Table B.4 presents data for 27 houses sold in Erie, Pennsylvania.

a. Fit a simple linear regression model relating selling price of the house to the current taxes ( x1 ).

b. Test for signifi cance of regression.

c. What percent of the total variability in selling price is explained by this model?

d. Find a 95% CI on β1 .

(a) Do part (a). Report your point estimates for all parameters of your model. Make a plot of the data and the fitted regression line.

(b) Do part (b) which asks you to run and interpret an F test.

(c) Part (c) asks you to interpret the sums-of-squares decomposition. Answer this question.

(d) Do part (d).
 

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V1 1 y 2 29.5 3 27.9 4 25.9 5 29.9 6 29.9 7 30.9 8 28.9 9 35.9 10 31.5 11 31 12 30.9 13 30 14 36.9 15 41.9 16 40.5 17 43.9 18 37.5 19 37.9 20 44.5 21 37.9 22 38.9 23 36.9 24 45.8 25 25.9 V2 V3 x1 x2 5.0208 1 4.5429 1 4.5573 1 5.0597 1 3.891 1 5.898 1 5.6039 1 5.8282 1 5.3003 1 6.2712 1 5.9592 1 5.05 1 8.2464 1.5 6.6969 1.5 7.7841 1.5 9.0384 1 5.9894 1 7.5422 1.5 8.7951 1.5 6.0831 1.5 8.3607 1.5 1 8.14 9.1416 1.5 4.9176 1 V6 x5 2 V4 V5 x3 x4 3.531 1.5 2.275 1.175 1 4.05 1.232 1 4.455 1.121 1 4.455 0.988 1 5.85 1.24 1 9.52 1.501 0 6.435 1.225 2 4.9883 1.552 1 5.52 0.975 1 6.666 1.121 2 5 1.02 0 5.15 1.664 2 6.902 1.488 1.5 7.102 1.376 1 7.8 1.5 1.5 5.52 1.256 2 5 1.69 1 9.89 1.82 2 6.7265 1.652 1 9.15 1.777 2 8 1.504 2 7.3262 1.831 1.5 3.472 0.998 1 =V7 x6 7 6 01 6 1.0 6 6 7 6 6 6 01 5 1.0 5 8 7 6 7 01 6 6 8 6 8 7 8 7 V8 x7 st 4 3 3 3 3 3 3 3 3 2 3 2 4 3 3 3 3 3 et 4 3 4 3 4 st 4 V9 x8 62 40 54 42 56 51 32 32 30 30 32 46 50 22 17 23 40 22 50 44 48 3 31 42 V10 x9 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 V1 1 y 2 29.5 3 27.9 4 25.9 5 29.9 6 29.9 7 30.9 8 28.9 9 35.9 10 31.5 11 31 12 30.9 13 30 14 36.9 15 41.9 16 40.5 17 43.9 18 37.5 19 37.9 20 44.5 21 37.9 22 38.9 23 36.9 24 45.8 25 25.9 V2 V3 x1 x2 5.0208 1 4.5429 1 4.5573 1 5.0597 1 3.891 1 5.898 1 5.6039 1 5.8282 1 5.3003 1 6.2712 1 5.9592 1 5.05 1 8.2464 1.5 6.6969 1.5 7.7841 1.5 9.0384 1 5.9894 1 7.5422 1.5 8.7951 1.5 6.0831 1.5 8.3607 1.5 1 8.14 9.1416 1.5 4.9176 1 V6 x5 2 V4 V5 x3 x4 3.531 1.5 2.275 1.175 1 4.05 1.232 1 4.455 1.121 1 4.455 0.988 1 5.85 1.24 1 9.52 1.501 0 6.435 1.225 2 4.9883 1.552 1 5.52 0.975 1 6.666 1.121 2 5 1.02 0 5.15 1.664 2 6.902 1.488 1.5 7.102 1.376 1 7.8 1.5 1.5 5.52 1.256 2 5 1.69 1 9.89 1.82 2 6.7265 1.652 1 9.15 1.777 2 8 1.504 2 7.3262 1.831 1.5 3.472 0.998 1 =V7 x6 7 6 01 6 1.0 6 6 7 6 6 6 01 5 1.0 5 8 7 6 7 01 6 6 8 6 8 7 8 7 V8 x7 st 4 3 3 3 3 3 3 3 3 2 3 2 4 3 3 3 3 3 et 4 3 4 3 4 st 4 V9 x8 62 40 54 42 56 51 32 32 30 30 32 46 50 22 17 23 40 22 50 44 48 3 31 42 V10 x9 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0

Answer rating: 100% (QA)

Solution a Construct the simple linear regression model by using MINITAB MINITA... View the full answer