Regression

Regression models are used to predict the value of a dependent variable from a set of independent variables, and to inform us about the strengths and forms of the potential relationships between each independent variable and the dependent variable in our dataset. While we will only cover linear and logistic regression in this chapter, it is worth noting that there are more types of regression, such as Poisson regression, for predicting count variables, such as the number of tattoos a person has, and ordinal regression, for predicting ranked variables, such as questionnaire answers ("Really Bad", "Bad", "Decent", "Good", "Really Good"), where the difference between "Decent" and "Good" is not necessarily the same as between "Really bad" and "Bad".

Each of these regression models relies on a set of assumptions about the data. For instance, in order to meaningfully use and interpret a linear regression...