1. Which set of data would probably show a strong negative linear correlation?

• Heights volleyball players can jump and the strength of their leg muscles
• Number of people at a water park and the air temperature
• Scores on a mathematics test and the number of hours spent studying for it
• Resale values of computers and their ages

2. A set of data with a correlation coefficient of -0.55 has a

• strong negative linear correlation
• moderate negative linear correlation
• weak negative correlation
• little or no linear correlation

3. As the hot chocolates sales increase as does the number of people skating increase as well. This is an example of what type of causal relationship?

• cause and effect
• presumed
• common factor
• accidental

4. Mr. Ravesi conducted a survey and determined that students who missed more classes tended to achieve lower grades. The relationship had a moderately strong correlation. Which of the following is most likely to be the value of the correlation coefficient?

• 0.75
• 0.95
• - 0.25
• - 0.75
• none of these

5. Which describes a situation that is NOT Cause and Effect?

• the current world price of crude oil and the price of gasoline at the pump
• parents' educational level and their children's success in school
• intensity of a person's exercise and their heart rate
• the pace of the runner and the time taken to finish the race
• all of these are cause and effect relationships

6. Using a line-of-best-fit equation to predict values between actual data points is an example of:

• residuals
• extrapolation
• interpolation
• sampling

7. The equation of the line-of-best-fit to predict the mark (out of 100) on a test, M, versus the number of hours you studied, h, is found to be M = 10.5h + 40. What mark do you predict someone would earn if they studied for 4 hours?

• 50.5%
• 82%
• 84%
• 92%

8. Which set of data would probably show a strong positive linear correlation?

• Marks on a history test and the heights of the students.
• Height of corn in a field and the amount of precipitation during the growing season.
• The colour of cars sold and the annual income of the car buyers.
• The number of defective light bulbs produced and the time of the day when they were manufactured.