The following data give the selling price square footage num

The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the past 6 months. Develop three regression models to predict the selling price based upon each of the other factors individually. Which of these is better?

Solution

For selling price vs square footage:

Using technology, we get              
              
slope =    143.6534373          
intercept =    2880.775544          
              
Thus, the regression line is              
              
Price^ =    143.6534373footage + 2880.775544
              
Also, getting the correlation,              
              
r =    0.853964441          
              
Thus, the coefficient of determination is              
              
r^2 =    0.729255267          

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For selling price vs bedrooms:

Using technology, we get              
              
slope =    88233.87097          
intercept =    70491.93548          
              
Thus, the regression line is              
              
Price^ =    88233.87097bed   +   70491.93548
              
Also, getting the correlation,              
              
r =    0.808411425          
              
Thus, the coefficient of determination is              
              
r^2 =    0.653529032          

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For selling price vs age,

Using technology, we get              
              
slope =    -5100.525818          
intercept =    428849.9941          
              
Thus, the regression line is              
              
Price^ =    -5100.525818age   +   428849.9941
                              
Also, getting the correlation,              
              
r =    -0.772135257          
              
Thus, the coefficient of determination is              
              
r^2 =    0.596192855          
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Comparing the r^2 values, we see that the model PRICE VS SQUARE FOOTAGE IS THE BEST, followed by price vs bedrooms, and lastly, price vs age.