1. Quick Start Expectations 1.Fill in planner and HWRS HW: p.100, #6-8, 19, 20, 25 2.Get a signature on HWRS 3.On desk: calculator, journal, HWRS, pencil, pen 4.Warm Up: next slide… back of HWRS

2. Warm Up 1). 5n – 9 = -16 + 6n2.) 5x – 5 = 3x – 9 3.) 7 - 2x = x - 14

6. A correlation of 1 means that there is a perfect linear relationship between two variables with a positive slope.

7. 0.4 -0.4-0.80.0 0.8

9. Yes. These data points are tightly clustered in an upward sloping linear trend, so a linear model would give accurate predictions.

10. The correlation coefficient is closest to r = 1.

11. Yes. As a roller coaster drops, its speed increases. So, the longer the drop is, the faster the coaster will travel. Coasters with longer drops will be able to achieve greater speeds.

13. The points are less tightly clustered in an upward sloping trend, so a linear model is going to be less reliable as a predictor of top speed for a given track length.

14. The correlation coefficient is closest to r = 0.5.

15. No. A coaster could have a long track, but height doesn’t change much. Without large drops, coasters would not achieve high speeds.

18. The points are less tightly clustered than in question C, so a linear model will not be a reliable predictor of top speed for a given ride time.

19. The correlation coefficient is closest to r = 0 … possibly 0.5

20. No Outlier No Outlier No

21. Answers vary… (2800, 120) (1300, 100)

23. These data points are tightly clustered in a downward sloping linear trend, so a linear model would give accurate predictions of number of riders based on rider age.

24. No. Although the data have a correlation, the variables do not have a causal relationship.

25. 4. Are any of the data points outliers? If so, estimate the coordinates of those points. The correlation coefficient is closest to r = -1 (14, 45) (16, 60) (17, 70) (72, 2) (77, 2) (80, 1)

26. Yes, it is possible to have a strong correlation coefficient that indicates a strong relationship even when there are a few outliers.