Statistics Examples and Topics
Statistical analysis of Article
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The studies related to spreading and control of diseases use large samples. A large percentage of such studies suggest that nearly 90% of the US population has been vulnerable to one unexpected event. These events include accidents, disasters, and other critical happenings. This high exposure to such critical situations is alarming. There are serious long-term outcomes of such happenings including mental and physical health problems, structural and functional changes in the brain. Trauma is clearly related to the outcomes discussed but there are no mechanisms that can ensure a proactive attitude toward the traumatic conditions.
This study is significant because youth who is exposed to some traumatic happenings may react differently to stress as compared to those who has not been exposed to such traumatic issues in their childhood. A clear impact of trauma can be seen in the ways youth deals with the stress in their future lives. Exposure to trauma changes the way one deals with stress and make decisions CITATION Kat15 \l 1033 (Howell, Kaplow, M.Layne, & A.Benson, 2015). People exposed to some sort of trauma sometimes overreact to situations involving stress. These individuals also find it difficult to implement coping strategies suggested by the practitioners. The exposure to stress coupled with trauma may result in the increased volume of the right amygdala. The more perceived threat will make the individuals withdraw from a certain situation rather than seeking advice from someone else. Trauma may result in a loss of working memory and control over diverting attention. This lack of attentional control will lead to poor coping strategies against stress. Living in a war zone may be considered one of the traumatic situations faced by some people. Involving people in some activity if their interest may result in relief from stress.
There is not much literature available to study the relationships between exposure to stress and poor psychological health. The study of this relationship can be improved by developing skills for better stress management which will decrease the likelihood of post-traumatic mental problems for the affected people. If the emotional and other factors are separated while studying coping behavior, they will be less effective to cater to the stress-related issues CITATION Jud16 \l 1033 (A.Cohen, P.Mannarino, & Deblinger, 2016). It is required that the impact of coping strategies is studied on those individuals who have been exposed to some traumatic happening and those who have not faced any such situation. The relationship between trauma and coping mechanisms will also help in finding youth who are at risk of poor outcomes or they need some guidance regarding their problems.
There has been considerable work on various coping mechanisms and several frameworks have been proposed these behaviors. Coping is defined as an effortful response to stress. In the early development of coping mechanisms, a major focus was placed on individuals and the environment in which they lived. Emotion-focused coping and problem-focused coping techniques were evolved through early research on the subject. There was a difference of focus in the two proposed mechanisms. Emotion-focused coping focused on the internal state to reduce and manage stress whereas the problem-based coping focused on the external environment to cope with stress. Both types of coping fall under two major types namely engagement and disengagement coping. In engagement coping, the stressors are managed which are the emotions of a person. This also includes problem-focused coping and a part of emotion-focused coping including the development of humorist approach to the problem. In the current study, the authors have used the term positive emotion-focused coping strategies. The disengagement strategies include strategies such as avoidance or denial to avoid the stressors. These are referred to as negative emotion-focused coping because all the negative thoughts are used to determine the coping strategy. Since youth are going through various developments in their personalities, there are changes in their preference of coping strategies as well.
There have been a large number of studies that have seen the relationship between exposure to trauma and coping strategies. People who have been exposed to some traumatic situation are more likely to use negative emotion-based coping strategies. An example can be given about the youth involving in sexual trauma and those involving in non-sexual trauma condition CITATION Cin15 \l 1033 (Mels, Derluyn, Broekaert, & Garcia-Perez, 2015). There has been a tendency of avoidance among those exposed to sexual-based trauma as compared to those who have been exposed to non-sexual based trauma. This avoidance affects the relationship between trauma and the symptoms shown thereafter. People who have been exposed to some traumatic situations more than once are expected to use negative emotion-based coping strategy. There has been less investigation concerning the problem based on coping. A small number of studies have concluded that the outcomes of problem-based coping are some positive psychological outcomes. The use of problem-based coping on the trauma affected people will hurt the required factors. There have been certain limitations to the past studies undertaken to see the changes in different individuals while coping with stress in life. Adaptive techniques of stress management include behaviors such as solving some problem or supporting the victims on some social platform. Maladaptive techniques include avoidance by people or an effort to indulge in some activity that drives their focus away from the stress creating factors. The subject needs further research to show the circumstances under which the three distinct coping strategies can be further divided into adaptive or maladaptive CITATION Lin15 \l 1033 (Xia, Dung, & Hollon, 2015).
The current study has three major aims to be achieved. Firstly, an examination was made to see if there is any difference in coping strategies for the current level of stress if the subject is exposed to different types of trauma. The first testing statement included that exposing to any kind of trauma will result in more negative emotion-focused coping. Due to the lack of literature, the direction of positive problem-focused coping skills was not tested. Second aim to see how the timing of exposure to trauma was associated with the choice of coping techniques and if there is a difference between people who have been exposed to traumatic conditions at an early age and those who are not. If a person is exposed to trauma at a very early age, he is expected to use negative emotion-based coping techniques. The last aim of the study is to analyze how the total number of trauma exposures were associated with the usage of various coping strategies. The test statement was that the increase in the number of traumatic exposures will result in greater usage of negative emotion-focused coping and less positive emotion-focused coping. In crux, there were three hypotheses for the current study.
H01: The young people who are exposed to any form of trauma are more likely to use negative emotion-focused coping skills.
H02: The young people who have been exposed to the first traumatic condition at a very early age are most likely to use negative emotion-focused coping techniques.
H03: The more traumatic events a person faces, the more likely he is to use the negative emotion-focused coping techniques.
This study uses data from the National comorbidity Survey Replication. This was the first study that provided data on mental disorders in English speaking US youngsters. The data was collected with the help of face to face survey from 10148 kids aged 13-18 years. Dual framed sample technique was used by the researchers. Historically, samples used in research in the US have used the area probability sampling and face to face interviews. More recently central telephone interviewing has replaced the traditional ways of sampling because they are more cost-effective and there will be lower response errors as the respondents and interviewers can be constantly monitored from a central place. One of the shortcomings of the telephone surveys is that there is a certain percentage of people who do not have the telephones available with them. This set of people are automatically excluded from the studies using telephone survey CITATION JiH16 \l 1033 (Lee, Seo, & Lee, 2016). Certain characteristics of the population does not possess telephones, they are poor, young and living in the rural areas of the country. To overcome the non-coverage bias of single survey methods, researchers use a dual-frame method that retains the cost-saving characteristics of telephone interviewing and increases the coverage by using the area probability sampling. There is an area sample as well as s telephone sample. The mixed model mode conducts face-to-face interviews with the area sampling people and telephone interviews with those having telephones. The allocation of resources to both types of sampling will require an assessment of costs and errors associated with the individual methods. Certain parameters will have different values when different forms of dual-frame samples are developed. To assure that the dual-frame design is appropriate, accurate estimates of these parameters should be calculated.
In the current study, there are two major frames used to conduct the research. One sample frame was collected involving the adults while the other sample frame included the adolescents. Several adults were 904 while that of adolescents was 9244.
There were 18 traumatic situations used in the CIDI (Composite International Diagnostic Interview) which is especially used all over the world to conduct studies involving disease spread and control. This kind of interview has all the characteristics that are required to extract relevant information from the respondents for the study undertaken. The responses were recorded against queries like whether the respondents have come across any traumatic situation. The next description was about the age at which they came across such an incident. There were four groups developed based on prior research. These were interpersonal violence, accidents, and injuries, social networks or witnessing events and other events. In consistence with the past studies, the adolescents were divided into three distinct age groups which were early childhood (0-5) years of age, middle childhood (6-10) years of age and adolescence (11-18) years of age. The structure of interviews was such that it helped the adults to recall every bit of information required to make the study more useful. The adolescents reported their ages accurately at the time of first traumatic experience. This is shown by the figure of correlation coefficient r= 0.81 in the original survey between the first traumatic experience age of the child as reported by parents and children themselves. This response of ages and exposure to trauma have helped the researchers to develop four independent variables. 1. Whether a person is exposed to traumatic conditions or not. 2. What types of trauma have been experienced by the individual? 3. Age at the time of first exposure to each trauma or trauma types and 4. A total number of traumatic incidents that have been reported and the total number of traumas across each type.
This study uses seventeen measures to identify the various coping styles used across the different age groups. These measures have already been used in some previous studies. The adolescents were asked to recall any incident that resulted in stress for them. They were then asked how they will cope up with this situation given the scale from 1 to 4 showing most likely to least likely. The problem-focused coping included comments such as identifying a problem and seek ways to make the situation better. Positive emotion-based coping has been represented by avoidance or by keeping your feelings to your self and to keep a sense of humor. The last set included negative emotion-focused coping that was represented by getting mad and break something, avoiding any social interactions and be alone for most of the time. The questions were designed similarly so that legitimate responses can be received from the respondents. Due to the usage of a similar scale, a higher score on the questionnaire will mean that more people have used a particular coping strategy. Internal consistency means that the items in a test which measure the same thing, produce identical results over time. This is judged by calculating the correlation between the different items used to measure the same constructCITATION Ret \l 1033 (Vaske, Beaman, & Sponarski, 2017). The methods used in this study namely the COPE and WCQ have shown above-average internal consistency and test-retest reliability.
Subparts of the major variables are also studied to analyze whether there is a significant difference among groups when an association between exposure to trauma and coping responses to stress are considered. Gender differences were studied to know if the responses for males differ from females. Differences in regions and environments are also studied to account for the complex survey design.
The primary analysis in this study includes several regression models which have been developed after adjusting for the covariates. The regression analysis is prone to certain assumptions, variables considered in the regression analysis are assumed to have a linear relationship with each other. The linear relationship can be seen by making a scatter diagram involving a dependent and an independent variable. All variables that are included in regression analysis are assumed to be multivariate normal which can be checked with the help of a histogram or Q-Q plot. A goodness of fit test can also be used to check the normality of these variables. A model in which data is not normally distributed, some transformation should be applied to data e.g. log transformation. One of the most important assumptions is the existence of multicollinearity in the data. This means that the independent variables used in the data have strong relationships within themselves and such relationships do not allow the researcher to study the relationship between dependent and independent variables properly. To test for the presence of this problem, any of the three methods can be used. A correlation matrix is developed to see the correlation scores between all the independent variables in the form of coefficients. For a satisfactory result, scores on the correlation matrix should be very close to zero. The tolerance method measures if there is a significant influence of one independent variable on the other variables. If the tolerance score for any variable is less than 0.01, there is certainly some existence of multicollinearity. The variance inflation factor is the reciprocal of the measure of tolerance. A score of more than 100 in this measure shows that multicollinearity exists. To solve the problem of multicollinearity, some of the independent variables can be removed from the analysis. In the current study, all the three coping domains were weakly correlated to each other. The last assumption of the linear regression is the existence of homoscedasticity which means that there are equal residuals across the whole regression line.
Sensitivity analysis was undertaken to see if the existence of depressive episodes has any impact on the association between exposure to trauma and coping behaviors. There was a repetition of all primary data analysis excluding those adolescents who had not been reported for depressive episodes over the last year by the medical history. This strategy was implemented to analyze the robustness of the findings of this investigation. The strengths of the association between trauma and coping strategies were found to be low for all types of coping strategies. The main focus of sensitivity analysis was on the association between depression and negative emotion-focused coping because of r= -0.40.
The results of the study show that 59% of the sample had been exposed to at least one traumatic event. Adolescents who were poor and belonged to the minorities reported more traumatic exposure. Adolescents did not use problem-focused coping only in special cases e.g. exposure to rape or witnessing fights at home or any other social trauma situation. There was no significant difference between those who were exposed to trauma and those who were not when the usage of positive emotion-focused coping is considered. There was a difference in usage of negative emotion coping techniques between the trauma affected and unaffected people. In different age groups, there was no significant difference when positive emotion-focused coping is considered. There was a use of negative emotion-focused coping at all age levels whenever adolescents were exposed to any form of trauma. Within age groups, there was a no different reaction to trauma when positive emotion-focused coping is considered while all age groups consistently show negative emotion-focused coping in all age groups in the first instance of trauma. At an accumulated level, there is no association between traumatic experiences and any form of copingCITATION Rac \l 1033 (A.Vaughn-Coaxum, Wang, Kiely, R.Weisz, & C.Dunn, 2018)y.
This study can be improved by analyzing the relationships between exposure to trauma and various forms of coping over a longer period. This study is based on the responses of adolescents whose responses cannot be considered reliable because they had to recall on certain past events. It may also happen that the excluded sample may also have been exposed to some traumatic events. A longitudinal study can be conducted so that any changes in coping behavior in response to trauma can be studied which will also provide a better understanding on the impact of age differences in the relationship between traumatic happenings and type of coping. There have been previous studies whose results have been reaffirmed by this study. Another missing aspect is the usage of drugs by adolescents as a result of traumatic events. The article uses primary data generated from interviews and surveys and the sample selection has been done with the help of the national surveys. The general quality of the data is good as suggested by the internal consistency and test-retest reliability. All the hypotheses developed have been accepted after thorough data analysis. This is an exploratory form of study that has tried to explore some form of relationships and associations. The regression analysis has been used to analyze the relationships between variables whereas the chi-square test was used to study the associations. If I was the author, I would have conducted a comparative study between a developed country and an underdeveloped country.
BIBLIOGRAPHY A.Cohen, J., P.Mannarino, A., & Deblinger, E. (2016). Treating Trauma and Traumatic Grief in Children and Adolescents. Gilford Publications.
A.Vaughn-Coaxum, R., Wang, Y., Kiely, J., R.Weisz, J., & C.Dunn, E. (2018). Associations Between Trauma Type, Timing and Accumulation on Current Coping Behaviors in Adolescents: Results from a Large, Population-Based Sample. Journal of Youth and Adolescence, 842-858.
Howell, K. H., Kaplow, J. B., M.Layne, C., & A.Benson, M. (2015). Predicting Adolescent posttraumatic stress in the aftermath of war: Differential effects of coping strategies across trauma reminder, loss reminder and family conflict domains. Anxiety, Stress, and coping.
Lee, J. H., Seo, M., & Lee, M. (2016). Profiles of Coping Strategies in Resilient Adolescents. Psychological Reports, 49-69.
Mels, C., Derluyn, I., Broekaert, E., & Garcia-Perez, C. (2015). Coping behaviors and post-traumatic stress in War-affected Eastern Congolese Adolescents. Stress & Health, 83-88.
Vaske, J. J., Beaman, J., & Sponarski, C. C. (2017). Rethinking Internal Consistency in Cronbach's Alpha. Leisure Sciences, 163-173.
Xia, L. X., Dung, C., & Hollon, S. D. (2015). Interpersonal Self Support, Venting Coping and Post- Traumatic Stress Disorder Symptoms Among Adolescent Earthquake Survivors. Current Psychology, 14-25.
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Table 1: Descriptive statistic
The data shows an interval of occurrence of an eruption of various Geysers. The graphs indicate a large variation in intervals from one eruption to the other. The change intervals can be seen from one graph to another of the first day, moving to the median intervals. Based on the table, there is a clear difference in means of Geysers that took place during the day within a specific time. The analysis of the table shows that Castle Geyser occurs several within an interval during the day followed by Spouter Geyser. The data shows that Castle Geyser has a mean of 11.8982, Spouter Geyser has 4.26062 and Grand Geyser has a means of 8.0. This means that the regions experience more Castle Geyser activities during the day compared to the rest. It is also evident that Castle Geyser has a median of 13.2333, which means that striking correlation between the duration of one eruption and the interval of eruption is higher for Castle Geyer. And therefore, it shows that the regions experience several Castle Geysers within the shortest time possible compared to spouter, grand and old faithful and daisy geyser. However, the data also shows that Daisy and Old faithful Geyser rarely occurs in the region.
The graph and data also show that the internal distribution of Castle Geyser is high compared to the spouter and the rest of the Geysers which take place in the region. The standard deviation of 3.5536 and Spouter 1.024 and the rest are having lower STD Dev. This means that the castle geyser is more likely to take place within a specific time compared to the rest. It is also evident the intervals of occurrence of the castle is closed and therefore, it takes place often and in multiple and huge compared to the rest. The quartile of castle geyser is high and therefore, the magnitude of each eruption is higher.
ANOVA (Analysis of variance) is the collection of the statistical models plus their association projection procedures used for analysis of the dissimilarities among the group means in any sample. It was developed by an evolutionary biologist and statistician Ronald Fisher. In ANOVA setting, the variance observed in the specified variable is sectioned into parts attributable to the different variation sources (Zwanenburg et al., 2011). ANOVA also provided the statistical test if the means populations of various groups are equal and hence generalizes the test to over two groups. It is useful for testing or comparing three or more group’s means for statistical significance. It is theoretically comparable to the two-sample multiple t-tests, however, is more conventional leading to less type I errors and hence suited for the broader range of the practical issues.
When one wants to use the ANOVA for analysis, the data should be checked first if it can be analyzed using the method. It is necessary to review the data because it is appropriate to use ANOVA if the data passes the six assumptions which are needed to make the result valid (Zwanenburg et al., 2011). Practically, checking of the six assumptions utilizes more time of the analysis as it involves clicking some buttons in the SPSS Statistics while performing the analysis however it is not difficult.
The assumptions include:
• The dependent variable needs to be measured at the ration level
• Independent variable needs to compromise of two or more independent groups
Independent observations are needed that means there is an absence of an association between observations in between groups and every group.
Significant outlier should not be there.
The dependent variable requires to be around distributed ordinarily for every group of the independent variable.
The dependent variable requires to be almost normally dispersed for every set of the independent variable.
Homogeneousness of variances should be present.
Zwanenburg, G., Hoefsloot, H. C. J., Westerhuis, J. A., Jansen, J. J., & Smilde, A. K. (2011). ANOVA-principal component analysis and ANOVA-simultaneous component analysis: A comparison. Journal of Chemometrics, 25(10), 561. Retrieved from https://search.proquest.com/docview/907080082?accountid=41759
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H01: There are no negative consequences of alcohol and other drug usage before the school leaving celebrations
Ha1: There are significant negative consequences of alcohol and other drugs usage before the school leaving celebrations
H02: There are no negative consequences of alcohol and other drug usage during the school leaving celebrations
Ha2: There are significant negative consequences of alcohol and other drug usage during the school leaving celebrations
The independent variable is alcohol usage on an average celebration day, other drug use, protective behavior strategy score, gender, accommodation/location, and survey modality. Hangover, emotional outburst, vomiting, heated argument, accident/injury, physical aggression, blackout, inability to payout for things, unprotected sex, and unhappiness towards the sexual situation, sexual risk/problem and any legal risk/problem have been used as the dependent variables. Another variant of this study can be conducted to compare the differences among groups e.g. between genders. The extent to which the independent variables explain the variation in the dependent variable should also be observed. A lower value in this regard will mean that more variables should be added to the analysis.
The dual-frame sampling method was used in this study. The main advantage of this method over the single-frame design is that a large number of people can be added to the dual-frame method. As in this study, one frame included the people who gave online responses and people from the other frame were conducted with the help of face to face interviews. In dual-frame sampling, the number of persons is increased and it provides a better representation of the population under study. There were surveys conducted before and after the celebrations took place. There were some small gifts given to the participants which also kept them interested in the surveys. The face-to-face interviews gave better responses as compared to the online survey but there were not many resources available to undertake the whole research with the interviews. Another advantage of such a technique is to minimize the bias related to the similarity of responses from all the respondents. Another positive aspect of personal interviews in this study is that these include actual happenings as responses.
The first survey included 56% of females which make 303 out of 541. Out of these females, 275 were 17 years old and 28 were 18 years old. 87% of these females were enrolled in an independent school. 52% of the people filled the online surveys. The survey after celebrations were conducted by using 405 people out of which 203 were females. Out of these females,191 were aged 17 years and the rest were aged 18 years or above. 192 of these females were from some independent school. Table 4 in this study shows that 209 of the respondents indulged in a hangover, 139 were involved in an emotional outburst.
As an inferential statistic, a series of Wilcoxon signed-rank tests were used because we had to determine that both the samples have been drawn from the same population. The relationships between the dependent and independent variables were studied by using the logistic regression model. A logistic regression model is very similar to the multiple regression model and all the assumptions of multiple regression apply to it. Since the dependent variables are nominal as all of these cannot have a direct numerical value and there are many independent variables in this study so logistic regression analysis is the most appropriate to use. Males consumed 18.44 and females 13.24 Australian standard drinks on an average day when they celebrated their school leaving. This consumption was higher than their last social event consumption and ecstasy consumption. The number of drinks consumed per hour remained similar across the different contexts. A majority of respondents experienced at least one of the negative outcomes specified in the study.
Odds ratio tells us the chance of occurrence of an event provided that some other event has already taken place. It can also be stated in terms of the association between exposure and an outcome. The odds ratio shows the odds that an outcome will occur given a particular exposure. Another concept related to the odds ratio is the confidence interval. The higher odds ratio will have a huge difference between the lower and upper limit of the confidence interval. The interpretation of this huge difference between boundaries of confidence intervals is that the probabilities of the occurrence of such an event are very high. As depicted by the table, the odds ratio for indulging in unprotected sex against those using the safety strategies is very high. This shows a very high probability of indulging in unprotected sex if the safety strategies are not implemented. The confidence interval limits are very wide in this scenario which shows a very high probability of a person indulging in unprotected sex if the safety strategies are not implemented properly. If we observe in detail, there are high odds ratios in most cases in the analysis. Gender is the only criterion that has odd ratios that are lower than 1 showing that there is less chance in getting involved in certain activities based on gender. The lowest score is between survey modality online and regrets on a sexual encounter.
I think that the sample taken is representative of all the school students across the country. The reason for this statement is that the students were not only given the questionnaires in person but also online surveys were conducted. The questionnaires in person were given to the student from a single geographical area while the online surveys can collect data nationwide. Using dual-frame sampling also ensured that the samples taken were representative of the population. Since the celebrations at the end of schools continue to be popular, there is a need to extend the study to schools all across the country so that solution to youth alcohol and drugs consumption can be found.
The study explored the factors associated with diabetes for the people who reported their disease by themselves. The study was conducted from the year 2001 to 2008. The major aim of the study can be presented in the form of a hypothesis statement as follows:
H0: The self-reporting of diabetes in patients has not improved over the period from 2001 to 2008.
H1: The self-reporting of diabetes has improved over the period 2001-2008.
To study the patients in greater detail, they have been divided into various age groups and also into male and female respondents. Although this study does not involve the comparison across groups, a variant of this study can do the group-wise comparisons. In that case, there were many more hypotheses to be formed and analyzed.
The demographic variables included in the study are age, sex, and income. As far as age is concerned, there are eight distinct groups formed. The first group was below fifteen years of age and then groups were formed with 10 years gap in each class. The last group comprised of the people above seventy-five years of age. As far as the representation is concerned, the age group from 35-44 years had the most number of people as depicted by the highest percentage. The least representative group was the people who were aged more than seventy-five years. For the years 2005 and 2008, the group having the highest respondents has changed to aged 45-54. There was no change to the least represented group throughout the study. The representation of certain groups was decreased throughout the study but the group aged 45-54 showed a consistent increase in the number of people taking part in the study. As far as gender is concerned, there has been a consistency in the representation of two genders in the study group. There are slightly more women participating in this study as compared to men. There are certain groups formed based on the income of households included in the study. Four distinct groups are starting from people having an income of less than 10000 Hong Kong dollars. The last group for income is greater the 50000 Hong Kong dollars. The highest number of people fall in the income bracket of 10000-24999 representing 42.4% of the total subjects. The least representative group is the one that has income higher than 50000 Hong Kong dollars. This ranking of income groups prevails all through the study period.
A binary logistic regression model was used in the study. This method is used when a categorical variable which is usually dichotomous has to be predicted using a set of predictor variables. Another analysis that can be used is the discriminant function which is undertaken when the independent variables are continuous and nicely distributed. Logit regression is used in a situation where all the independent variables are categorical and logistic regression is used when the independent variables are a mix of categorical and continuous variables and if they are not nicely distributed. This study uses logistic regression which is already popular with those studies involving the presence or absence of some disease in some population. As in this study, logistic regression involves that the subjects are analyzed based on their set of scores on several independent variables. This study uses the dichotomous variable of presence or absence of diabetes in the subjects. There is no relationship between the independent variables showing good quality of research. The presence of such a relationship is referred to as multicollinearity. The R squared figure for the regression analysis was calculated at 19.8% which showed that the independent variables have explained 19.8% variation in the dependent variable. This figure is low and should be improved by adding more relevant variables to this study.
When the authors split the data, analysis based on age and gender, the increase in the prevalence of reporting in male respondents was lower than the females. The males showed an increase of 27.8 and 47.9% in 2005 and 2008 whereas females showed an increase of 31.8 and 69.3 % respectively. The lowest income groups showed the maximum increase in prevalence when age and gender variable were separately studied. As far as the individual groups are concerned, there were no significant differences among the respondents based on gender and age groups. Gender is found to have no association with the prevalence of diabetes but the association of disease and older age were more identified in the males as compared to females. Overall the self-report of Diabetes increased by 50% during the period studied.
As per the odd ratios, the males who are aged more than 65 years are the most like to have the disease which is represented by the AOR figure of 141.1.
The first limitation is that the self-reported prevalence of diabetes has not been reported in the past literature anywhere and using this method can be misleading. Self-reported diabetes has not been validated as opposed to that which is reported through a proper medical examination. Another limitation is that there are certainly other variables that affect diabetes and are not considered in this study. One variable of interest can be the person being over or underthe weight. As mentioned earlier, the explained variation for regression model as depicted by the R squared value of 19.8% is very low. This value can be improved by adding other relevant variables to the model. Another limitation is that the population in China is huge and this sample is not representative of the whole population.
Pages: 7 Words: 2100
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Dependent variable is the salary paid to a player of a baseball team. Baseball is the national game of the US and a large number of stakeholders are interested in this game. Various stakeholders are interested in this variable especially the owners of team and investors. The dependent variable is explained by scores, winning percentage, batting average, home runs, runs, earned run average and pitching saves.
The primary independent variable is pitching saves defined by the number of saves, or percentage of saving opportunities converted successfully. This is the most important variable because this one act of saving the runs is the most critical to the results of any match. Hitting home run is a chance item that may or may not happen in every match. Payroll is also a variable that is not directly associated with the performance in the game or match. Thus, we have considered pitching saves as the most critical independent variable among all. The positive aspect in measuring winning percentage is that this variable is measured without any ambiguity CITATION Phi82 \l 1033 (Porter & W.Scully, 1982). There are more wins reported by the teams whose players have saved more runs.CITATION Ste \l 1033 (Hall, Szymanski, & S.Zimbalist, 2002).
Salaries = Saves + winning percentage + runs
Payroll is the amount of salary paid to the player in a month. This is chosen as the dependent variable because salary is very important to the player as well as to the team. Teams pay salaries to players so they can help them win matches. More effective players receive higher salaries and this effectiveness is shown by the number of runs scored or number of saves made.
Winning percentage is defined as the number of matches won by a team as a percentage of total matches played over the period of study.
Saves show the saves per match made by the pitcher over the period of study.
The usefulness of this study is that it will help various baseball players to identify the factors affecting their salary structure. The dependent variable is the amount of salary that an individual player will receive every month. This amount is adjusted as a percentage of average salaries paid by a team in the specified period CITATION She \l 1033 (Hassan, n.d.).The study is important because salaries are often the biggest expenses made by a team. The teams will also determine the increase in salaries of certain players on the bases of their performance. There are three independent variables namely score, winning percentage and saves. Score is defined as the runs scored by a player over a period of one-month, winning percentage is calculating by taking a percentage of matches won as compared to the total matches played and saves are recorded as the shots saved by a player on position other than the pitcher. The expected sign of the independent variable is positive as the high scoring players will be offered a higher salary. Similarly, the teams who have higher winning percentages are expected to pay their players a higher salary CITATION Ger131 \l 1033 (T.Mangine, et al., 2013).
The information regarding salaries paid to players is gathered from the websites of various teams. These websites also hold the record of team’s winning percentage and saves made by renown players CITATION MLB19 \l 1033 (MLB.com, 2019). The data for salaries will be presented in dollars whereas the data of saves and runs will be presentenced in numbers. The data for winning will be presented in form of percentages. Since all the variables are having different units, the logs of all variables are taken to make the analysis more logical and comprehendible. The limitation to this data is that the websites update the data regularly every 5 years and the previous data is deleted from the databases. We have gathered only three years data i.e. for 2017,2018 and 2019.
Salary = a + b1*score + b2* winning percentage+ b3 * saves
When regression is run, the r square is calculated as 0.84 which means that the independent variables in the model explain 84% variation in the dependent variable. The remaining 16% variation is defined by the error term and is not defined by the variables included in this model. The strongest independent variable is the winning percentage as depicted by the highest value of its coefficient of 5.67 which shows that every unit change in the salary of baseball players increases the winning percentage by 5.67 units. The coefficient of scores comes to 3.02 and that of saves is 1.02. This means that the least impact on salaries is shown by the variable saves. The regression analysis is prone to certain assumptions, variables considered in the regression analysis are assumed to have a linear relationship with each other. The linear relationship can be seen by making a scatter diagram involving a dependent and an independent variable. All variables that are included in regression analysis are assumed to be multivariate normal which can be checked with the help of a histogram or Q-Q plot. A goodness of fit test can also be used to check the normality of these variables. A model in which data is not normally distributed, some transformation should be applied to data e.g. log transformation. One of the most important assumptions is the existence of multicollinearity in the data. This means that the independent variables used in the data have strong relationships within themselves and such relationships do not allow the researcher to study the relationship between dependent and independent variables properly. To test for the presence of this problem, any of the three methods can be used. A correlation matrix is developed to observe the correlation scores between all the independent variables in the form of coefficients. For a satisfactory result, scores on the correlation matrix should be very close to zero. The tolerance method measures if there is a significant influence of one independent variable on the other variables. If the tolerance score for any variable is less than 0.01, there is certainly some existence of multicollinearity. The variance inflation factor is the reciprocal of the measure of tolerance. A score of more than 100 in this measure shows that multicollinearity exists. To solve the problem of multicollinearity, some of the independent variables can be removed from the analysis or some new variables can be added to the analysis. In the above analysis there is a problem of multicollinearity that exists between winning percentage and the other two variables namely saves and scores. It is suggested that some other variables may also be added to the analysis to cure this problem.
The adjusted R square is the value that shows power of a regression analysis. The value of 0.76 in this model shows that 76 % variation in the dependent variable is shown by the independent variables. The rest 24% variation is account ted for by the error term or other variables that have not been included in the model. The choice of independent variables is reasonable as a majority of variation is defined by the variables included in the model. If this value is lower than 50%, there is a need to include or exclude variables from the model.
The F test of overall significance shows whether the linear regression model provides a suitable fit to the data than a model that contains no independent variables. F tests can evaluate multiple values at the same time which allows that fits of more than one models can be judged at the same time. This feature is not available with the t- tests which can analyze a single value at a time. The p-value is 0.002 which shows a good fit for the model as this test is conducted at a significance level of 0.05 and p- value is less than the level of significance.
The t- tests for the scores variable show that the probability value is less than 0.05 which shows that there is no significant difference between the mean score of the variable scores and the score of same variables from the population. The null hypothesis is accepted regarding no significant difference between the sample and population values.
The value of t-tests for the variable winning percentage shows that p-value is 0.08 so in order to accept that this value is not significantly different from the value of same variable in the population, we have used a level of significance at 0.10. The null hypothesis is accepted regarding no significant difference between the sample and population values for the said variable.
The value of t tests for the variable saves shows that the probability value is 0.004 which is significant at 0.05 level of significance and shows that the value of this variable is not significantly different from the value of similar variable from the population. This shows that for this variable, there is no significant difference between the sample score and the population scores.
Yes, the signs of all the independent variables are found to be positive as depicted because the more scores will mean more winning percentage and ultimately more salaries to the players.
BIBLIOGRAPHY Hall, S., Szymanski, S., & S.Zimbalist, A. (2002). Testing causality between team performance and Payroll: The cases of major league Baseball and Soccer. Journal of Sports Economics.
Hassan, S. (n.d.). https://pdfs.semanticscholar.org/6f39/892055920f0a7855fbc82f50db2f5b46bcf8.pdf. Retrieved from https://pdfs.semanticscholar.org: https://pdfs.semanticscholar.org/6f39/892055920f0a7855fbc82f50db2f5b46bcf8.pdf
MLB.com. (2019, July 31). https://www.mlb.com/orioles/official-information. Retrieved from https://www.mlb.com: https://www.mlb.com/orioles/official-information
Porter, P. K., & W.Scully, G. (1982). Measuring managerial efficiency: The Case of Baseball. Southern Economic Journal, 642-650.
T.Mangine, G., J.R.Hoffman, Vazquez, J., Pichardo, M., Fragala, M. S., & Stout, J. R. (2013). Predictors of Fielding Performance in Professional Baseball Players. International Journal of Sports Physiology and Perfromance, 510-516.
Pages: 5 Words: 1500
BUS 320 Foundations of Statistics
Name of the Writer
Name of the University
Average time taken
Upper control limit
Lower control limit
BUS 320 Foundations of Statistics
Average time taken
Upper control limit
Lower control limit
The above Control Chart shows the movement of the average time taken for five customers at a given hour to receive their order. The data for this graph has been collected from the closest fast food restaurant (Mc Donald). The data collected for the graph above is shown in the table above the graph. This table, tabulates the average time in minutes it took for five individuals to get their order within the chosen fast food restaurant. Using the data in the table the control chart was formed. Furthermore, the data in the table was used to find the mean, mean range, standard deviation, central line, upper control limit and the lower control limit.
If the data is analyzed properly, it can be seen that the average time it took to serve individuals during the period from 12:00 to 14:15 is around four minutes. Considering this is the lunch time and there is a high amount of people present who want to be served, the average time taken is exceptional. The upper limit calculated for the data is around 9.49 minutes, the lower limit calculated for the data is around -1.49 minutes and the central line was calculated at 4 minutes. If the graph trajectory is to be analyzed, none of the data moves out of the UCL or the LCL. This means that the data is cohesive and without fault. Furthermore, there are two points that also lie on the central line that give the realness of the data. A further analysis of the control chart shows that the time at 13:00 hours surpasses whom if refurbished can have positive effect on the the zone A and the time for 12:15 moves further down into zone B. Both of these zones are at equidistant to the central line and show the variation that is occurring within the values.
How statistics (including probability) can benefit businesses by increasing the bottom line
It is an age old tradition in the business world to use statistics and probability in order to improve business functions. This is because in the face of uncertainty statistics including probability has been the go to tool for business managers. This is because it helps in minimizing the rise in uncertainty. Furthermore, statistical thinking and its methods have become the basis for business decision making and is helping in furthering the growth of businesses (Mariappan, 2019). In simple words, the term statistics refers to the quantitative terms that are an expression of numerical values and information. Objects, activities, subjects and phenomena are some of the aspects that the information might relate to. At the micro level this is the data that is produced by small and large firms. However, the size, these firms produce gauge amounts of data. The data provided by thee companies include data on production, inventories sales, capital employed and expenditure
The data produced by these firms is usually collected through field research and scientific techniques are employed in order to collect the said data. However, this data needs to be regularly updated as the previous data collected has only one time use. On the basis of academics, students are intimately aware of the statistics in the form of a module in subjects such as mathematics, chemistry, economics and physics. This discipline, scientifically deals with data and is referred to as the science of data (Crisostomo & Chauhan, 2019). Additionally, in order to deal with the data that is collected, statistics has developed methods that are appropriate for either presenting, analyzing, collecting or summarizing data. After the data is dealt with statistics is the branch of mathematics that helps businesses and countries to decide regarding a certain situation using the data provided. This way any level of uncertainty within decision making is reduced tremendously.
For businesses statistics is a very important tool to increase its bottom line. For every business out there turning a profit is very important. Furthermore, their aim is to increase the level of their profits from the last year. In this way they are always looking for new ways to improve their bottom line. This is because they either want to expand their operations or just to keep their investors happy. The reasons can be immense and can be very specific in nature as it all depends on the type of business being looked at. This makes the use of statistics very important for business functions.
Specifically, there are multiple ways through which statistics can easily help in improving the bottom line of businesses. Primarily, statistics can help in managing the performance of a corporation. It is a known fact that the bottom line of companies is dependent on the level of performance that they show. If there is any lag in the performance within the business or the company, it will be very detrimental for the company itself (Coleman, 2016). Statistics can help managers in improving efficiency within the operations of the business. This will be done by analyzing the performance and the production of the employees within the company. For example, a manager can look at the units produced within a specific timeframe or the time taken for an employee to complete a given task. By using statistical techniques, managers can analyze the data collected regarding the different scenarios and focus on the areas within the business that are lagging behind and that need improvement.
Additionally, statistics can also be used in order to formulate alternative scenarios during the decision making process. Most businesses are made up of a combination of departments and subdivisions that help contribute their part in the bottom line of the company. In this way, managers from different departments have to work together to formulate strategies in order to increase the overall productivity of the company (Sandoz, et al, 2017). These managers have to effectively take part in the decision making process and make the best decision possible in order to create a greater profit margin and increase the bottom line of the company. An example of this is when textile industries have to choose between buying a machine that can improve the overall productivity but is costly and requires training for use. However, there is also the option of old machines, whom if refurbished can have a positive effect on the bottom line but not as much as the new one. These are some cases when statistics can be useful in decision making.
Lastly, in order to grow and increase its bottom line, businesses have to constantly innovate and research their technologies, operational methods and multiple other things. This is another place where statistics can really help businesses. Statistics can help in market research and product development. This way businesses can look for new exciting products that are needed in the market and produce them. Using statistics, a sample group would be taken to analyze the new products developed. Their actions and response would be observed towards the product testing and would be then documented (Wang et al, 2018). This data would be essential in the launch of the product as it will let businesses the defects within their products and the areas that they need to work on. Businesses would be able to fulfill a need present in the market and create a new customer base for themselves. If their products perform especially well then they will be able to retain those customers as well. This way the company would be able to increase its profits and significantly benefit the bottom line of the company.
Coleman, S. Y. (2016). Data-mining opportunities for small and medium enterprises with official statistics in the UK. Journal of Official Statistics, 32(4), 849-865.
Crisostomo, M. E., & Chauhan, R. S. (2019). Using the O ccupational I nformation N etwork (O* NET) to demonstrate the importance of understanding statistics for undergraduate students. Teaching Statistics, 41(3), 89-93.
Mariappan, P. (2019). Statistics for Business. Chapman and Hall/CRC.
Sandoz, E. K., Butcher, G., & Protti, T. A. (2017). A preliminary examination of willingness and importance as moderators of the relationship between statistics anxiety and performance. Journal of Contextual Behavioral Science, 6(1), 47-52.
Wang, P., Palocsay, S. W., Shi, J., & White, M. M. (2018). Examining Undergraduate Students’ Attitudes toward Business Statistics in the United States and China. Decision Sciences Journal of Innovative Education, 16(3), 197-216.
Pages: 4 Words: 1200
Business Statistics: Confidence Intervals
In statistics, Confidence Interval refers to the probability that the parameter of a population would fall between two set values from a specific or certain proportion of times. Confidence Intervals measures the level and degree of certainty or uncertainty in the sampling method. A CI can take any figures or numbers of probabilities where the most common being a 95a5 or 99% confidence level.
The confidence intervals are almost about the risk. In business operations or activities, confidence intervals are used to estimate the range in which the real or actual answer lies. T=it is done by considering the sample size and potential variation in a specific population. The confidence intervals are considered and known as the bright sign that results in better outcomes which are used for making business decisions while it is considered as a significant and appropriate as well because it provides a very specific range to the business analysts for analysis.
There are several other facts and reasons that clarify that confidence intervals are more valuable to the business. While some most crucial ones are;
Market Research is considered as the most critical and important job for every business to be done with perfection while confidence intervals give appropriate percentage probability that includes actual values. For example, sales can be ranged by businesses that are likely to fall.
It is not possible to predict future business risk 100% but confidence intervals are the process that enables businesses to manage the risk of a non-occurrence accordingly.
Confidence intervals are valuable because businesses can have a range of possible values for costs and revenues to make crucial financial decisions.
Additionally, one of the reasons due to confidence intervals are significant in business because no business or analyst cannot get data or information from entire population such as customers. While confidence intervals enable and allow them to work on the specific range of the sample.
I would use confidence intervals method almost all the time when I get engaged in doing market research and forecasting budget and future sales.
Cumming, Geoff. Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, 2013.
Greenland, Sander, et al. "Statistical Tests, P Values, Confidence Intervals, and Power: a Guide to Misinterpretations." European Journal of Epidemiology 31.4 (2016): 337-350.
Topic: Impact of social status on young girls
Research question: Does social status affect confidence and self-esteem of young girls negatively?
Primary survey is conducted for collecting data on impact of social status on young girls. A survey is designed for targeting young girls who are studying at colleges and high-school. A closed-ended questionnaire is developed for collecting information from the targeted population. The questionnaire comprise of three sections; the first one uncovers demographic information of the respondents including their age, household income, education level and education of parents. The second section inquires information about the emotions related to status consciousness. In total 10 emotions are examined in this section. The third section evaluates the self-worth associated with the social status. This section also include 5 questions that examines feelings of respect, pride and usefulness. The study targeted 30 adolescent girls who were enrolled in college/ university. Stratified random sampling is selected for choosing a small sample from the larger population.
The 10 emotions examined through the instrument include; amusement, anger, compassion, desire, embarrassment, fear, guilt, jealousy, sadness and tension CITATION Mic143 \l 1033 (Krus & Park, 2014). These emotions explains the negative feelings associated with the social status and rank. This is used for evaluating how emotional ratings are influenced by social status.
SPSS is used for analysis of the data collected through primary survey from the participants.
25 years and more
What is your parent’s education?
Below college level College degree
The survey collected information for assessing emotions of the girls associated with status consciousness. In total 10 variables were selected for determining the relationship of emotions with consciousness of the respondents. The respondents were asked if social status makes them feel amused. The results obtained from the survey indicates that most of the girls (33%) states that they feel amusement very frequent and 33% mentioned that they feel amused always. The remaining 17% stated that they feel amused rarely.
Figure 1 Feeling of amusement
Figure 2 Feeling of anger
Figure 3 Feeling of compassion
The questionnaire also inquired the respondents about their desires for attaining social status. The results indicates that majority (50%) stated that they very frequently have a desire of attaining high social status. The remaining 33% said that they always felt the desire of attaining social status and 17% felt it only occasionally.
Figure 4 Desire for high social status
The respondents were also asked if they feel embarrassed for their social status. Their responses depicts that 33% always felt embarrassed and 33% had a feeling of embarrassment very frequently. The remaining 17% stated that they either felt embarrassed occasionally or rarely.
Figure 5 Feeling of embarrassment
To evaluate emotions of the participants associated with social status their feelings of sadness were determined. These findings indicate that 33% of the participants felt sad very frequently, 50% felt sad always and about social status. The other 10% felt sad occasionally.
Respondents were asked if social status raise feelings of guilt or not. The responses depicts that majority (50%) believed that they always experienced feeling of guilt. The remaining 33% stated that they experienced feeling of guilt very frequently and 10% experienced it only occasionally
The findings of the survey confirm that social status impacts self-confidence and self-esteem of young girls negatively.
Anderson, E. (2000). Code of the Street: Decency, Violence, and the Moral Life of the Inner City. NY: Norton.
Bécares, L., & Priest, N. (2015). Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach . PlosOne .
Bonilla-Silva, E. (1997). Rethinking racism: toward a structural interpretation. . American Sociological Review , 62 (3), 465–480.
Cuncic, A. (2018). How to Be Less Self-Conscious in Social Situations . Retrieved 04 23, 2019, from https://www.verywellmind.com/how-can-i-be-less-self-conscious-in-social-situations-3024823
Charmaraman, L., & Grossman, J. M. (2010). Importance of race-ethnicity: An exploration of Asian, Black, Latino, and Multiracial adolescent identity . Cultur Divers Ethnic Minor Psychol , 16 (2), 144–151.
Hall, R. E. (2001). The Ball Curve: Calculated Racism and the Stereotype of African American Men . Journal of Black Studies , 32 (1).
BIBLIOGRAPHY Krus, M. W., & Park, J. W. (2014). The undervalued self: social class and self-evaluation . Front Psychol , 5.
Causation and Correlation
Correlation is the tendency of two variables to tend to move together. The correlation could be negative meaning that the variables tend to move in a different direction or positive meaning that the variables tend to move in a similar direction. Pepperberg (2001) explained that causation involves causes and effect that is when one thing tends to happen it would be the outcome in another thing. It is mostly hard to identify a real causal relationship. In order to differentiate between correlation and causation, an example in Computer Networking Field will be used. The instance is the assertion that eating a lot of ice cream can assist in boost the scores of the students on the reading scale of PISA. There is a presence of correlation between the two data sets however there is an absence of evidence to ground the causation of one data and the other data. Increase consumption of ice-cream shows that there will be an increase in the student scores while decrease in the ice cream consumption leads to decrease in the students score. Whereas there may be a clear connection between ice cream consumption and IQ, the data does not conclusively reveal anything apart from the apparent correlation present.
Follow up post 1
From the main discussion post, there is a difference between the correlation and causation. Correlation is the tendency of two variables to tend to move together. While causation involves causes an effect that is when one thing tends to happen, it would be the outcome in another thing. It is easier to establish correlation while it is difficult to determine causation.
Follow up post 2
The example provided in the main discussion did not demonstrate causation, but it showed a correlation. Increase consumption of ice-cream indicates that there will be an increase in the student scores hence the correlation, but there is an absence of cause and effect that is linked with causation. In causation, cause and effect needs to be present while in correlation it is not a must.
Pepperberg, I. M. (2001). The conundrum of correlation and causation. Behavioral and Brain Sciences, 24(6), 1073-1074. Retrieved from https://search.proquest.com/docview/212229516?accountid=41759
My online class experience was amazing because it provided me opportunities to learn different statistical concepts. The online course offered familiarity about many important concepts such as population, sample size and parameters. I learned the technique of targeting a specific population in statistical study. The role of sample size and appropriate method for its estimation was explained. I enhanced my knowledge about different statistical concepts that are also applicable in research studies. I developed the ability of understanding sequences of frequencies and descriptive statistics. My knowledge of probability and statistical inference also improved to larger degree. Through this course I managed to learn the techniques of building probability for learning the possibilities of occurrence of an event. These concepts are important for taking a statistical exam. I also availed the opportunity of applying these concepts in research studies.
I developed expertise of using main descriptive statistics that include tendency, variability and location. By understanding the differences I managed to use them in solving of many statistical problems. My learning was not limited to these concepts because the course offered insights into many other important aspects of the subject. The best thing about the course was to learn the use of graphs and charts. This allowed me to create by own graphical visuals by using datasets and relating them to the variables. I also managed to learn different statistical software such as SPSS that is one of the commonly used software in academics. I developed the knowledge of adding raw data from surveys and questionnaire in the software and coded them. The course shared elaborated guideline for performing different statistical techniques such as computation of means, standard deviations and frequencies. I learned to run regression model and coding the dependent and independent variables. The software was also used for creating scatter plots that helped in understanding the frequencies and distribution of population.
The course shared different learning materials that allowed me to build clear knowledge about the use of bivariate and normal distributions. There distributions can be used in statistical research and other areas where statistical concepts are applicable. The course offered me knowledge for constructing hypothesis for studying assumptions. I developed the competency of differentiating between the null and alternative hypothesis and their function in different researches. My encounter with different statistical concepts will allow me to handle real life data such as inquiring on a general problem of gender discrimination and pay gaps. I would be able to select appropriate variables for determining the relationship between them. By running statistical techniques in statistical software such as SPSS computation of different frequencies, distributions and ranges is possible. I would manage to analyze data and run various tests such as t-test and regression analysis. After entering the data in software with proper coding I would manage to analyze correlations that will depict the relationship between variables.
I believe that the online course on statistics offered me numerous opportunities of applying various statistical techniques and concepts. After completing the course I felt that my knowledge of statistics improved significantly. My enhanced knowledge of statistics has helped me to perform quantitative research by collecting data from surveys and questionnaire. The information of the techniques and statistical method will help me to use them for calculating the research results. I think my enhanced knowledge of statistics have made me self-sufficient in differentiating between important concepts and using them for calculating results of surveys.
The practical problem discussed in the study “Do Health Promotion Behaviors Affect Levels of Job Satisfaction and Job Stress for Nurses in an Acute Care Hospital?” The study aims to find out the relationship between health care promotional behavior and job satisfaction and job stress in a hospital setting. It is discussing the issue of increasing job stress and job satisfaction among the nurses who have the most important job of providing health care services to others.
Effect of the issue on my nursing practice:
As nurses are responsible for providing optimal care of various types of patients and their emotional and physic health are important determents of their performance. Due to the high level of job stress, this is the unhealthiest workforce among health care professionals. Thus, stress management in nursing is a topic that needs to be fully analyzed.
Being a nurse, I find this job very stress full, the twelve hours shift have a huge impact on the physiology of an individual. Delivery health care services without any rest periods make a person emotionally and physically exhausted. The heavy workload and the stressful environment of a hospital trigger mental health issues and physical illness in the individuals working there. Nurses like me, are greatly affected by job stress and we need to improve our coping capabilities at work. Job stress affects nursing practices as a negative impact on the physical and mental health of an individual. It makes health care professional lose interest in the job. I could not be able to concentrate on the patient outcome when I am working in long shift and thus it affected my performance as a nurse.
The purpose of the study:
The purpose of this study is to find out the relationship between the Job stress, job satisfaction, and health care promotional behavior of nurses in an acute care hospital setting. The health promotional model used to make the nurses understand the important determinants of health behaviors and those determines are useful for a healthy life. The study is focusing on the factors that promote HPM and thus decreases job stress and increase job satisfaction of nurses.
The study has clearly stated its purpose and the variables that it is going to test to identify their relationship. The research question is not separately written but can be identified from the research objective. The objective of the research is to find out the relationship between and out the relationship between the job stress, job satisfaction, and health care promotional behavior of nurses. The research question and the purpose of the study are overlapping and giving similar meaning.
The researcher has not clearly identified the hypothesis of the study but that can be identified from the research objective of the study. The objective of the study says that the research is conducting to identify the relationship between job stress, job satisfaction and the HBP in nurses.
The purpose of the study is to find the relationship between the relationship between job stress, job satisfaction and the HBP in nurses. The research hypothesis and research question revolve around this research purpose.
However, the researcher explains the research purpose further and states that the purpose of the study that is to identify the gap in the literature regarding nurse reported HPB and the issues in the work environment. The hypothesis is designed to find out the relationship between job stress, job satisfaction and the HBP in nurses. It is not related to any specific issues in the work environment but the factors that influence HPBs and impact job satisfaction and job stress. Thus, both the hypothesis and the research purpose re interrelated and reflects the objective of the research.
The null hypothesis for this rash could be, the factor that there is no relationship between job stresses, (HPBs) nurse-reported health-promoting behaviors (HPBs) and job satisfaction in a hospital setting.
The target population:
The population includes RNs who are responsible for acute care in faith-based community hospital located in the Southeastern United States. The sample 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.
The sample shows uneven characteristics that might affect the results of the study. Majority of the participant is of old age and have the same gender and professional experience. Females and old age individuals have little capability to cope with stress and this behavior can affect the overall result of the study. The researcher should have selected the target population either of the same age group with a similar mechanism to tackle work-related stress thus the result of the study would be more accurate.
Selection methods of the subjects:
The subjects were chosen conveniently in the one selected hospital. Most of the participant belongs to the acute care specialty area and those were selected through a self-report. The sample includes more white people than people of another race. It excluded the nurses who might be having some health issue or who might be pregnant. The size of the sample is determined by the availability of the participants in the hospitals. As the main focus of the study is to find out job satisfaction, jobs stress and HPBs in Acute care hospital setting so the majority of the participant were having acute care specialty area.
The research variables describe in the research paper are job satisfaction, job stress, and health promotion behavior. The variables were described fully in the paper. Health-Promoting behaviors include spiritual growth, physical activities, nutrients, health responsibilities, interpersonal relationship, and stress management. The Health-promoting behavior was measured using a health-promoting lifestyle Profile that included 52 items, 6 subscales, and 4 Likert scales. However, job stress was measured with the help of Job stress scales which was based on 8 subscales. Those subscales included rime proprieties, patient outcome, work environment, competency, staffing. Emotional support and team respect. The high score shows a high level of job stress. While job satisfaction was measured using a McCloskey Mueller Satisfaction Scale which is a Likert scale consisting of 31 items. This variable was described by the help of units like the family and work balance, coworkers, awards, scheduling, raise and recognition. The high level of MMS shows high job satisfaction.
The validity of the instrument used:
The validity of the instrument used to collect data can find out by identifying the item used. Self-reporting was used to calculate and those days are analyzed using a Likert scale. The reliability of MMs can be identified by the fact that bit has been used as a tool to identify job satisfaction in the field of nursing from centuries. The health Promoting Lifestyle is valid because it is used to identify HPB from ages. The Criterion-related validity is rs+0.29-0.49 and it means it is valid. The reliability scale for MMS is 0.64 that is quite near to +1 so it is reliable. Moreover, the scales are accurately described in the research paper. It says that if the score is high so is the attributes attached to it.
Data collection procedure:
Data were collected using an electronic questionnaire and that was kept anonymous. The survey was of 30 minutes. It is not given in the paper how often the data was collected.
The data collected through an online survey was uploaded on SPSS and the relationship between job satisfaction, HPB and job stress was identified using Pearson’s R-value. The variance was used to find out the strongest relationship between job satisfaction, HPB and job stress. The relationship between job stress and subscale of HPB are analyzed further by multiple regression. The procedure of data analysis is perfectly described and the data was organized in table forms as well.
The table shows that the instrumental range of MMS ranges from 1 to 5 and the mean is 3.73 while job stress scales 1 -4 and Mean SD is 2.97. HPLP ranges from 1-4 and the standard deviation is 2.74.
The value of the statistic deviation shows that the results do not vary that much and the stability in the observation describes the validity of the methods used. There is no assumption in the data given but the overall results of the test are given.
Level of measurement for each variable:
The means core for job stress was 2.969 and that ranges from 1-4. This value shows a moderate level of stress and the job satisfaction score mean value was 3.726 with a range of 1-5. The values for HPB was 2.742 that indicates a very moderate level of HPB. Cronbach’s of the item shows how closely related these are and it shows internal consistency of .89-.94. The alpha for MMS was .93, for Job stress scale it was .89 and for the HPLP II it was .94. As the coefficient of 0.7 or higher shows good internal consistency and this value is acceptable in most of the quantitative researches.
Moreover, the statistical test that was suitable for the types of data collected through the survey. For instance, the data collected to identify the value of job satisfaction is analyzed with the help of McCloskey Mueller Satisfaction Scale and that is the best scale to identify job satisfaction.
Demonstration of statistical significance for each value:
The output of the statistical test is THE p-value that shows the probability of observing the differences. The P-Value for HPB and for job stress is (P<.05). Job stress and the subscale of HPB is P <. 001. The p-value for spirituality is (P < .05). it means that spirituality shows statistically significant effects of interaction and non-other sub-level show that.
The findings of the study show that the strongest relationship between HBNP and job satisfaction the lowest was job stress. Job stress is strongly related to the HPB but the relationship between competence and HPB could not be fully identified from the finding. It was found out that competence increase in a highly stressful situation and this find can be further studied to check the implications. However, spirituality showed statistically significant and has a strong relationship with job stress and job satisfaction. The research question and hypothesis are fully answered by identifying the relationship between the variable.
The limitations of the study were given which says that the cross-sectional analyses include only one hospital. The employees were not playing multiple roles but the majority of them were having undergraduate degrees. The number of response is affected by the length of the survey and it should be enhanced. The data was collected from faith-based institute so the response showing spirituality was quite obvious so a generalization could be easily made.
The relationship between competence and job stress was positive and it was the unexpected thing that was found out because generally, these two are inversely proportional.
Clinical significance of the study:
A relationship between job stress, HPB and job satisfaction is to find out using empirical data. This relationship says that if health behavior is promoted in nurses then positive outcome van is achieved. The nurse with high value for HPB shows less stress. Thus, this affects their competency. The result of the study will benefit the individual who cannot cope with job stress and show pour performance. However, I will promote healthy behavior in order to get over of job stress.
Pages: 6 Words: 1800
MSN5250: Statistics for Advanced Nursing Practice Team:____________
Critique Worksheet for Group Project Part A
Elements of Critique
State the practice problem/issue that is the focus of the study.
How does this practice problem/issue affect your nursing practice?
Nursing is an important activity in the health industry. This is one of the most important tasks in any hospital where it is mandatory to look after patients as best as it is possible. The issue that is being discussed in this article is the lack of significant role by nurses in the well-being of those patients who are at critical stages and even battling for their survival. Instead of due diligence and more care, the nurses are poorly prepared in EOL for dying patients and their families. Thus, the issue of nursing is discussed in this article by the Alison Stacy, Margaret Rosenzweig, and the Bonnie Freeman to develop and transform the nursing practice in hospital and life-saving institutes with the help of effective tools and strategies.
This study solves the severe nursing problems in health practicing agencies. This study can improve the nursing profession in a way that it will make the nurses and the health practitioners more sympathetic to those who need more care and attention. This practice by using the CARES method will solve the problem of both patients and nurses they will be facilitated by the due attention and pragmatic measures. This study is an effective contribution to understanding and improving the talent and skills of nurses and their performances.
In your own words, state the purpose of the study.
It has been witnessed by the common mundane and in the medical researches that the nursing profession is not attentive to the real needs of the patients. There are serious shortcomings in the nursing professions as the patients who desperately need the life-saving measures are often not provided with the due attention and care in hospitals. The nurses prefer to live by their robotic ways of doing things and have no attention towards the use of EOL for critical patients. As a result, in general, lose their lives in this quagmire. EOL care is not properly offered to them and this takes the lives of a huge number of patients. Keeping in view the tragic situation of life, the researchers attempted to resolve the issue by giving due attention to CARES and EOL in hospitals and for the families as well. The purpose of the study is clearly expressed in a way that it aims to boost the knowledge and skills of the nurses in their field so that they become a good contribution to the health industry and become a better support system to the patients. For this problem, effective CARES tool helps at the End of Life experiences of families of those dying patients with the use of Final Journey.
Is the research question clearly stated?
What is the research question?
Does it match the purpose of the study?
Yes, the research question is clearly stated in the study which is done to improve the nursing profession by accoutering them with an adequate level of comfort, knowledge, and expertise.
The research question in this study is to investigate and examine questions that the usage of the CARES tools helps in augmenting nursing knowledge and comfort related to EOL. The other question was about the use of Final Journey whether or not it helps in improving the End of Life experiences of families of the dying patient.
Yes! these questions match the purpose of the study because the researchers are clear on exploring the real knowledge that would make the EOL and the Final Journey more effective in the nursing profession because it is about making health care more effective and more empathetic in nature and essence. Nurses to need to be attentive to the real needs of the dying patients and of their families.
Is the research hypothesis clearly stated?
What is the research hypothesis?
Does the hypothesis reflect the purpose of the study?
Formulate a null hypothesis for this study.
Yes! the research question is clearly and specifically stated in this study of the Alison Stacy, Margaret Rosenzweig, and the Bonnie Freeman. In this research, the scientific method of research is adopted in the same way just like any other medical study in the world where a hypothesis is formed and then researchers strived to prove it.
The research hypothesis evaluates how CRAES tools and Final Journey is influential in improving the talent and capacity of nurses and the paramedic staff in hospitals as it is observed that nurses are inadequately trained to deal with the EOL and the Use of CARES in easing the life of critical patients and their families.
Yes! This hypothesis resounds with the objectives of the study that were intended by the researchers. The hypothesis and the objectives share a cooperative relationship with each other.
The null hypothesis on this type of study will be the zero effect EOL and CARES to improve the quality of nursing in hospitals, and for the critical patients.
Who is identified as the target population?
How were the subjects chose (e.g., randomly, conveniently)?
Who is included (e.g., males, females, children, adults)?
Who is excluded (e.g., elderly, pregnant women, minorities)?
How large is the sample?
How was the sample size determined?
In every research, the relevancy and appropriateness of the targeted population are very important for the envisioned research question and the research hypothesis. This is the reason that researchers logically selected the data from the persons that were quite relevant in nature and essence. So, the targeted population is the convenient sample space of the registered nurses was used on a 16-bed medical progressive care unit at a western hospital in Pennsylvania.
The subjects were not randomly selected for this research question. Some conditions were part of choosing sample space for the study. Thus, a 16-bed in a progressive unit was chosen for the study question while keeping in mind the frequency of caring for patients at EOL.
The adequate sample space was fixed according to the criterion of the researchers to meet their specific need of the hypothesis. Keeping this prime objective, they set up a few conditions that nurses must be practicing in a progressive health care unit. Moreover, the condition of EOL was fixed for those nurses as well that were counted in the sample space. Mostly the nurses were in the age range of 30-39 with the experience of 6 years in the field. They were having a bachelor's degree as well.
The other practicing nurses were excluded from this study who did not meet the inclusion criterion.
The sample space is not large as it has only 9 nurses16 bed unit in observation and the limited number of nurses working there. Moreover, that progressive unit had only 30 patients
The sample size was determined on the basis that it should not create any confusion in the proposed study question.
List the research variables.
How are the variables described?
What instruments or tools were used to collect data?
How did you determine if the instruments are valid? Reliable?
Are the instruments adequately described for you to understand what the score means?
The research variables are the EOL, CARES tools, comfort and pain management for patients, airway and emotional support, and poise and the skill of practicing nurses.
The variables are described as clearly to fully understand the proposed research question.
The instruments that were used to collect data are surveys, questionnaires to collect data about the research question. Moreover, the interview questions were also developed to get feedback from the nurses.
This was the subjective study as it took into account the human sentiments. So, all the methods were reliable in that sense.
Yes, all the instruments were clearly described for the readers and practitioners.
State the data collection procedures.
How often was data collected and for how long?
The data was collected with descriptive pre-intervention and postintervention surveys and with a variety of interviews from those participating nurses and patients. Besides, live interviews were conducted for the process of data collection.
The project took place over five months from September 2017 through January 2018. Eleven nurses were participants, but only nine performed the operations and the two did not for unknown reasons.
Were data analysis procedures clearly described?
Were data logically organized/presented in tables, graphs and/or charts? Describe.
What statistical tests were used to analyze data?
What assumptions in the data must be met for the type of statistical tests used? How do you know if these assumptions were met?
What were the levels of measurement for each variable in the study?
Were statistical tests suitable to the types of data collected/levels of measurement?
What was the alpha for each statistical test?
Describe how statistical significance was demonstrated (or not) for each variable.
Yes, the data analysis procedures were described in this research article. Every step was mentioned numerically to analyze the hypothesis.
The tables were listed visibly in the study. One table was about the sample space in the study and the other one was about a paired sample test. Moreover, there was a graphical representation of the surveys as well.
The statistical test that was used in this study were mostly descriptive as it was about the human care and sympathy. Thus, a pre- and post-quasi-experimental design with a pre-educational and post educational intervention was used to understand the influence of tools. Then reporting procedures were used such as Presurvey and post-surveys to evaluate changes in knowledge and comfort connected to EOL
The assumptions were met in the statistical surveys as it was exhibited in the various tables given in the finding.
Yes, the statistical surveys tests with themes were accurate with the type of data collected.
A paired-sample t-test was conducted using SPSS for comparing Preintervention and postintervention survey with a predetermined significance level of less than 0.05.
Several nursing themes were fixed to effectively draw out the experiences of nurses along with various types of interviews from families and nurses.
Discuss the study results. What were the findings?
Is the research question/hypothesis answered?
Were the study limitations described? Discuss.
Can generalizations be made? Discuss.
Were there any unexpected findings? Discuss.
The results of the study are such that the Use of the CARES tool helps provide nurse the optimum evidence-based EOL care to critical d near to dying patients and their families. Moreover, the Final Journey is also an opportunity to progress the EOL knowledge as perceived by families of dying patients. Furthermore, this study noted that Improving QOD in hospitals is even more imperative. Refining EOL care in nursing and cultivating the EOL experience for families of the dying are the main objectives for quality improvement with the help of CARES tools.
Yes, the research question and hypothesis are duly answered catered in this study. All postintervention survey scores improved with the proper utilization of CARES tool.
Yes! the study also clearly explained the limitations. It is stated that the convenience was very small and that was operating in the small unit from a single hospital. Moreover, the use of self-developed preintervention and postintervention survey was chosen as the method of evaluation which was unreliable as compared to a validated preexisting tool in the assessment of EOL care. Moreover, the limitation was also about the element of subjectivity in the research. Also, the information obtained regarding feedback from families about Final Journey was secondary and was increased biases in the research.
When the subjective tools were used, it left the room for generalization and this effectiveness of EOL and CARES tools was generalized for all types of patients.
It was noted that some nursed performed better in the tests even when they had a deficient experience in the field.
Discuss the study recommendations.
Is there an identified need for further research?
The study recommends the there should be an improved quality of QOD in hospitals and there should be more use of CARES tools by the practicing nurses. There should be more focus on Final Journey and EOL.
The area of further research is exploring the maximum potential of CARES for quality improvement.
Do study findings have clinical significance?
Who will benefit from results of the study?
Discuss implications of the study for nursing practice.
What changes could you make in your practice based on the results of this study?
The study has huge clinical significance in a sense that it is making medical research more humane a tragedy is never readily accepted. This is about harmonizing the needs of dying patients and families with professional health ethics and code of conduct.
The nurses at the progressive care units or in general will be benefited from the study.
This study will improve the nursing profession in the medical field as they will be more refined in their fieldwork. The critical patients will be more facilitated as it will simplify their lives in that critical period.
There should be some changes in the hospital and the nursing profession as this study calls for more employment of CARES tools for EOL patients and families. This step is even more effective when it accompanies the use of Final Journey in the practice. The nursing profession must be reformed in a way that is more humane and empathetic rather than just mechanical or robotic in outlook.
Pages: 6 Words: 1800
[Name of the Writer]
[Name of the Institution]
In this assignment we will discuss the data provided in the article White House Falsely Claims Trump Has Created More Jobs for Black Americans Than Obama Did. (2019) Published in the Newyork Times. According to the report the Trump government claimed that we had created three times more jobs for African-Americans than Obama's government. However, the statistics show that the unemployment rate in African-Americans was about 10 percent when Obama’s government took charges. African-Americans comprised a large portion of the country population. They are highly ignored by the government and pulled back to the darkness from many centuries. It has a negative impact on the country's overall economic, and political situations. The new governments of the country have focused on this issue, and they are seriously coping with this issue. Trump government had claimed that they had increased the employment level of black Americans three times from Obama's era. Moreover, he claimed that they had created more than 700,000 jobs for the black community of the country. However, their initial claim was rejected by business experts and declared it as a false claim. The government officials have apologized for the wrong statement due to the confusion in the data.
It raised to 16.8 by March 2018, the highest unemployment rate of black Americans in Obama’s government. However, the unemployment reduced after touching its peak value, and at the end of Obama's tenure, it was nearly 7 percent. During the Trump government, the unemployment level in African-Americans reduced up to 6.6 percent. The data could be either misinterpreted by Trump government because at the mid of Obama’s tenure it was about 17 percent, which is now reduced up to 6.6 percent. Trump government has also claimed that they have produced about 708,000 jobs so far for African-Americans. The given data suggests that the unemployment of black Americans has been controlled during the Obama government.
In this article, the descriptive statistics were used to analyze the wide range of data. The data were analyzed by using simply utilizing the variety of highest and lowest value in this article. This article would be more authentic if they would use the measure of central tendency. I calculated the mean, mode, and median of the provided data. The mean of the data was 11.5 percent, which shows that during the last ten years the unemployment was approximately 11.5 percent. The data suggest that mostly the unemployment rate remained near to the value of the mean. Moreover, the mode of the provided data suggests was 8.4 which indicates that this value occurred many times during ten years. The data was misinterpreted because the Trump government claimed that they had declined the unemployment rate in black Americans by three times of Obama's government, which were the wrong calculations of the statisticians of the government. The findings of the new government were to somewhat subtle, in previous government it reached to the highest value of 17 percent. So 6.6 is almost three times of 17 percent.
This article revealed important information regarding the employment rate of black Americans. The employment level of the African-Americans is raising with time, Obama belonged to the same race, so he did remarkable work for the uplifting of this community. People's perceptions regarding Trump presidency were not good, people from other ethnic groups and races felt unsecured at the beginning. However, he worked on the uplift of the African-American community and had two claims. First, they created 700,000 jobs for black Americans, second, they created three times more jobs for the said community during his short time. This article aimed to discuss the clarification for these claims. The government officials apologized for the second claim of the creation of three times more jobs than Obama’s era for black Americans. While the government confirmed the initial claim of creating 708,000 jobs for African-Americans. Trump's government had some misconceptions, and the data might interpret wrong. The government compared the rate of unemployment in the current government with the highest value of the Obama government.
The article used simple range and calculations which created problems, and the initial statement of the president was rejected. For fair analysis the government has to use all the techniques of statistics such as measures of Frequency, measures of central tendency, measures of dispersion or variation - range, variance, standard deviation, and measures of position - percentile, and quartiles. All these measures provide precise and reliable information about big data. The data used in this article is given as under.
Table SEQ Table \* ARABIC 1Employment of African Americans
White House Falsely Claims Trump Has Created More Jobs for Black Americans Than Obama Did. (2019). Nytimes.com. Retrieved 17 February 2019, from https://www.nytimes.com/2018/08/14/us/politics/fact-check-trump-jobs-black-americans.html?rref=collection%2Ftimestopic%2FBureau%20of%20Labor%20Statistics
Pages: 3 Words: 900
Summary of the Article
School segregation has been one of the most crucial issue for United States since its inception. Even today many of the parents cannot find school of their choice for their children. A recent article in The New York Times provides statistics of segregation in San Francisco. Although efforts have been made from government side but the problem still persists. Black or Hispanic people are the ones affected the most and a case explained in the article, a Hispanic child gets admission where the percentage of white student population is only 3. Secondly, income has also been the determinant of getting into public schools where low income parents are left behind. Government has tried many ways to eliminate school segregation like improving transportation, merit based admissions and admissions based on residential location but proved ineffective. Racial segregation in the district schools has been more in the year 2015 than it were in the 1990 according to research studies. District school segregation has been more in San Francisco than any other part of the country and students have been admitted to schools mostly consisting students of their race. The mentioned factors to segregation are unavailability of transport and ineffective government policies like lottery. Out of 54 thousand students in the district only 4 thousand have the privilege to school buses. Second is the choice which is not fulfilled given the discriminatory facts in the system. Segregation is also worsened due to the loopholes in the system which gives advantage to the wealthier like school visits during working hours, costs associated in checking out every school in the area. Segregation also affects the wealthier families as they are left behind in the lottery system of the administration and private school enrollment is more compared to other major cities like New York. Parents are ultimately forced to use all their resources for their children better education.
Descriptive Statistics have been used in this article to show the total number of schools, number of students enrolled in those schools. Measure of central tendency has been used to illustrate average number of students based on income, race and geographic location. Measure of Dispersion has been used in way to illustrate the choices of the parents and how far they are deviating from their best choices like out 15 choices a parent getting admission of her child in the second last choice. Measure of position has been used in the article to show different enrollment statistics in the private and public schools
Application in the Real World
Segregation of any form has been one of the major issues faced by the United States, a place of diversity and consists of population who have migrated from every corner of the world. Segregation of any form would affect the expectations of the population leading to frustration and social crimes. It is better to have statistics of segregation of any form and anywhere in the country in order to address the issues affectively. As a student of statistics I should be in position to understand the descriptive statistics of any form. It would ultimately help me in my professional career and current education requirements as I would be able to analyze data from different sources and in different form.
Reasons for Data in the Article
There are various reasons for using different data in the article. As the article consists of data related to distribution of the population based on race, ethnic background and income that is why Frequency distribution has been used. The article contains information of range of different categories of school that is why different range of schools have been mentioned in any particular area. Data also contains average number of students in schools, maximum and least number of students enrolled on racial basis. Quartiles have been used to show the school enrollment of different students in public and private schools.
BIBLIOGRAPHY \l 1033 Goldstein, D. (2019, 04 25). New York Times. Retrieved from www.nytimes.com: https://nyti.ms/2IEWQRf
Organizations operate through making decisions. The manager controls, plans, leads, staffs and organizes her team through execution of judgments. The quality and effectiveness of the decisions determine the success of the manager. As managers, I have been faced with a situation that needed to solve a problem in the past. Addressing the issue often requires the knowledge of probability. The decisions making process starts when the manager identifies the problem
The manager needs to look at various alternatives for solving a problem. After the analysis of multiple options, as manager, I am supposed to select the best one that has the highest probability of having fewer risks and higher returns. The best alternative selected has the most advantages with fewer disadvantages. The process of selection could be direct for instance the alternative with the most benefits and with the fewest drawbacks. The optimal solution is the combinations of various options (Xie & Goh, 2014). In other scenarios, the best choice is not very obvious. The manager’s needs to make decisions on the alternative that is the most effective and feasible combined with the one that has the lowest cost to the form. Probability estimates whereby the analysis of every alternative chance of the success takes place come to plan in this state of the decision-making process.
In most cases, the manager selects the alternative that has the highest probability of succeeding. As a manager, I am paid to make decisions and to attain good results from the choices. The decisions made needs to have positive outcomes. For a successful outcome, every person in the organization needs to know their role.
Xie, M., and T. N. Goh. "The use of probability limits for process control based on geometric distribution." International Journal of Quality & Reliability Management 14.1 (2014): 64-73.
When to Not Use Graphs
[Name of the Writer]
[Name of the Institution]
Despite the fact that how the graphs at times tend to augment the presentation of the data, the key fact that has to be kept in mind is that the presentation of the data has to be done in a manner that the critical information has to be reflected. Without the reflection of the key information, no matter how much critical information is being provided during the course of the data, it is not going to be providing any value. One of the key things that has to be kept in mind when it comes to the data is that how the ranges are supposed to be working out. If the data is of such nature that it does not represent sufficient information at the given point of time, the critical aspect is that how ranges are needed to be consistent when it comes to the presentation of such data. The other thing that has to be kept in mind when presenting such data is that the ranges are supposed to be small. If the data values are fluctuating a lot, then it is not really advisable that such a data has to be presented in a graphical manner. The most common example in this regard is how the economic variables and income estimations are carried out. The problem with such a data is that the values have so much variance and the data ranges are so much broader that it is not extremely useful to put and present such data in the form of the graph. It is very important to make sure that some sort of restraint is being exercised when it comes to the way presentation of the graphical information that has great variance.
Everitt, B. S., Everitt, B. S., Everitt, B. S., Statisticien, G. B., Everitt, B. S., & Statistician, G. B. (2018). Graphical techniques for multivariate data (No. 04; BF39, E8.). New York: North-Holland.
Random samples obtained from a population are, by their nature, unpredictable. It would not be expected that two random samples of the same size and taken from the same population have the same sample mean or are completely similar; It can be expected that any statistic, such as the sample mean, calculated from the means in a random sample, changes its value from one sample to another, therefore, the distribution of all possible values of a statistic is studied. Such distributions are very important in the study of inferential statistics because inferences about populations are made using sample statistics.
Since the values of a statistic, such as x, vary from one random sample to another, it can be considered as a random variable with its corresponding frequency distribution. The frequency distribution of a sample statistic is called the sample distribution. In general, the sampling distribution of a statistic is that of all its possible values calculated from samples of the same size. As an example, considering that the random samples of size 20 have been selected in a large population. Sample x is calculated for each sample; the collection of all these sample means is called the sample distribution of means. A sample distribution is generated by extracting all the possible samples of the same size from the population and calculating their statistic. If the population from which the samples are taken is normal, the sample distribution of means will be normal regardless of the sample size (Lipson, 2003).
A sampling can be done with or without replacement, and the starting population can be infinite or finite. When considering all the possible samples of size n in a population, for each sample the statistic (mean, standard deviation, proportion ...) can be calculated that will vary from one to another. Thus, a distribution of the statistic is obtained that is called sampling distribution (Lumen, 2018).
Lipson, K. (2003). The Role of the Sampling Distribution in Understanding Statistical Inference. Retrieved from https://files.eric.ed.gov/fulltext/EJ776331.pdf
Lumen. (2018). Sampling Distributions | Boundless Statistics. Retrieved from https://courses.lumenlearning.com/boundless-statistics/chapter/sampling-distributions/