Week 4Confidence Intervals and Chi Square (Chs 11 - 12).docx
- 1. 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.
- 2. 1 Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)? Mean St error t value Low to High Males
- 4. 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?
- 7. Yes/No Can the means be equal? Why?
- 8. How does this compare to the week 2, question 2 result (2 sampe t-test)? 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?
- 9. 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.
- 10. 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.) What are the hypothesis statements:
- 11. Ho: Ha:
- 12. Note: You can either use the Excel Chi-related functions or do the calculations manually. Data input tables - graduate degrees by gender and grade level OBSERVED A B C D E F Total If desired, you can do manual calculations per cell here. M Grad
- 13. A B C D E Fem Grad M Grad Male Und Fem Grad Female Und
- 16. For this exercise - ignore the requirement for a correction factor Fem Grad for cells with expected values less than 5. Male Und Female Und
- 19. Interpretation: What is the value of the chi square statistic:
- 20. What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis:
- 21. 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:
- 23. 4 Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population? What are the hypothesis statements:
- 24. Ho: Ha:
- 25. Do manual calculations per cell here (if desired) A B C D E F A B
- 26. C D OBS COUNT - m M OBS COUNT - f F
- 27. Sum = EXPECTED
- 29. What is the value of the chi square statistic: What is the p-value associated with this value:
- 30. 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:
- 31. What does this correlation mean?
- 32. 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?