1-6: Some Key Topics from Midterm

7: ANOVA

8: Correlation & Simple Linear Regression

9: Multiple Regression

• Know how to interpret the regression coefficients for models with numerical and/or categorical predictors, including without and with interaction
• Know how to make predictions using a multiple regression equation
• Know the hypotheses for the regression global $F$-test, and know how to interpret the results
• Know the hypotheses for the regression partial $F$-test, and know how to interpret the results
• Know the hypotheses for the individual $t$-tests for regression coefficients, and know how to interpret the results
• Know how backwards stepwise selection works and how to read its R output
• Know how to calculate degrees of freedom for multiple regression
• Understand how to interpret and compare $R^2$ and adjusted $R^2$ (do not need to know the equation for adj. $R^2$ )

10: Classification

• Know the equation to convert between log odds ratios and probabilities
• Know how to use a logistic regression model to predict a log odds ratio
• Know how to interpret the slope(s) of and the R output for a logistic regression model
• Know how to read the R output for Multinomial model summaries and predictions
• Understand how to compare the fit of Multinomial models
• Understand the idea behind the $K$-Nearest Neighbors algorithms