1. Stat 100 Feb. 6

2. What to do Read Ch. 10, Try 1-10 Read Ch. 11, Try 1-3, 7-11

3. Case Study Relationship between amount of electronic media use and weight concerns in teenage girls. Study of n=837 ninth grade girls in San Jose, CA

4. What the researchers did Surveyed girls on weight concerns, appearance appearance concerns, actual weight, and amount of time spent watching music videos

5. Conclusion was … Frequent music video watching may be a risk factor for increased weight concerns. Researchers reported a “statistically significant” correlation between weight concerns variable and time spent watching music videos

6. Statistical Significance of a Correlation Sample data are evidence the correlation is not 0 in the larger population

7. In the San Jose study.. For weight concern variable and time watching videos, r=.08 Scatter plot would be quite scattered and trend would be nearly horizontal

8. Example of r =.08

9. How could such a weak correlation be “significant?” Sample size affects statistical significance With a big sample, a weak correlation may achieve the label “statistically significant” This only means there’s enough evidence to say the true correlation is not 0. That’s not saying much.

10. Does sample represent the larger population of teen girls in U.S.? A sample from San Jose may not represent all girls in country. Also, sample was restricted to 9th graders Also, ethnic mix was different from national mix.

11. Does watching music videos lead to increased weight concerns? Probably not. Observed correlation was weak. Sample may not be representative And, there was confounding. Heavier girls tended to watch more music videos. These girls have more weight concerns.

12. Possible interpretations of observed relationships Changes in explanatory variable cause changes in response variable Changes in response variable cause changes in explanatory variable There’s a “cause and effect” relationship but confounding variables complicate things The observed relationship is caused entirely by a third variable

13. Example 1 A study reports that older men who walk one mile per day have less cardiovascular disease than men who don’t walk that much What are some interpretations of this result?

14. Example 2 Teacher salaries and beer sales for each year from 1950 to 2000 show a positive correlation What’s the interpretation?

15. Example 3 Number of deaths from automobile accidents and beer sales for each year from 1950 to 2000 show a positive correlation What’s the interpretation?

16. Example 4 In a randomized experiment, teenagers with high blood pressure were randomized to three different dose levels of a calcium supplement Those using the highest level showed the biggest drop in blood pressure What’s the interpretation?