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MG315

Chapter 12:

What two reasons do we use for the F-Statistic for? (10/200 points)

For testing the hypothesis of equality of two population variances.

For testing the equality of more than two means

What is the critical F value for a hypothesis test with a sample from 5 populations with 20 observations total. Use a .05 significance level. (10/200 points)

We have to see (N-k) and (k-1) degrees of freedom. This makes (20-5) and (5-1). From the table it is 3.87.

Given a calculated F value of 3.5, would you reject or fail to reject the null hypothesis with the critical F value from question 2? (5/200 points)

We will reject the null hypothesis as the calculated value is greater than the critical value.

Arbitron Media Research Inc. conducted a study of the iPod listening habits of men and women. One facet of the study involved the mean listening time. It was discovered that the mean listening time for men was 25 minutes per day. The standard deviation of the sample of the 13 men studied was 8 minutes per day. The mean listening time for the 12 women studied was also 25 minutes, but the standard deviation of the sample was 12 minutes. At the 0.05 significance level.

Given that the null hypothesis is the two variances are equal, do we reject the null hypothesis or fail to reject? (15/200)

We take v1 as the men and v2 as the women

Then df numerator are 13-1 = 12 and df denominator are 25-2 = 23.

The F calculated is s1/s2 that is 8/12= 0.67

The F critical value is 2.04. Thus we accept the null hypothesis of equality of variances.

Chapter 14:

Suppose we wanted to see the cost of gas a person pays per month. We use miles per gallon of their vehicle as the first independent variable, the average distance driven as the second independent variable, and cost of tires as the third independent variable.

Use the following regression output to build a multiple regression equation: (10/200 points)

Coefficient

Standard Error

T

P-value

Constant

84.998

1.863

45.61

0.000

X1

2.391

1.2

1.99

0.051

X2

-0.4086

0.1717

-2.38

0.02

X3

1.153

.789

1.54

0.1587

Cost of gasoline = 2.391* miles per gallon + -0.4086* average distance travelled + 1.153* cost of tires

Which independent variable(s) are statistically significant? (5/200 points)

At 0.05 level of significance only X2 namely the average distance travelled is statistically significant.

Interpret the data in terms of the variables given the output. (10/200)

A unit change in miles per gallon will bring a 2.391 units change in the cost of gasoline. A unit change in average distance travelled will bring an opposite direction change in the cost of gasoline. A change in the cost of tires will bring a 1.153 units change in the cost of gasoline.

The director of marketing at Reeves Wholesale Products is studying monthly sales. Three independent variables were selected as estimators of sales: regional population, per capita income, and regional unemployment rate.

Yhat = 64,100 + 0.394 X1 + 9.6X2 – 11,600X3

Note: Here, the variables X1, X2, and X3 refer to regional population, per capita income, and regional unemployment rate respectively.

What does the 64,00 mean in terms of the equation (aka what is it called?) (5/200 points)

This is the intercept of this equation showing the value of sales when the values of all independent variables is zero.

What are the estimated monthly sales for a particular region with a population of 796,000, per capita income of $6,940 and an unemployment rate of 6.0%? (15/200 points)

= 796000*0.394+6940*9.6-11600*0.06

= 313624+ 380248-696

= $ 693176

Chapter 1:

Describe the difference between inferential and descriptive statistics. (10/200 points)

Descriptive statistics provide the numerical information about the data in the form of mean, median or mode. The inferential statistics help to make inferences about the data and to test these inferences.

Why don’t we just measure populations? Why do we use samples to infer about populations? (10/200 points)

Using populations will be very expensive and time consuming. Samples are groups taken from the populations that are assumed to have same characteristics as the population.

Chapter 2:

Ten people were asked how many siblings they have. Below is the data:

2, 4, 1, 2, 1, 3, 5, 0, 1, 3, 0

Create a frequency distribution table. (20/200 points)

The data is discrete in nature and the range Is less than 10 so we will use the numbers to show the groups.

Group

Frequency

0

2

1

3

2

2

3

2

4

1

5

1

Add on a cumulative frequency column and compute the cumulative frequencies. (15/200 points)

Group

Frequency

Cumulative Frequency

Relative Frequency

0

2

2

2/11

1

3

5

3/11

2

2

7

2/11

3

2

9

2/11

4

1

10

1/11

5

1

11

1/11

Add on a relative frequency column and compute the relative frequencies. (15/200 points)

Chapter 3:

A sample of retailers reported that they had the following number of copies of statistics textbooks in inventory:

57, 81, 69, 84, 85, 79, 71, 74, 55.

What is the mean number of textbooks in inventory? (10/200 points)

Mean is 72.78 calculated by dividing sum of values on number of values.

What is the median number of textbooks in inventory? (10/200 points)

Median is the middle value of arranged data which comes out to be 74.

What is the range of the number of textbooks in inventory? (5/200 points)

Range is calculated as the difference of highest and lowest value which is 85-55 = 30

The standard deviation is 10.98. What is the variance? (round to two decimals) (5/200 points)

Variance is the square of SD thus, 10.98^2 = 120.56

Chapter 4:

Create a Box and Whisker Plot using the following data. Be sure to give the 5 quartiles. (15/200 points)

12, 20, 15, 17, 32, 21, 45, 15

The lower boundary of the above box is the lower quartile of the data. The black line in the box is depicting median. The top boundary of the box shows the third quartile. The lines above and below the tables show the maximum and minimum values in the data.

Subject: Statistics

Pages: 8 Words: 2400

Statistical Analysis

[Enter the name of Student here]

[Enter the name of Institution here]

Problem

A company that makes shoes is facing financial problems and wants to reduce production. It wants that shoe sizes are specified for men and women so that the company makes only the specified shoe sizes for males and females.

Samples

A small sample set of 35 individuals has been provided in the study. It is assumed that this sample adequately represents the whole population.

Statistical Analysis

Analysis of Normality

There are various methods used to analyze the normality of any data set. In the data available, we will use the histogram and two tests of normality to assess it. Following are the SPSS outputs for height and shoe size

The above diagrams show that shoe size and height variables are not normally distributed. The height variable is negatively or left skewed whereas the shoe size variable is positively or right skewed. In order to further test the normality, we will discuss the existence of outliers and then apply some tests of normality to accept or reject the underlying hypothesis.

Outliers Analysis for Shoesize

The above diagram shows that there are no outliers in the variable shoe size which means that there is no extra ordinary value in the data set that differ significantly from mean. The shape of histogram is justified by a greater difference between the median value and the top limit of the data i.e. 14.

The above diagram shows the outliers analysis for the variable height. There are no outliers which means that there are no values that differ significantly from the mean or median. In this case, the shape of histogram is depicted in the fact that lowest value is further away from median as compared to the highest value.

Tests of Normality

Kolmogorov-Smirnova

Shapiro-Wilk

Statistic

df

Sig.

Statistic

df

Sig.

Height

.118

35

.200*

.978

35

.677

ShoeSize

.195

35

.002

.928

35

.055

The above table shows the tests for normality. The Shapiro Wilk test has been used to assess whether the sample has been obtained from a normal population, and whether the sample is distributed normally on its own as well. The sample size is too small to assess whether it has been taken from a normal population or not. The significance figures in Shapiro Wilk test will only let us reject null hypothesis regarding normality in the sample.

Once we have ascertained that the sample is normally distributed, we will now apply the tests to show if this company can use only one shoe size for both genders and for different heights of people. For gender analysis, we will use the t- test for analysis while for the height scenario, we will use the correlation.

A t- test is based upon several underlying assumptions that are discussed in the following lines:

Assumption 1

The dependent variable used in analysis should be continuous in nature which means that it should be measured on the ratio or interval scale. In our example the dependent variable is shoe size which is a continuous variable in a sense that it can take on any value between any two given numbers. A discrete variable on the other hand can take only whole number values. Thus, the first assumption for the t-test has been met.

Assumption 2

The independent variable should be categorical in nature which means that there should be two or more distinct groups in the independent variable under consideration. Our analysis has the independent variable in the name of gender which has two distinct groups namely male and female.

Assumption 3

This assumption states that the individual observations included in the analysis should be independent from each other. There should be different participants in each group and there should be no participant who belongs to both groups. This assumption has been fulfilled as this study includes gender as an independent variable. There is no way that an individual can fall into both distinct groups. Another aspect is that subjects on which test has been performed should not be same. If one or more respondents or subjects fall into more than one groups, the paired sample t-test should be used. A situation where this test is applicable would be a weight loss campaign before and after some particular exercise. In this kind of experiment, subjects will be same on which test will be conducted before and after the exercise.

Assumption 4

There should be no significant outliers in the data. An outlier is a value that is significantly different from all the other values. Basically, outliers are values in the data which are very much different from all other values. The outliers create the issue of validity of the results of t-test. The outliers have already been discussed in relation to the shoe sizes and heights of the respondents.

Assumption 5

The dependent variable should be approximately normally distributed for each group of the independent variable. The word approximately is used because t-test is quite robust to violation of normality in data. The analysis of normality in the above section has shown that the variable shoe size is approximately normally distributed.

Assumption 6

The variances have to be assumed to be equal if independent samples t-test has to be used. The Levene’s test in the t-test allows to check the equality or homogeneity of variances.

Group Statistics

Gender

N

Mean

Std. Deviation

Std. Error Mean

Shoe Size

Male

17

11.294

1.8033

.4374

Female

18

7.111

1.1318

.2668

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

ShoeSize

Equal variances assumed

3.070

.089

8.270

33

.000

4.1830

.5058

3.1540

5.2120

Equal variances not assumed

8.165

26.649

.000

4.1830

.5123

3.1312

5.2348

The above tables show the results of t-tests that have been run to assess if there are any differences between genders regarding their shoe sizes. There are several figures to be assessed in the above tables. Firstly, lavene’s test has shown a value greater than 0.05 which shows that variances of both groups namely male and female are same. This fulfills the assumption of homogeneity of variances for the t-test. Next important aspect in the above table is the significance column in the above table. The values in this column are less than 0.05 which is the level of significance which means that there is a significant difference between the groups of male and females with their respective shoe sizes which means that the company cannot provide one shoe size for both the genders and it has to provide at least two shoe sizes for the different genders.

After analyzing the relationship between shoe sizes and gender, we will analyze the relationship between shoe sizes and heights of the respondents. Following is a table for cross tabulation between height and shoe size.

Height * ShoeSize Crosstabulation

Count

ShoeSize

Total

5.0

6.0

6.5

7.0

7.5

8.0

9.0

9.5

10.0

10.5

11.0

11.5

12.0

13.0

13.5

14.0

Height

60.0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

62.0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

63.0

1

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

2

64.0

0

0

1

1

0

0

0

0

0

0

0

0

0

0

0

0

2

65.0

0

0

2

0

0

0

0

0

0

0

0

0

0

0

0

0

2

66.0

0

0

0

2

0

0

0

0

0

0

0

0

0

0

0

0

2

67.0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

1

68.0

0

0

0

1

0

1

0

1

0

0

0

0

0

0

0

0

3

69.0

0

0

0

0

0

0

0

1

1

0

0

0

1

0

0

0

3

70.0

0

0

0

0

3

0

1

0

0

0

0

1

0

0

0

0

5

71.0

0

0

0

0

1

0

0

0

0

1

1

0

0

0

0

0

3

72.0

0

0

0

0

0

0

0

0

1

1

1

0

1

1

0

0

5

73.0

0

0

0

0

0

0

0

0

0

0

1

0

1

0

0

0

2

75.0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

76.0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

1

77.0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

Total

1

2

4

5

4

1

1

2

2

2

3

1

3

1

1

2

35

A cross tabulation involving height and shoe sizes has been shown above. There is no clear evidence in above table regarding any relationship between the height and shoe size. When we run the correlation analysis between the two variables, we come to know that the correlation coefficient for males is higher than that of females. A bigger shoe is required for men as compared to women. There is a higher demand of shoe size 11-12 in males and 6.5-7.5 in females.

Conclusion

First conclusion is that there is a significant difference between the two genders when they are studied keeping the shoe size in view. There is a positive correlation between heights of people and their shoe sizes. Thus, there is a need of different sizes of shoes made by the company. A shoe size of 11.5 will be suitable for men and 7 will be suitable for women.

Subject: Statistics

Pages: 5 Words: 1500

Hate crime analysis

[Name of the Writer]

[Name of the Institution]

Hate crime analysis

Introduction

LGBTQ is the population that has been targeted for hate crimes. The context of hate crime is as old as the 15 century and 16 centuries when belonging to any of the groups of LGBTQs can lead an individual to capital punishment or felony. Even this population was considered as sinful or mentally ill. It is important to note that as a community and a population, LGBTQ people have a high rate of assault, stigma, poverty, and violence that is hate-motivated. There are a lot of factors that lead to victimization, where one of the major factors is sexual orientation. LGBTQ Community comprises of people who have a different approach to martial life, as compared to the other members of the society (Antebi-Gruszka et al. 2019). The categories in LGBTQ are, lesbians, gay, bisexuals and transgenders and queers. Within this sexual orientation, racism is a subfactor that leads to the victimization of this population. It is important to note that the victimization range from emotional to physical torture that behoves this population group to confine themselves in the dark boundaries of society, such as crime and evil acts. There are a lot of LGBTQs, who are threatened assaulted, beaten, robbed, abused and sometimes they are emotionally tortured (Gerstenfeld et al. 2019). There are a lot of cases, that are heard every now and then because this particular community is not accepted and they are not treated as a part of society.

Applicable case examples

There are a lot of examples that decipher the victimization of this particular group. According to one of the transgender individual, he said that he used the washroom and then the girls were not willing to use the same washroom as if I was having some serious type of or contagious infection (Messinger et al. 2019). According to another LGBTQ community member, she said that she was assaulted when she was holding hands with her lesbian partner. She said that someone held her back and thrust her into him. Afterwards, he attacked her several times verbally. According to another tarns individual, he was beaten up by few of the men who were on the street and when he went to the police station, the police just listened to him and then they did nothing. He was left with no choice, other than to make his way back home. According to another case, that was brought to the highlights of media was, one of the gay couples was beaten up by a community in which they bought a new house (Gerstenfeld et al. 2019). According to them, they were having a normal routine of returning from their work when they see the neighbours standing with some weapons and sticks. They started beating the couple and then they were thrown out of the building. Although they fulfilled all the requirements of the house, still they were pushed to leave society. When they went to the police, they said that their existence could cast a negative impact on the people so they should try to find a house in their community where they could live freely (Gerstenfeld et al. 2019).

State Laws

There are a lot of state laws that have been formulated with the passage of time to criminalize hate crime such as The United Nation Human Rights Committee, The UN Committee on the cultural, social and economic rights and the UN Committee on the elimination of all the forms of discrimination are all universal approaches that are trying to overcome the differences that the LGBT community has the face within society and their victimization because of hatred. Equality Act 2010 is another major platform that replaced the other laws such as Sex Discrimination Act 1975, Employment Equality Act 2003, Race Relations Act 1976 and then Employment Equality Act 2006, that were all designed to support LGBT community and this law is a universal approach that addresses equality for all the members of this community (Messinger et al. 2019).

Data Collection

The data would be collected by using different databases such as ProQuest and google scholar. After the choice of the database, some keywords would be identified as “victimization", "LGBT", and "Hate crime". After that, all available and displayed research articles would be chosen and few of them will be finalized so that highly relevant and to the point, information can be collected. This method will be used because it is one of the most effective and best ways of collecting relative and critical information that can provide authentic and to the point data (Chakraborti et al. 2015).

Criminological theories

The criminological theory that deals with the selected population is Queer criminology. This theory considers the experiences of the LGBTQ people as offenders or victims as required. It explores the instructions given by the criminal justice system as a mechanism that can help to control Queer identities and protect their lives and lifestyle by ensuring safety and providing them with legal rights. It is important to note that mainstream criminology has failed to address the experiences and victimization of the LGBTQ community so, Queer Criminology is a theory that can help to ensure that the concerns and the hate motivate crime and victimization can be stopped (Antebi-Gruszka et al. 2019).

This theory is selected because LGBTQ is termed as one of the separated dimensions or aspects of the society, they are segregated from the normative structure of normal society so this theory as the name shows can be one of the best choices for addressing the issues of victimization. Also, this theory has strict and strong rules that can help to address the concerns of this population in particular (Antebi-Gruszka et al. 2019).

References

Antebi-Gruszka, N., Mor, Z., & Shilo, G. (2019). Mental distress, well-being, and stress-related growth following an anti-LGBQ hate crime among LGBQ young adults in Israel: The effect of familiarity with the victims and the mediating role of emotional support. Journal of homosexuality, 1-19.

Chakraborti, N., & Garland, J. (Eds.). (2015). Responding to hate crime: The case for connecting policy and research. Policy Press.

Gerstenfeld, P. B. (2019). Hate Crimes against the LGBTQ Community. The Encyclopedia of Women and Crime, 1-5.

Messinger, A. M., & Koon-Magnin, S. (2019). Sexual Violence in LGBTQ Communities. In Handbook of Sexual Assault and Sexual Assault Prevention (pp. 661-674). Springer, Cham.

Subject: Statistics

Pages: 3 Words: 900

Hypothesis

Student’s Name

Institution

STEP 1:

A sample collected is true random when it is not bias to any issue. In order to ensure that the sample is random, various methods can be used to draw the sample from a population and will be using random numbers. This numbers can be selected using sample of n=12 men and n=8 women from the population of men and women and this should be done using random numbers and therefore, this will make sure that the sample obtain is truly random.

STEP 2:

Men

Women

Time

Exercise per week

Favorite

Time

Exercise per week

Favorite

1

0.22

3

Aerobic

1

0.22

3

Aerobic

2

0.22

3

Push up

2

0.2

3

Push up

3

0.2

5

Aerobic

3

0.19

5

Aerobic

4

0.19

4

Aerobic

4

0.18

4

Cardiovascular

5

0.18

4

Cardiovascular

5

0.2

4

Push up

6

0.2

0

N/P

6

0.22

0

N/P

7

0.22

2

Aerobic

7

0.19

5

Aerobic

8

0.26

3

Aerobic

8

0.25

2

Flexibility

9

0.23

3

Cardiovascular

10

0.22

4

Aerobic

11

0.25

0

NP

12

0.2

3

Aerobic

Mean

0.216

2.833333333

5

0.20625

3.25

Std

0.024

1.527525232

2

0.02264

1.669045921

STEP 3:

Mean for men = 2.833mean for Women =0.206

Standard Deviation for men= 1.5275Standard deviation for women = 1.6690

2.) The Median number of days per week exercised

Media for number of days for men = 3 and for women 3.5

3.) The Mode of the favorite exercise

The mode of the favorite exercise for men is Aerobics and for women is Aerobics and Cardiovascular

4.) The 90% confidence interval of the mean

The needed 90% confidence interval of the mean for women = (0.205, 0.252) and for men (0.20, 0.23)

STEP 4:

The two groups are significant differences. The hypothesis tested obtained that alpha is more than 0.05 and therefore, the null hypothesis is rejected. This means that there are significant differences between the two groups.

Subject: Statistics

Pages: 1 Words: 300

661 N University Drive

apt 102

Pembroke Pines, FL 33024

United States (USA)

August 1, 2019

Alison Stacy, DNP, RN,

Roxane R. Chan, Ph.D., RN

Rebecca H. Lehto, Ph.D., RN

Dear:

I am writing this letter with great pleasure to applaud your significant efforts in the health industry. This complexity of health industry often becomes a conundrum for new entrants like me who are about to dive in this deep venture. Nevertheless, I get highly driven by your research articles and findings in the medical field. Similarly, I have gone through your featured article "Improving Knowledge, Comfort, and Confidence of Nurses Providing End-of-Life Care in the Hospital Setting Through Use of the CARES Tools "in detail. Beyond a shadow of a doubt, this research article is an important piece of writing as it clarified the significance of CARES tools and Final Journey related to end of life in the nursing profession.

However, I believe that this article is quite limited in nature as it took only eleven nurses in a private hospital and even among those eleven nurses, two did not participate for some reasons. This sample space is limited for the effectiveness of any study. The article should have been more diverse and a greater number of nurses in other wards should have been a part of these findings. This current sample space is easy to manipulate and this is the reason that most medical researchers are getting defective as the European Journal of Clinical investigation illustrates it.

In this study, the variables under consideration are the CARES tools, End of life experiences, knowledge, confidence, and the comfort of the nurses, and the Final Journey. It is no blinking the fact that all these variables were investigated by surveys, interviews and different questionnaires. All these methods have an inherent sense of subjectivity which is obvious. This subjectivity and bias have become the reason of flawed medical processes and this thesis is elaborated in the Medscape Journal where chief health care practitioners view that medical research is getting misleading, and it needs to be fixed.

Therefore, it is utmost necessary to make medical researches and findings as objective as it is possible. These medical findings and researches are often the rationale for future health policies, so one needs to be objective and diverse in nature and purpose. Besides, the sample space has to cater all and sundry to accommodate all varied concerns.

I hope that you will give serious consideration to my response to your work and will make it even more enlightening in the future.

Regards,

Midiala Suarez

Subject: Statistics

Pages: 1 Words: 300

Letter to the Author of the Research Study

[Name]

[Institution]

[Date]

Dear Authors,

I write in response to the publication you have written about the Health Promotion Behaviors affect the level of job satisfaction and jobs tress for nurses in an acute care hospital. I find your work quite fascinating but I have some criticism as well regarding your work. Your target population shows that the sample you have selected includes 750 nurses and 46 percent out of them were above 50 years old and up to 94 percent were female. White employees were 74 percent and 80 percent of the population were well educated with bachelor degrees. Most of the participants had experience of 20 years ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"BrCVvlq6","properties":{"formattedCitation":"(Williams, Costley, Bellury, & Moobed, 2018)","plainCitation":"(Williams, Costley, Bellury, & Moobed, 2018)","noteIndex":0},"citationItems":[{"id":10,"uris":["http://zotero.org/users/local/EWcKoWvh/items/LIQI6FXU"],"uri":["http://zotero.org/users/local/EWcKoWvh/items/LIQI6FXU"],"itemData":{"id":10,"type":"article-journal","title":"Do Health Promotion Behaviors Affect Levels of Job Satisfaction and Job Stress for Nurses in an Acute Care Hospital?","container-title":"JONA: The Journal of Nursing Administration","page":"342-348","volume":"48","issue":"6","author":[{"family":"Williams","given":"Heather L."},{"family":"Costley","given":"Teresa"},{"family":"Bellury","given":"Lanell M."},{"family":"Moobed","given":"Jasmine"}],"issued":{"date-parts":[["2018"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Williams, Costley, Bellury, & Moobed, 2018).

The issue with this set of the population is that the employees belong to a faith-based institute and the majority of them are female and of old age. Their age and gender will divert the result of the study. The data is collected in a single hospital. If the target group consisted of people of different age group. People with different level of experience, with different stress management skills and with different education level then the findings might not just show healthy behavior as a key to cope with stress. I would suggest more in-depth research on the given topic which will be including various hospital, employees with different degrees, different age groups and different level of experience.

Our point of view on your research work is that the target group and sample size needs some modification to get more accurate results. Moreover, the research paper has highlighted the most important issue faced by nurses and the finding has great implication for nursing practice.

Best regards,

Your name

Reference

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Williams, H. L., Costley, T., Bellury, L. M., & Moobed, J. (2018). Do Health Promotion Behaviors Affect Levels of Job Satisfaction and Job Stress for Nurses in an Acute Care Hospital? JONA: The Journal of Nursing Administration, 48(6), 342–348.

Subject: Statistics

Pages: 1 Words: 300

Statistics

Name of the Writer

Name of the University

Statistics

Chapter 8

True

Mean= 3.3

The sample size has an avid effect on the standard error of the mean. This is because sample size or (n) is considered the denominator in the standard error formula (Hamaker & Ryan, 2019). So as the sample size increases leading to less variation in the results and showing a decrease in the standard error.

Mean = $35420

Standard Deviation = $2399

Standard Error=σ√N

= 2399√287

= 141.61 = 142

Chapter 9

Z-score for 95% confidence

Subtracting confidence level from and then dividing by 2 = (1-0.95)2 = 0.025

Subtracting value from 1 = 1 – 0.025 = 0.975

Z-score = 1.96

b) At 95% confidence interval

= Z*σ√N

= 1.96*25 √250

= 3.10

Lower end of range = (20 - 3.10) = 16.90

Upper end of range = (20 + 3.10) = 23.10

Confidence interval = (16.90, 23.10)

Subtracting confidence level from and then dividing by 2 = (1-0.90)2 = 0.05

Subtracting value from 1 = 1 – 0.05 = 0.95

Z-score = 1.65

At 90% confidence interval

= Z*σN

= 1.65*3 33

= 0.86

Lower end of range = (10.7 – 0.86) = 9.84

Upper end of range = (10.7 + 0.86) = 11.56

Confidence interval = (9.84, 11.56)

Chapter 10

7.

This is a two-tailed test. The reason for this is that this test is using a significance level of 0.05 and the testing is done in the direction of the relationship hypothesized on both sides (Kock, 2015). This is because a two-tailed test allocates one half of alpha to one side and one to the other.

Z-score at 0.05 significance level is 1.64

= 1.6+0.04 = 1.64

Z-statistic = x-μoσN

Z-statistic = 215-2201564

Z-statistic = -2.67

According to the z-table, the p-value is 0.0038

According to the p-value obtained, we reject the null hypothesis as the value is less than 0.05 level of significance.

8.

Null hypothesis: H0=90 min

Alternative hypothesis: H1>90 min

Z-statistic = x-μoσN

Z-statistic = 96-901218

Z- statistic = 2.12

P-value using the test statistic above was found to be 0.0170

We reject the null hypothesis that typical park visitor spends around 90 mins in the park but we accept the alternative hypothesis which states that visitors stay longer than 90 mins in the park.

Chapter 12

9.

a)The dependent variable is the number of sales.

b)Mean of x (Number of airings) = 4

Mean of y (Number of sales) = 17

Standard deviation of x = 1.58

Standard deviation of y = 6.12

Sum of Corresponding standardized value (zxzy)= 3.72

Correlation Coefficient = 3.725-1

Correlation Coefficient = 0.93

Calculations:

Locations

X

Y

(Zx)

(Zy)

(ZxZy)

Providence

4

15

0.00

-0.33

0.00

Springfield

2

8

-1.26

-1.47

1.86

New Haven

5

21

0.63

0.65

0.41

Boston

6

24

1.26

1.14

1.45

Hartford

3

17

-0.63

0.00

0.00

mean= 4

mean = 17

sum= 3.72

Std dev=1.58

Std dev= 6.12

Correlation coeff

0.93

c)A positive value for the correlation coefficient shows that as the value of x increases so does the value of y. Similarly, even x decreases then the values for y would also decrease (Emerson, 2015).

10. The following sample observations were randomly selected.

X53634468

Y131571213119

a)Value for x-bar = 4.88

Value for y-bar = 11.43

Calculations:

X

Y

5

13

3

15

6

7

3

12

4

13

4

11

6

9

8

x-bar=4.88

y-bar=11.43

b)Value for x-bar = 4.88

Value for y-bar = 11.43

standard deviation of x = 1.73

standard deviation of y = 3.38

Sum of Corresponding standardized value (zxzy)= -8.89

Correlation Coefficient = -8.898-1

Correlation Coefficient = -1.27

Calculations:

X

Y

Zx

Zy

(ZxZy)

5

13

0.07

0.46

0.03

3

15

-1.08

1.06

-1.15

6

7

0.65

-1.31

-0.85

3

12

-1.08

0.17

-0.18

4

13

-0.51

0.46

-0.24

4

11

-0.51

-0.13

0.06

6

9

0.65

-0.72

-0.47

8

1.81

-3.38

-6.11

X-bar=4.88

Y-bar=11.43

Sum=-8.89

c)Slope =y2-y1x2-x1

Slope =13-115-4

Slope = 2

d)y=a+bx

Taking (4,11) and putting it in the equation

11=a+2(4)

11=a+8

a=3

e)The equation of the regression line is as follows: yhat=3+2x

References

Emerson, R. W. (2015). Causation and Pearson's correlation coefficient. Journal of visual impairment & blindness, 109(3), 242-244.

Hamaker, E. L., & Ryan, O. (2019). A squared standard error is not a measure of individual differences. Proceedings of the National Academy of Sciences, 116(14), 6544-6545.

Kock, N. (2015). One-tailed or two-tailed P values in PLS-SEM?. International Journal of e-Collaboration (IJeC), 11(2), 1-7.

Subject: Statistics

Pages: 7 Words: 2100

Central tendency

Student’s Name

Institution

Date

Central tendency

The analysis of data using the central tendency requires the use of mean, median and mode. However, as an IT center representative, the use of mode as central tendency would be the best technique to identify the technical problems and the call frequent or issues in the call center. According to Clifford and Alexander (2002), the mode is the figure or value, which appears most frequently in a data. As an IT call center representative, using the mode as a central tendency would be to identify the most reported technical problem by clients. In order to find the mode, the data is arranged in sequence and the frequently appeared figure is the mode. And therefore, in the case of technical issues, the frequently reported technical problems among all the problems reported would be the mode. It is, therefore, means that mode would be the best way to identify the technical problem, which is being encountered by clients based on the report gathered by the call center.

The most reported technical issues are a software update, internet connectivity printer failure or not working. Based on the data, most clients have problems related to software on their computers, printer failure or not working and internet connectivity. It is because using the mode as a central tendency, software related issues, internet connectivity, and printer failure appears more frequent compared to other technical issues reported by clients.

Follow up post 1

The central tendency mode is the idea in identifying issues and finding out the common value. And based on the technical report, it is evidence that printer, internet, and software are common issues faced by clients. These issues cannot be identified using median and mean and this makes the mode to be the best central tendency, which should be used in a Call Center by an IT call center representative.

Follow up post 2.

The mode provides a clear understanding of the happenings with data. And based on the analysis, the internet connective appears more often on the data and therefore, it is the mode. However, it is followed by software and then printer related issues. In this case, it can be concluded that mode is an important central tendency, which can be used to analyze data for easy understanding and interpretation.

References

BIBLIOGRAPHY Clifford, K., & Alexander, P. (2002). Data analysis as the search for signals in noisy processes. Journal for Research in Mathematics Education, 33 (4), 12-38.

Subject: Statistics

Pages: 1 Words: 300

[Name of the Writer]

[Name of Instructor]

[Subject]

[Date]

Normal Distribution

Answer 1

The Z- scores are important because they can be used to calculate the probabilities of certain events that are assumed to be normally distributed. There is also a possibility to compare scores from two different normal distributions which can have considerable research implications. In order to compare two discrete variables, the standardized normal distribution is used to compare these scores under a single normal distribution.

Answer 2

No this is not true that z scores do not have any units. Basically, the z scores are used to measure the distance between a certain value from a sample and population mean in terms of standard deviation. The unit of Z score is the population standard deviation.

Answer 3

The normal distribution is a continuous probability distribution that contains a formula for converting the original scores to the z- scores. This makes it a suitable option to compare values having different units because the units are converted to the same ones i.e. population standard deviation. The distribution is called standard normal distribution so it also allows comparing scores from two different normal distributions.

Answer 4

The formula for the normal distribution allows us to compare two z scores and decide on the performance or other criteria directly. This is because the numerator of the normal distribution formula shows the difference between the population mean and the given value. In general, more difference between the given value and population mean will result in a higher z score. This will mean that this value will need higher boundaries to fall under the standard normal curve. On the other hand, a smaller value will have a lower score on the normal distribution table which will mean that it will need narrower boundaries to fall under a normal distribution curve. The population standard deviation remains the same for the two values compared.

Work Cited

Mario F. Triola. Essentials of Statistics (6th Edition) 6th Edition. Pearson publishers, 2019.

Subject: Statistics

Pages: 1 Words: 300

Author

Institutional Affiliation

Course Number

Date

Probability

Probability is a mathematical subject which falls under the category of applied mathematics and has a wide range of application in applied science. By definition, probability computes the likelihood of an event and its value remains between 0 and 1 inclusive. This essay will discuss the concept of mutually exclusive events and independent events and both the concepts fall under the scope of probability. Under the concept of mutually exclusive events, no two events can occur at the same time.

Flipping a coin for a toss is the best example that can be discussed to explain mutually exclusive events and their dependence or influence on one another. When the occurrence of one event gets nullified by the occurrence of other(s), the events will be termed as mutually exclusive. For instance, when some tosses a coin then the outcome will either be a head or a tail. Let suppose A is the event when the head appears after tossing the coin and B is the event when the tail appears after tossing the coin. Mathematically mutually exclusive events will be written as follows.

P (A ∩ B) = 0

P (A ∪ B) = P(A) + P(B)

P (A ∣ B) = 0

P (A ∣¬ B) = P(A) / 1- P(B)

The above formulas represent mutually exclusive events.

Mutually exclusive events cannot be independent because none of the aforementioned events in case of flipping a coin influence each other. Mathematically, it can be written as:

P (A ∩ B) = P(A) P(B)

P (A ∪ B) = P(A) + P(B) - P(A) P(B)

P (A ∣ B) = P (A)

P (A ∣¬ B) = P(A)

The above equations demonstrate that both A and B are independent.

The condition under which P (A and B) = P(A)*P(B) is true when both the events are independent of each other i.e. the occurrence of the head is not being influenced by the tail and vice versa. This statement will hold if and only if both the events are independent.

The condition under which P (A and B) = P(A)*P(B) is false when both the events are mutually exclusive i.e. if tail occurs after flipping a coin head cannot occur and vice versa.

Subject: Statistics

Pages: 1 Words: 300

Probability Techniques

Your Name (First M. Last)

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

Probability Techniques

Probability is the discipline that has changed the course of science. It has opened the door of unlimited possibilities in the science and mathematics that we would have never encountered otherwise. Evaluating the likelihood of occurrence of any event before it happens has helped to make the scientific processes smooth and ready for new challenges.

Computer scientists measure the success and failure of any program before even running it. Probability also becomes a reason for building any computer program and software. Nowadays in time of social media, probability algorithms are used to target the particular audience on the basis of their search results. These results are matched with the other themes on the basis of probability to provide the better user experience. Modern search engines also use probability to extract the useful information for their users ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"YWqrZ231","properties":{"formattedCitation":"(\\uc0\\u8220{}PageRank Algorithm - The Mathematics of Google Search,\\uc0\\u8221{} n.d.)","plainCitation":"(“PageRank Algorithm - The Mathematics of Google Search,” n.d.)","noteIndex":0},"citationItems":[{"id":1230,"uris":["http://zotero.org/users/local/KZl8ZL3A/items/6D54PWT5"],"uri":["http://zotero.org/users/local/KZl8ZL3A/items/6D54PWT5"],"itemData":{"id":1230,"type":"webpage","title":"PageRank Algorithm - The Mathematics of Google Search","URL":"http://pi.math.cornell.edu/~mec/Winter2009/RalucaRemus/Lecture3/lecture3.html","accessed":{"date-parts":[["2019",2,2]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (“PageRank Algorithm - The Mathematics of Google Search,” n.d.). In technical terms, transferring data using fast protocols never guarantee the delivery of 100 % data packet. For this reason, developers evaluate the probability of events of failures and set a stochastic limit to tell the user about the poor communication situation. Error Correction codes that are used to handle the duplication of data in case of errors in hard disks and SSD ’s; also use probability and statistics.

Nowadays MENET-IoT is used in big multimedia data, due to its cost-effective nature and mobility but it faces serious problems of energy consumption and congestion for handling MBD data. In order to solve this problem, Low Energy Adaptive Clustering Hierarchy (LEACH) is suggested that requires the use of probability in the cycling method to find which cluster heads are used in the setup phase ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"gdABRRNq","properties":{"formattedCitation":"(Al-Qarni, Almogren, & Hassan, 2018)","plainCitation":"(Al-Qarni, Almogren, & Hassan, 2018)","noteIndex":0},"citationItems":[{"id":1232,"uris":["http://zotero.org/users/local/KZl8ZL3A/items/2ESDHW2Z"],"uri":["http://zotero.org/users/local/KZl8ZL3A/items/2ESDHW2Z"],"itemData":{"id":1232,"type":"article-journal","title":"An efficient networking protocol for internet of things to handle multimedia big data","container-title":"Multimedia Tools and Applications; Dordrecht","page":"1-18","source":"ProQuest","abstract":"In recent years, the emergence of multimedia big data (MBD) due to the excessive use of mobile Internet of Things (ioT) is imposing various challenges to develop efficient communication with the digital world. In this aspect, Mobile Adhoc Network based IoT (MANET-IoT) system is becoming popular due to its greater mobility support and cost-effective nature. A mobile ad hoc network (MANET) consists of randomly placed, battery-powered, moving nodes without an infrastructure that can administer and control traffic in the IoT network. In the MANET-IoT network, the major problems include energy consumption and congestion control to handle MBD data. In this paper, we present two proposals for solving these problems. In the first proposal, a new clustering approach that depends on a well-known protocol called the Low Energy Adaptive Clustering Hierarchy (LEACH) been used in a wireless sensor network (WSN) with modification to adapt to the MANET-IoT’s mobility. Our proposal for applying LEACH to a MANET-IoT consists of rounds, each containing three ordered phases as follows: (1) the announcement phase, in which all nodes announce their remaining energy and the node with the original message also announces itself; (2) the setup phase, in which all cluster heads are selected based on the probability factor with a cycling method; and (3) the steady state phase, in which message delivery to all nodes occurs using several types of links. The second proposal is to provide congestion control for all mobile nodes by link utilization that can support different data rates depending on the link status. Simulation results comparing our modified LEACH protocol to state-of-the-art protocols with utilized links show a great enhancement in energy consumption, received data, throughput, and delay.","DOI":"http://dx.doi.org/10.1007/s11042-018-6883-7","ISSN":"13807501","language":"English","author":[{"family":"Al-Qarni","given":"Bandar H."},{"family":"Almogren","given":"Ahmad"},{"family":"Hassan","given":"Mohammad Mehedi"}],"issued":{"date-parts":[["2018",11]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Al-Qarni, Almogren, & Hassan, 2018).

While testing various software, probability concepts are also used. In this research paper probability of program failures and probability of correctness is used in the testability phase of a program to estimate its correctness. Bayesian interference and probability are used to argue the usefulness of the programs during testing ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"xgBXo85x","properties":{"formattedCitation":"(Bertolino & Strigini, 1996)","plainCitation":"(Bertolino & Strigini, 1996)","noteIndex":0},"citationItems":[{"id":1233,"uris":["http://zotero.org/users/local/KZl8ZL3A/items/G6EHLCWC"],"uri":["http://zotero.org/users/local/KZl8ZL3A/items/G6EHLCWC"],"itemData":{"id":1233,"type":"article-journal","title":"On the use of testability measures for dependability assessment","container-title":"IEEE Transactions on Software Engineering; New York","page":"97-108","volume":"22","issue":"2","source":"ProQuest","abstract":"Program testability is, informally, the probability that a program will fail under test if it contains at least one fault. When a dependability assessment has to be derived from the observation of a series of failure-free test executions (a common need for software subject to ultra-high reliability requirements), measures of testability can - in theory - be used to draw inferences on program correctness (and hence on its probability of failure in operation). The concept of testability and its uses in dependability assessment, criticizing, and improving on, previously published results are examined.","DOI":"http://dx.doi.org/10.1109/32.485220","ISSN":"00985589","language":"English","author":[{"family":"Bertolino","given":"Antonia"},{"family":"Strigini","given":"Lorenzo"}],"issued":{"date-parts":[["1996",2]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Bertolino & Strigini, 1996).

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Al-Qarni, B. H., Almogren, A., & Hassan, M. M. (2018). An efficient networking protocol for internet of things to handle multimedia big data. Multimedia Tools and Applications; Dordrecht, 1–18. http://dx.doi.org/10.1007/s11042-018-6883-7

Bertolino, A., & Strigini, L. (1996). On the use of testability measures for dependability assessment. IEEE Transactions on Software Engineering; New York, 22(2), 97–108. http://dx.doi.org/10.1109/32.485220

PageRank Algorithm - The Mathematics of Google Search. (n.d.). Retrieved February 2, 2019, from http://pi.math.cornell.edu/~mec/Winter2009/RalucaRemus/Lecture3/lecture3.html

Subject: Statistics

Pages: 1 Words: 300

Quantitative Analysis Approach

Name

Institutional Affiliation

Quantitative Analysis Approach

Quantitative analysis is currently the most preferred data analysis approach to make informed decisions. Through qualitative analysis, data is collected and evaluated for understanding business performance and behaviour. Therefore, Analytical approach allows results reported in arithmetic terms be given a certain percentage of confidence. In this project, a case study of Brazil Sugarcane Cultivation for Bioethanol Production is reviewed, and the outcomes are examined using sensitivity and post-optimality study.

Sensitivity study calculations mostly aims variables affected by other variables variations such as input variables. The study assists in pinpointing the significant variables that mainly influence the benefits and cost of the project (Lee & Lim et al., 2017). For example, expenses, operating costs and legal costs, revenues and financial interests are encompassed in this stage. It is calculated dividing the change in the change in input over the change in output.

Sensitivity of NPV to each input

Revenue = productivity data * Sugar price on conveyance belt + [total reducing sugar * raw material quality* productivity data]

= 13085.54 + [0.95 *135.10] * 501

= 13085.54 + 128.345*501

= 13085.54 + 64300.845

= 77,385.385

Cash flow= sugar field reform cost +Revenue – production cost

= 2652.24 + [0.0154*(77,385.385-8663.43)]

=2652.24 + 1058.32

=3710.56

NPV at 16.3% discount rate = cash flow/ [1 + 16.3%) 1

= 3710.56/[17.3%]

Change in NPV=21448.32-1759.49/1759.49

=11.2%

Sensitivity in NPV =11.2%/0.01

=11.2%

The sensitivity of the Net Present Value is 11.2% which will happen if the sales price rise by 1%. Alternatively if the sales reduce by 1% the NPV value will decrease by 11.2% (Simões, Cervi & Batistela, 2018). The above calculations not only show the connection between output and input, but it also explains how sensitive output is to each input. Net present value is crucial and most sensitive to estimate the production costs of variables such as, fertilizers, agricultural pesticides, seedlings, mechanized operations, soil correctives, sugarcane cutting costs, loading and transportation.

References

Simões, D., Cervi, R. G., & Batistela, G. C. (2018). Quantitative Analysis of the Economic Risk of Sugarcane Cultivation for Bioethanol Production: A Case Study in Brazil. BioResources, 13(3), 6497-6509.

Lee, B., Chae, H., Choi, N. H., Moon, C., Moon, S., & Lim, H. (2017). Economic evaluation with sensitivity and profitability analysis for hydrogen production from water electrolysis in Korea. International Journal of Hydrogen Energy, 42(10), 6462-6471.

Subject: Statistics

Pages: 1 Words: 300

Name: __________________________________________________________________________________

Statistics I, Quiz #4

A survey was conducted of students’ residences. Data was gathered from a random sample of 1000 students. The data is summarized in the table below.

Gender and Residence of Students

Males

Females

Apartment off campus

50

90

Dorm room

150

210

With Parent(s)

100

50

Sorority/ Fraternity House

200

150

Start with fractional probabilities, convert to decimals, and round final answers to three decimal places.

1.) What is the probability that a student is female and lives in a dorm?

The probability of female students living in Dorm room = 210

Total students = 1000

P(female at dorn) = 210/1000

= 0.210

2.) What is the probability that a student is female given that she lives in a dorm?

The probability of female students living in Dorm room = 210

P(female at dorn) = 1/210

= 0.004

3.) What is the probability that a student lives in a dorm given that she is a female?

Total female students living in a dorn = 210

Total female students =500

P(female student living in dorn) = 210/500

= 0.420

4.) What is the probability that a student lives in a dorm or an apartment off campus?

Total students living in dorn = 360

Students living in Apartments = 140

Probability= 140/1000

= 0.140

The exercises on the back page relate to the binomial probability distribution. You will need to obtain your answers from MS Excel.

Round final answers to three decimal places.

For each question, you may want to show the data you enter into Excel for partial credit.

number of success

number of trials

probability of success

cumulative probability (true or false) ?

A recent report shows that 80% of elementary school teachers have a computer at home. 12 elementary school teachers are randomly selected.

5.)Find the probability that exactly 10 of them have a home computer.

Probability = 10/12

= 0.8333

6.) Find the probability that 8 or less have a home computer.

P = 8/12

P = 0.666

P is less or equal to 0.666

7.)Find the probability that 12 or fewer have a home computer.

P = 12/12

Probability is equal or less than 1.000

8.) Find the probability that 7 or more have a home computer.

P = 7/12

P = 0.583

Probability is equal to 0.583 or more.

9.)Find the probability that 9 or 10 have a home computer.

P= 9/12

P = 0.750

P= 10/12

P= 0.833

For 9 the probability is 0.750 and for 10 it will be 0.833

Subject: Statistics

Pages: 1 Words: 300

Reflection Paper

Student’s Name:

Institutional Affiliation:

Reflection

Introduction

I have learned a lot in the statistics class this semester. This paper will involve reflective writing to recount what I have been able to learn in the statistics class. Reflective writing aims to assist in determining from the specific practical experience (DeGroot & Schervish, 2012). It helps in making connections between what was taught in theory and what I am needed to do in practice. This reflection will include descriptive statistics, inferential statistics, hypothesis development, and testing, selection of the appropriate statistical tests and evaluation of the analytical results.

Descriptive statistics

I learned that the descriptive statistic is the summary statistic that describes features of the collection information quantitatively. I understood the dissimilarity between the descriptive statistics from the inferential statistics is that the descriptive statistics main aim is to summarize the sample instead of using the data in learning more concerning the population that the data sample is representing (DeGroot & Schervish, 2012). Overall, it means that the descriptive statistics are not generated from the probability theory and usually are nonparametric statistics. The teacher mentioned that even when the data analysis draws the main conclusions by using the inferential statistics, the descriptive statistics are mostly presented. The descriptive statistics can be applied in the Business Information Systems to provide the necessary information concerning the dataset variables and highlighting the association between the variables.

Inferential statistics

I learned that the inferential statistics utilize the random sample of data derived from the population for the description and in making of inference concerning the society. The inferential statistics are significant when investigating every member of the whole people when it is not possible or convenient (Hogg et al., 2015).. For instance, when measuring the diameter of every nail which is manufactured in the mill is impractical; however one can measure the diameter of the representative random sample of the pins. I understood that the information from the sample could be used to generalize the widths of the nails. Inference statistics application in the Business Information System to permit the company to test the hypothesis and come up with the conclusion concerning the information.

Hypothesis development and testing

I learned that the hypothesis is directly connected to the theory; however, it compromises operationally variables that are defined and in the testable form. Assumption permits us to establish via research if the method is accurate. The teacher mentioned that there is a research hypothesis and the null hypothesis (DeGroot & Schervish, 2012). The null hypothesis is abbreviated H0 with the research hypothesis being abbreviated H0=1. To make research as specific one of the two outcomes are addressed. In concluding that their absence of difference between the means, the null hypothesis is accepted.

Nevertheless, when the null is not true, it is rejected and the conclusion I made that the alternative hypothesis is correct. Hypothesis testing I act in statistics where the analysts carry out the test concerning the population parameter. The employed methodology by the analysts will depend on the kind of the used data and the analysis reasons. In Business Information Systems, hypothesis testing could be set up to determine how the increase in labor will affect productivity.

Selection of appropriate statistical tests

When selecting the proper statistical analyses, it was a challenge to me. There is the strategy that is applied when selecting the proper statistical test: it involves the selection of the correct test and afterward the variety of the appropriate sample size for the test. The level of measurement is defined as every variable to be comprised in an analysis (Hogg et al., 2015). The variables needed to be regular or categorical, ratio level, interval, rank or ordinal ordered. It is done for both the dependent and independent variables. Secondly in the selection of the correct statistical analyses, one will need to clarify what is required to be found. Thirdly the power analysis or the sample size calculation is directly connected to the statistical test that is selected. The estimate of the sample size is based on the alpha, effect size, and power. In Business Information Systems, the selection of the appropriate statistical tests is significant because it will affect the quality of the results.

Evaluating statistical results

We learned that the statistical analysis is the quantitative method for establishing the probabilities between groups of data. The study assists in elaborating the patterns or trends found in the research of the topic. The result is analyzed to establish if it is accurate. Evaluation of the statistical analysis is done to check if the null hypothesis is disapproved, to investigate the data quality (DeGroot & Schervish, 2012). The evaluation methods include the ANOVA test. The ANOVA is the tool of evaluation that makes sure that the averages exist within every variable of the test group. When there is not, the sizes of the samples in the analysis might be incorrect. Regression is set up which is the general tool of statistics to establish if the variables have a relationship. The qualitative analysis aspect is analyzed. The researchers provide a summary of what the information means and the entire scope of the study is assessed, and the conclusion is made on the analysis. In the Business Information System, evaluation of the statistical results is essential to check if the quality of the data.

Conclusion

In conclusion, I learned statistics which is essential for my career. The statistics topics include descriptive statistics, inferential statistics, hypothesis development, and testing, selection of the appropriate statistical tests and evaluation of the statistical results. The topics are very important for my course, Business Information systems.

References

DeGroot, M. H., & Schervish, M. J. (2012). Probability and statistics. Pearson Education.

Hogg, R. V., McKean, J., & Craig, A. T. (2005). Introduction to mathematical statistics. Pearson Education.

Kenney, J. F. (2013). Mathematics of statistics. D. Van Nostrand Company Inc; Toronto; Princeton; New Jersey; London; New York,; Affiliated East-West Press Pvt-Ltd; New Delhi.

Subject: Statistics

Pages: 3 Words: 900

Research Design and its applications

[Name]

[Institute]

[Date]

Research Design and its applications

Ethics in Research

Researchers have found out that there are different dimensions of ethics in research. The first is Procedural ethics. It is a mandatory kind of ethics and needs to be followed by everyone while conducting research. The Institutional Review Board ensures that ethical procedure like signing consent before asking candidates to be the part of a research, right to privacy, protecting human from harm and deception. Such terms come under procedural ethics and those should strictly be followed by every researcher. The second is Situational ethics that deals with a moment that comes up in a field or in a particular situation and needs to be taken care of. Like someone might have felt some discomfort due to a specific research question and that should be properly handled. The third one is relational ethics that recognize mutual respect and values each other dignity. It connects the researcher with the participants of the research ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"U0OFYI1T","properties":{"formattedCitation":"(Ellis, C, 2007)","plainCitation":"(Ellis, C, 2007)","noteIndex":0},"citationItems":[{"id":176,"uris":["http://zotero.org/users/local/LY9XXHSK/items/3XYQM9HM"],"uri":["http://zotero.org/users/local/LY9XXHSK/items/3XYQM9HM"],"itemData":{"id":176,"type":"article-journal","title":"Telling secrets, revealing lives: Relational ethics in research with intimate others. Qualitative inquiry,","page":"3-29.","author":[{"literal":"Ellis, C"}],"issued":{"date-parts":[["2007"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Ellis, C, 2007).

Unethical Research Practices:

Some of the business researchers have to test their products on animals or humans and they do it without telling the side effect of the products. Some of the pharmaceutical companies do research and experiment to promote their drugs. Like research has been done on the effect of Syphilis on patients. It was done on live black people who have no excess to medical care. It was actually an experiment to help affected people but then it turned out to be an experiment on the live people. It was a deception and the principals of ethics have not been followed. The principles like, consent of the people, human dignity, respect of the affected vulnerable people, minimizing harm and maximizing the benefits of research were not followed. As the participant were not informed that they were tested and the doctor is not just examining them for their own sake ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"F5ICQonq","properties":{"formattedCitation":"(Reverby, S. M., 2001)","plainCitation":"(Reverby, S. M., 2001)","noteIndex":0},"citationItems":[{"id":175,"uris":["http://zotero.org/users/local/LY9XXHSK/items/WT384YZ9"],"uri":["http://zotero.org/users/local/LY9XXHSK/items/WT384YZ9"],"itemData":{"id":175,"type":"article-journal","title":". More than fact and fiction: Cultural memory and the Tuskegee syphilis study. Hastings Center Report,","page":"22-28.","volume":"31(5),","author":[{"literal":"Reverby, S. M."}],"issued":{"date-parts":[["2001"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Reverby, S. M., 2001)

The ethical considerations like the consent of the participants are very important. Secondly, the identity and information of the participant should not be revealed thirdly such life-threatening experiments would not be conducted on humans and animals.

These should be avoided in research by signing consent paper from the candidates and by explaining them the nature and objectives of the research properly.

A survey should be started with a consent paper and the nature of the question should not be disrespectful and should not harm someone's emotions and those should not be controversial.

My research topic and question:

My research topic is workplace gender discrimination and the research question that I am going to address in the whole research is, “what are the causes of gender discriminations in the workplace?”

Hypothesis

H1. Gender discrimination in the workplace is the result of poor company morale.

H0: Gender discrimination in the workplace is not the result of poor company morale.

Generating Survey Questions

Answer the following questions in 1-2 lines.

• What could be the possible causes of workplace gender discrimination?

Click here to enter text.

• Do you think that poor company morale is the main cause of workplace gender discrimination?

Click here to enter text.

• Do you think that female employee is not as capable as males?

Click here to enter text.

• Which gender effects more as a result of gender discrimination in the workplace?

Click here to enter text.

• Do you think that the culture of the organization has to do with the workplace gender discrimination and what would you suggest to minimize it?

• Click here to enter text.

The survey will focus on both the gender and all the employees of the company including managers and everyday employees. The specific organization that will have high rates of gender discrimination in the workplace will be preferred to examine, but if such organizations will not be available then it will be done across the field. The research will include a sample size of 100-200 employees from such organizations.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Ellis, C. (2007). Telling secrets, revealing lives: Relational ethics in research with intimate others. Qualitative inquiry,. 3-29.

Reverby, S. M. (2001). . More than fact and fiction: Cultural memory and the Tuskegee syphilis study. Hastings Center Report,. 31(5), 22-28.

Subject: Statistics

Pages: 2 Words: 600

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