QUESTION 1

You want to conduct a simple linear regression to find out if grades in school predict whether a graduate is employed or unemployed. Why can't this be done?

a. Because more variables are needed.

b. Because the dependent variable is nominal rather than continuous.

c. Because there are too few degrees of freedom.All of the above.

QUESTION 2

Which is NOT true of simple linear regression?

a. It can be used to identify predictors of continuous outcome variables.

b. It can be used to predict the outcome of a binary variable (e.g., pass/fail) with continuous variables.

c. It can be used to quantify a relationship between two continuous variables.

d. It can be used to model a linear relationship between variables.

QUESTION 3

You want to see if the learners' Graduate Record Exam (GRE) scores are predictive of grades once enrolled at the university. If in your model R2 = .17 how much of the variation in grades is explained by GRE scores?

a. 1.7%

b. 83%

c. 17%

d. There is not enough information provided to tell.

QUESTION 4

What would the null hypothesis be for the research question explored using simple linear regression in the previous question?

a. There is a positive linear relationship between GRE scores and grades.

b. There is a negative linear relationship between GRE scores and grades.

c. There is a linear relationship between GRE scores and grades that could be either positive or negative.

d. There is no linear relationship between GRE scores and grades.

QUESTION 5

What can you conclude from this output from a linear regression analysis of GRE scores and grades that results in the following data: the slope coefficient (B) for GRE scores is .037 with a significance level of .018?

a. You should reject the null hypothesis, and accept the alternative hypothesis that there is a linear relationship between GRE scores and grades because the p-value is less than .05.

b. You should reject the null hypothesis, and accept the alternative hypothesis that there is a linear relationship between GRE scores and grades because the standard error is less than 1.0.

c. You should accept the null hypothesis and reject the alternative hypothesis that there is a linear relationship between GRE scores and grades because the p-value is less than .05.

d. You need more information to draw any conclusions.