Exam 2 review

Lecture 22

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Spring 2024

2024-04-09

Warm up

Warm up

Announcements

Same as Exam 1!

• Exam format / flow

• Asking questions during exam / office hours

Application exercise

Application exercise: ae-16-equality-randomization

• Go back to your project called ae.
• If there are any uncommitted files, commit them, and push.
• Then pull and work on ae-16-equality-randomization.qmd.

Recap of AE

• A hypothesis test is a statistical technique used to evaluate competing claims (null and alternative hypotheses) using data.
• We simulate a null distribution using our original data.
• We use our sample statistic and direction of the alternative hypothesis to calculate the p-value.
• We use the p-value to determine conclusions about the alternative hypotheses.

Questions

Questions

• Do we need to know functions and iteration for the exam? Since we didn’t work very much with them after the HW videos.

• When we run bootstrapping simulations for different types of statistics (proportions, means, medians, slopes, etc), are there any differences in the methodology? Or does it all rely on calculating sample statistics for many simulations and then taking the middle 90 or 95% as a confidence interval estimate for that statistic.

• How do you determine whether a relationship is nonlinear without visualization?

• Could you go over how logistic regression works again? Especially how a log(p/1-p) transformation of binary data creates a linear model?

• When do you use logarithmic functions in linear regression, rather than a logistic regression?