Question 21) The correlation coefficient has values between ?1 and +1.

21) The correlation coefficient has values between ?1 and +1.

22) Errors are also called residuals.

23) The regression model assumes the error terms are dependent.

24) The regression model assumes the errors are normally distributed.

25) The errors in a regression model are assumed to have an increasing mean.

26) The errors in a regression model are assumed to have zero variance.

27) If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.

28) Often, a plot of the residuals will highlight any glaring violations of the assumptions.

29) The error standard deviation is estimated by MSE.

30) The standard error of the estimate is also called the variance of the regression.

31) An F-test is used to determine if there is a relationship between the dependent and independent variables.

32) The null hypothesis in the F-test is that there is a linear relationship between the X and Y variables.

33) If the significance level for the F-test is high enough, there is a relationship between the dependent and independent variables.

34) When the significance level is small enough in the F-test, we can reject the null hypothesis that there is no linear relationship.

35) The coefficients of each independent variable in a multiple regression model represent slopes.

36) For statistical tests of significance about the coefficients, the null hypothesis is that the slope is 1.

37) Both the p-value for the F-test and r2 can be interpreted the same with multiple regression models as they are with simple linear models.

38) The multiple regression model includes several dependent variables.

39) The multiple regression model includes several intercept terms.

40) The multiple regression model includes multiple slope coefficients.

41) In regression, a binary variable is also called an indicator variable.

42) Another name for a dummy variable is a binary variable.

43) Dummy variables for regression analysis can take on a value of either -1 or +1.

44) The best model is a statistically significant model with a high r-square and few variables.

45) The adjusted r2 will always increase as additional variables are added to the model.

46) The value of r2 can never decrease when more variables are added to the model.

47) A variable should be added to the model regardless of the impact (increase or decrease) on the adjusted r2 value.

48) Multicollinearity exists when a variable is correlated to other variables.

49) If multicollinearity exists, then individual interpretation of the variables is questionable, but the overall model is still good for prediction purposes.

50) Transformations may be used when nonlinear relationships exist between variables.