For this assignment, you will undertake an analysis based on a selfdesigned fictitious study that utilizes statistical methodologies. You will first develop a fictitious problem to examine  it can be anything. For example, maybe you want to look at whether scores on a standardized college placement test (like the SAT) are related to the level of income a person makes 10 years after college; Or, whether those who participate in a Leadership Training program rated as better managers compared to those who do not; Or, whether ones political affiliation is related to gender.
These are just a few examples; be creative and think about what piques your interest. You might also address a problem that you may want to look at in future research for a dissertation.
Your analysis report should include the following components:
Describe your research study.
State a hypothesis.
List and explain the variables you would collect in this study. There must be a minimum of three variables and two must meet the assumptions for a correlational analysis.
Create a fictitious data set that you will analyze. The data should have a minimum of 30 cases, but not more than 50 cases.
Conduct a descriptive data analysis that includes the following:
.a measure of central tendency
.a measure of dispersion
.at least one graph
Briefly interpret the descriptive data analysis.
Conduct the appropriate statistical test that will answer your hypothesis. It must be a statistical test covered in this course such as regression analysis, single ttest, independent ttest, crosstabulations, Chisquare, or OneWay ANOVA. Explain your justification for using the test based on the type of data and the level of measurement that the data lends to for the statistical analysis.
Report and interpret your findings.
Wages are affected by different factors from personal to group factors. When company or organization is determining the salary and wages structure of employers, these factors should be put into consideration labour unions, personal perception of wage, cost of living, government legislation, ability to pay and demand and supply. At personal level factors that determine wages include worker’s age and capacity, educational qualifications, worker experience, promotions possibilities, gender and profit earned by the organization.Waged laborer’s can be divided into employed which are self employment and waged employment and unemployment. Organization perceives wages as hourly or salaried wages. Wages include relief pay, family allowance, financial support and any other benefit originating from the firm.
Personal characteristics affect the wages level. Education qualification influences the wage one get. According Lazear E. (2005) degree education result to higher pay than those employees who have only college or high school qualification. The study established positive linear correlation between wages and education. An increase in education qualification resulted to a higher pay and vice versa.
In terms of age the older the employees the higher the experience and thus earn higher pay than those who are younger. Old age is associated with experience and higher skills to do job better compared to those who are young. Though higher age is associated with higher risk in term of energy and more time consumed doing a job, (Kidd P., 1993).
Gender disparity also affects mechanism and passion of doing a task. Male are associated with high vigor and mechanism and thus in jobs that require a lot of energy and mechanism tend to get higher pay. Females attract more pay in jobs such as customer relation, marketing and front office. These factors also affect male and female choices in employment, (Kunhn P. & Schuetze H., 2001).
The study tries to answer the question of wages imbalance in different organization. What are the factors that lead to this imbalance? Why are some employees doing the same job and in same organization getting different wages?
 Number hours one spends working affect wages one get?
The report tries to identify if there exists any difference between employees working long hours and those who are working in short hours.
 Gender affects wages of employee?
The study will try to identify how gender affects wages of employees.
The data set had seven variables that is
 age of employee in years, gender of employee(1 represent male, 2 female)
 Measure of education qualification that is number of degree employee posses.
 Number of hours the employee spend in work in a year.
 Salary of employee in dollar per year
 Number of kinds the employee have as measure of family responsibility.
 Marital status of the employee (0 unmarried and 1 Unmarried).
The 30 persons sample was chosen at random to represents a population of people who are in the area. They had formal education and were below age of 35 years. Number of degrees, age of respondent, wages and hours of working hours are numerical data while gender of respondent is categorical data. As shown below by a screen shot
The sampling frame where data was obtained is from my neighbors and friends who are waged employees. They consist of male and female employees, according to demographic setting of our locality males are many compared to females. The target population was first divided into groups’ males and females, and then respondents to the survey were chosen at random. This sampling method ensures one obtains a representative sample of the population that is all characteristics of population are present in the population and each member of population has each chance of being in the sample this eliminates biases that could originate from the researcher. The type of study was survey method where questionnaires were used as tool of data collection. The questionnaires had set of questions require to obtain the data which the respondents was required to answer. They were self administered to the sample to reduce non response. Excel software was used in data entry and data cleaning and also data analysis and report was written in Microsoft word.
Research hypothesis
Excel software was used as tool of analysis. The data analysis made use of inferential and descriptive analysis method. Descriptive statistics are used to describe and summarized any data set. They are used to describe what is in data or distribution of the data and provide a comparison that can be used to compare set of data. When computing descriptive statistics one is at risk of distorting the data. They include measure of location such as mean, median and mode and measure of dispersion such as variance, range, quartile and deciles. In this study it explored univariate variables. Inferential statistics infer and make conclusion on the population using sample characteristics. They include regression analysis, hypothesis testing and confidence level testing. The study utilized independent ttest is used to test significance, it checks if the mean of two independent samples is significance different. Scatter diagram is a graphical method to check linear relationship between two variables. When a linear relationship is identified correlation analysis is used to check if the relationship is causal or not causal. The report will cover the following histogram of number of hours worked and wages to check their distribution. Scatter plot of working hours and gender to check any association or linear relationship between the two variables. The relationship between wages and working hours is tested using regression analysis. Regression analysis model which was utilized was where y is response variable (wages) and x is predictor (working hours of employee per year).
Descriptive statistics
To compute mean and variance the variable under study must be quantitative variables. The average age of waged employee is 28.66 years. The standard deviation of age of employees 1.24, this implies that majority of observed age lied near the mean. The average wage that the employees earned in a year is 51200.87 dollars. The wage earned had an average working hours of 2996.10 hours in year. This implies that earned on average 17.09 dollars per hour. The average number of degree an employee has acquired is 2 and every employee has one child on average. In order for one to earn 17.09 dollars per hour one must posse’s two degrees.
Histogram is used to describe the distribution of data under study. The distribution of number of hours an employee worked is skewed to the right thus is not normally distributed, majority of employees who responded to the questionnaire work for at most 5000 hours per year. The data have extreme values, where we have some working for more than 40000 hours in year. This affect the mean and measure of dispersion giving a force picture of the data.
Many waged workers earn a wage of below 100,000 dollars in year; given their level of education and number one spend in the firm working. Few of employees managed to earn higher than this and formed outliers in wage data.
In order to plot the scatter plot the two variables must be quantitative variables. The scatter plot of working hours and gender showed no linear relationship between working hours and gender. This implies that gender is not associated with working hours. The correction factor is almost 0 where gender change working hours neither increase nor decrease. The two variables are independent of each other and do not affect each other.
Description of data
Relationship between working hours and wage
The first step in checking the relationship between two variables which are continuous and quantitative we plot a scatter diagram of the variables to identify linearity. In the scatter plot the predictor variable is working hours and response variable wages.
Majority of employees work between 2000 hours and 3000 hours earning below $100000 and few work above 6000 hours though the wages is not increasing above $100000. There is no linear relationship between the two variables that is working hours and wage one earns. The amount of hours spent by employee working does no determine the wage he gets. Increase in working hours does not increase wage earned, neither decrease in working hours does not reduce wage earned neither does increase in working hours reduce wage earned. Some employees work for not more than 2,000 hours per year and are getting a salary of more than 100,000 dollars. Others work for not less than 6,000 hours in a year and earn less than 100,000 dollars. Thus hours have no linear association with wage of employee. The distribution has extreme values and outliers that make data to be skewed. These outliers affect average and also affect the correlation giving false picture of data; therefore it is necessary to check if they are real data or just errors.
The Pearson Moment Product Correlation coefficient is used to check the strength of association between two variables and if the relationship is causal or not causal. The Pearson Moment Product Correlation coefficient between working hours of employees and wage is 0.299. This is a weak positive linear association where wage increase also working hours increase and if wage decrease also working hours decrease. The correlation coefficient is near 0 where there is no association.
The equation of linear regression model is given by
Where y is response variable (wages earned) and x is predictor variable (number of working hours per year).
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.093646 

R Square 
0.00877 

Adjusted R Square 
0.02794 

Standard Error 
34028.03 

Observations 
29 

ANOVA 

Df 
SS 
MS 
F 
Significance F 

Regression 
1 
2.77E+08 
2.77E+08 
0.238872 
0.628968 

Residual 
27 
3.13E+10 
1.16E+09 

Total 
28 
3.15E+10 

Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
42710.94 
12411.24 
3.441311 
0.001899 
17245.18 
68176.71 
17245.18 
68176.71 
7780 
1.844106 
3.77314 
0.488746 
0.628968 
5.89774 
9.585949 
5.89774 
9.585949 
R – Squared of the model is 0.093 which implies that working hours as predictor only explain 9% variation in employees’ wages. A very low value of Rsquared depicts inadequacy of the model thus our model is bad fit. R – Squared is 0.093 thus when one is predicting a person’s total income, he will make 9% fewer errors by basing the predictions on the person’s hours of work and predicting from the regression line, as opposed to ignoring this variable and predicting the mean of income for every case. Thus number of working hours of employee can be dropped from the regression model.
The model has slope of 1.84 which means that an increase in one hour of employee’s working hours will change the employee annual wage by 1.84 dollars. When working hours is dropped from the model one is expected to get an income of 42710.94 dollars. The pvalue is 0.63 which is greater than 0.05 (significance level) thus we fail reject null hypothesis that the predictor is not significance in the model and conclude that the working hours is statistically insignificance in predicting the wage of employee.
Data analysis methodology
Gender is categorical data and wages is continuous data. To check significance between two groups we utilize independent ttest. Student t test assumes that the data is normally distributed and sample size is small. It also assumes that the two samples have equal variance.
tTest: TwoSample Assuming Equal Variances 

Variable 1 
Variable 2 

Mean 
56460.3 
20000 

Variance 
1.88E+09 
379517907 

Observations 
20 
10 

Pooled Variance 
1.88E+09 

Hypothesized Mean Difference 
40682 

Df 
19.00 

t Stat 
0.09 

P(T<=t) onetail 
0.46 

t Critical onetail 
1.73 

P(T<=t) twotail 
0.93 

t Critical twotail 
2.09 
The pvalue of two tailed ttest is 0.93 which is less than the significance level of 0.05 thus we reject null hypothesis that the means are same and conclude that the mean salary of male and female is different. Gender affects wages, different genders get different wages. This may because female spend less time working as they prefer to take care of their families. At 95% level we can say that mean wages of different gender is statistical significance different.
Conclusion
The average age of waged employee is 28.66 years. The standard deviation of age of employees 1.24, this implies that majority of observed age lied near the mean. The average wage that the employees earned in a year is 51200.87 dollars. The wage earned had an average working hours of 2996.10 hours in year. This implies that earned on average 17.09 dollars per hour. The average number of degree an employee has acquired is 2 and every employee has one child on average. In order for one to earn 17.09 dollars per hour one must posse’s two degrees. The Pearson Moment Product Correlation coefficient between working hours of employees and wage is 0.299. This is a weak positive linear association where wage increase also working hours increase and if wage decrease also working hours decrease. The correlation coefficient is near 0 where there is no association. The model of predicting wages using working hours of employee was a bad fit. Working hours can not be used to predict wages and we dropped it from the model and conclude that wages is affected by other factors other than working hour of the employee. Gender affects wages, different genders get different wages. This may because female spend less time working as they prefer to take care of their families. The mean salaries of female are different from that of male.
References
Desaro S. (2011). A Students guide to conceptual side of inferential statistics.
Kidd, P. (1993) Immigrant Wage Differential and the Role of SelfEmployment in Australia. Australia Economic Papers , vol. 32, pg. 92115.
Kunhn, P. & Schuetze, H. (2001) SelfEmployment Dynamics and SelfEmployment
Trends: A Study of Canadian Men and Women, 19821998: Canadian Journal of Economics , vol. 34, pg.760784.
Kothari, C. R, (2004), Research Methodology Methods and Techniques New Age International. (P) Limited, Publishers :New Delhi
Lazear, E. (2005) Entrepreneurship: Journal of Labor Economics , vol. 23, pg. 649680.
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th Edition. Pearson Education Limited: UK.
Perry, J. & Perry, E. (2014). Contemporary Society: An Introduction to Social Science, 12th Edition. Pearson Education, Inc.: Singapore
Tashakkori & C. Teddlie (2003). Handbook of mixed methods in social & research. Thousands Oaks, CA: Sage.
Whitley E, Ball J. (2002). Statistics review 1: Presenting and summarizing data. Crit Care.
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