7. If r = -1, then we can conclude that there is a perfect linear relationship between the dependent and independent variables. A. True B. False

8. The _____ measures the strength and direction of the linear relationship between the dependent and the independent variable. A. sample correlation coefficient B. distance value C. y Intercept D. residual E. coefficient of determination

9. A sample correlation coefficient of 0.22 was found between outdoor humidity and unexcused absences at a construction site. What can the HR director conclude? A. 48.4% of the absences can be predicted by the outdoor humidity B. 0.48% of the absences can be predicted by the outdoor humidity C. 0.22% of the absences can be predicted by the outdoor humidity D. 4.84% of the absences can be predicted by the outdoor humidity E. 22.0% of the absences can be predicted by the outdoor humidity

10. Regression models that employ more than one independent variable are referred to as multiple regression models. A. True B. False

11. An outlier may be an incorrectly entered data value. A. True B. False

12. A fitted multiple regression model yields an R² of 0.64. Interpret this value. A. 64% of the independent variables used in the regression model are highly correlated and may falsely suggest a curvilinear relationship exists between x and y when the true relationship is linear. B. 64% of the variation in the observed value of the dependent variable is explained by the regression model. C. The correlation between each pair of independent variables is 0.8 or -0.8. D. The fitted model predicts the dependent variable accurately 64% of the time. E. 64% of the independent variables in the model are capable of accurately predicting the dependent variable.