#8: Split the dataframe Q8 into a training sample (75%) and holdout sample (25%). Build a predictive logistic regression model predicting Buy from all predictors (no interactions), choosing as your final model the one suggested by the one standard deviation rule. Fit the model on the training sample, then report its misclassification RATE on your holdout sample. Note: in the Two places where it is necessary, be sure to set the random number seed to 320. #9: Build a partition model predicting Buy on the training set you made in #8. Copy/paste the value of cp corresponding to the tree suggested by the one standard deviation rule. Again, in the place(s) where it is necessary, be sure to set the random number seed to 320. #10: Use the Q10 dataframe. Consider three standard multiple linear regression models predicting Sales from both predictors and no interaction (i.e., ignoring the time series nature of the data): one with a linear trend, one with a quadratic trend, and one with a cubic trend. In the model with lowest of the three AICs, report the STANDARD ERROR of the difference in average sales between First Quarter and Third Quarter