Attached to the end of the page is a portion of a printout from a stepwise regression analysis. a) Any of the F statistics on the printout can be computed via the formula: F = (SSReg( Model A ) – SSReg( Model B ) ) / C MSResidual( Model A) Identify what Model A, Model B, and the constant C are in order to obtain the F = 1.33 value for the variable x8 .b) Based on the printout for Step 6 of the stepwise selection procedure, what will be the next change in the model, in Step 7 of the procedure? (In other words, will a particular term be dropped, or added, or will nothing occur? Assume that the significance level for entry and staying are a = .15.) **Results for the Stepwise Regression Analysis****Stepwise Selection: Step 6****Statistics for Removal** Degrees of Freedom (DF) = 1, 1116 | Variable | Partial R-Square | Model R-Square | F Value | Pr > F ||----------|------------------|----------------|---------|--------|| X9 | 0.0202 | 0.2650 | 31.60 | <.0001 || X1 | 0.0153 | 0.2699 | 23.95 | <.0001 || X10 | 0.0962 | 0.1890 | 150.20 | <.0001 || X2 | 0.0116 | 0.2736 | 18.08 | <.0001 || X6 | 0.0225 | 0.2627 | 35.16 | <.0001 |**Statistics for Entry** Degrees of Freedom (DF) = 1, 1115 | Variable | Tolerance | Model R-Square | F Value | Pr > F ||----------|-----------|----------------|---------|--------|| X7 | 0.207460 | 0.2855 | 0.48 | 0.4906 || X4 | 0.215074 | 0.2852 | 0.01 | 0.9334 || X11 | 0.933262 | 0.2925 | 11.52 | 0.0007 || X5 | 0.894028 | 0.2857 | 0.81 | 0.3682 || X8 | 0.671736 | 0.2861 | 1.33 | 0.2494 || X3 | 0.632098 | 0.2852 | 0.02 | 0.8816 |This table presents the results of a stepwise regression analysis, detailing variables considered for removal and entry during step 6. For removal, variables with significant F values < 0.0001 suggest a strong impact. For entry

Stepwise selection: step 6Statistics for RemovalD.F = 1,1116Step 6PartialmodelVariableR-squareR-squareF Value F"}">Pr > FX90.02020.26531.6< 0.0001X10.01530.269923.95< 0.0001X100.09620.189150.2< 0.0001X20.01160.273618.08< 0.0001X60.02250.262735.16< 0.0001Statistics for EntryD.F = 1,1115ModelVariableToleranceR-squareF-value F"}">Pr > FX70.204760.28550.480.4906X40.2150740.28520.010.9334X110.9332620.292511.520.0007X50.8940280.28570.810.3682X80.6717360.28611.330.2494X30.6320980.28520.020.8816a) Model A: This corresponds to the model where variable X8 is included.Model B: This refers to the model where variable X8 is excluded.Constant C: This represents a constant value that scales the difference between the regression sum of squares from Models A and B when multiplied by the mean square residual of Model A, yields the desired F-value for variable X8.SSReg(Model A): It represents the regression sum of squares when variable X8 is included in the model.SSReg(Model B): It denotes the regression sum of squares when variable X8 is excluded from the model.MSResidual(Model A): This value represents the mean square of residuals for the model with X8 included.b) When conducting a stepwise regression, variables with P-values below the significance level are considered significant and retained in the model. In the given scenario, P-value(0.0007) for X11 is below the significance level, so it is likely that X11 will remain in the model in the next step, as it is considered significant based on the significance level of 0.15.Any variable with P-values above the significance level are not considered significant and they can be removed from the model.The variable with the highest P-value above the significance level (α = 0.15) is X4 (P-value = 0.9334). In the next step(Step 7) of the procedure, it is likely that X4 will be removed from the model as its p-value is above the chosen significance level.a) Model A: This corresponds to the model where variable X8 is included.Model B: This refers to the model where variable X8 is excluded.Constant C: This represents a constant value that scales the difference between the regression sum of squares from Models A and B when multiplied by the mean square residual of Model A, yields the desired F-value for variable X8.SSReg(Model A): It represents the regression sum of squares when variable X8 is included in the model.SSReg(Model B): It denotes the regression sum of squares when variable X8 is excluded from the model.MSResidual(Model A): This value represents the mean square of residuals for the model with X8 included.b) When conducting a stepwise regression, variables with P-values below the significance level are considered significant and retained in the model. In the given scenario, P-value(0.0007) for X11 is below the significance level, so it is likely that X11 will remain in the model in the next step, as it is considered significant based on the significance level of 0.15.Any variable with P-values above the significance level are not considered significant and they can be removed from the model.The variable with the highest P-value above the significance level (α = 0.15) is X4 (P-value = 0.9334). In the next step(Step 7) of the procedure, it is likely that X4 will be removed from the model as its p-value is above the chosen significance level.