RT IlI - Final Model Interpretations-Answer the following questions about your t

Question

RT IlI - Final Model Interpretations-Answer the following questions about your t madel east Squares Linear Regression of Asking redictor ariables Coefficient onstant Std Error T 0.0000 0.0002 0.0031 0.0048 0.2438 0.0595 VIF 0.0 33.7 41.9 57.4 126.7 63.9 26448.2 836.836 20.78 -0.2614904 0.08 Mileage x1sq68.9208 x1x2 x1sqx2 Model 2956-030.09322 2.956E-03 0.01883 1332. 73 -0.01883,?? -_114079 -83226.4 0.03672 0.31 0.6164 Adjusted R 0.5728 Mean Square Error (MSE) 1405638 Standard Deviation 1157.39 1110.8 PRESS 6.28E+12 Source sS Regression 5 2.563+11 5.126E+10 14.14 109.11 0.0000 DF MS Residual 44 1.595E+11 3.626E+09 Total 49 4.158E+11 Identify the least squares prediction equation (use #'s) for your best model after all your testing was completed (you do not need to show the printouts of any additional tests conducted, just the results of your best model). Use the values from the printout and write the prediction equation below. (3 points) 7, 8. sh (United States) F3 F6

Explanation / Answer

Solution8:

E(y)=26448.2-0.26149*mileage+0.002956*xlsq+68.9208x1x2-0.01883*x1sqx2-83226.4 model

Solution9:

R sq=0.6164

=0.6164*100=61.64%

61.64% variation in y is explained by model

explained variation=61.64%

unexplained variation=100-expalined=100-61.64=38.36%

solution10:

we would use model in practise

as F(5,44)=109.11,p=0.0000

p<0.05

Model is significant

We can use the model in practise.

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