Question

1) Explain the Regression Sum of Squares (RSS) 2) Explain the Residual Sum of Squares (RSS)...

1) Explain the Regression Sum of Squares (RSS)

2) Explain the Residual Sum of Squares (RSS)

3) What is the formula for calculating Mean Squares?

Regression Output - 2

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3722496657566

1

3722496657566.014

4132

.000b

Residual

2196018240410

2438

900745791.801

Total

5918514897976

2439

a. Dependent Variable: Sale Price

b. Predictors: (Constant), Appraised Land Value
Math   Statistics-And-Probability   
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Answer #1

Solution: 1) Explain the Regression Sum of Squares (RSS)

Answer: Regression sum of squares measures how much of the variation in the dependent variable is explained by the regression model. It is the sum of the squares of the deviations of the predicted values from the mean value of a response variable. Larger the value, better is the model. Mathematically it is:

and in the given output we have:

Regression Sum of Squares  

2) Explain the Residual Sum of Squares (RSS)

Answer: Residual Sum of Squares measures overall difference between actual data and the values predicted by an estimation model. It represents the unexplained variation in the regression model. Smaller residual sum of squares means that the model fits the data well. Mathematically it is:

and in the given output we have:

Residual sum of squares

3) What is the formula for calculating Mean Squares?

Answer: The formula for Mean squares is:

For Mean squares for Regression, the formula is:

For mean squares for Residual, the formula is:

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