3. If we had a tabulation of the errors, then if we then squared and summed them

Q: 3. If we had a tabulation of the errors, then if we then squared and summed them

3. It is referring to the SS of residual errors, the sum of squared errors. [Residual errors are the differences between the actual and predicted values of the line.] Thus, C. 7940. 4. This one is referring to the R-squared value. This is also called the coefficient of determination, the proportion of your data explained by your variation. Thus, B. 98.2% or 98%.

ID: 3437452 • Letter: 3

Question

3. If we had a tabulation of the errors, then if we then squared and summed them they would

equal ___.

a. 0

b. 794

c. 7,940

d. 446,174

e. 438,234

4. The variation of Y explained by X1, X2, X3, and X4 is approximately

a. 40%

b. 98%

c. 28%

d. 71%

e. None of these or unable to determine

Coef 138315 -11.56 -1127.7 -1813.7 8125 SE Coetf 14750 13.13 463.2 227.4 2155 Predictor Constant 0.38 0.000 0.399 0.035 0.000 0.004 -0.88 -2.43 X2 X2 X4 3.7 s 2818 R-sq 98.2% R-Sq (adj ) = 97.5% = Analysis of Variance Source Regression Residual Error 1 Total DF 4 438234 7940 446174 MS 109558 794 137.90 0.000 14

Explanation / Answer

It is referring to the SS of residual errors, the sum of squared errors.

[Residual errors are the differences between the actual and predicted values of the line.]

Thus, C. 7940.

This one is referring to the R-squared value. This is also called the coefficient of determination, the proportion of your data explained by your variation.

Thus, B. 98.2% or 98%.

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3. If we had a tabulation of the errors, then if we then squared and summed them

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