Synchronous: Applied Linear Regression

Monday, June 20, 2022 - Friday, June 24, 2022 - 1:30 PM - 5:30 PM

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Course Description

This course will provide an introduction to the basics of regression analysis. The class will build on the material from the Introduction to Biostatistics course and it will systematically cover correlation, simple and multiple regression models, omnibus and partial F-test, understanding of interaction terms and interpretation, using dummy coding for categorical variables, regression diagnostic and model selection tools. Finally, we will end by exploring polynomial, and logistic regression models. The course will describe the theory and underlying assumptions of linear regression models.


Course Objectives

By the end of the course, students will be able to:

  • Describe the theory behind and underlying assumptions of linear regression models
  • Assess the use of regression analysis techniques in the context of experimental and sampling designs
  • Apply regression techniques to analyze real data and interpret the results in a meaningful context for application by public health practitioners
  • Conduct regression analyses and basic programming in SAS
  • Evaluate computer output containing statistical procedures and graphics and interpret output for use in a public health context
  • Apply diagnostic tools to assess the validity of regression models
  • Employ variable selection and model building strategies in linear regression models

 


Prerequisites

Material covered in the Introduction to Biostatistics episummer@columbia course or general knowledge of hypothesis test and confidence intervals.


Course Reading List

There are no required readings but the following textbook is suggested if needed:

Applied Regression Analysis and Other Multivariable Methods, Fourth Edition

ISBN: 0495384968

Publisher: Cengage Learning

Author(s): Kleinbaum, Kupper, Nizam, and Muller

Publication Date: April 23, 2007

Additional material for the course will be provided to the students as needed.

 


Instructor


Martina Pavlicova, PhD, MS

Dr. Martina Pavlicova graduated with PhD from the Statistics Department at the Ohio State University, with a primary focus on spatial statistics and statistical issues when analyzing functional magnetic resonance imaging. Currently, her research interests include spatial statistics, statistical issues when analyzing functional magnetic resonance imaging, multiple comparison problems, clinical trials, generalized longitudal mixed effect statistical models, zero-inflated models, and analysis of categorical data. She is also interested in new methods of teaching statistics and biostatistics and developing new teaching approaches for non-statistics master and PhD students. Additionally, Dr. Pavlicova serves as senior biostatistician on several clinical trials in psychiatry.



Course Fee

Early registration discount before April 1, 2022: $900.00
After April 1, 2022: $1,000.00

 

 


Register



Location

Synchronous Course

 

 

The Zoom link for this live webinar course will be made available to course registrants prior to the start of class.


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