Covers regression analysis and introduction to linear models. Topics include point estimation, confidence intervals, hypothesis testing, simple linear regression, multiple regression, analysis of covariance, and non-linear regression. The course uses statistical software and emphasizes hands-on applications to data sets from a variety of settings.
Advanced presentation of statistical methods for comparing populations and estimating and testing associations between variables. Topics include point estimation; confidence intervals; hypothesis testing; ANOVA models for 1, 2, and k way classifications; multiple comparisons; chi-square test of homogeneity; Fisher's exact test; McNemar's test; measures of association, including odds ratio, relative risks, Mantel-Haenszel tests of association, and standardized rates; repeated measures ANOVA; simple regression; and correlation.