Multiple and Logistic Regression in R
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
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Course Description
In this course you'll take your skills with simple linear regression to the next level. By learning multiple and logistic regression techniques you will gain the skills to model and predict both numeric and categorical outcomes using multiple input variables. You'll also learn how to fit, visualize, and interpret these models. Then you'll apply your skills to learn about Italian restaurants in New York City!
- 1
Parallel Slopes
FreeIn this chapter you'll learn about the class of linear models called "parallel slopes models." These include one numeric and one categorical explanatory variable. - 2
Evaluating and extending parallel slopes model
This chapter covers model evaluation. By looking at different properties of the model, including the adjusted R-squared, you'll learn to compare models so that you can select the best one. You'll also learn about interaction terms in linear models. - 3
Multiple Regression
This chapter will show you how to add two, three, and even more numeric explanatory variables to a linear model. - 4
Logistic Regression
In this chapter you'll learn about using logistic regression, a generalized linear model (GLM), to predict a binary outcome and classify observations. - 5
Case Study: Italian restaurants in NYC
Explore the relationship between price and the quality of food, service, and decor for Italian restaurants in NYC.
Collaborators
Nick SolomonPrerequisites
Correlation and Regression in R
Ben Baumer
Assistant Professor at Smith College
Ben is an Assistant Professor in the Statistical & Data Sciences Program at Smith College. He completed his Ph.D. in Mathematics at the Graduate Center of the City University of New York in 2012. He is an Accredited Professional Statistician™ by the American Statistical Association and was previously the Statistical Analyst for the Baseball Operations department of the New York Mets.
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