Can you please show me how to solve this problem?

We investigate the use of the test set approach to estimate the test error rates of three different linear regression models fitted to the Auto dataset, which contains 392 observations. Each model uses a different degree of polynomial for the horsepower predictor. A random sample of 196 observations is used as the training set, and the test error is computed on the remaining observations. library (ISLR2) set .seed (302) train lm.fit mean((mpg - predict(1m.fit, Auwto))[-train]~2) [i] 27.19734 lm.fit2 mean ((mpg - predict(lm.fit2 , Auto))[-train] 2) [i] 19.89617 Ilm.fit3 mean((mpg - predict(1m.fit3 , Auto))[-train]~2) [i] 19.86668 (a) Write down the mathematical expressions for the three models using Y for the response and x for the predictor. (b) Report the estimated test error for each model. Based on these results, which model has the best test performance? (c) For the model selected in part (b), write R. code to estimate the test error using leave- one-out. cross-validation