Then the mean square for error, MSE, equals SSE/26.

Name: Then the mean square for error, MSE, equals SSE/26.
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Quizplus · Accepted Answer

This statement is true. MSE (mean square error) is calculated by dividing the sum of squared errors (SSE) by the degrees of freedom, which in this case is 26 (n-2, where n is the sample size and 2 represents the number of estimated parameters in a simple linear regression model). So, MSE = SSE/26.

Then the Mean Square for Error, MSE, Equals SSE/26