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  3. Generalized Linear Models in R
  • 1

    GLMs, an extension of your regression toolbox

    Free

    This chapter teaches you how generalized linear models are an extension of other models in your data science toolbox. The chapter also uses Poisson regression to introduce generalize linear models.

  • 2

    Logistic Regression

    This chapter covers running a logistic regression and examining the model outputs.

  • 3

    Interpreting and visualizing GLMs

    This chapter teaches you about interpreting GLM coefficients and plotting GLMs using ggplot2.

  • 4

    Multiple regression with GLMs

    In this chapter, you will learn how to do multiple regression with GLMs in R.

Initializing

Exercise

Default trend lines

ggplot2 gives us powerful tools to visualize our data and "see" what is going on. However, sometimes we need to wrangle our data to give us what we need. During this exercise, you will look and see if the length of commutes changes the chance somebody rides the bus. You will do this by changing the points to be non-overlapping (or jittered) using geom_jitter() and adding smoothed trend line with geom_smooth().

Instructions

100 XP
  • Using the data.frame bus, plot MilesOneWay on the x-axis and Bus2 on the y-axis.
  • Jitter the width of the data by 0 and the height by 0.05.
  • Label the axes on the plot. Use "Probability of riding the bus" on the y-axis and "One-way commute trip (in miles)" on the x-axis.
  • Save this plot as gg_jitter then add geom_smooth()