R Tutorial: The modeling problem 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. --- Now that you have some background on modeling, let's introduce the goal of modeling for explanation and use the evals teaching score data as an example. Recall that of the elements of the general modeling framework, we previously covered the the outcome variable y and explanatory/predictor variables x. Now let's first study f, which defines an explicit relationship between y and x, and then the error component epsilon. Let's address some points about the modeling problem. -Usually you won't know the true form of f nor the mechanism that generates the errors epsilon. -However you will know the observations y and x, as they are given in our data -Using y and x, the goal is to construct or "fit" a model f-hat that approximates the true f, but not epsilon. -In other words, you want to separate the signal from the noise. -With the fitted model f-hat, you can apply it to x to obtain fitted or predicted values of y called y-hat. In this course, you'll keep things simple and only fit models that are linear. But first, let's now perform an EDA of the relationship between the variables in our modeling for explanation example. Earlier you performed a univariate EDA on the outcome variable score and the explanatory variable age. By univariate we mean they only considered one variable at a time. The goal of modeling, however, is exploring relationships between variables. So how can you visually explore such relationships? Using a scatterplot! You use a geom_point() to create a scatterplot with x mapped to age and y mapped to score. This will mark each instructor's age and score with a point. Let's ask ourselves, is the relationship positive, meaning as professors age do they also get higher scores? Or is it negative? It’s hard to say, as the p