coefficient of determination correlation coefficient direct relationship inverse relationship negative relationship outcome variable partial correlation Pearson correlation coefficient perfect relationship positive relationship predictor variable r2 | an effect size that reveals the percentage of variability in one variable that is accounted for by the other variable. the variable in a relationship test, Y, that is predicted from the other variable, X. Sometimes called the dependent variable. a relationship in which high scores on X are associated with low scores on Y. Also called a negative relationship. a relationship in which high scores on X are associated with high scores on Y. Also called a positive relationship. a relationship in which high scores on X are associated with low scores on Y. a relationship between two variables in which the value of one can be exactly predicted from the other. the variable in a relationship test, X, that is used to predict the other variable, Y. Sometimes called the independent variable. a statistic that summarizes, in a single number, the strength of a relationship between two variables. formal name for the effect size r2. a relationship in which high scores on X are associated with high scores on Y. a correlation between two variables from which the influence of a third variable has been mathematically removed. a statistical test that measures the degree of linear relationship between two interval/ratio-level variables. |
Is there a relationship between how long a person’s foot is, in inches, and how tall, in inches, he or she is. Connect with ten friends, ask them their heights, and then measure how long their feet are. Which variable is X and which is Y? Why did you make that decision? Compute a Pearson r. Is there a relationship? Is it strong? (Be sure to keep track of who is in your sample and who isn’t. That answer will be important in Chapter 14’s DIY.)