Question

Least Squares Linear Regression of Rent Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843 0.41717 T 2.81 0.40 P 0.0072 0.6945 Mean Square Error (MSE) Standard Deviation 458532 677.150 R2 Adjusted R2 AICC PRESS 0.0032 -0.0175 656.27 2.34E+07 DF F 0.16 P 0.6945 1 Source Regression Residual Total MS 71610.6 458532 SS 71610.6 2.201E+07 2.208E+07 48 49 20.14 0.0006 Lack of Fit Pure Error 42 6 2.185E+07 155000 520346 25833.3 Cases Included 50 Missing Cases 0 7. 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) ŷ =1276.56+0.16486*Size 8. Interpret both s and R2 associated with your best model. (4 points)

Answer 1

7.

The least squares prediction equation from the output is,

$\hat{y}$ = 1276.56 + 0.16486*Size

9.

From the output,

s = 677.150

The rent represented in your sample data differ from the predictions made by the regression equation by  $1276.56 "on average".

$R^2$ = 0.0032

0.32 % of variation in Rent is explained by the model with Size as the predictor variable.