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Map of Torsaker on Determining the fit of the model. R2 (pronounced are squared) is one measure for how well the regression model fits the data. R2 is calculated by taking the Total Sum of Squares (TSS), which is the total variation in the dependent variable around its mean without estimating the regression [TSS=S(y µy)2 where µy is the average of y], and compared it to the errors explained by the regression: econometrics 223 R2 = 1 ESS/TSS where ESS is the sum of the squared errors. R2 can range between 0 and 1. The higher the value of R2, the better the fit of the model. One drawback of R2 is that it does not give an objective answer to the question Is this a good model? The F statistic performs a test of the null hypothesis that the estimated regression does no better than a simple average: F = R2/(k-1) (1-R2)/(n-k) If the calculated F value is greater than the critical value found by computer software (such as SAS or Excel) or found in statistical tables of the F distribution, then one can reject the null hypothesis that the independent variables provide no explanation of the dependent variables. The alternative is that at least one variable is statistically significant. Map of Torsaker 2016.

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Map of Torsaker

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Map of Torsaker

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