Hi,

I have a question in which table shows step wise solution

1. These methods of variable selection can be quick and cheap to achieve but can present some issues. Describe 3 issues that arise with these methods of variable selection.
2. You are tasked to build a multiple linear regression model from a dataset containing a large number of potential predictors. Describe 5 reasons why having too many predictors in your model is not desirable, even if they yield reasonable p-values in the model summary.
3. Multicollinearity:
   1. Briefly describe what multicollinearity is and whether or not you want this to be present in your multiple linear model
   2. Describe five methods that can help you search for the presence of multicollinearity in your model.
   3. Using the model you built above through 'stepwise' selection, investigate the five methods described above for the presence of multicollinearity and list which variables, if any, should be excluded from the model

No more variables to be added/removed. Final Model Output Model Summary R 0.954 RMSE 3. 222 R-Squared 0. 910 Coef. Var 11. 103 Adj. R-Squared 0. 909 MSE 10. 381 Pred R-Squared 0. 909 MAE 2. 381 RMSE: Root Mean Square Error MSE: Mean Square Error MAE : Mean Absolute Error ANOVA Sum of Squares DF Mean Square F Sig. Regression 611716.123 14 43694. 009 4208.962 0. 0000 Residual 60833.743 5860 10.381 Total 672549. 866 5874Parameter Estimates model Beta Std. Error Std. Beta t Sig lower upper (Intercept) 2.921 1. 051 2.779 0. 005 0. 860 4.981 motor_UPDRS 1. 207 0. 006 0. 917 207 . 311 0. 000 1. 195 1. 218 sex1 -1. 705 0. 106 -0. 074 -16. 095 0. 000 -1.913 -1. 497 age 0. 074 0. 005 0. 061 14 . 361 0. 000 0. 064 0. 084 subject 0. 046 0. 004 0. 053 11. 880 0. 000 0. 038 0. 054 DFA -4. 077 0. 769 -0. 027 -5. 304 0. 000 -5.583 -2. 570 RPDE 2. 517 0. 586 0. 024 4. 291 0. 000 1. 367 Shimmer . APQ11 3. 666 -46. 606 6.774 -0. 087 -6. 881 0. 000 -59. 884 Shimmer . APQ5 -33. 327 99.969 16.283 0. 156 6.140 0. 000 68. 049 131. 890 PPE -5.617 0. 898 -0. 048 -6.257 0. 000 -7. 377 -3. 857 HNR -0. 107 0. 022 -0. 043 -4.767 0. 000 -0. 150 Jitter . Abs -0. 063 15327 .605 2545 . 325 0. 052 6. 022 0. 000 10337 . 830 20317 . 380 NHR -6.475 1. 562 -0. 036 -4.145 0.000 -9.537 -3. 412 Shimmer -41.296 10. 078 -0. 100 -4. 098 0. 000 -61. 052 -21. 540 test_time 0. 003 0. 001 0. 013 3. 195 0. 001 0. 001 0. 004 test_time_hr NA NA 144. 649 NA NA NA NA test_time_min NA NA -12262. 349 NA NA NA NA