Answer:
The following combination describes a regression line that is a good fit for the data
a. Larger R-sq and small Se
Step-by-step explanation:
In regression analysis, we measure the goodness of fit in terms of two parameters.
1. R² ( R-squared or also called the coefficient of determination)
2. SE ( Standard Error)
1. R-squared
The R-squared indicates the relative measure of the percentage of the variance with respect to the dependent variable.
R-squared is measured in percentage so it doesn't have any unit.
The greater the R-squared percentage, the better is the goodness of fit.
2. Standard Error
The SE basically indicates that on average how far the data points are from the regression line.
The unit of the standard error is the same as the dependent variable.
The lower the SE, the better is the goodness of fit.
Therefore, the correct option is (a)
a. Larger R-sq and small Se
