Introduction to Modeling

In this mission, you’ll learn about the fundamentals of building models with R. We’ll cover concepts, principles, and rules that will be critical not only for the remainder of this Linear Modeling in R course, but for all of your future work modeling with R.

This mission starts with the basics: what is a model? Why are models useful in data science? What kinds of questions can different sorts of models help us answer?

Then, we’ll take a closer look at modeling in R and start digging into the specifics. There are two primary goals we might have for a model: predicting the future (prediction model) or understanding how different variables are affecting a result (inference model). In this course, we’ll dig deeper into both types of model, the concepts that underlie them, and how we can approach building both predictive and inference models that are statistically sound.

Objectives

• Understand what a model is and why we model
• Learn the concepts underlying prediction and inference models

Lesson Outline

1. Introduction
2. Motivation
3. General Model Form
4. Prediction
5. Inference
6. Error
7. Estimating f with Parametric Models
8. Residuals
9. Comparing Model Predictions to Reality
10. Recap
11. Takeaways