- Use the following dataset for questions 1a and 1b: (2pts)
| Hours of Studying | GPA |
| 5 | 2.50 |
| 24 | 2.65 |
| 7 | 2.70 |
| 22 | 2.80 |
| 16 | 4.00 |
| 10 | 3.00 |
| 20 | 3.00 |
| 14 | 3.60 |
| 18 | 3.50 |
- Does the relationship between hours of studying per week and GPA, based on your reading of this dataset, appear to be direct/positive, indirect/negative, or curvilinear? How did you know? (Try and figure out if high numbers on “X” associate with high numbers on “Y” or low numbers on “Y”, that might give you a clue.) (Hint: look at the ‘When correlations lie’ ppt slides)
- Based on your answer from above, what conclusion can you make about the relationship between hours of studying and GPA? What would you tell an incoming class of freshman at ASU given these results?
- Indicate (by bolding or highlighting) which correlation coefficient in each of the following pairs is stronger. (Hint: you should bold or highlight four times) (4pts)
- .15 or -.15
- .63 or .55
- -.88 or -.50
- -.90 or .95
- One of the researchers working for you at the tech company delivers some data you asked for about associations between consumers and their likelihood of purchasing a new tablet. The two variables of particular interest are: 1) hours per day spent on technology and 2) likelihood of purchasing a new tablet. The researcher tells you that the Pearson Product Moment Correlation Coefficient is .45 for these two variables and is significant at the p < .05 level. You have a briefing with management now, and they don’t want to hear the “statistics jumble” that they pay you to sort through. What number or indicator could you present to them that would clearly explain this relationship? Consider how researchers interpret an r statistic and what we learned was the most precise method of doing so. (2pts)
- If I say two things are negatively correlated, explain (in words) what that means. (1pt)
- Inspired by your presentation on correlations, management begins scouring research reports for other associations between variables and buying likelihood. They come across a rather shaky report, which indicates that the number of checking and savings accounts a person is strongly correlated (or, as they’ve been saying, “causes”) with whether or not they will buy a new tablet. Management now wants to ask the board about getting into a small banking operation, so as to get more folks to buy the new tablet. You recognize the lunacy of this plan, and need to stop them. What do you tell them? (1pts)
