Section 9.2: Correlation

1. State what it means, loosely, for two quantitative variables to be associated.
2. Interpret a scatter plot of two quantitative variables.
3. State the trend of two quantitative variables given a scatter plot.
4. State the strength of the trend of two quantitative variables given a scatter plot.
5. Recognize the names linear correlation coefficient, Pearson correlation coefficient, and ‘the’ correlation coefficient as synonyms.
6. Identify the notation used for the sample correlation \(r\) and the population correlation \(\rho\) (the lowercase Greek letter rho).
7. State the range of values that the sample correlation \(r\) and the population correlation \(\rho\) can take, and what those values correspond to in terms of the presence / absence of a linear trend in the sample data.
8. State when the sampling distribution used for hypothesis testing and confidence intervals is appropriate for a given data set.
9. State the null and alternative hypotheses for a claim about the correlation between two outcomes in a population.
10. Use the \(P\)-value provided by Minitab to test a claim about the correlation between two outcomes in a population.
11. Perform the hypothesis testing routine in the right column of page 334 of Triola & Triola for a claim about a population correlation.
12. Interpret a confidence interval, like the one provided by this web applet, in terms of the correlation between two outcomes in a population.
13. Distinguish between correlative and causative statements about two outcomes, and give examples of correlative statements that do not imply causative statements.