RT III - Final Model Interpretations-Answer the following questions about your m
ID: 2908716 • Letter: R
Question
RT III - Final Model Interpretations-Answer the following questions about your model east Squares Linear Regression of Asking t redictor ariables Coefficient onstant Mileage2.9 VIF 0.0 33.7 41.9 57.4 126.7 63.9 std Error T 836.836 20.78 0.0000 0.0002 0.0031 0.0048 0.2438 0.0595 26448.2 0.261491.574E-04 0.08 2.956E-03 x1sg68.9208 2.956-030.09322 x1x2 x1sqx2 Model 1332.73 0.52 0.03672 0.31 -0.01883 114079 -83226.4 Mean Square Error (MSE) 1405638 0.6164 Adjusted R 0.5728 1110.8 6.28E+12 Standard Deviation 1157.39 PRESS Source DF sS MS Regression 5 2.563+11 5.126E+10 14.14 109.11 0.0000 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) F3Explanation / Answer
the prediction equation would be like this
E(y)=26448.2-0.2615*Mileage+0.0003Xlsq+68.9208*x1x2-0.0188*x1sqx2-0.8323*Model
and R2 for this model is 0.6164, its means 61.64% variablity in the dependent varaiable y , was explained by t he indpendent variables .
since the variables x1sqx2 and Model are not significanct as thier p-value is more than typical alpha=0.05, so we should remove these variables from the model and re-analyse the data and find the improved regression equation. for re-analysis raw-data is required.
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