Fit another regression, adding an interaction between beauty and age (keep the other variables as controls). Interpret the regression coefficients and residual standard deviation. 2. Based on this model, what would be the predicted evaluation score for the youngest, median, and oldest instructor in the dataset at the lowest and highest score of beauty (hint: this is six estimates)? Assume that we are comparing white men for these predictions. 3. Using predict, make predictions for the youngest, median, and oldest instructor across all levels of beauty (hint: Use the newdata argument and set beauty=seq(-2,2,.1)). Plot lines for each age (so three lines). Be sure to differentiate the lines with color or line type (lty in base R or linetype in ggplot). Interpret the results. BONUS: Use posterior_linpred and posterior_predict to plot uncertainty in your predictions