1. 1. What is the probability that a randomly selected person is a woman who likes P.E.? 2. Given that you select a man, what is the probability that he likes Social Studies? 3. What is the probability that you select a person who likes Math? 8/50 =.16 10/20 =.50 18/50 =.36

2. Correlation, Linear Regression, & Exponential Regression

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

4. Residual is another word for ERROR

5. To find the residual you take the ACTUAL data and SUBTRACT the PREDICTED data.

6.  Determines the effectiveness of the regression model

7. A residual plot is another type of SCATTERPLOT that shows the relationship of the residual to the x value.

8.  If it the regression model is appropriate, then the residual plot will have a RANDOM scatter.  If the residual plot creates a pattern then the regression model is NOT A GOOD FIT. Pattern = Problem

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

12. 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.

14. 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.

16. 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.

18. 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.

19. 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

20. Total Time (minutes) Total Distance (miles) Predicted Total Distance Residuals (observed – predicted) 325154.4-3.4 193031.9 2847 47.5 3656 61.3 1727 28.5 2335 38.8 4165 70.0 2241 37.1 3773 63.1 2854 47.5

21. 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

22. Total Time Residual

23. Total Time Residual

24. Residuals Task – Carnival

25. Residuals CW worksheet