Review Lectures 18 and 19 in Module 5, sections 10.2-10.4 in chapter 10 of the textbook, then complete problems 1 and 2.For problem 2, use SAS to perform the analysis and show the pertinent SAS output along with your explanation of the results.

1.A 31-run experiment was conducted with 6 independent predictor variables. The table below gives the SSresfor all possible fitted models in the six variables.Select a "best" model using each of the approaches in a-f stated below.

Variables in ModelSSresVariables in ModelSSresVariables in ModelSSresVariables in ModelSSresnone11.058x3, x41.453x2, x3, x41.041x1, x3, x4, x50.561x10.607x3, x59.891x2, x3, x59.362x1, x3, x4, x60.555x210.795x3, x68.848x2, x3, x68.838x1, x3, x5, x60.561x310.633x4, x51.397x2, x4, x51.030x1, x4, x5, x60.553x41.522x4, x61.507x2, x4, x61.192x2, x3, x4, x50.958x59.922x5, x67.680x2, x5, x67.679x2, x3, x4, x60.995x69.196x1, x2, x30.498x3, x4, x51.383x2, x3, x5, x67.675x1, x20.499x1, x2, x40.450x3, x4, x61.437x2, x4, x5, x61.024x1, x30.600x1, x2, x50.477x3, x5, x67.678x3, x4, x5, x61.342x1, x40.582x1, x2, x60.367x4, x5, x61.352x1, x2, x3, x4, x50.412x1, x50.597x1, x3, x40.581x1, x2, x3, x40.441x1, x2, x3, x4, x60.311x1, x60.576x1, x3, x50.582x1, x2, x3, x50.475x1, x2, x3, x5, x60.364x2, x310.168x1, x3, x60.567x1, x2, x3, x60.365x1, x2, x4, x5, x60.313x2, x41.218x1, x4, x50.569x1, x2, x4, x50.413x1, x3, x4, x5, x60.545x2, x59.479x1, x4, x60.558x1, x2, x4, x60.323x2, x3, x4, x5, x60.939x2, x69.192x1, x5, x60.574x1, x2, x5, x60.364x1, x2, x3, x4, x5, x60.307

a. Backward elimination using F-test and removal?=0.05

?=0.05

b. Forward selection using F-test and entry?=0.05

?=0.05

c. Stepwise selection using F-test and entry and removal?=0.05

?=0.05

d.Backward elimination based on Mallows' Cp

e. Forward selection based on Mallows' Cp

f.Stepwise selection based on Mallows' Cp

Variables in SSres Model Variables in Model SSres Variables in Model SSres Variables in Model SSres none 11.058 x3, x4 1.453 x2, x3, x4 1.041 x1, x3, x4, x5 0.561 x1 0.607 x3, x5 9.891 x2, x3, x5 9.362 x1, x3, x4, x6 0.555 x2 10.795 x3, x6 8.848 x2, x3, x6 8.838 x1, x3, x5, x6 0.561 x3 10.633 x4, x5 1.397 x2, x4, x5 1.030 x1, x4, x5, x6 0.553 X4 1.522 X4, x6 1.507 x2, x4, x6 1.192 x2, x3, x4, x5 0.958 x5 9.922 x5, x6 7.680 x2, x5, x6 7.679 x2, x3, x4, x6 0.995 X6 9.196 x1, x2, x3 0.498 x3, x4, x5 1.383 x2, x3, x5, x6 7.675 x1, x2 0.499 x1, x2, x4 0.450 x3, x4, x6 1.437 x2, x4, x5, x6 1.024 x1, x3 0.600 x1, x2, x5 0.477 x3, x5, x6 7.678 x3, x4, x5, x6 1.342 x1, x4 0.582 x1, x2, x6 0.367 x4, x5, x6 1.352 x1, x2, x3, x4, x5 0.412 x1, x5 0.597 x1, x3, x4 0.581 x1, x2, x3, x4 0.441 x1, x2, x3, x4, x6 0.311 X1, x6 0.576 x1, x3, x5 0.582 x1, x2, x3, x5 0.475 x1, x2, x3, x5, x6 0.364 x2, x3 10.168 X1, x3, x6 0.567 x1, x2, x3, x6 0.365 x1, x2, x4, x5, x6 0.313 x2, x4 1.218 x1, x4, x5 0.569 x1, x2, x4, x5 0.413 x1, x3, x4, x5, x6 0.545 x2, x5 9.479 x1, x4, x6 0.558 x1, x2, x4, x6 0.323 x2, x3, x4, x5, x6 0.939 X2, x6 9.192 x1, x5, x6 0.574 x1, x2, x5, x6 0.364 x1, x2, x3, x4, x5, x6 0.307