R Tutorial: Background on modeling for explanation

Want to learn more? Take the full course at https://learn.datacamp.com/courses/modeling-with-data-in-the-tidyverse at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello, welcome to the next course in DataCamp's "Learn the tidyverse" track: "Modeling with data in the tidyverse". In this course, you'll leverage the data wrangling and visualization toolbox you developed in previous courses to learn about modeling. The ideas behind modeling are crucial to many fields, including statistics, causal inference, machine learning, and artificial intelligence. You'll start by equipping yourself with some theory and terminology related to modeling. In Chapters 2+3, you'll learn one of the most widely used techniques for modeling: linear regression. You'll end by assessing the quality of models. For example, -how well does a model fit given data? OR -how good are a model's predictions? Let's start with the general modeling framework as expressed by following formula where you have -y, an outcome variable, the phenomenon you wish to model -x, a set of explanatory or predictor variables used to inform your model. The arrow on the x indicates that x can be a vector, in other words a series of values. -f, a mathematical function making explicit the relationship between y and x. f(x) is also called the "signal" -and finally epsilon, an unsystematic error component. epsilon is also called the noise. Let's first focus only on y and x, and revisit f and epsilon later. Previously I called x both explanatory and predictor variables. Which term you use when roughly depends on which modeling scenario you're addressing: -If you want to explain what factors are associated with or cause the outcome variable, you are "modeling for explanation" and thus x are "explanatory" variables. -If you want to make predictions of the outcome variable, you are "modeling for prediction" and thus x are "predictor" variables. Let's st