In this question, our response variable is cmort (Mt) from astsa package: Average weekly cardiovascular mortality in Los Angeles County; 508 six-day smoothed averages obtained by filtering daily values over the 10 year period 1970-1979. For the predictor variables, we're going to consider tempr (Tt), part (Pt), trend (t), and it's modified variables. To be more specific, we're going to consider . t.cm: t temp: T - T temp2: (T-T.) part: P partL4: Pt-4 (a) Using ts.intersect() function, create a dataset containing your response variable and all predictor variables in matching timeframe. After that, divide that dataset into two parts, training (containing first 400 observations) and test dataset (containing rest). temp-tempr-mean (tempr) temp2=temp^2 t.cm-time (cmort) partL4=lag (part, -4) (b) Fit a linear regression model Mt Bo + Bit + B2 (Tt - T) + B3(Tt - T) + B4Pt+B5Pt-4 + Wt to your training dataset. print out the result. (c) Find the accuracy of your model using the test data. What is your test RMSE?