Use the following scenario to answer the question. Scenario: Consider the follow

Question

Use the following scenario to answer the question. Scenario: Consider the following summary output from Microsoft Excel for a simple regression of company XYZ weekly stock prices vs. the S&P500; stock index, for the period January 2004 through August 2006. Consider XYZ's stock price to be the dependent variable and the S&P500; index to be the independent variable SUMMARY OUTPUT Regression Statistics Multiple FR R Square Adjusted R Standard Error Observations 0.93 0.87 0.86 3.20 140 ANOVA MS Significance Regression Residual Total 9067.88 9067.8 884.90 6.84E-62 138 139 XXXXXX 10.24 10482.0 Intercept S&P500; Index Coefficients Standard tStat P-value Lower 95% U 4.914 -24.74 1.37E-52 0.004 29.75 6.84E-62 121.65 0.122 131.37111.93 0.13 0.11

Explanation / Answer

In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (SSE), is the sum of the squares of residuals (deviations predicted from actual empirical values of data). ... A small RSS indicates a tight fit of the model to the data.

Here,

SSE/138 = 10.24

Thus, SSE = 1414

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