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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.
- Week 1 - Introduction to Regression
This session starts where the Data Analysis Tools course left off. This first set of videos provides you with some conceptual background about the major types of data you may work with, which will increase your competence in choosing the statistical analysis t...
- Week 2 - Basics of Linear Regression
In this session, we discuss more about the importance of testing for confounding, and provide examples of situations in which a confounding variable can explain the association between an explanatory and response variable. In addition, now that you have statis...
- Week 3 - Multiple Regression
Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variab...
- Week 4 - Logistic Regression
In this session, we will discuss some things that you should keep in mind as you continue to use data analysis in the future. We will also teach also you how to test a categorical explanatory variable with more than two categories in a multiple regression anal...
Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.
Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.