Locate the values of SSE, s2, and s on the printout below.

Model Summary

Model

R

R Square Adjusted

R Square Std. Error of the Estimate

1.859.737.689 11.826

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 4512.024 1 4512.024 32.265.001

Residual 1678.115 12 139.843

Total 6190.139 13

Question 3 options:

SSE = 4512.024; s2 = 4512.024; s = 32.265

SSE = 4512.024; s2 = 139.843; s = 11.826

SSE = 6190.139; s2 = 4512.024; s = 32.265

SSE = 1678.115; s2 = 139.843; s = 11.826

Question

Business

Cooper Koch

9 February, 11:10

Locate the values of SSE, s2, and s on the printout below.

Model Summary

Model

R

R Square Adjusted

R Square Std. Error of the Estimate

1.859.737.689 11.826

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 4512.024 1 4512.024 32.265.001

Residual 1678.115 12 139.843

Total 6190.139 13

Question 3 options:

SSE = 4512.024; s2 = 4512.024; s = 32.265

SSE = 4512.024; s2 = 139.843; s = 11.826

SSE = 6190.139; s2 = 4512.024; s = 32.265

SSE = 1678.115; s2 = 139.843; s = 11.826

+3

Answers (1)

Know the Answer?

Barton · Answer 1

SSE = 1678.115; s2 = 139.843; s = 11.826

Explanation:

Consider the following formulas:

SSE: This value provides a measure of how well the line of best fit approximates the data set.

S^2: The variance is mathematically defined as the average of the squared differences from the mean

S: is the expectation of the squared deviation of a random variable from its mean.