Data Set 12-4: $$\begin{array} { c c c r c } \text { Subjects } & X _ { l } & X _ { 2 } & X _ { 3 } & \Sigma \ \hline 1 & 3 & 0 & 7 & 10 \ 2 & 5 & 4 & 7 & 16 \ \hline \Sigma & 8 & 4 & 14 & 26 \end{array}$$ -In Data Set 12-4, MS_erroris equal to A) 5.00 B) 4.00 C) 2.00

Question

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The Mean Square Error (MSE) is calculated by dividing the sum of squared errors by the degrees of freedom. In this case, the sum of squared errors (SSE) is not directly given, but we can infer some information from the table. The total sum of squares (SST) can be calculated from the given sums, but without specific information on the mean or the sum of squares due to regression (SSR), we cannot directly calculate SSE. However, given the options and the context, if we assume the question is about calculating a mean square of some kind (possibly a misunderstanding in the question's phrasing), and considering the basic calculations possible from the table (which do not directly lead to an MSE calculation without additional context), the correct choice might be inferred or guessed based on typical operations with such data. Without the ability to calculate MSE directly from the provided data, the choice is based on an assumption that cannot be validated with the given information.

Data Set 12-4 -In Data Set 12-4, MSerror is Equal to A) 5

Data Set 12-4 -In Data Set 12-4, MS_erroris Equal to
A) 5