Di Monaco et al. (A-11) used multiple regression to predict bone mineral density of the femoral neck

(among other locations). Among 124 Caucasian, healthy postmenopausal women, they found that weight ð Þ p < :001 , age ð Þ p < :01 , and total lymphocyte count ð Þ p < :001 were each useful in predicting bone mineral density. In addition, R2 ¼ :40.

For each of the data sets given in Exercises 17 through 19, do as many of the following as you think appropriate:

(a) Obtain the least-squares multiple regression equation.

(b) Compute the sample coefficient of multiple determination.

(c) Compute the sample coefficient of multiple correlation.

(d) Compute simple coefficients of determination and correlation.

(e) Compute partial correlation coefficients.

(f) Construct graphs.

(g) Formulate relevant hypotheses, perform the appropriate tests, and find p values.

(h) State the statistical decisions and clinical conclusions that the results of your hypothesis tests justify.

(i) Use your regression equations to make predictions and estimates about the dependent variable for your selected values of the independent variables.

(j) Construct confidence intervals for relevant population parameters.

(k) Describe the population(s) to which you think your inferences are applicable.