What is the standard error of estimate?
Click on the arrows to vote for the correct answer
A. B. C. D. E.E
All the descriptions are correct for the standard error of estimate.
The standard error of estimate (SEE) is a statistical measure that quantifies the accuracy of predictions made by a regression model. It is used to assess how well the model fits the observed data points and provides an estimate of the typical distance between the predicted values (Y') and the actual values (Y) of the dependent variable.
Option A, "Based on squared vertical deviations between Y and Y'," is incorrect. The squared vertical deviations between Y and Y' are used to calculate the sum of squared errors (SSE), not the standard error of estimate.
Option B, "None of these answers," is incorrect. There is a correct answer among the options provided.
Option C, "Measure of the accuracy of the prediction," is correct. The standard error of estimate measures the accuracy of predictions made by a regression model. It represents the standard deviation of the residuals, which are the differences between the observed values and the predicted values. A lower standard error of estimate indicates a better fit of the model to the data, implying higher accuracy in predicting the dependent variable.
Option D, "Cannot be negative," is incorrect. The standard error of estimate can take positive values. It represents a measure of dispersion, similar to a standard deviation, and can be either positive or zero.
Option E, "All of these answers," is incorrect. Only option C, "Measure of the accuracy of the prediction," is correct.
Therefore, the correct answer is C: Measure of the accuracy of the prediction.