Ch. 11 R.M.S Error for Regression error = actual – predicted = residual RMS(error) for regression describes how far points typically are above/below the.

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Presentation transcript:

Ch. 11 R.M.S Error for Regression error = actual – predicted = residual RMS(error) for regression describes how far points typically are above/below the regression line. Calculating RMS(error)=square root(sum of the square errors divided by the total number of values).

68% of the points should be 1 RMS(error) from the regression line 95% of the points should be 2 RMS(error)s from the regression line Examples (Ch.11 Set A #4, 5, 7 p. 184) RMS(error) for regression line of y on x is the square root(1-rsquared)xSDy. (Use the SD of the variable being predicted.)

Special cases of RMS(error) for different values of r. Residual plots for single variable and scatter diagrams Homoscedasticity (football-shaped scatter diagram) Heteroscedasticity: different scatter around the regression line If a scatter diagram is football-shaped, take the points in a narrow vertical strip and they will be away from the regression line by amounts similar to the RMS(error).

Examples (Ch.11 Set D #3 p.193) In order to use the normal approximation, the scatter diagram should be football- shaped with points thickly scattered in the center and fading at the edges.