Sum squared error formula

• [DOC File]Columbia University in the City of New York

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  SSE stands for “sum of squares due to error” - this is simply the sum of the squared residuals, and it is the variation in the Y variable that remains unexplained after taking into account the variable X. The interpretation of equation (2) is now clear.

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• [DOC File]1

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  Sum squared resid 8650.172 Schwarz criterion 9.207052. Log likelihood -89.07479 F-statistic 1.797005. Durbin-Watson stat 0.836087 Prob(F-statistic) 0.196750. Regression #3 Dependent Variable: Y. Method: Least Squares. Included observations: 20

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• [DOC File]Derivation of the Ordinary Least Squares Estimator

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  The sum of squared residuals can be written mathematically as (3) where n is the total number of observations and ∑ is the summation operator. The above equation is known as the sum of squared residuals (sum of squared errors) and denoted SSE. Using the definitions of and , the SSE becomes (4)

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• [DOC File]Some of the basics of summation can be “summarized” with ...

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  The following fact is useful when deriving the Least Squares formula as the solution to the problem of minimizing the sum of squared residuals with respect to the slope estimate: Show that: Fact 4: Also, sometimes it is useful to express the formula for the slope estimate in a way that clearly shows it to be linear function in the Yi data. Fact 5:

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• [DOC File]Chapter 11 – Simple linear regression

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  Choose the line that minimizes the sum of squared vertical distances from observed values (Yi) to their fitted values Note: b0 is the Y-intercept for the estimated regression equation

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• [DOC File]Derivation of the Ordinary Least Squares Estimator

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  In matrix form, the estimated sum of squared errors is: (10) where the dimensions of each matrix are shown below each matrix and the symbol represents the matrix transpose operation. The following example illustrates why this definition is the sum of squares. Example Sum of Squared …

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