Question 1

A standardized regression equation with Olympic athletes' sprint times as the dependent variable features two X variables. The beta-weight for sprinters' height (X1) is -0.18, and the beta-weight for sprinters' weight (X2) is 0.26. Which of the following can we conclude from these results?

Accepted Answer

A)  Weight has a stronger effect than height on race times. 
B)  Height has a stronger effect than weight on race times. 
C)  The coefficient of determination is 0.08. 
D)  The coefficient of determination is 0.44.

Question 2

A theory suggests that X should have a positive effect on Y. Upon analyzing a set of data, the correlation between X and Y is 0.55, and remains just as strong when Z is controlled. What does this tell us about the theory in question?

Accepted Answer

A)  The theory was on the right track, but had the direction of the effect reversed. 
B)  The theory is proven correct. 
C)  The theory is supported, but other control variables need to be considered. 
D)  Nothing can be learned about the theory.

Question 3

Researchers determine that age has a positive correlation with trust of one's neighbours. They then ask whether this correlation is due to the fact that older people tend to have lived for longer amounts of time in their neighbourhood relative to younger people. What type of variable is "time lived in neighbourhood" for this example?

Accepted Answer

A)  a beta variable 
B)  a zero-order variable 
C)  a dependent variable 
D)  a control variable

Question 4

What types of data are required for partial and multiple correlation and regression?

Accepted Answer

A)  nominal and continuous 
B)  inferential and bivariate 
C)  interval-ratio 
D)  ordinal and discrete

Question 5

A standardized regression equation is stated as  
. Which of these conclusions is consistent with this formula?

Accepted Answer

A)  Z is caused by both X and Y. 
B)  There is a direct relationship between X and Y after controlling for the effects of Z. 
C)  The independent variables have strong, nearly equal relationships with the dependent variable. 
D)  This is a spurious relationship.

Question 6

Which of the following best describes an intervening relationship between the variables "literacy," "economic productivity," and "wealth" for a sample of countries?

Accepted Answer

A)  Literacy increases wealth, no matter the value of economic productivity. 
B)  Literacy increases economic productivity, which increases wealth. 
C)  Wealth is caused by neither literacy nor economic productivity. 
D)  Economic productivity increases wealth only when there are high levels of literacy.

Question 7

Consider the multiple regression equation Y = a + b1X1 + b2X2. What is the ideal correlation between X1 and X2, assuming the assumptions of multiple regression are fully met?

Accepted Answer

A)  r = 0 
B)  r = 0.5 
C)  r = 1 
D)  r = 2

Question 8

Briefly explain the difference between additive and interactive effects of independent variables in multiple regression.

Accepted Answer

The answer of Briefly explain the difference between additive and...

Question 9

Which of the following is a standardized multiple regression equation with two independent variables?

Accepted Answer

A)  Zy = a - b1Z1 - b2Z2 
B)  Zy = b1Z1 + b2Z2 
C)  Zy = b1Z1 - b2Z2 
D)  Zy = a + b1X1 + b2X2

Question 10

The correlation in a large company between self-confidence and income is 0.25. After controlling for level of authority on the job, the original correlation dips to 0.11. What is this evidence of?

Accepted Answer

A)  a direct relationship between self-confidence and income 
B)  a spurious relationship between self-confidence and income; job authority causes both 
C)  an intervening relationship between self-confidence and income; self-confidence affects income through increasing people's job authority 
D)  either a spurious or an intervening relationship; it is impossible to differentiate the two possibilities on statistical grounds

Question 11

What is the major advantage of a standardized multiple regression equation?

Accepted Answer

A)  a simple comparison of each variable's relative importance 
B)  predicting values of the dependent variable (  ) in the variable's original units 
C)  the ability to incorporate multiple independent variables; the bivariate nature of unstandardized regression 
D)  assessing non-linear associations between independent and dependent variables

Question 12

How should the symbol b be interpreted in a multiple regression equation?

Accepted Answer

A)  as the value of Y when all independent variables have a score of 0 
B)  as the amount of Y for a unit change in the independent variable while controlling for the effects of all other variables 
C)  as the correlation between two independent variables 
D)  as the effect of X1 on X2 while controlling for the effect of Y

Exam 14: Partial Correlation and Multiple Regression and Correlation

A standardized regression equation with Olympic athletes' sprint times as the dependent variable features two X variables. The beta-weight for sprinters' height (X₁) is -0.18, and the beta-weight for sprinters' weight (X₂) is 0.26. Which of the following can we conclude from these results?