The information below represents the relationship between the selling price (Y, in $1,000) of a home, the square footage of the home (X1), and the number of rooms in the home (X2). The data represents 60 homes sold in a particular area of East Lansing, Michigan and was analyzed using both multiple and simple linear regression models. The first two tables relate to the multiple regression analysis including both variables while the latter two tables show the results of the simple linear regression for each individual independent variable. MLR MODEL (Size and Number of Rooms) Multiple R 0.9408 R-square 0.8851 Adjusted R-square 0.8660 Standard Error of Estimate 20.8430 Regression Coefficients Coefficient Std Error t-value p-value Constant -13.9705 49.1585 -0.2842 0.7811 Size 7.4336 1.0092 7.3657 0.0000 Number of Rooms 5.3055 8.2767 0.6410 0.5336 SLR MODEL #1 (Size) The following table is for a simple regression model using only size. (R2 = 0.8812) Coefficient Std Error t-value p-value Constant 14.771 19.691 0.7502 0.4665 Size 7.816 0.796 9.8190 0.0000 SLR MODEL #2 (Number of Rooms) The following table is for a simple regression model using only number of rooms. (R2 = 0.3657) Coefficient Std Error t-value p-value Constant -93.460 108.269 -0.8632 0.4037 Size 41.292 15.082 2.7379 0.0169