- Logistic regression. Select a binary reponse and continue explanatory/predictor variable.
- (1 p) Plot the data.
- (1 p) Summarize the \(\hat{p}\) values for each value of the \(x\)-variable. Also, calculate the empirical logits.
- (1 p) Plot the \(\hat{p}\) values vs the \(x\)-variable and plot the empirical logits vs the \(x\)-variable.
- (1 p) Describe the logit-vs-\(x\) plot. Is it linear? If not, consider a transformation of \(x\) to improve linearity; describe the transformation you chose if you needed one.
- (1 p) Fit the
`glm()`

model and assess the deviance lack-of-fit test. - (1 p) Calculate the confidence bands around the model fit/predictions. Plot on both the logit and \(\hat{p}\) scales.
- (1 p) Interpret the sign (\(+\) or \(-\)) of the slope parameter and test whether the slope is different from zero, \(H_A: \beta_1 \ne 0\).

Poster topics.

- Choose two analyses you’ve done this semester to present in your poster project. At least one should include the relationship between two variables. The questions below help you start thinking about what you’d like to present on and what that might look like in poster form.

(1 p) State the first research question, the method used to answer it, and a brief statement of what you found (1-3 short sentences).

(1 p) State the second research question, the method used to answer it, and a brief statement of what you found (1-3 short sentences).

- (1 p) (Freebee) What’s one thing you’re grateful you learned in this class and why?

Turn in your master HW file with these sections appended to the bottom.