Answer:
Explanation:
The standard error of estimate is a statistical measure that represents the average distance that the observed values of a dependent variable deviate from the predicted values of that variable, based on a given regression model. In other words, it measures the extent to which the predicted values from a regression model differ from the actual observed values.
The standard error of estimate is calculated as the square root of the mean squared error (MSE) or the residual sum of squares (RSS) divided by the degrees of freedom (n - k - 1), where n is the sample size and k is the number of predictor variables in the regression model.
It is commonly used as a measure of the accuracy of a regression model and can be used to assess the goodness of fit of the model. A lower standard error of estimate indicates that the predicted values are more closely aligned with the observed values, and thus the model is a better fit for the data. Conversely, a higher standard error of estimate indicates a poorer fit between the predicted and observed values.