1. A dependent t-test is used to test for:

A) Differences between means of groups containing different people when the data are normally distributed, have equal variances and data are at least interval.

B) Differences between means of groups containing different people when the data are not normally distributed or have unequal variances.

C) Differences between means of groups containing the same people when the data are normally distributed, have equal variances and data are at least interval.

D) Differences between means of groups containing different people when the data are not normally distributed or have unequal variances.

2. An independent t-test is used to test for:

A) Differences between means of groups containing different people when the data are normally distributed, have equal variances and data are at least interval.

B) Differences between means of groups containing different people when the data are not normally distributed or have unequal variances.

C) Differences between means of groups containing the same people when the data are normally distributed, have equal variances and data are at least interval.

D) Differences between means of groups containing different people when the data are not normally distributed or have unequal variances.

3. A way of representing discrete variables in multiple regression is by constructing:

A) Stupid Variables

B) Dummy Variables

C) Imitation Variables

D) Fake Variables

4. R² is:

A) Indicates the slope of the regression line.

B) The proportion of variance in the outcome accounted for by the predictor variable or variables.

C) The proportion of variance in the predictor accounted for by the outcome variable.

D) Tells you if there is a significant difference between variables.

5. In order to conduct a bivariate regression the two variable need to have a linear relationship.

A) True

B) False

6. Correlational studies allow the researcher to:

A) test for differences between two variables

B) predict the effect of one variable upon another

C) make causal inferences about the relationship between two variables

D) identify the relationship between two variables

7. What is meant by a 'spurious' relationship between two variables?

A) One that is so illogical it cannot possibly be true

B) An apparent relationship that is so curious it demands further attention

C) A relationship that appears to be true because each variable is related to a third one

D) One that produces a perfect negative correlation on a scatter diagram

8. When interpreting a correlation coefficient, it is important to look at:

A) The significance of the correlation coefficient.

B) The magnitude of the correlation coefficient.

C) The +/ - sign of the correlation coefficient.

D) All of the above

9. What does the multicollinearity assumption mean?

10. A type 1 error is when:

A) We conclude that there is a meaningful effect in the population when in fact there is not.*

B) We conclude that there is not a meaningful effect in the population when in fact there is.

C) We conclude that the test statistic is significant when in fact it is not

D) The data we have typed into SPSS is different to the data collected.

11. What would be the level of measurement for a variable representing the number of assaults in WV in May of 2012?

A) Interval

B) Ratio

C) Nominal

D) Ordinal

12. The correlation between two variables A and B is .12 with a significance of p < .01, what can we concluded?

A) That there is a substantial relationship between A and B.

B) That there is a small relationship between A and B.

C) That variable A causes variable B.

D) All of the above

13. A scatterplot show:

A) The frequency with which values appear in the data.

B) The average value of groups of data.

C) Scores on one variable plotted against scores on a second variable.

D) The proportion of data falling into different categories.

14. What are some advantages of multiple regression compared to bivariate regression?

15. What level of measurement should your predictor and outcome variable be in order to run a bivariate regression.

16. describe why it is important to meet statistical assumptions.

17. What is the difference between inferential and descriptive statistics?

18. Imagine that you are conducting a regression with multiple predictor variables. You are trying to determine what variables influence illicit drug use. This is your outcome variable, it was measured as a continues (ratio level) variables asking the respondents how many times they have used an illicit drug in the last month.

Here are your predictor variables.

• Age (Ratio level variable)
• Close Peer Drug Use (Categorical Yes or No Answer, No is the comparison category) It is not required that you interpret this one. But, bonus points if you can interpret this one correctly. Remember categorical variables are interpreted differently.
• Stress Level (Ratio Level - 20-point scale with higher score meaning higher level of stress).

Here is the results of your multiple regression:

• F test = p = .001
• R² = .37
• B slope for Age = -2.13 (p = .002)
• B slope for Stress Level = 3.312 (p = . 004)
• B slope for Peer Drug Use = 4.1 (p = .05).

Please provide the correct interpretations for these results