Question

If you saw that a scatterplot was someWhat linear between variable A and B and you wanted to see if A predicted the value of B so you run a PROC REG. Which of the different parts of the output would you look at to determine if the linear regression model is "good"?

1 R-Square
2 Basic Statistic for the Variables of Interest
3 P-value from the Analysis of Variance
4 Residual graphs
5 Dependent Mean
6 Parameter Estimates
7 P-value for the Parameter Estimates

A. 1 and 4
B. 2 and 5
C. 3 and 6
D. 7

Answer 1

To determine if the linear regression model is good, you would look at R-Square and Residual graphs in the output of PROC REG.

Step-by-step explanation:

To determine if the linear regression model is good, you would look at R-Square and Residual graphs in the output of PROC REG.

R-Square measures the proportion of the variation in the dependent variable that can be explained by the independent variable. A high R-Square value (close to 1) indicates a good fit of the regression model.

Residual graphs help to assess the residuals (the differences between the observed and predicted values) for any patterns or deviations from randomness. If the residuals are randomly scattered around zero, it suggests a good fit of the model.