1) It is appropriate to use a simple linear regression when we have

a. an ordinal independent variable and a scale dependent variable.

b. an ordinal independent variable and an ordinal dependent variable.

c. a scale independent variable and a scale dependent variable.

d. a scale independent variable and an ordinal dependent variable.

2) What is the null hypothesis for a correlation test?

a. There is a significant difference between the two variables.

b. There is not a significant difference between the two variables.

c. The independent variable predicts the dependent variable.

d. The independent variable does not predict the dependent variable.

3) What is the alternate hypothesis for a correlation test?

a. There is a significant difference between the two variables.

b. There is not a significant difference between the two variables.

c. The independent variable predicts the dependent variable.

d. The independent variable does not predict the dependent variable.

4) When we use a regression model to make predictions, we will usually over- or underestimate each score. The amount by which we over- or underestimate a score is called

a. the residual.

b. the linearity.

c. the regression.

d. the coefficient.

5) Suppose a regression analysis produces anR2coefficient of .55. What can we conclude from these results?

a. The model is not useful.

b. The model explains a very small amount of the variation in the dependent variable.

c. The model explains most of the variation in the dependent variable.

d. The model is not statistically significant.

6) Suppose a regression analysis produces an ANOVA statistic with ap-value of .101. What can we conclude from these results?

a. The model results in significantly better predictions than estimates based solely on the mean.

b. The model does not result in significantly better predictions than estimates based solely on the mean.

c. The findings are substantively significant.

d. The findings are not substantively significant.

7) Suppose that, according to our coefficient table, thep-value associated with our independent variable is .000. What can we conclude from these results?

a. The independent variable is a statistically significant predictor of the dependent variable.

b. The independent variable is not a statistically significant predictor of the dependent variable.

c. The results are practically significant.

d. The results are not practically significant.

8) Suppose a regression analysis produces anR2coefficient of .02. What can we conclude from these results?

a. The independent variable is a statistically significant predictor of the dependent variable.

b. The independent variable is not a statistically significant predictor of the dependent variable.

c. The results are practically significant.

d. The results are not practically significant.

9) The following regression equation can be used to predict children's height (in feet) based on their age (in years):

Height = 2.34 + .22(Age)

Based on this equation, what is the expected height of an eleven-year-old boy?

a.4.54 feet

b. 4.76 feet

c. 5.02 feet

d. 5.19 feet

10) The following regression equation can be used to predict children's height (in feet) based on their age (in years):

Height = 2.34 + .22(Age)

Based on this equation, what is the expected height of a seven-year-old girl?

a. 3.66 feet

b. 3.70 feet

c. 3.81 feet

d. 3.88 feet