Methods for Joint Modeling of Longitudinal and Health Outcome Data, Bayesian Hierarchical Modeling, Mixture Modeling, Functional and Imaging Data Analysis, Kernel Machine Regression/Classification, Bayesian Support Vector Machine.
Simple linear regression analysis, inference on regression parameters, residual analysis, prediction intervals, weighted least squares. Multiple regression analysis, inference about regression parameters, multicollinearity and its effects, indicator variables, selection of independent variables. Non-linear regression. Prerequisite: STAT 266, or STAT 235 with consent of the Department.Fall Term 2020
Methods of data analysis useful in applied research, including repeated measures and longitudinal data analysis, non-linear regression, survival analysis, multivariate techniques. Applications to real data will be emphasized, including case studies and real data applications. Each researcher works on a project to present, highlighting the methods used in the project. Prerequisite: STAT 252 or 337 or consent of the instructor.Fall Term 2020
Data analysis, problem solving, oral communication with clients, issues in planning experiments and collecting data; practical aspects of consulting and report writing. Corequisite: STAT 568 and 578 or their equivalents.Winter Term 2021