Introduce the concepts of positive, zero, and negative correlations with examples (eating and weight, the number of notes they take and the price of tea in China, number of suicidal thoughts and happiness). You can also have them learn about correlation coefficients by using the authors' tutorial called " The Correlator."

Once students understand the Pearson *r*, you could discuss the following three topics:

- The different types of correlation coefficients. These are summarized in table 7-4 (p. 191).
- The coefficient of determination. Begin by showing students that +1 and -1 relationships are perfect, so r-squared for each of them should be (and is) 1.00. Zero correlations indicate no relationship, so knowing one variable shouldn't help at all in knowing the other variable (and zero squared is zero). Having thus reviewed two essential points about correlation coefficients (#1 negative correlations are not weaker than positive correlations and #2 zero correlations indicate no relationship), go on to discuss the
*r*-squared values commonly found in psychology (.09 to .25). Explain that although these r-square's indicate that we fail to account for a great deal of the variability in human behavior, that's to be expected because (1) we have measurement error, and (2) we wouldn't expect a single variable (e.g., IQ) to account for all the variance in another variable (college performance). From this point, go on to debate the value of the SATs, the utility or futility of using*r*-squared to address the nature-nurture controversy (emphasizing the notions of correlation and causality and apparent size of effect and restriction of range); whether*r*-squared might be more informative than*p*values, or multiple regression.

- Multiple regression. Two useful references are

the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Kachigan, S. K. (1982). Multivariate statistical analysis: A conceptual introduction.

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