This course provides graduate students with a foundation in applied regression analysis techniques used in health, social, and behavioral sciences. The major topics covered in this course are univariate/multivariate linear regression, advanced data screening, model building, variable selection, regression diagnostics, univariate/factorial analysis of variance, and repeated measures design analysis of variance. Conceptual understanding and critical evaluation of statistical models is emphasized. Students will gain practical experience in using statistical software packages for the analysis of data.
Outcomes: Upon successful completion of the course, the student will be able to: 1) Apply general linear modeling techniques in answering research questions; 2) Analyze bivariate and multivariate associations; 3) Interpret results of statistical testing using general linear modeling techniques; 4) Understand the process of model building and variable selection; 5) Evaluate assumptions of general linear models; 6) Apply regression diagnostic techniques.
Outcomes: Upon successful completion of the course, the student will be able to: 1) Apply general linear modeling techniques in answering research questions; 2) Analyze bivariate and multivariate associations; 3) Interpret results of statistical testing using general linear modeling techniques; 4) Understand the process of model building and variable selection; 5) Evaluate assumptions of general linear models; 6) Apply regression diagnostic techniques.