Course
Modeling with Data in the Tidyverse
IntermediateSkill Level
Updated 09/2022RProbability & Statistics4 hr17 videos49 Exercises3,900 XP27,103Statement of Accomplishment
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Prerequisites
Data Manipulation with dplyr1
Introduction to Modeling
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".
Modeling with Data in the Tidyverse
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FAQs
What type of modeling does this course focus on?
The course centers on linear regression, one of the most commonly used modeling approaches, covering both basic single-variable and multiple regression with the tidyverse.
What datasets are used for hands-on practice?
You will model teaching evaluation scores for University of Texas instructors and house prices from the Seattle metropolitan area housing market.
Does the course explain the difference between explanatory and predictive modeling?
Yes. Chapter 1 introduces both approaches, explaining when you want to understand why something happens versus when you want to predict future outcomes.
What R prerequisites are needed?
You need Data Manipulation with dplyr and Introduction to the Tidyverse. These provide the core data wrangling and visualization skills used throughout.
How will I learn to choose between different models?
Chapter 4 covers model assessment measures that evaluate how well models fit data or predict outcomes, giving you criteria to determine which model is best.
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