# Question

Using the sample data given in the file P10_10.xlsx, use multiple regressions to predict the selling price of houses in a given community. Proceed as follows.

a. Add one explanatory variable at a time and estimate each regression equation along the way.

Report and explain changes in the standard error of estimate se, R2, and adjusted R2 as each explanatory variable is added to the model. Does it matter which order you add the variables? Try at least two different orderings to answer this question.

b. Interpret each of the estimated regression coefficients in the full equation, that is, the equation with all explanatory variables included.

c. What proportion of the total variation in the selling price is explained by the multiple regression equation that includes all four explanatory variables?

a. Add one explanatory variable at a time and estimate each regression equation along the way.

Report and explain changes in the standard error of estimate se, R2, and adjusted R2 as each explanatory variable is added to the model. Does it matter which order you add the variables? Try at least two different orderings to answer this question.

b. Interpret each of the estimated regression coefficients in the full equation, that is, the equation with all explanatory variables included.

c. What proportion of the total variation in the selling price is explained by the multiple regression equation that includes all four explanatory variables?

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