Find three models you might think are meaningful for the prediction with different number of variables

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Homework:

Please complete this homework by filling the template provided on Canvas. Any submission which is not R will not be accepted. The code will need to properly run without any error

Exercise 1 - LR and Out of Sample Prediction

We generate 99 independent variables uniformly distributed between -100 and 100 of size 500 observations each. (see code below) We also generate the dependent variable y = 3 + 10*V99, where V99 is the last covariate and add some noise (see code below).

1. Construct 3 models: one linear model with no variables, one with all the variables and one with only the variable V99

2. Compute the MSE of each model

Exercise 2 Cross Validation

We are interested in predicting the quality of wines using chemical indicators. To do so, we have a disposal two data sets for white and red wine, reporting the variable quality on a scale from 0 to 10.

1. find the file read wine

2. Find three models you might think are meaningful for the prediction with different number of variables

3. Compute the in-sample mean squared

4. Compute the out-of-sample mean squared error using a test-training set approach (remember to set the seeds)

5. (challenging) Compute the out-of-sample mean squared error using 10-folds cross validation 

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