Question

The following estimated regression equation based on 10 observations was presented. 9= 23.1670 + 0.5706x₁ + 0.4920x₂ 1502 = 0.0565. Here, SST= 6,726.125, SSR = = 6,213.375, Sb₁ = 0.0815, and s. Compute MSR and MSE. (Round your answers to three decimal places.)

MSR = ?
MSE = ?

Answer 1

To compute MSR and MSE, divide the sum of squares regression by the degrees of freedom for regression and the sum of squares residual by the degrees of freedom for error, respectively.

Step-by-step explanation:

To compute MSR and MSE, we need to know the formulae and the given values. MSR (Mean Square Regression) is calculated by dividing the sum of squares regression (SSR) by the degrees of freedom for regression, which is 2 in this case.

Hence, MSR = SSR/2. MSE (Mean Square Error) is computed by dividing the sum of squares residual (SSE) by the degrees of freedom for error, which is the total number of observations minus the number of independent variables, or n - k. Therefore, MSE = SSE/(n-k).

Given that SSR = 6,213.375, MSE = 20.8542, and n = 10, we can calculate MSR and MSE as follows:

MSR = SSR/2 = 6,213.375/2 = 3,106.688

MSE = SSE/(n-k) = 20.8542/(10-2) = 20.8542/8 = 2.6068