Research Methods: Regression Analysis with Python

In this course you will learn how to use Python with the most salient libraries to gain data driven insights into typical research problems from various disciplines. This course is not a complete beginners’ course, yet it allows you to enter with just some fairly-basic statistics knowledge. This course is also not meant to comprehensively cover programming and algorithms, yet we will introduce you to some important concepts and show how these can be applied for novel approaches to your data.

The course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using the freely available, easy-to-learn, yet powerful Python language, 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.

Topics: Introduction to Python, Introduction to Regression, Basics of Linear Regression, Mutiple Regression, Logistic Regression, Python