ID Salary Compa Midpoint Age Performance Service Gender Rating Raise Degree Gender 1 Gr Students: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments. The ongoing question that the weekly assignments will focus on is: Are males and fe Note: to simplfy the analysis, we will assume that jobs within each grade comprise eq The column labels in the table mean: ID - Employee sample number Salary - Salary in thousands Age - Age in years Performance Rating - Appraisal rating (e Service - Years of service (rounded) Gender - 0 = male, 1 = female Midpoint - salary grade midpoint Raise - percent of last raise Grade - job/pay grade Degree (0= BS\\BA 1 = MS) Gender1 (Male or Female) Compa - salary divided by midpoint emales paid the same for equal work (under the Equal Pay Act)? qual work. employee evaluation score) Week 4 Confidence Intervals and Chi Square (Chs 11 - 12) For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions. For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed. 1 Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. Mean St error t value Low to High Males Females Interpretation: 2 Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How does this compare to the findings in week 2, question 2? Difference St Err. T value Low to High Yes/No Can the means be equal? Why? How does this compare to the week 2, question 2 result (2 sampe t-test)Results are the same - means are not equal. a. Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples? 3 We found last week that the degree values within the population do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders. Do males and females have athe same distribution of degrees by grade? (Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.) Ignore any cell size limitations. What are the hypothesis statements: Ho: Ha: Note: You can either use the Excel Chi-related functions or do the calculations manually. Data InTables The Observed Table is completed for you. OBSERVED A B C D E F Total If desired, you can do manual calculations per cell here. M Grad 1 1 1 1 5 3 12 A B C D E F Fem Grad M Grad 5 3 1 1 1 2 13 Male Und Fem Grad 2 2 2 1 5 1 13 Female Und Male Und 7 1 1 2 1 0 12 Female Und 15 7 5 5 12 6 50 Sum = EXPECTED M Grad Fem Grad Male Und Female Und For this exercise - ignore the requirement for a correction for expected values less than 5. Interpretation: What is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis: If you rejected the null, what is the Cramer's V correlation: What does this correlation mean? What does this decision mean for our equal pay question: 4 Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population? Again, ignore any cell size limitations. What are the hypothesis statements: Ho: Ha: A OBS COUNT - m B C D E Do manual calculations per cell here (if desired) A B C D E F M F OBS COUNT - f F Sum = EXPECTED What is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis: If you rejected the null, what is the Phi correlation: If calculated, what is the meaning of effect size measure: What does this decision mean for our equal pay question: 5. How do you interpret these results in light of our question about equal pay for equal work