris":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"uri":["http://zotero.org/users/local/yvjivw9i/items/ZBQXAUU3"],"itemData":{"id":439,"type":"post-weblog","title":"Changing Attitudes on Same-Sex Marriage | Pew Research Center","abstract":"In Pew Research Center polling in 2001, Americans opposed same-sex marriage by a margin of 57% to 35%. Since then, support for same-sex marriage has steadily grown.","URL":"https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/","language":"en-US","author":[{"family":"NW","given":"1615 L. St"},{"family":"Washington","given":"Suite 800"},{"family":"Inquiries","given":"DC 20036 USA202-419-4300 | Main202-419-4349 | Fax202-419-4372 | Media"}],"accessed":{"date-parts":[["2019",5,27]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (NW, Washington, & Inquiries, n.d.).

Irrefutably, the legal debate over the matter of the same-sex-marriage has ended in the United States because of court rulings. Question pertinent to the political and cultural acceptance of LGBT rights and same-sex marriage endure. The evidence and statistics mentioned offering a potential platform to supplement the argument pertinent to the opposition of same-sex marriage. The institution of marriage has culturally been stipulated as a ritual between a woman and a man. The same observation occurred throughout the history of mankind. The advanced state of liberalism has deprived children of the blessings, love and care of a mother. Hence, the children are deprived of the essential emotional security offered by the mothers. Moreover, permitting gay couples to marry can potentially weaken the structure of marriage. It is deemed the culmination of revisionism and can have emotional intensity. To negate the legal provisions, marriage is deemed a privilege by the cynics, not the right. Several rulings of the court have explicitly highlighted that marriage revolves around the dimensions of freedom and falls under the provision of the Fourth Amendment. However, the cynics believe marriage is a privilege and the amendment only grants rights.

Likewise, same-sex or gay marriage will ultimately accelerate the accumulation of gays into the mainstream heterosexual culture to cast adverse impacts on the heterosexual community. The pop-culture and surge of absurd trends and campaigns on social networking platforms are the potential elements and factors which fueled the rise of same-sex marriages in the United States. Thus, a critical appraisal of the matter indicates it is essentially a violation of the sacred ritual of marriage and religious confrontation ought not to be deemed bigotry. Instead, the religious and political opposition is the means to encourage and enlighten people to assimilate the severe abominations associated with the practice of same-sex marriages.

Conclusion

The deliberated paper indicates the empirical evidence pertaining to both the advocates and opponents of same-sex marriage in the United States of America (USA). Besides, statistics and empirical evidence supplement rational of both sides without any discrimination. The advocates primarily glorify and base their argument based on the Fourth Amendment. Several court rulings have strengthened the belief and offered value to the spread of same-sex marriage. As reflected in the research above, the culture of same-sex marriage has witnessed a tremendous acceleration since the past five decades. Government, court and proponents of same-sex marriage played an instrumental role. The opponents also base their opposition on the grounds of morality, historical context and culture of marriage. Some of these views sound rational but they lack the constitutional support as is the case with the proponents of same-sex marriages. This aspect strengthens same-sex marriages and discards the views of the cynics in true letter and spirits. In addition, each human being is entitled to marry and exercise the will regardless of sexual orientation. It is explicitly promulgated in the Human Rights Charter. Therefore, same-sex marriage ought not to be culturally, morally, religiously or socially confronted by citizens in the United States. Though the number of supporters has surged rapidly, there still exist certain groups which are inclined towards abashing same-sex marriage thoroughly on several political and online platforms.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY A Right to Marry? Same-sex Marriage and Constitutional Law. (n.d.). Retrieved May 27, 2019, from Dissent Magazine website: https://www.dissentmagazine.org/article/a-right-to-marry-same-sex-marriage-and-constitutional-law

NW, 1615 L. St, Washington, S. 800, & Inquiries, D. 20036 U.-419-4300 | M.-419-4349 | F.-419-4372 | M. (n.d.). Changing Attitudes on Same-Sex Marriage | Pew Research Center. Retrieved May 27, 2019, from https://www.pewforum.org/fact-sheet/changing-attitudes-on-gay-marriage/

Suicide 2007 And 2017

STATISTICS ASSIGNMENT

Name of Student

Name of Institution

Contents

TOC \o "1-3" \h \z \u Introduction PAGEREF _Toc8792932 \h 2

Array Table 2007 PAGEREF _Toc8792933 \h 2

Arrayed table 2017 PAGEREF _Toc8792934 \h 3

Descriptive statistics with grouped data PAGEREF _Toc8792935 \h 4

Mean 2007 PAGEREF _Toc8792936 \h 5

Median 2007 PAGEREF _Toc8792937 \h 5

Mode 2007 PAGEREF _Toc8792938 \h 6

Standard deviation 2007 PAGEREF _Toc8792939 \h 6

Mean 2017 PAGEREF _Toc8792940 \h 7

Median 2017 PAGEREF _Toc8792941 \h 7

Mode 2017 PAGEREF _Toc8792942 \h 8

Standard Deviation 2017 PAGEREF _Toc8792943 \h 8

Scatter Diagram PAGEREF _Toc8792944 \h 9

Correlation PAGEREF _Toc8792945 \h 9

Regression PAGEREF _Toc8792946 \h 10

Coefficient of Determination PAGEREF _Toc8792947 \h 11

Calculation of estimated values PAGEREF _Toc8792948 \h 12

95% confidence interval for population mean PAGEREF _Toc8792949 \h 13

Conclusion PAGEREF _Toc8792950 \h 14

References PAGEREF _Toc8792951 \h 16

Introduction

Statistics is seen by the people in many different ways. Generally, it is considered to be a study that deals with some numerical characteristics of the data. In the view of other people, it is more concerned with the collecting, interpreting and presenting large amounts of numerical data. In the first place the word statistics means numerical facts systematically arranged. In this sense the word statistics is always used as a plural. The major uses of the statistical information is to inform the general public about some happenings, to show what has already happened, to justify some claim that has already been made and to develop some relationships between some factors.

The present analysis makes use of almost all the above uses of Statistics. The data has been provided in the raw form. Some analysis has been made from the raw data to observe the characteristics. The data is converted to the grouped data and various measures of central tendency will be calculated. One of the major measures of dispersion namely the standard deviation is also calculated. The relationship between the data is checked by using the correlation and regression analysis.

Array Table 2007

4.95-8.35

5

8.35-11.75

18

11.75-15.15

18

15.15-18.55

6

18.55-21.95

3

21.95-25.35

1

Above is the histogram for the number of suicides in 2007, this shows that the data may not be normal but it will be positively skewed. This aspect is also shown by the characteristics of mean, median and mode. In a positively skewed distribution, mean is greater than median and median is greater than the mode. The right most values in the diagram shows the outliers.

Arrayed table 2017

Classes

F

5.95-10.55

7

10.55-15.15

16

15.15-19.75

16

19.75-24.35

9

24.35-28.95

3

The above graph shows the number of suicides in 51 states of the USA for the year 2017. The distribution is positively skewed which is shown by the longer tail on the right side. This aspect is also depicted by the fact that mean is greater than median and median is greater than the mode.

Descriptive statistics with grouped data

A measure of central tendency is a single value that is assumed to lie in the center of the data. The measure helps us to know how much the values tend to move towards the center or mean. This measure tries to describe some certain characteristics of the data with the help of these values. The mean, median and mode are all valid measures of central tendency. These are appropriate to be used with different data characteristics. In the following lines we calculate the mean, median and mode for the two sets of data provided.

For the data pertaining to 2007, we will use the following table to calculate all the descriptive statistics.

Class boundaries

Frequency

Cumulative frequency

X

fx

Fx2

4.95-8.35

5

5

6.65

33.25

221.1125

8.35-11.75

18

23

10.05

180.9

2020.05

11.75-15.15

18

41

13.45

242.1

3075.3425

15.15-18.55

6

47

16.85

101.1

1703.53

18.55- 21.95

3

50

20.25

60.75

820.125

21.95-25.35

1

51

23.65

23.65

559.3225

Mean 2007

Mean = ∑fx/∑f

= 644/51

= 12.62

This is the simple average of the data. This shows the number of suicides in a state if all states had the same number of suicides. This shows the average rate of the suicides per state of the country. There are certain advantages and disadvantages associated with the use of this figure as an average. This is very simple to calculate but it also has some disadvantages. First of all, it is affected by the extreme values. It is also affected by the change of origin and scale which means that if some number is added to the data, the same number is added to the mean and if some number is multiplied by the data, the same number is multiplied by the mean. A small or large mean is not representative of the data as a whole due to these drawbacks. This is the best measure when the data is symmetrical or continuous. This value does not necessarily come from the data itself. This method of average produces the lowest quantity of error when compared to the actual values.

Median 2007

Median = L + h/f (n/2-c)

51/2= 25.5

=11.75+3.4/18(25.5-23)

= 11.75+3.4/18(2.5)

= 11.75+0.189(2.5)

=11.75+0.4725

= 12.225

In simple terms, median is the middle value of the data. This value lies in the center of the data having 50% of the values to its each side. The basic assumption of calculating the median is that the values are evenly distributed in the group. The process of calculating median starts with dividing the total number of values by 2. A value higher than the resulting value is looked in the cumulative frequency column. This decides the median class. The lower-class boundary and frequency are taken from this class. This measure is preferred over other measures of central tendency when the distribution is skewed.

Mode 2007

638175154940Mode = L+ fm-f1X h

(Fm-f1)+ (fm-f2)

304800163829008.35+18-5 X 3.4

(18-5) +(0)

=8.35+3.4

= 11.75

The mode is defined as the most repeated value from the data. The fm in the formula is the highest frequency of the distribution which is 18 in this case. F1 is the frequency that is above the highest frequency and f2 is the frequency below the highest frequency. The value of lower-class boundary is obtained from the class with the highest frequency. Mode is not a representative of the data from which it is calculated.

Standard deviation 2007

Standard Deviation = √∑fx2/∑f – (∑fx/∑f) 2

=8399.4825/51-(12.62) ^2

=164.69-159.2644

=5.425^0.5

=2.33

This is a measure dispersion and does not have a simple interpretation as the arithmetic mean. This is a very important concept that serves as a basic measure of variability in the data. A smaller value of standard deviation as calculated above shows that most of the values are very close to the mean of the data. A larger value shows that the observations are scattered and are not very closely gathered around the mean.

For the year 2017, we will be using the following table to calculate the descriptive statistics.

Class boundaries

Frequency

Cumulative frequency

X

fx

Fx2

5.95-10.55

7

7

8.25

57.75

476.4375

10.55-15.15

16

23

12.85

205.6

2641.96

15.15-19.75

16

39

17.45

279.2

4872.04

19.75-24.35

9

48

22.05

198.45

4375.8225

24.35-28.95

3

51

26.65

79.95

2130.6675

Mean 2017

Mean = ∑fx/∑f

= 841/51

= 16.49

Mean is higher as compared to the suicides data of 2007. This means that the average number of suicides have increased over the 10-year period. If we see the data more closely, the difference between the values of certain states is more as compared to others. One particular example of this aspect can be Montana state. The values in 2017 have been higher than 2007 for almost all the states.

Median 2017

Median = L + h/f (n/2-c)

51/2= 25.5

= 15.15+4.6/16(25.5-23)

= 15.86

The median is the central value of the data and the above value shows that 50% of the data lies on both sides of this value.

Mode 2017

638175154940Mode = L+ fm-f1X h

(Fm-f1)+ (fm-f2)

504825163830=10.55+16-7 X 4.6

(16-7)+0

52387517335400=10.55 + 9X 4.6

9

=10.55+4.6

= 15.15

The mode is the most repeated value of the data. This value is suitable when we need to find the occurrences of certain values within the given data.

Standard Deviation 2017

Standard Deviation = √∑fx2/∑f – (∑fx/∑f) 2

= 14496.9275/51- (16.49) ^2

=284.25- 271.92

= 12.33^0.5

= 3.51

This is one of the most important measures that have to be calculated from the given data. This shows how much variability is present in the data. This also shows how much the data is dispersed away from the average value. The value of standard deviation in 2017 is higher than 2007 which means that the variability of the data has increased over a period of 10 years.

Scatter Diagram

This is the first step of determining whether any relationship exists between the independent and dependent variables. This has been done by plotting each pair of the independent, dependent variable on a graph paper. The above diagram shows the scatter diagram to show some relationship between the suicides in 2007 and in 2017 in 51 states of the USA. This is the first step of finding if there is any relationship between any two given variables. This just draws the pairs of values on a single graph. If the paired values are clustered closely together, there exists a strong relationship between the variables. The more scattered the diagram, the weaker will be the relationship.

Correlation

13335018288000r = n∑XY-∑X∑Y

√ (n∑X2- (∑X) ^2) (n∑Y2-(∑Y)^2

∑X= 644.8, ∑Y= 841.5, ∑XY= 11431.18, ∑X2 = 8774.4, ∑Y2 = 15095.45, n = 51

Putting the values in the formula

17145017398900r = 51*11431.18-(644.8)(841.5)

√(51*8774.4- (644.8)2)(51*15095.45-(841.5)2

9525016446400= 582990.18- 542599.2

√(447494.4-415767.04)(769867.95-708122.25)

466724184150= 40390.98

√31727.36*61745.7

295274212725= 40390.98

44260.90

=0.9125

A correlation coefficient between variable is a measure of strength or weakness of relationship between them. The correlation coefficient may take values between -1 and +1. A value closer to -1 will show a strong negative relationship between the variables while a value closer to +1 will show a strong positive relationship between the variables. This figure is important as it will help us to predict the change in one variable given the change in the other variable. In the above calculation we see that the correlation coefficient is +0.92. This shows a strong positive correlation between the numbers of people committing suicide in 2007 and 2017. The problem with this measure is that it only allows us to see the direction of the change in one variable as a result of a change in the other variable. The exact magnitude of the change in one variable as a result of the change in the other variable is not shown by this measure.

Regression

This form of data analysis is applied when a relationship between a dependent and an independent variable has to be developed. Unlike the correlation that shows the relationship between two variables, the regression analysis allows the researchers to predict the value of one variable based on the change in the other variable. In our analysis, we have taken the suicides in 2017 as dependent variable while suicides in 2007 are taken as the independent variable.

Suicides in 2017 = a + b (suicides in 2007)

a = Mean of 2017 values – b (mean of 2007 values)

190500189230b = n∑XY - ∑X∑Y

n∑X2- (∑X) 2

∑ XY = 11431.18, ∑X = 644.8, ∑ Y = 841.5, ∑ X2 = 8774.4, n = 51

Putting values

-13335017335551*11431.18 – (644.8)(841.5)

51*8774.4- (644.8)2

114300221615= 582990.18 – 542599.2

447494.4 – 415767.04

114300164465= 40390.98

31727.36

b = 1.273

a = Mean of 2017 values – b (mean of 2007 values)

= 841.5/51-(1.273) (644.8/51)

= 16.5-(1.273)12.64)

= 16.5-16.09

= 0.405

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

The above equation shows the relationship between the suicides committed in 2017 and in 2007. The suicides committed in 2007 is taken as the independent variable while suicides in 2017 has been considered as the dependent variable. The value of b is 1.273 which shows that one unit change in the independent variable will bring 1.273 units change in the dependent variable. The value of intercept is 0.405 which shows the value of the dependent variable if the value of independent variable is zero.

Coefficient of Determination

The coefficient of determination is represented by R2. This is also called the explained variation of the model. This depicts the extent to which the variation in the dependent variable is explained by the independent variables included in the model. This is calculated by squaring the value of correlation coefficient r. The value of coefficient of determination lies between 0 and 1. The value of 0 means that the dependent variable cannot be predicted by the independent variable. The value of 1 for the coefficient shows that the independent variable will predict the dependent variable exactly without any error. In the above data, we have calculated a value of 0.8464 for coefficient of determination which shows that the independent variable is responsible for 84.64% change in the dependent variable.

Calculation of estimated values

After the above equation has been developed, the next step is to estimate the values of dependent variable against some specific values of independent variable. We take 5 values from the data given in 2007 column and put them in the equation formed.

Value of 2007

Equation

Result

6

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

8.043

7.7

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

10.2071

9

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

11.862

10.8

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

14.1534

12.4

Suicides in 2017 = 0.405 + 1.273 (suicides in 2007)

16.1902

The above table shows the 5 selected values of the suicides in 2007 and their corresponding values in 2017 calculated through the regression equation. The results show considerable differences between the actual values and the estimated values. The difference between the actual values and the estimated values show the error associated with the model. The error generally occurs because in any given model, it is not possible for the researcher to consider all the variables affecting the dependent variable so the variables that are ignored are shown as the error term.

95% confidence interval for population mean

When we compute a confidence interval for the population mean, we want to know the percentage chance that the sample mean will be different from the population mean by a certain value. There are 3 values in the formula that have to be calculated so that the confidence interval can be found. The formula used to calculate the value of confidence interval is as follows:

CI = X + z α/2. σ mean, X - z α/2. σ mean

To apply the above formula, we will use the data from the year 2007 and 2017 one by one.

For 2007, the mean will be 12.64, standard deviation of this sample will be 3.52. Since we are calculating a 95% confidence interval so the value of z will be 1.96. When we put these values in the formula, we get

CI = 12.64+ (1.96) (3.52), 12.64-(1.96) (3.52)

= 12.64+6.8992, 12.64-6.8992

= 19.54, 5.74

This shows that any value that is in the data has a 95% chance of falling between 5.74 and 19.54. In other words, we are 95% confident that any given value will lie in this given interval.

For the year 2017, the mean will be 16.5, the value of z will be the same as used in the previous working as 1.96. The standard deviation will be 4.92. Putting these values in the formula, we get

= 16.5+ (1.96)(4.92), 16.5- (1.96)(4.92)

= 16.5+9.64, 16.5-9.64

= 26.14, 6.86

This shows that any value that is in the data has a 95% chance of falling between 6.86 and 26.14. In other words, we are 95% confident that any given value will lie in this given interval. We have used the z distribution in the above formulas because the sample sizes are greater than 30 in both cases. The above formula also show that we are 95% confidence that the sample mean will be within 1.96 standard deviations of the mean.

Conclusion

The current analysis has been done to see the characteristics of data for suicides in 2007 and 2017 for 51 states of the USA. Firstly, the raw data has been observed for any irregularities or outliers. The normality of both sets of data has been seen by making the histogram for each. The descriptive statistics have been calculated for both the data sets. The major findings are that the mean of 2017 values is much higher than 2007 values. Same applies for the values of median and mode. One of the most important measurements for the analysis is the standard deviation which is higher in case of 2017 as compared to 2007. This shows that the variability of the data in 2017 is higher than in 2007. This also means that the difference of values from their respective means has increased over a period of 10 years under consideration.

The second part of the analysis tries to find if there is any relationship between the figures of suicides in 2007 and 2017. The starting point for this part is the scatter diagram that shows whether the data is clustered closely or scattered here and there. The scatter diagram for the data shows that the data is clustered together closely. The diagram uses the pairs of data to plot them on the graph. Once the scatter diagram has been made, the next step is the calculation of correlation. This shows the direction of the relationship between the two variables. The answer to this calculation is 0.9125 which shows a strong positive correlation between the two variables considered. The next step is to develop a model that will help us to predict one variable on the basis of the other. For this analysis, the number of suicides in 2017 has been predicted on the basis of number of suicides in 2007. There are 2 important numbers in this analysis namely intercept that is depicted by a and the slope that is depicted by b. The regression analysis has shown that one-unit change in the independent variable brings about a change of 1.273 units in the dependent variable. If the independent variable is kept at 0, the value of dependent variable will be 0.405. The coefficient of determination shows the power of the model. The value of the coefficient of determination comes out to be 0.8464. This shows that the independent variable accounts for 84.64% change in the dependent variable.

The 95% confidence interval values show the limits within which there is a 95% chance for the population mean to lie based on the calculations of the sample data. This aspect is differently stated in terms of level of significance This shows that the level of significance for this study will be 5%. This is calculated as 100-95.

References

BIBLIOGRAPHY Sagepub. (2010, March). https://www.sagepub.com/sites/default/files/upm-binaries/35399_Module5.pdf. Retrieved from https://www.sagepub.com/sites/default/files/upm-binaries/35399_Module5.pdf: https://www.sagepub.com/sites/default/files/upm-binaries/35399_Module5.pdf

Up To The Writer

People’s Attitude in Using Emoji/Emoticon in Emails

[Author’s name]

People’s Attitude in Using Emoji/Emoticon in Emails

Abstract

It is a growing trend that people used different non-verbal cues in the form of emojis to convey their message. This specific phenomenon also exists in the form of an email. Email is characterised as the effective form of communication to convey a message to the receiver. There are different predictors that influence the approach of using emojis in emails. The focus is to determine the impact of people’s attitude and nature in the form of using emojis in emails. Statistical approaches in the form of regression analysis and ANOVA were utilised to test all the four hypotheses crafted for the study. The outcomes of both the tests enhance this domain that attitude of people directly linked with the perspective of using emojis in emails for different purposes.

People’s Attitude in Using Emoji/Emoticon in Emails

Introduction

Use of emoji/emoticons in conversations is the growing trend that influences the lives of individuals in different forms. It is established that the phenomenon of using emoticons in different forms. It is perceived that communication of people can never be imagined without the consideration of the emoji/emoticons. It is worthy to mention that the enhancing idea of emoji/ emoticons in conversation can also observe in the form of emails. Email is identified as an important indicator when it comes to the approach of conversation. People consistently use email as a mode of communication to convey important messages to others. It is interesting to explore the increasing perspective of emoji/emoticons specifically for the case of emails. It is necessary to explore specific attitudes and perceptions adopted by the people when it comes to the idea of using different emoji/emoticons in case of emails. This form of consideration also helps to determine how effectively emoji/emoticons present the moods and actual attitude of people. It is also true that there is a presence of limited empirical studies that significantly focus to determine the impact of emoji/emoticons in the form of communication. It is vital to examine the impact of emoji/emoticons in the entire perspective of communication. This form of exploration helps to figure out the difference between the conventional methods of communication and the increasing inclination of emoji/emoticons ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"HaoZY9MH","properties":{"formattedCitation":"(Stark & Crawford, 2015)","plainCitation":"(Stark & Crawford, 2015)","noteIndex":0},"citationItems":[{"id":1036,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/6ZN3SK42"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/6ZN3SK42"],"itemData":{"id":1036,"type":"article-journal","title":"The conservatism of emoji: Work, affect, and communication","container-title":"Social Media+ Society","page":"2056305115604853","volume":"1","issue":"2","author":[{"family":"Stark","given":"Luke"},{"family":"Crawford","given":"Kate"}],"issued":{"date-parts":[["2015"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Stark & Crawford, 2015). Here the particular focus is to critically discuss the overall nature and predictors of people’s attitude when it comes to the use of emoji/emoticon in emails.

It is important to observe the nature of people associated with the entire idea of using emoji/emoticons in emails in different forms. Exploration of the nature of emails is essential to determine the actual attitude linked with the idea of using emoji/emoticons in communication mode. Former research work focused to explore whether it is fine to use emoji/emoticon in formal business emails to convey a message to the receiver. This form of consideration is closely linked with the nature and different predictors of people’s behaviours that enhance their inclination to use emoji in different forms of communication such as the approach of emails. Undoubtedly, emojis are increasing practical measures in the form of computer-mediated communication. People utilise this approach to convey their message to the sender without the use of appropriate words. It is important to indicate that the phenomenon of using emoji/emoticons in emails directly linked with the behavioural aspects of the individuals. The actual attitude of people in the form of using emoji/emoticons can observe with the consideration of different relevant and crucial predictors. Currently, using emoji/emoticon is the increasing approach concerning the idea of computer-mediated communication (CMC).

The practical implications of considering emoji/emoticons involve critical consideration of different important predictors. Thorough consideration of these aspects helps to determine the people’s inclination towards the utilisation of emoji/emoticons in the form of business emails. It is observed that people used emojis to convey nonverbal cues to the receiver in computer-mediated communication such as in the form of an email. The approach of emails is established as the alternative mode of communication to convey different important messages to each other. It is vital to explore this aspect in the form of consideration of different elements that are directly linked with the attitude of individuals when it comes to using emojis/emoticons. Personality is characterised as an important indicator to make better inferences about the overall attitude of the individual when it comes to utilisation of emojis/emoticons in the form of emails ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"r7qmTbYt","properties":{"formattedCitation":"(Walther & D\\uc0\\u8217{}Addario, 2001)","plainCitation":"(Walther & D’Addario, 2001)","noteIndex":0},"citationItems":[{"id":1034,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/QVBWV25S"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/QVBWV25S"],"itemData":{"id":1034,"type":"article-journal","title":"The impacts of emoticons on message interpretation in computer-mediated communication","container-title":"Social science computer review","page":"324-347","volume":"19","issue":"3","author":[{"family":"Walther","given":"Joseph B."},{"family":"D’Addario","given":"Kyle P."}],"issued":{"date-parts":[["2001"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Walther & D’Addario, 2001). It is established that usually email is considered as the formal mode of communication that makes it essential to overview the behavioural perspective of people. It is important to figure out the actual context of the use of email. This domain is better examined in the form of use of emails for business purpose or for personal use. Proper identification of the personality eventually helps to better examine the true aim of sending emoji/emoticons in the form of an email. Consideration of the overall frequency of using emoji is also important to determine the actual interpretation of the message which is come up with the approach of emoji/emoticons. The broad factor of the frequency of emoji use can better apprehend through the certain perspectives of writing email, sending an email, and the inclination of an individual to post it on any social media platform ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"j85X6zHM","properties":{"formattedCitation":"(Prada et al., 2018)","plainCitation":"(Prada et al., 2018)","noteIndex":0},"citationItems":[{"id":1035,"uris":["http://zotero.org/users/local/7Hi3kAOD/items/RICXT7Z3"],"uri":["http://zotero.org/users/local/7Hi3kAOD/items/RICXT7Z3"],"itemData":{"id":1035,"type":"article-journal","title":"Motives, frequency and attitudes toward emoji and emoticon use","container-title":"Telematics and Informatics","page":"1925-1934","volume":"35","issue":"7","author":[{"family":"Prada","given":"Marília"},{"family":"Rodrigues","given":"David L."},{"family":"Garrido","given":"Margarida V."},{"family":"Lopes","given":"Diniz"},{"family":"Cavalheiro","given":"Bernardo"},{"family":"Gaspar","given":"Rui"}],"issued":{"date-parts":[["2018"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Prada et al., 2018). Detailed exploration of the existing research works on the phenomenon of people’s attitude in using emoji/emoticons in emails helps to develop appropriate research questions. Construction of the research questions established as the suitable roadmap to effectively meet the domains of descriptive and inferential statistics. Concerning the actual issue, research questions for this study are formulated as follows:

RQ1: What are people’s attitudes towards the use of emoji/emoticon in emails?

RQ2: Which variables predict people’s attitude towards the use of emoji/emoticon in emails?

Development of suitable research questions further assists the researcher to draw required hypotheses. Formulation of appropriate hypotheses is used to test the statement crafted by the researcher. Development of the research questions assists to determine that people’s attitude and use of emojis in emails are linked with each other in both directions. This form of exploration further helps to draw suitable hypotheses. Hypotheses recognised as the testable form of research questions developed for the study. Hypotheses for the study crafted as:

H1: People’s attitude does have a significant association with emoji/emoticons used in emails

Ho: People’s attitude does not have a significant association with emoji/emoticons used in emails

When it comes to considering emoji/emoticons as independent variable than hypotheses are formulated as follows:H1: Emoji/emoticons does have a significant association with people’s attitude when it comes to using emojis in emails.

Ho: Emoji/emoticons do not have a significant association with people’s attitude when it comes to using emojis in emails.

Method

It is important for the researcher to have a clear idea of using appropriate research methods according to the requirements of the study. Adoption of significant methods closely aligned with the predictors of the study appeared in the form of dependent and independent variables. There is a need for offering a clear understanding of the participants for this study. The sample of 120 participants is selected to test the crafted hypotheses for both regressions. The particular individuals were selected through the approach of random sampling from the ones who regularly use the approach of emails for communicating with others.

Different statistical methods are considered to attain desired forms of descriptive and inferential statistics for the research work. Consideration of these methods helps to make better inferences about the association between the variables of people’s attitude and use of emoji/emoticons in emails.

Measures of frequency and central tendency are used to determine the descriptive statistics for all the considered predictors. This form of consideration helps to assess the overall score of each predictor. When it comes to the consideration of inferential statistics than the broad idea of hypotheses is applied to attain outcomes for the study. The statistical approach of regression and ANOVA are considered to figure out the existing association between the factors of people’s attitude and use of emoji/emoticon in emails. The perspective of regression will be helpful to determine the trend of the relationship exist between dependent and independent variables for this study. Test of ANOVA also recognised as the analysis of variance. ANOVA is a suitable approach to compare the statistical significance of multiple groups appeared in the form of predictors.

Results

Empirical results are existing both the considered regressions to determine better outcomes about the formulated hypotheses. Regression and ANOVA testing are applied to both groups. One form of regression is classified with the approach when people’s attitude is the dependent variable whereas in other considered group, using emojis/emoticons established as the dependent variable.

RQ

Model Summary

Model

R

R Square

Adjusted R Square

Std. The error of the Estimate

1

.406a

.165

.162

1.484

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

606.208

4

151.552

68.813

.000b

Residual

3076.734

1397

2.202

Total

3682.942

1401

R2

Model Summary

Model

R

R Square

Adjusted R Square

Std. The error of the Estimate

1

.107a

.011

.009

1.173

ANOVA

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

22.245

4

5.561

4.039

.003b

Residual

1920.954

1395

1.377

Total

1943.199

1399

Discussion

The method of regression provides an overview of both the research questions used for this study. Both the procedures of ANOVA and regression are separately used for both the research questions to determine better inferences about the idea of people’s attitude when it comes to using emojis in emails. The first research question provides direction to assess the existing association between people’s attitude and use of emojis in emails. The statistical approach of ANOVA helps to evaluate the perspective of regression in the form of analysis of variances. The statistical phenomenon of ANOVA also helps to determine the existing association between dependent and independent variables. It is vital to discuss the results for both the research questions separately to examine the actual association exists in the form of people’s attitude and use of emojis/emoticons in emails.

The first table provides a model summary for the first research question. The main focus of this form of consideration is to check the existing association between people’s attitude and the use of emojis in email. The value of R square is identified as .165 that indicate the existing association between both forms of variables. It is worthy to mention that the statistical value of R square indicates the overall significance of the model through the inclusion of people’s attitude and use of emojis in email. The value of r square ranges between o to 1 that helps to determine how effectively independent variable explain the overall movement in the model. It is established that R Square have significant value in the form of .165 that explains that people’s attitude is closely linked with the perspective of using emojis in emails. The ANOVA table show the value of significance that helps to check the suitability of the overall model.

The results for second research questions also helps to determine the existing relationship between different predictors of people’s attitude and their impact on the use of different emojis in emails. The significance value for the second research question reflects as .003 that indicate the significant association between the predictors and the use of emojis in emails. The outcomes help to determine that people’s attitude positively influence the approach of using emojis in the emails.

Conclusion

To conclude the discussion about the focal point of using emojis in emails, it is worthy to indicate that this growing trend is closely influenced by the people’s attitude and nature. There is consideration of different predictors that explain the entire perspective of the use of emojis in emails that are influenced by the nature of people. Two significant models are used to make inferences about both the research questions to assess the existing association between people’s attitude and use of emojis in emails. The second crafted questions assist to determine all the relevant predictors that play their role in the entire scenario of using emojis in the form of an email.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Prada, M., Rodrigues, D. L., Garrido, M. V., Lopes, D., Cavalheiro, B., & Gaspar, R. (2018). Motives, frequency and attitudes toward emoji and emoticon use. Telematics and Informatics, 35(7), 1925–1934.

Stark, L., & Crawford, K. (2015). The conservatism of emoji: Work, affect, and communication. Social Media+ Society, 1(2), 2056305115604853.

Walther, J. B., & D’Addario, K. P. (2001). The impacts of emoticons on message interpretation in computer-mediated communication. Social Science Computer Review, 19(3), 324–347.

Appendix

Week 3 Case Study - A BETTER Secret Sauce Of Stats Success

Week 3 Case Study: A Better Secret Sauce of Stats Success

Name of the Writer

Name of the University

Week 3 Case Study: A Better Secret Sauce of Stats Success

Question 1

a) 0.50

Calculation

=151/300 = 0.50

b) 0.93

Calculation

=48++63+40+36+32+23+9+16+11/300 = 0.93

c) 0.67

Calculation

= 101/151 = 0.67

d) 0.17

Calculation

= 51/300 = 0.17

e) 0.01

Calculation

= 2/300 = 0.01

f)

Rating

Male

Female

21 - 24

25 - 34

35 - 49

4

0.17

0.13

0.12

0.11

0.08

5

0.23

0.28

0.16

0.21

0.13

Based on the calculations, the best target audience would be the females with ratings 5, which is 0.28

Calculation

Question 2

a) Mean = 4.39 and Standard Deviation = 37.70

Calculation

b) 0.15

Calculation

= 13/85 = 0.15

c) 0.54

Calculation

= 14+12+10+5+5/85 = 0.54

d) 0.14

Calculation

= 12/85 = 0.14

e) 0.21

Calculation

= 12/7+6+6+13+14+12 = 0.21

Question 3

a)

Drinking Temperature

Sample

Temperature

1 to 5

242

6 to 10

221

11 to 15

237

16 to 20

245

21 to 25

242

26 to 30

258

Total

1445

b)

Question 4

a)

Drinking Temperature

Sample

Temperature

Z score

1 to 5

242

26.10

6 to 10

221

23.68

11 to 15

237

25.53

16 to 20

245

26.45

21 to 25

242

26.10

26 to 30

258

27.95

Total

1445

Standard deviation

8.66

Mean

15.93

Calculation

b) 0.2

Calculation

= 42+39+44+39+43/1445 = 0.2

c) The probability is zero because in no sample of wine was the temp 60 or above 60 in the data given.

d) 42.08%

Calculation

= 52+50+52+50+49+52+52+50+49+49+49+54/1445 = 42.08%

Week 4 Case Study - EVEN MORE Secret Sauce Of Stats Success

EVEN MORE Secret Sauce of Stats Success

Student’s Name

Institution

Date

Q1: Determine the range of values in which you would expect to find the average weekly sales for the entire sales force in your company 90% of the time, and calculate the following.

Column1

Mean

3966.643077

Standard Error

323.0310322

Median

3907.096154

Mode

#N/A

Standard Deviation

2284.174334

Sample Variance

5217452.388

Kurtosis

-1.285406114

Skewness

0.096968234

Range

7164.865385

Minimum

569.25

Maximum

7734.115385

Sum

198332.1538

Count

50

Confidence Level (95.0%)

649.1551509

Based on the analysis of the average weekly sales of 50 employees, the range values for the expected average weekly sales for the company are 7164.86. However, the mean of average sales is 3966.64, the median is 323.03, and the standard deviation for the average weekly sales is 2284.17. However, the maximum range is 7734.11, and the minimum range is 569.25. The analysis of average weekly sales also indicates that the variance is 5217452.388. However, if the confidence level is increased to 95%, the average weekly sale of the company would increase as well. And based on the analysis of the weekly sales data, the average weekly increased from 541.578 to 649.156. This gives the company an increase of 107.578 of the average weekly sales. But if the company decides to increase the sample size to one hundred and fifty (150), the mean of the sample sales would increase as well and therefore, and the company would be able to get closer to the population.

Q2: Based on the calculated confidence interval for weekly sales on the sample of 50 reps at a 90% confidence level, calculate the following.

Average Weekly Sales

Mean

3966.643077

Standard Error

323.0310322

Median

3907.096154

Mode

#N/A

Standard Deviation

2284.174334

Sample Variance

5217452.388

Kurtosis

-1.285406114

Skewness

0.096968234

Range

7164.865385

Minimum

569.25

Maximum

7734.115385

Sum

198332.1538

Count

50

Confidence Level (90.0%)

541.5779655

The analysis of weekly sales of the rep indicates that the average weekly sale when the confidential level is 90% is 541.578.

Q3: Determine whether there is a statistically different average weekly sale between Sales Rep A and Sales Rep B by doing the following.

Rep B

Mean

4118.866667

Standard Error

278.2098469

Median

4396

Mode

#N/A

Standard Deviation

1523.818089

Sample Variance

2322021.568

Kurtosis

-0.943725802

Skewness

-0.375169677

Range

5290

Minimum

1032

Maximum

6322

Sum

123566

Count

30

Confidence Level (95.0%)

569.003017

Rep A

Mean

4972.266667

Standard Error

368.099649

Median

4703

Mode

#N/A

Standard Deviation

2016.164811

Sample Variance

4064920.547

Kurtosis

-0.836646761

Skewness

-0.293387285

Range

6817

Minimum

1052

Maximum

7869

Sum

149168

Count

30

Confidence Level (90.0%)

625.4480506

The analysis of the weekly sales for rep A and B indicates that there are differences in the mean of weekly sales registered by two sale reps. The average mean for rep A is 4972.27 and rep B registered an average weekly sale of 4118.866667. Therefore, the difference between the two reps is 854.

t-Test: Two-Sample Assuming Equal Variances

Weekly Rep A

Weekly Rep B

Mean

4972.266667

4118.866667

Variance

4064920.547

2322021.568

Observations

30

30

Pooled Variance

3193471.057

Hypothesized Mean Difference

0

df

58

t Stat

1.849552979

P(T<=t) one-tail

0.034737668

t Critical one-tail

1.671552763

P(T<=t) two-tail

0.069475336

t Critical two-tail

2.001717468

The analysis of the weekly sales for rep A and B indicates a unique trend of the sales of the product in a week. Based on the data, it is clear the two sales records for both reps A and B are the same. The analysis established that the P-Value of the two reps A and B is 0.069475. The P- value obtained is higher than the significant value of 0.05 provided, and therefore, the null hypothesis is accepted. This could mean that there is a relationship between weekly sales for Rep A and B. Though rep A made the highest sales compared to rep B, their weekly sales still have some similarities. However, the difference between rep A and B is 854. It means that rep A made a higher sale of 854 than rep B.

Q4: Determine whether this person's average weekly sales are higher than the average weekly sales for the 50 sales reps whose data you used to develop confidence intervals.

z-Test: Two Sample for Means

Average Weekly Sales

Rep A

Mean

3966.643077

4972.266667

Known Variance

0.05

0.05

Observations

50

30

Hypothesized Mean Difference

0

z

-19473.81708

P(Z<=z) one-tail

0

z Critical one-tail

1.644853627

P(Z<=z) two-tail

0

z Critical two-tail

1.959963985

The statistical analysis of the weekly sales for 50 employees and rep B indicates that rep B has a high average sales compared to the average sales for 50 employees. It is established that the average sales for 50 employees are 3966 while the average sales for Rep A are 4972. This means that the difference in average sales between 50 employees and rep B is 1006. It is, therefore; clear that rep A, made a high sales compared to the 50 employees. And thus, in terms of promotion, I can decide to promote rep B for registering higher sales during the month. It is also pointed out that the P- value for the average weekly sales for 50 employees and rep B is 0, P-value =0). The P-Value is, therefore, less than the significant value of 0.05 provided. This means that the null hypothesis is rejected and therefore, there is no direct relationship between the two variables. It could also mean that the average weekly sales of 50 employees and the weekly sales of rep A are not the same.