Score for this quiz: 14 out of 20 Submitted Nov 4, 2014 at 6:23pm This attempt took 3 minutes.
Question 1
1 / 1 pts
We use multiple regression for two reasons. One of them is to statistically control for extraneous variables. For example, examining the relationship between hours of study and test performance while controlling for previous achievement. Which of the following variable is the extraneous variable based on the example above?
hours of study test performance Correct!
previous achievement None of them
Question 2
1 / 1 pts
One of the reasons that we use multiple regression is to minimize errors of prediction. In other words, predictions are often more accurate using multiple sources of information.
Correct!
true false
Question 3 Multiple Regression means that there are multiple criterion variables in the study.
You Answered
true
Correct Answer
false
Multiple Regression means that there are multiple predictor (independent) variables in the study.
0 / 1 pts
Question 4
1 / 1 pts
Which of the following represents the yellow shaded area based on the diagram below?
The proportion of the variance in one predictor that is associated with the other predictor Correct!
The proportion of the variance in the criterion that is associated with the two predictors. The proportion of the variance in the two criteria that is associated with one predictor. all of above
Question 5
1 / 1 pts
R-square (R2) is used to represent the proportion of the variance in the criterion that was associated with our predictors. The capital “R” indicates more than one predictor is involved.
Correct!
true false
Question 6
1 / 1 pts
the R-square (R2) value told us the proportion of the variance in the criterion that was associated with our predictors. Which of the following is true about the R-square value?
R-square = SSreg / SSresidual Correct!
R-square = SSreg / SStotal R-square = SSresidual / SStotal None of them
Question 7 In multiple regression, we can test the null hypothesis below. Which of the following is correct for the null hypothesis below?
Correct Answer
To test if the population variance in the criterion that is associated with the predictors differs from 0. To test if the population slope for each preditor differs from 0.
0 / 1 pts
You Answered
Both of them
None of above
Question 8
1 / 1 pts
Which of the following statistics is used to test the null hypothesis in the previous question?
The t test Correct!
The F test The chi-square test None of them
Question 9
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Which of the following statement regarding the R-square value is NOT true?
Correct Answer
The R-square value provides an estimate of the amount of the sample variance in the criterion that is associated with the predictors.
Most of the cases, the R-square value is positively biased.
The R-square value needs to make bigger adjustments when the sample size is small and when the number of predictors is large
You Answered
None of above
The R-square value provides an estimate of the amount of the "POPULATION" variance in the criterion that is associated with the predictors.
Question 10 Which of the following formulas is correct for the F test in multiple regression?
1 / 1 pts
Correct!
Both of them
Question 11
1 / 1 pts
What does the adjusted R-square mean?
The unique contributions of each predictor to the prediction of the criterion Correct!
The adjustment of the biased R-square None of them
Question 12
1 / 1 pts
What does the partial R-square mean?
Correct!
The unique contributions of each predictor to the prediction of the criterion The adjustment of the biased R-square None of them
Question 13 Use the diagram below to answer questions 13-16. Question: What is the R-square for all predictors?
Yellow You Answered
blue
green Correct Answer
All of them
0 / 1 pts
Question 14
0 / 1 pts
Which of the following symbols is used to present the R-square for all predictors?
Correct Answer
You Answered
None of them
Question 15
1 / 1 pts
What is the unique contribution of predictor x1, partialling predictor x2 out?
Yellow and blue Green Blue Correct!
Yellow
Question 16 Which of the following symbols is used to present the unique contribution of predictor x1, partialling predictor x2 out?
Correct!
None of them
1 / 1 pts
Question 17
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If we would like to make a comparison about which predictor makes more unique contribution to prediction, which of the following statistics is used?
unstandardized regression coefficient Correct!
standardized regression coefficient the adjusted R-square the F test
Question 18
1 / 1 pts
If we would like to test if each regression coefficient differs statistically from zero, which of the following statistics is used?
The chi-square The F test Correct!
The t test The adjust R-square
Question 19
1 / 1 pts
Which of the following symbols is used to represent the null hypothesis in the previous question?
Correct!
None of them
Question 20
0 / 1 pts
Which does the multicollinearity assumption mean?
You Answered
It refers to a context where one of the predictors is completely redundant with the other predictors
When you ran a regression predicting the redundant predictor from the others you would get an R2 (R-square) value of 1.0.
Correct Answer
It indicates that a predictor is somewhat redundant, but not completely redundant with other predictors. None of them