An introductory statistics course covering descriptive statistics, probability, random variables, estimation, confidence intervals, and hypothesis testing. Introduction to statistical computing. A course in applied linear modeling. Emphasis on the theory and practice of multiple regression and analysis of variance, with extensions to path analysis and other simultaneous equation methods. An applied graduate level course in multiple regression and analysis of variance for students who have compelted an undergraduate course in basic statistical methods.
Emphasis is on practical methods of data analysis and their interpretation.
Primarily for doctoral students in the managerial, behavioral, social and health sciences. An applied graduate level course in multiple regression and analysis of variance for students who have completed an undergraduate course in basic statistical methods. Emphasis is on practical methods of data analysis and their interpretation. Primarily for doctoral students in the managerial, behavioral, social and health sciences. This course is designed to provide a working knowledge of the fundamental concepts of biostatistics.