10. For the house sales data in Table 9.4. Table 9.11 shows a computer printout for the regres- sion analysis relating selling price (thousands of dollars) to number of bedrooms TABLE 9.11 Variable PRICE BEDROOMS N Mean Std Dev 93 99.5333 44.1841 93 3.1828 0.6070 Sum of Mean Source DE Squares Model 1 62577.52 Square 62577.52 F Value 48.66 Prob>F 0.0001 Error 91 117028.35 1286.03 C Total 92 179605.87 Root MSE 35.86120 R-square 0.3484 Parameter Standard Variable DF Estimate INTERCEP 1 -37.229 Error 19.955 T for HO: Parameter-0 -1.866 Prob T 0.0653 BEDROOMS 1 42.969 6.160 6.976 0.0001

a) Report the prediction equation, and interpret the slope. Is the relationship positive, or negative?

b) Find the predicted selling price for a home with (i) two. (ii) three. (iii) four bedrooms.

c) The first observation in the data set has three bedrooms and a selling price of 48.5 thou- sand dollars. Find the residual, and interpret.

d) Using the sample slope and the standard deviations, find the Pearson correlation. In- terpret its value.

e) Report the coefficient of determination. and interpret its value Report the standard crior of the sample slope. Interpret g) Find the test statistic and P-value for testing Ho B=0 against H,,: 8 +0, and inter- pect. h) Construct a 95% confidence interval for . and interpret. i) Use the result of the previous part to form a 95% confidence interval for the difference in the mean housing prices for homes with X = 4 bedrooms and with X = 2 bedrooms. Interpret. j) Interpret the value labeled "Root MSE."