I was wondering if someone would be able to answer the question in the photos bellow

A. rehabilitation centre researcher was interested in examining the relationship between phys ical tness prior to surgery of persons undergoing corrective knee surgery and time required in physical therapy until successful rehabilitation. Patient records in the rehabilitation cen tre were examined. and 24 male subjects ranging in age from 13 years to 3|] years who had undergone similar corrective knee surgery during the past year were selected for the study. The number of days required for successful completion of physical therapy and the prior physical tness status [below averageI average. and above average) for each patient below. I .= 1 2 3 4 5 s r s 9 In 1 3mm; 19 42 13 4a 43 40 30 42 2 Average 30 35 3-: re 31 31 29 35 29 n 3 Abmhveragt 26 32 21 2D 2'1 22 The rehabilitation researcher wishes to use age of patient as a. concomitant variable. The ages [Xijj of patients in the study follow: I 2 3- 4 5 5: 2' l '9 112 l' 1 15.3 3D.D 26.5 23.1 .29.? 22.3 ISLE 29.3 2 20.3 25.2 29.2 20.9 21.5 22.1 19.? 24.2 30.2 22.9 3 22.2 23.? 18.9 IBJJ 21.3\" 20.0 [3.) Obtain the residuals e14. egg. and 35 for the covariance model Y- =H+T(X,-3X__.;]+Ej =p_+Ti+'f[:Xij J?_:I+E1-_jwl1ere i=1,....3, j=1,...,1 {b} State the generalized regression model to be employed for testing whether or not the treatment regression lines have the same slope. Conduct this test using c.- = .05 and the SAS output below. State the hypotheses. decision rule. and conclusion. (c) Using the SAS output of full and reduced regression models. test for treatment effects. Use a. = .13]. State the hypotheses. decision rule. and conclusion. Model I: Full Model with Interaction Number of Observations Read 24 Number of Observations Used 24 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 5 1082 05609 216.41122 655.36 <.0001 error corrected total root mse r-square dependent mean adj r-sq coeff var parameter estimates standard variable df estimate t value pr> It) Intercept 1 31.37644 0. 13524 232.01 <.0001 interact interac2 ii: reduced model without interaction analysis of variance sum mean source squares square f value pr> F Source OF Squares Square F Value Pr > F Model 3 1081.83425 360.61142 1169.72 <.0001 model error corrected total root mse r.square dependent mean adj r-sq cooff var parameter estimates standard variable df estimate t value pr> It Variable DF Estimate Error t Value Pr > It Intercept 1 31 42704 0.11586 271 26 <.0001 intercept model iii: reduced with covariate only analysis of variance sum mean mear source df squares square f value pr> F Source DF Squares Square F Value Pr > F Model 835.75055 835.75055 72.89 <.0001 model error corrected total root mse r-square dependent mean adj r-sq coeff var parameter estimates standard variable df estimate t value pr> It Variable DF Estimate Error t Value Pr > It Intercept 1 32.00000 0.69119 46.30