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About the content
Regression Analysis is the most common statistical modeling approach used in data analysis and it is the basis for more advanced statistical and machine learning modeling.
In this course, you will be given fundamental grounding in the use of widely used tools in regression analysis. You will learn the basics of regression analysis such as linear regression, logistic regression, Poisson regression, generalized linear regression and model selection.
Throughout this course, you will be exposed to not only fundamental concepts of regression analysis but also many data examples using the R statistical software. Thus by the end of this course, you will also be familiar with the implementation of regression models using the R statistical software along with interpretation for the results derived from such implementations.
This course is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.
- Basics of regression analysis such as linear regression, generalized linear regression and model selection
Fundamental grounding in the use of some widely used tools, but much of the energy of the course is focus on individual investigation and learning.
Prerequisite
A sound familiarity with undergraduate or graduate statistics and probability but also basic programming proficiency, linear algebra and basic calculus.
Syllabus
Weeks 3-4: Introduction to the Analysis of Variance (ANOVA) Model with data examples
Weeks 5-8: Introduction to most popular regression model: Multiple Linear Regression with data examples
Weeks 9-11: Introduction to Logistic Regression and Poisson Regression within the more general regression approach, generalized linear model, with data examples
Weeks 12-14: Introduction to multiple approaches to variable selection illustrated with an extensive data analysis example
Instructors
Nicoleta Serban
Associate Professor
Georgia Institute of Technology
Content Designer
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