Week 4 Chi Square salary

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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, we can 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 Results are mean +/-2.064*standard error Females 2.064 is t value for 95% interval Interpretation: 2 Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How do these results 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)? 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 gender-degree combinations have equal average compa values within the population. This does not mean, however, that graduate degrees are distributed equally across the grades. 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: Ho: Ha: 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 Do manual calculations per cell here (if desired) M Grad A B C D E F Fem Grad M Grad Male Und Fem Grad Female Und Male Und Female Und Sum = 0 EXPECTED M Grad For this exercise – ignore the requirement for a correction Fem Grad for expected values less than 5. Male Und Female Und Chitest value = Chiinv = Interpretation: 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: Ho: Ha: Do manual calculations per cell here (if desired) A B C D E F A B C D E F OBS COUNT – m M OBS COUNT – f F Sum = EXPECTED What does this decision mean for our equal pay question: 5. How do you interpret these results in light of our equity question?

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