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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 Exercises39,856 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.

    Play Chapter Now
    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.

    Play Chapter Now
    Model fit, residuals, and prediction
    50 xp
    R-squared vs. adjusted R-squared
    100 xp
    Prediction
    100 xp
    Understanding interaction
    50 xp
    Thought experiments
    50 xp
    Fitting a model with interaction
    100 xp
    Visualizing interaction models
    100 xp
    Simpson's Paradox
    50 xp
    Consequences of Simpson's paradox
    50 xp
    Simpson's paradox in action
    100 xp
  3. 3

    Multiple Regression

    This chapter will show you how to add two, three, and even more numeric explanatory variables to a linear model.

    Play Chapter Now
    Adding a numerical explanatory variable
    50 xp
    Fitting a MLR model
    100 xp
    Tiling the plane
    100 xp
    Models in 3D
    100 xp
    Conditional interpretation of coefficients
    50 xp
    Coefficient magnitude
    50 xp
    Practicing interpretation
    50 xp
    Adding a third (categorical) variable
    50 xp
    Visualizing parallel planes
    100 xp
    Parallel plane interpretation
    50 xp
    Higher dimensions
    50 xp
    Interpretation of coefficient in a big model
    50 xp
  4. 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.

    Play Chapter Now
    What is logistic regression?
    50 xp
    Fitting a line to a binary response
    100 xp
    Fitting a line to a binary response (2)
    100 xp
    Fitting a model
    100 xp
    Visualizing logistic regression
    50 xp
    Using geom_smooth()
    100 xp
    Using bins
    100 xp
    Three scales approach to interpretation
    50 xp
    Odds scale
    100 xp
    Log-odds scale
    100 xp
    Interpretation of logistic regression
    50 xp
    Using a logistic model
    50 xp
    Making probabilistic predictions
    100 xp
    Making binary predictions
    100 xp
  5. 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.

    Play Chapter Now
    Italian restaurants in NYC
    50 xp
    Exploratory data analysis
    50 xp
    SLR models
    100 xp
    Incorporating another variable
    50 xp
    Parallel lines with location
    50 xp
    A plane in 3D
    100 xp
    Higher dimensions
    50 xp
    Parallel planes with location
    100 xp
    Interpretation of location coefficient
    50 xp
    Impact of location
    100 xp
    Full model
    50 xp
    Wrap-up
    50 xp

Datasets

Average SAT scores by stateNew York City Zagat restaurant reviews

Collaborators

Nick Solomon

Prerequisites

Correlation and Regression in R
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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