1. Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and Analysis of Variance (ANOVA) Sum of squares and least squares criterion ANOVA as a type of regression Main effects and interactions Evaluating null hypotheses about model as a whole and about the individual coefficients Day 5. Logistic regression

2. Covariance and correlation Covariance: the amount of shared variance between paired variables describing same set of observations Correlation (Pearson’s product moment): (written r) covariance standardized to the range of each of the two variables ranges between 1 (for perfect association in same direction) and -1 (for perfect association in opposite direction) r 2 ranges between 0 (no shared variance) and 1 (for completely overlapped variance)

3. Regression Simple linear regression Like correlation, except differentiate functions of two variables, so that … one of the variables is the independent variable (which is used to predict variation) other is the dependent variable (it’s variation is being predicted) R 2 measures efficacy of model (like the squared correlation coefficient) Multiple linear regression Uses two or more independent variables to predict variance of dependent variable

4. Analysis of Variance (ANOVA) A family of subtypes of linear regression where all of the independent (predictor) variables are nominal variables related independent variables can be grouped into factors R 2 measures efficacy of model (as in other linear regression models)