1. Review Homework

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

3. Residual (or error)
Observed y MINUS predicted y

4. Determines the effectiveness of the regression model
Analyzing Residuals
Determines the effectiveness of the regression model

5. A scatterplot of Residuals vs. X
Residual Plots
A scatterplot of Residuals vs. X

6. Residual Plots Determine
If it 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

7. Example of Random Scatter

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

9. Linear model appropriate or inappropriate?

10. The only way to know is to see the residual plot.
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.

11. Linear model appropriate or inappropriate?

12. The only way to know is to see the residual plot.
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.

13. Linear model appropriate or inappropriate?

14. The only way to know is to see the residual plot.
1. Does their appear to be a pattern in the residual plot?
no, it seems that there is a pattern BUT it has an outlier
2. Does this support your original guess?
Yes-the linear model is appropriate

15. Example: Calculate Residual Tracking Cell Phone Use over 10 days
Total Time (minutes)
Total Distance (miles
Predicted Total Distance
Residuals
(observed – predicted) 32 51
54.4
-3.4 19 30
31.9 28 47 36 56 17 27 23 35 41 65 22 37 73 54
Data from TI Activity for NUMB3RS Episode 202

16. Example: Calculate Residual Tracking Cell Phone Use over 10 days
Total Time (minutes)
Total Distance (miles
Predicted Total Distance
Residuals
(observed – predicted) 32 51
54.4
-3.4 19 30
31.9
-1.9 28 47
47.5
-0.5 36 56
61.3
-5.3 17 27
28.5
-1.5 23 35
38.8
-3.8 41 65
70.0 -5 22
37.1
3.9 37 73
63.1
9.9 54
6.5
Data from TI Activity for NUMB3RS Episode 202

17. Good fit or not?

18. Classwork
Carnival Task

19. Homework
Worksheet