How does the distribution of credit hours look like? Does the mean number of credit hours per student appear to be significantly below 15 hours? Does the distribution of credit hours appear to be impacted by gender or class?
- What is the variable of interest?
- What graphical techniques will you use to describe your data?
- Why are those techniques appropriate? Why did you choose them?
What is the proportion of students at StatCrunchU that have CC debt? For those students who have CC debt, does the amount of credit card debt appear to be influenced by any one or any combination of the other variables?
- What is the variable of interest?
- What graphical techniques will you use to describe your data?
- Why are those techniques appropriate?
- Why did you choose them?
- What numerical measures will you use to describe your data?
- Why are those measures appropriate? Why did you choose them?
Suppose you want to compare the amount of student loan debt for males and females at StatCrunch U. The null hypothesis for this problem would be that there is no difference in the mean amounts of student loan debt for males and females and the alternate hypotheses would be that there is a difference in mean amounts of student loan debt. Using the sample you downloaded in StatCrunch Assignment 1A, conduct a randomization test for two means with 3000 randomizations.
a) Paste the output from your randomization test here.
b) What p-value did you get in your randomization? Explain in the context of the problem what the p-value means.
c) Do you think the data support the null hypothesis of no difference in mean student loan debt between males and females or the alternate hypothesis that there is a difference? Explain your answer.