Suppose that three classification models (IrFit, knnTune, and nbFit) were built and evaluated in RStudio, their returned ROC values are provided below: Logistic regression (IrFit): ROC value = 0.95 K-nearest neighbors (knnTune): ROC value= 0.80 Naïve Bayes (nbFit): ROC value = 0.82 Question: Which classification model would you choose? Also, write the R code for using the chosen model to predict the test set (i.e., predictors_test).

Female and Smoke are binary indicator variables ( 0 and 1). Age and Education is measured continuously in years. Loan Default is a binary indicator variable (0 = no default, 1=default). Are the predictor variables in Models A statistically significant at the 5% significance level? Carefully interpret the coefficients for Female and Education in Models A Generate a forecast from all 3 models when Age=10, Educ=10, Sex=1, Smoke=0 You are unsure if education has a linear effect on loan default. What are possible transformations you can do to check for non-linearity?

Interpret the following graphs for multiple linear regression and comment on the validity of model assumptions

This dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise!  (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important.  (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test.  (please fully describe steps if you are using Excel)  Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…