2. From the Carnegie Foundation math.mtsac.edu/statway/lesson_3.3.1_version1.5A

3.  Observed y MINUS predicted y

4.  Determines the effectiveness of the regression model

5.  A scatterplot of Residuals vs. X

6.  If the model is appropriate, then the plot will have a random scatter.  If another model is necessary, the plot will have a pattern. Pattern = Problem

8. Determine, just by visual inspection, if the linear model is appropriate or inappropriate.

10. 1. Does their appear to be a pattern in the residual plot? Yes, quadratic. 2. Does this support your original guess? You must now see that a linear model does NOT fit this data.

12. 1. Does their appear to be a pattern in the residual plot? Yes, it fans out as x increases. 2. Does this support your original guess? You must now see that a linear model does NOT fit this data.

14. 1. Does their appear to be a pattern in the residual plot? Yes, it looks quadratic. 2. Does this support your original guess? This was very tricky. The scale was very small. You must now see that a linear model does NOT fit this data.

16. 1. Does their appear to be a pattern in the residual plot? Yes, it seems decrease as x increases. 2. Does this support your original guess? This was tricky. You must now see that a linear model does NOT fit this data.

17. Total Time (minutes) Total Distance (miles) Predicted Total Distance Residuals (observed – predicted) 325154.4-3.4 193031.9 2847 3656 1727 2335 4165 2241 3773 2854

18. Total Time (minutes) Total Distance (miles) Predicted Total Distance Residuals (observed – predicted) 325154.4-3.4 193031.9 -1.9 2847 47.5-0.5 3656 61.3-5.3 1727 28.5-1.5 2335 38.8-3.8 4165 70.0-5 2241 37.13.9 3773 63.19.9 2854 47.56.5

20. Carnival Task

21. Worksheet