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  3. Modeling with Data in the Tidyverse
  • 1

    Introduction to Modeling

    Free

    This chapter will introduce you to some background theory and terminology for modeling, in particular, the general modeling framework, the difference between modeling for explanation and modeling for prediction, and the modeling problem. Furthermore, you'll start performing your first exploratory data analysis, a crucial first step before any formal modeling.

  • 2

    Modeling with Basic Regression

    Equipped with your understanding of the general modeling framework, in this chapter, we'll cover basic linear regression where you'll keep things simple and model the outcome variable y as a function of a single explanatory/ predictor variable x. We'll use both numerical and categorical x variables. The outcome variable of interest in this chapter will be teaching evaluation scores of instructors at the University of Texas, Austin.

  • 3

    Modeling with Multiple Regression

    In the previous chapter, you learned about basic regression using either a single numerical or a categorical predictor. But why limit ourselves to using only one variable to inform your explanations/predictions? You will now extend basic regression to multiple regression, which allows for incorporation of more than one explanatory or one predictor variable in your models. You'll be modeling house prices using a dataset of houses in the Seattle, WA metropolitan area.

  • 4

    Model Assessment and Selection

    In the previous chapters, you fit various models to explain or predict an outcome variable of interest. However, how do we know which models to choose? Model assessment measures allow you to assess how well an explanatory model "fits" a set of data or how accurate a predictive model is. Based on these measures, you'll learn about criteria for determining which models are "best".

Initializing

Exercise

Computing the R-squared of a model

Let's compute the \(R^2\) summary value for the two numerical explanatory/predictor variable model you fit in the Chapter 3, price as a function of size and the number of bedrooms.

Recall that \(R^2\) can be calculated as:

$$1 - \frac{\text{Var}(\text{residuals})}{\text{Var}(y)}$$

Instructions

100 XP

Compute \(R^2\) by summarizing the residual and log10_price columns.