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1. Complete the following analogy. INDEPENDENT : PREDICTOR :: DEPENDENT : (____)

2. To determine the correlation between a criterion variable and a set of predictor variables when the correlations are hypothesized to be linear, which test would be most appropriate?

3. The most widely used bivariate correlation coefficient is the

4. (obj. 8) An important reason for making a scattergram is that it

5. A limitation of the shotgun approach to identify variables that correlate with a criterion variable is that

6. A researcher administers 10 measures of stress and anxiety to a sample of evaluators working for state departments of education. What statistical technique can the researcher use to determine whether the information in these 10 measures can be reduced to a smaller number of variables?

7. A scattergram provides a visual representation of

8. An important factor in deciding which correlational technique to use is

9. If every increase in score on a test of literary knowledge is accompanied by an increase in score on an intelligence test, one can conclude that

10. In research investigating relationships between variables, it is best if

11. (obj. 14) A correlation coefficient of .20 between two variables

12. A researcher who obtains a correlation of -.72 between test anxiety and college grade point average would be justified in concluding that

13. The line of best fit can be calculated using the following equation (y = bx + a), where b represents the

14. Multiple regression, path analysis, hierarchical linear modeling, and structural equation modeling are similar in that all of them

15. Cross-validation of findings from a prediction study is particularly important for

16. To hold intelligence constant when correlating scores on a Spanish achievement test and a measure of auditory acuity, a researcher should use

17. Suppose a researcher generates a correlation matrix for four variables. How many correlation coefficients would be in the matrix?

18. If every point in a scattergram plotting variable *X *and variable *Y *falls on the line of best fit, we can conclude that

19. Prediction research can be done for the purpose of

20. If the relationship between two variables is markedly nonlinear, the researcher should determine the degree of relationship by computing a

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