# Question

Consider the following hypothetical data (in the file named pr1_mr2iv.sav)

The research question is: How well can Systolic Blood Pressure (SBP) be predicted from Anxiety and Weight combined? Also, how much variance in blood pressure is uniquely explained by each of these two predictor variables?

a. As preliminary data screening: generate a histogram of scores on each of these three variables, and do bivariate scatters plot for each pair of variables. Do you see evidence of violations of assumptions? For example, do any variables have non normal distribution shapes? Are any pairs of variables related in a way that is not linear? Are there bivariate outliers?

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b. Run a regression analysis to predict SBP from Weight and Anxiety. As in the example presented in the chapter, make sure that you request the Part and Partial correlation statistics, and a graph of the standardized residuals (ZRESID) against the standardized predicted values (ZPRED).

c. Write up a Results section. What can you conclude about the predictive usefulness of these two variables, individually and combined?

d. Does examination of the plot of standardized residuals indicates any serious violation of assumptions? Explain.

e. Why is the b coefficient associated with the variable Weight so much smaller than the b coefficient associated with the variable Anxiety (even though Weight accounted for a larger unique share of the variance in SBP)?

f. Set up a table (similar to the one shown as Table 11.1) to summarize the results of your regression analysis.

g. Draw a diagram (similar to Figure 11.13) to show how the total variance of SBP is partitioned into variance that is uniquely explained by each predictor, variance that can be explained by either predictor, and variance than cannot be explained; and fill in the numerical values that represent the proportions of variance in this case.

The research question is: How well can Systolic Blood Pressure (SBP) be predicted from Anxiety and Weight combined? Also, how much variance in blood pressure is uniquely explained by each of these two predictor variables?

a. As preliminary data screening: generate a histogram of scores on each of these three variables, and do bivariate scatters plot for each pair of variables. Do you see evidence of violations of assumptions? For example, do any variables have non normal distribution shapes? Are any pairs of variables related in a way that is not linear? Are there bivariate outliers?

Question Continue In Next Page.

b. Run a regression analysis to predict SBP from Weight and Anxiety. As in the example presented in the chapter, make sure that you request the Part and Partial correlation statistics, and a graph of the standardized residuals (ZRESID) against the standardized predicted values (ZPRED).

c. Write up a Results section. What can you conclude about the predictive usefulness of these two variables, individually and combined?

d. Does examination of the plot of standardized residuals indicates any serious violation of assumptions? Explain.

e. Why is the b coefficient associated with the variable Weight so much smaller than the b coefficient associated with the variable Anxiety (even though Weight accounted for a larger unique share of the variance in SBP)?

f. Set up a table (similar to the one shown as Table 11.1) to summarize the results of your regression analysis.

g. Draw a diagram (similar to Figure 11.13) to show how the total variance of SBP is partitioned into variance that is uniquely explained by each predictor, variance that can be explained by either predictor, and variance than cannot be explained; and fill in the numerical values that represent the proportions of variance in this case.

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