Regression Modeling



E.g., 2016-10-24
E.g., 2016-10-24
E.g., 2016-10-24
Oct 21st 2016

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Average: 6.1 (27 votes)
Aug 15th 2016

This course provides theoretical and practical training on the increasingly popular logistic regression model, which has become the standard analytical method for use with a binary response variable. This is a hands-on, applied course where students will become proficient at using computer software to analyze data drawn primarily from the fields of medicine, epidemiology, and public health.

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