Using the Body Fat data set, fit a simple linear regression model for relating weight ( ) to abdomen circumference ( ) Then, (a) use the residuals to check the regression assumptions (b) provided the regression assumptions appear to be satisfied, test whether or not there is a linear relationship between weight and abdomen circumference (c) estimate the mean weight for an adult male having an abdomen circumference of 85 cm (d) compute a 95 confidence interval for the mean weight for an adult male having an abdomen circumference of 85 cm (e) predict the weight for an adult male having an abdomen circumference of 85 cm (f) compute a 95 prediction interval for the mean weight for an adult male having an abdomen circumference of 85 cm Regression Equation BMI 3 583 0 31315 Abdomen S R sq R sq (adj) 1 418 85 07 85 01 Settings Variable Abdomen SE Fit 0 09157 Setting 95

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Question: Using the Body Fat data set, fit a simple linear regression model for relating weight (????) to abdomen circumference (????). Then, (a) use the residuals

Using the Body Fat data set, fit a simple linear regression model for relating weight

(????) to abdomen circumference (????). Then,

(a) use the residuals to check the regression assumptions.

(b) provided the regression assumptions appear to be satisfied, test whether or not there is a linear relationship between weight and abdomen circumference.

(c) estimate the mean weight for an adult male having an abdomen circumference of 85 cm.

Regression Equation BMI = -3.583 + 0.31315 Abdomen S R-sq R-sq (adj)

(d) compute a 95% confidence interval for the mean weight for an adult male having an abdomen circumference of 85 cm.

(e) predict the weight for an adult male having an abdomen circumference of 85 cm.

(f) compute a 95% prediction interval for the mean weight for an adult male having an abdomen circumference of 85 cm.

Regression Equation BMI = -3.583 + 0.31315 Abdomen S R-sq R-sq (adj) 1.418 85.07% 85.01% Settings Variable Abdomen SE Fit 0.09157 Setting 95

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