Question

1. Regression analysis procedures have as their primary purpose the development of an equation that can be used for predicting values on some DV for all members of a population.

T

F

2. A secondary purpose is to use regression analysis as a means of explaining causal relationships among variables.

T

F

3. In order to make predictions, three important facts about the regression line must be known. One of them is: The point at which the line crosses the X-axis.

T

F

4. The regression line is essentially an equation that expresses X as a function of Y.

T

F

5. Residuals (errors of prediction) are essentially calculated as the difference between the actual value and the predicted value for the IV.

T

F

6. The reason that we obtain the best-fitting line as our regression equation is that we mathematically calculate the line with the smallest amount of total squared error.

T

F

7. Multiple regression is used to predict the value of a single DV from a weighted, linear combination of IVs.

T

F

8. The coefficient of determination in multiple regression is the proportion of DV variance that can be explained by at least one IV.

T

F

9. Multicollinearity is desirable in multiple regression.

T

F

10. Multicollinearity tends to increase the variances in regression coefficients, which ultimately results in a more stable prediction equation.

T

F

Answer 1

Qut 1 True

Qut 2 True, Because From the regression equation we can see that which variable is cause by other varibale by using regression analysis.

Qut 3 True

Qut 4 True

Qut 5 True , because residual error = difference between observed and estimated value

Qut 6 false

Qut 7 True Bcs the assumption is that the IV are linearly related.

Qut 8 True

Qut 9 False , It may be present or not. its not necessary to be desirable.

Qut 10 False , No as Multicollinearity tends to increase the variances in regression coefficients, The model become more Unstable