COURSE DESCRIPTION
Modern multivariable statistical analysis based on the concept of generalized linear models which includes linear, logistic, and Poisson regression, survival analysis, fixed-effects analysis of variance and repeated measures analysis of variance. This course emphasizes the underlying similarity of these methods, the choice of the right method for specific problems, common aspects of model construction, the testing of model assumptions through influence and residual analyses, and the use of graphical and other methods to present results that are readily understood by health researchers. This is a second course in biostatistics, covering multi-predictor methods, including exploratory data analysis and multiple regressions (linear and logistic). The third course (KUI: 77814) will cover more details on categorical data (logistic and log linear modeling) and survival analysis (time to event issues). Emphasis is on the practical and proper use of statistical methodology and its interpretation. The statistics package STATA will be used throughout the course. Student interests on analyzing a big data set (i.e. IDHS or SUSENAS) they are suggested to take fourth course (KUI: 77815).
GOALS AND COURSE OBJECTIVES
The goal of this course is providing knowledge and skill of the students for analyzing of data using a multivariable technique. At the end of the course, students will be able to:
- compare the roles of descriptive versus inferential statistics,
- assess characteristics of the problem to help choose the appropriate analytic technique,
- compare techniques appropriate for handling a single outcome variable and multiple predictors,
- evaluate data limitations and their consequences, and
- evaluate the results of analysis of data using a multivariable or multi-predictors.