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11-42. Refer to the NFL team performance data in Exercise 11-4. (a) Calculate R2 for this model and provide a practical interpretation of this quantity. (b) Prepare a normal probability plot of the residuals from the least squares model. Does the normality assumption seem to be satis ed (c) Plot the residuals versus y and against x. Interpret these graphs. 11-43. Refer to the data in Exercise 11-5 on house selling price y and taxes paid x. (a) Find the residuals for the least squares model. (b) Prepare a normal probability plot of the residuals and interpret this display. (c) Plot the residuals versus y and versus x. Does the assumption of constant variance seem to be satis ed (d) What proportion of total variability is explained by the regression model 11-44. Exercise 11-6 presents data on y steam usage and x average monthly temperature. (a) What proportion of total variability is accounted for by the simple linear regression model

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(b) Prepare a normal probability plot of the residuals and interpret this graph (c) Plot residuals versus y and x Do the regression assumptions appear to be satis ed 11-45 Refer to the gasoline mileage data in Exercise 11-7 (a) What proportion of total variability in highway gasoline mileage performance is accounted for by engine displacement (b) Plot the residuals versus y and x, and comment on the graphs (c) Prepare a normal probability plot of the residuals Does the normality assumption appear to be satis ed 11-46 Consider the data in Exercise 11-8 on y green liquor Na2S concentration and x paper machine production Suppose that a 14th sample point is added to the original data, where y14 59 and x14 855 (a) Prepare a scatter diagram of y versus x Fit the simple linear regression model to all 14 observations.

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(b) Test for signi cance of regression with 005 (c) Estimate 2 for this model (d) Compare the estimate of 2 obtained in part (c) above with the estimate of 2 obtained from the original 13 points Which estimate is larger and why (e) Compute the residuals for this model Does the value of e14 appear unusual (f ) Prepare and interpret a normal probability plot of the residuals (g) Plot the residuals versus y and versus x Comment on these graphs 11-47 Refer to Exercise 11-9, which presented data on blood pressure rise y and sound pressure level x (a) What proportion of total variability in blood pressure rise is accounted for by sound pressure level (b) Prepare a normal probability plot of the residuals from this least squares model Interpret this plot (c) Plot residuals versus y and versus x Comment on these plots 11-48.

Each method may be tested without requiring the developer to create a test project. Clicking the link for each method displays information about the method. The Invoke button can be clicked to execute the method. Figure 16.7 shows the information for the RenderText method, which is the main method. This method accepts various parameters and returns a URL to the image that has been created. To see the image, the URL can be copied and pasted into the address bar in Internet Explorer. After entering the information into the test page and clicking the Invoke button, a new page is displayed. This page will contain an XML response with the image URL as follows:

Exercise 11-10 presents data on wear volume y and oil viscosity x (a) Calculate R2 for this model Provide an interpretation of this quantity (b) Plot the residuals from this model versus y and versus x Interpret these plots (c) Prepare a normal probability plot of the residuals Does the normality assumption appear to be satis ed 11-49 Refer to Exercise 11-11, which presented data on chloride concentration y and roadway area x (a) What proportion of the total variability in chloride concentration is accounted for by the regression model (b) Plot the residuals versus y and versus x Interpret these plots (c) Prepare a normal probability plot of the residuals Does the normality assumption appear to be satis ed 11-50 Consider the rocket propellant data in Exercise 11-12 (a) Calculate R2 for this model Provide an interpretation of this quantity..

(b) Plot the residuals on a normal probability scale. Do any points seem unusual on this plot (c) Delete the two points identi ed in part (b) from the sample and t the simple linear regression model to the remaining 18 points. Calculate the value of R2 for the new model. Is it larger or smaller than the value of R2 computed in part (a) Why (d) Did the value of 2 change dramatically when the two points identi ed above were deleted and the model t to the remaining points Why 11-51. Show that an equivalent way to de ne the test for signi cance of regression in simple linear regression is to base the test on R2 as follows: to test H0: 1 0 versus H1: 1 0, calculate F0 R2 1n 1 22 R

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