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Use the Student_Data which consists of 200 MBA students at Whatsamattu U. It includes variables regarding their age, gender, major, GPA, Bachelors GPA, course load, English speaking status, family, and weekly hours spent studying.
1. The battle of the sexes lives on still today. Since admission standards do not address gender whatsoever, there should be an equally diverse group of men and women in school, but do they perform equally well. Using the sample of 200 students, conduct a hypothesis test for two independent samples to determine if the mean GPA differs for men and women. Use a .05 significance level.
2. Can a student keep up their grade performance at the next level? Is a strong GPA at the Bachelors level a good predictor of a strong GPA at the Masters level, or are GPAs naturally going to decline since graduate school is tougher, or will GPAs automatically be higher in graduate school because of the 3.00 requirement to graduate and the treatment of a C as subpar instead of average? Using the sample of 200 students (in the data file), conduct a hypothesis test for paired samples and test if there is a difference in the mean GPA from the Bachelors to the Masters programs. Use a .05 significance level.
3. Given the reasons why people get their Masters, you surmise that men are more likely to declare a major than women. Using the sample of 200 students (in the data file), conduct a hypothesis test of proportions to determine if the proportion of women with “no major” is greater than the proportion of men with “no major”. Use a .05 significance level.
4. You have probably heard that if you want something done, give it to a busy person. So is one’s employment status a factor in their academic performance? Using the sample of 200 students (in the data file), conduct a hypothesis test using Analysis of Variance to determine if there is a difference in the mean GPA for those who are unemployed vs. work part-time vs. work full-time.