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11-31. Refer to the data in Exercise 11-1 on y intrinsic permeability of concrete and x compressive strength. Find a 95% con dence interval on each of the following: (a) Slope (b) Intercept (c) Mean permeability when x 2.5 (d) Find a 95% prediction interval on permeability when x 2.5. Explain why this interval is wider than the interval in part (c). 11-32. Exercise 11-2 presented data on roadway surface temperature x and pavement de ection y. Find a 99% con dence interval on each of the following: (a) Slope (b) Intercept (c) Mean de ection when temperature x 85 F (d) Find a 99% prediction interval on pavement de ection when the temperature is 90 F . 11-33. Exercise 11-4 presented data on the number of games won by NFL teams in 1976. Find a 95% con dence interval on each of the following: (a) Slope (b) Intercept (c) Mean number of games won when opponents rushing yardage is limited to x 1800 (d) Find a 95% prediction interval on the number of games won when opponents rushing yards is 1800. 11-34. Refer to the data on y house selling price and x taxes paid in Exercise 11-5. Find a 95% con dence interval on each of the following: (a) 1 (b) 0 (c) Mean selling price when the taxes paid are x 7.50 (d) Compute the 95% prediction interval for selling price when the taxes paid are x 7.50. 11-35. Exercise 11-6 presented data on y steam usage and x monthly average temperature. (a) Find a 99% con dence interval for 1. (b) Find a 99% con dence interval for 0. (c) Find a 95% con dence interval on mean steam usage when the average temperature is 55 F .

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(d) Find a 95% prediction interval on steam usage when temperature is 55 F . Explain why this interval is wider than the interval in part (c). 11-36. Exercise 11-7 presented gasoline mileage performance for 20 cars, along with information about the engine displacement. Find a 95% con dence interval on each of the following: (a) Slope (b) Intercept (c) Mean highway gasoline mileage when the engine displacement is x 150 in3 (d) Construct a 95% prediction interval on highway gasoline mileage when the engine displacement is x 150 in3. 11-37. Consider the data in Exercise 11-8 on y green liquor Na2S concentration and x production in a paper mill. Find a 99% con dence interval on each of the following: (a) 1 (b) 0 (c) Mean Na2S concentration when production x 910 tons day (d) Find a 99% prediction interval on Na2S concentration when x 910 tons day. 11-38. Exercise 11-9 presented data on y blood pressure rise and x sound pressure level. Find a 95% con dence interval on each of the following: (a) 1 (b) 0

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For security reasons, dynamic discovery is turned off in Visual Studio .NET but may be enabled by editing the machine.config file and adding the following line in the httpHandlers section:

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(c) Mean blood pressure rise when the sound pressure level is 85 decibals (d) Find a 95% prediction interval on blood pressure rise when the sound pressure level is 85 decibals. 11-39. Refer to the data in Exercise 11-10 on y wear volume of mild steel and x oil viscosity. Find a 95% con dence interval on each of the following: (a) Intercept (b) Slope (c) Mean wear when oil viscosity x 30 11-40. Exercise 11-11 presented data on chloride concentration y and roadway area x on watersheds in central Rhode Island. Find a 99% con dence interval on each of the following: (a) 1 (b) 0 (c) Mean chloride concentration when roadway area x 1.0% (d) Find a 99% prediction interval on chloride concentration when roadway area x 1.0%. 11-41. Refer to the data in Exercise 11-12 on rocket motor shear strength y and propellant age x. Find a 95% con dence interval on each of the following: (a) Slope 1 (b) Intercept 0 (c) Mean shear strength when age x 20 weeks (d) Find a 95% prediction interval on shear strength when age x 20 weeks.

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Fitting a regression model requires several assumptions. Estimation of the model parameters requires the assumption that the errors are uncorrelated random variables with mean zero and constant variance. Tests of hypotheses and interval estimation require that the errors be normally distributed. In addition, we assume that the order of the model is correct; that is, if we t a simple linear regression model, we are assuming that the phenomenon actually behaves in a linear or rst-order manner. The analyst should always consider the validity of these assumptions to be doubtful and conduct analyses to examine the adequacy of the model that has been tentatively entertained. In this section we discuss methods useful in this respect.

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