After taking advice from Dr. Hansz and Ramya I have developed a new model that expands upon the one previously posted. My "Location" paper is focusing on the variable: distance from a franchise restaurant. I split the distance into 4 dummy variables: B(Distance, 1/3, 2/3, 3/3, 4/3). My previously posted model had 3 of 4 distance variables kicked out for different reasons.
Following advice, I added a "Pool" dummy variable. It changed everything. Now 3 of 4 distance variable are inside the model explaining the Sales Price. The problem is that I lost .o1 R^2. The new model has a R^2 value of .777 instead of .787. Both models show strength, aptness, and follow the rules of regression.
I am inclined to choose the new model. Any advice?
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Richard: If the new model is just as strong as the old model (i.e., both adjusted R-squared measures are similar and independent variables in both models are significant), the signs on the Beta coefficients are sensible, and the final new model's strength is dependent to a greater degree on your variables of interest; then I would keep the new model. Does the 77.7% model fit agree with what you've been reading? If so, then this should strengthen your confidence in the final new model. It is my understanding that the y-intercept coefficients (Beta-not) give a measure of all that the model does not explain. A higher y-intercept number means that there is a lot NOT being explained by the model. How do the y-intercept coefficients of both models compare? Is the y-intercept for the final new model LESS THAN the y-intercept for the final old model? Hopefully, yes...or at least they are close to one another...
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