Consider 'car price' data set. The data shows different information about technical features of cars and the corresponding car price. What is the simplest and most informative regression model that you can make to predict car price? (To answer this question, firstly, make at least 10 multiple regression models by considering different combinations of predictor variables for your own use, and then select the best one; finally, copy and paste the output of your preferred model into a word file as your answer.) Hint 1: To avoid multicollinearity issue, try to consider some uncorrelated variables as predictors. Hint 2: The short video 'Comparing regression models' on BS can help you compare different models that you make. Explain in 5-7 lines how you support and check the validity of your preferred model by using R- Squared, F-test, t-test, and residual plots. Explain in 1 line how your model can be used to predict a car price.