Fundamental Statistics For The Behavioral Sciences 8th Edition By David – Test Bank
Chapter 11—Multiple Regression
MULTIPLE CHOICE QUESTIONS
11.1 The difference between multiple regression and simple regression is that
a) multiple regression can have more than one dependent variable.
b) *multiple regression can have more than one independent variable.
c) multiple regression does not produce a correlation coefficient.
d) both b and c
11.2 Assume that we generated a prediction just by adding together the number of stressful events you report experiencing over the last month, the number of close friends you have, and your score on a measure assessing how much control you feel you have over events in your life (i.e., prediction = stress + friends + control). The regression coefficient for stressful events would be
d) There is no way to know.
11.3 In the previous question the intercept would be
d) There would be no way to know.
11.4 In multiple regression the intercept is usually denoted as
11.5 Given the following regression equation ( = 3.5 X1 + 2X2 + 12), the coefficient for X1 would mean that
a) two people who differ by one point on X1 would differ by 3.5 points on .
b) *two people who differ by one point on X1 would differ by 3.5 points on , assuming that they did not differ on X2.
c) X1 causes a 3.5 unit change in the dependent variable.
d) X1 is more important than X2.
11.6 In the previous question, a student who scored 0 on both X¬1 and X2 would be expected to have a dependent variable score of
d) the mean of Y.
11.7 If one independent variable has a larger coefficient than another, this means
a) that the variable with the larger coefficient is a more important predictor.
b) that the variable with the larger coefficient is a more statistically significant predictor.
c) that the variable with the larger coefficient contributes more to predicting the variability in the criterion.
d) *We can’t say anything about relative importance or significance from what is given here.
11.8 If we want to compare the contribution of several predictors to the prediction of a dependent variable, we can get at least a rough idea by comparing
a) the regression coefficients.
b) *the standardized regression coefficients.
c) the variances of the several variables.
d) the simple Pearson correlations of each variable with the dependent variable.
11.9 When we speak of the correlations among the independent variables, we are speaking of
d) multiple correlation.
11.10 Before running a multiple regression, it is smart to look at the distribution of each variable. We do this because
a) we want to see that the distributions are not very badly skewed.
b) we want to look for extreme scores.
c) we want to pick up obvious coding errors.
d) *all of the above