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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4 Hours19 Videos59 Exercises38,333 Learners
4250 XP

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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. 1

    Parallel Slopes

    Free

    In this chapter you'll learn about the class of linear models called "parallel slopes models." These include one numeric and one categorical explanatory variable.

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    What if you have two groups?
    50 xp
    Fitting a parallel slopes model
    100 xp
    Reasoning about two intercepts
    50 xp
    Visualizing parallel slopes models
    50 xp
    Using geom_line() and augment()
    100 xp
    Interpreting parallel slopes coefficients
    50 xp
    Intercept interpretation
    50 xp
    Common slope interpretation
    50 xp
    Three ways to describe a model
    50 xp
    Syntax from math
    100 xp
    Syntax from plot
    100 xp
  2. 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.

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Datasets

Average SAT scores by stateNew York City Zagat restaurant reviews

Collaborators

Nick Solomon
Ben Baumer Headshot

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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