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R2 (R-Square) |
Coefficient of determination; indicates how much variation in the response is explained
by the model. The higher the R2
, the better the model fits your data. |
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Adjusted R-Square |
Accounts for the number of predictors in your model and is useful for comparing
models with different numbers of predictors. The formula is:
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1 -
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MS Error |
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SS Total / DF Total
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Sum of squares (SS)
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The sum of squared distances. SS Total is the total variation in the data. SS Regression
is the portion of the variation explained by the model, while SS Error is the portion
not explained by the model and is attributed to error.
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| Degrees of freedom (d.f.)
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Indicates the number of independent pieces of information involving the response
data needed to calculate the sum of squares. The degrees of freedom for each component
of the model are:
DF Regression = p
DF Error = n - p - 1
Total = n - 1
where n = number of observations and p = number of predictors.
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MS Regression |
Mean square regression. The formula is:
SS Regression
DF Regression
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MS Error
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Mean square error, which is the variance around the fitted regression line. MS Error = s2. The formula is:
SS Error
DF Error
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F |
If the calculated F-value is greater than the F-value from the F-distribution, then at least one of the coefficients is not equal to zero. The F-value is used to determine the p-value. The formula for the calculated F-value is:
MS Regression
MS Error
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Residuals
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The difference between the observed values and predicted values.
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