What does the Standard Error (SE) of Estimate indicate?

The Standard Error of Estimate quantifies the average distance that observed values fall from the regression line in a regression analysis. This statistical measure provides an insight into the accuracy of predictions made by the regression model. A smaller Standard Error indicates that the predictions are closer to the actual data points, reflecting a better fit of the model to the data. This characteristic is crucial for understanding the reliability of the model's predictions and helps in assessing how well the independent variables explain the variation in the dependent variable.

In contrast, the other options refer to different statistical concepts. Variance measures the spread of data points in a dataset, while the total number of data points is simply a count of observations in the dataset, and the confidence level typically pertains to the reliability or certainty of an estimate derived from a sample rather than specifically to the distance of data points from the regression line.