Please use R and share the code please.

Transient points of an electronic inverter are influenced by many factors. The file "inver- ter.csv' contains data on the transient point (y, in volts) of PMOS-NMOS inverters and five candidate regressors: x1 = width of the NMOS device, 22 = length of the NMOS device, x3 = width of the PMOS device, x4 = length of the PMOS device, and x5 = temperature (C) a. Fit a multiple linear regression model that uses all regressors to these data. Test for significance of regression using a = 0.01. Find the P-value for this test and use it to draw your conclusions. b. Test the contribution of each variable to the model using the t-test with a = 0.05. What are your conclusions? c. Delete x5 from the model. Test the new model for significance of regression. Also test the relative contribution of each regressor to the new model with the t-test. Using a = 0.05, what are your conclusions? d. Notice that the MSE for the model in part (c) is smaller than the MSE for the full model in part (a). Explain why this has occurred. e. Calculate the studentized residuals. Do any of these seem unusually large? f. Suppose that you learn that the second observation was recorded incorrectly. Delete this observation and refit the model using x1, X2, 23, and 24 as the regressors. Notice that the multiple R2 for this model is considerably higher than the multiple R2 for either of the models fitted previously. Explain why the R2 for this model has increased. g. Test the model from part (f) for significance of regression using a = 0.05. Also investigate the contribution of each regressor to the model using the t-test with a = 0.05. What conclusions can you draw? h. Plot the residuals from the model in part (f) versus y and versus each of the regressors X1, X2, 23, and x4. Comment on the plots.inverter Observation x1 x2 X3 x4 x5 y 3 3 3 3 0 0.787 2 8 30 8 8 0 0.293 3 3 6 6 6 0 1.71 4 4 4 4 12 0 0.203 5 8 7 6 5 0 0.806 6 10 20 5 5 0 4.713 7 8 6 3 3 25 0.607 8 6 24 4 4 25 9.107 9 4 10 12 4 25 9.21 10 16 12 8 4 25 1.365 11 3 10 8 8 25 4.554 12 8 3 3 3 25 0.293 13 3 6 3 3 50 2.252 14 3 8 8 3 50 9.167 15 4 8 4 8 50 0.694 16 5 2 2 2 50 0.379 17 2 2 2 3 50 0.485 18 10 15 3 3 50 3.345 19 15 6 2 3 50 0.208 20 15 6 2 3 75 0.201 21 10 4 3 3 75 0.329 22 3 8 2 2 75 4.966 23 6 6 6 4 75 1.362 24 2 3 8 6 75 1.515 25 3 3 8 8 75 0.751