1. 11-4: An article in Technometrics by S. C. Narula and J. F. Wellington [Prediction, Linear Regression, and a Minimum Sum of Relative Errors (Vol. 19, 1977)] presents data on the selling price and annual taxes for 24 houses. The data are shown in the following table.

Sale Price/100	Taxes (Local, School, County)/1000	Sale Price/100	Taxes (Local, School, County)/1000
25.9	4.9176	30.0	5.0500
29.5	5.0208	36.9	8.2464
27.9	4.5429	41.9	6.6969
25.9	4.5573	40.5	7.7841
29.9	5.0597	43.9	9.0384
29.9	3.8910	37.5	5.9894
30.9	5.898	37.9	7.5422
28.9	5.6039	44.5	8.7951
35.9	5.8282	37.9	6.0831
31.5	5.3003	38.9	8.3607
31.0	6.2712	36.9	8.1400
30.9	5.9592	45.8	9.1416

1. Assuming that a simple linear regression model is appropriate, obtain the least squares fit relating selling price to taxes paid. What is the estimate of 2 ?
2. Find the mean selling price given that the taxes paid are x=7.30 .
3. Calculate the fitted value of y corresponding to x=5.6039 . Find the corresponding residual.
4. Calculate the fitted yi for each value of xi used to fit the model. Then construct a graph of yi versus the corresponding observed value yi and comment on what this plot would look like if the relationship between y and x was a deterministic (no random error) straight line. Does the plot actually obtained indicate that taxes paid is an effective regressor variable in predicting selling price?