Explanation:
The standard error of the estimate is calculated as the square root of the mean square error (MSE). The MSE is derived by dividing the sum of squares error (SSE) by its degrees of freedom, which is (n - 2) for a simple linear regression. Here, MSE = SSE / (n - 2) = 280 / (30 - 2) = 10.0. The standard error of the estimate is then √10 ≈ 3.2. Option A incorrectly represents the F-statistic (MSR/MSE = 25/10 = 2.5), while Option C is the MSE itself (10.0).
An analyst estimates the following information from a simple linear regression: Sum of squares error (SSE): 280 Sum of squares regression (SSR): 25 Number of paired observations: 30 The standard error of the estimate is closest to:
A
2.5
B
3.2
C
10.0
No comments yet.