This chapter was written and developed by Beatriz Negreiros
Linear models are a class of statistical models that aim to establish a linear relationship between a dependent variable and one or more independent variables. These models assume that the relationship between the variables can be represented by a straight line in a multidimensional space.
In a simple linear regression model, there is only one independent variable, while in multiple linear regression models, there are multiple independent variables. The dependent variable, also called the target variable or response variable, is the variable that we want to predict or explain based on the independent variables.
Linear models have several advantages. They are relatively simple to understand and interpret, and they provide insights into the relationships between variables. Additionally, linear models are computationally efficient and can be applied to large datasets. However, linear models assume a linear relationship between variables, which may not hold true in all cases. We will cover this in detail in the section about structural and estimation errors in Linear Regression. Thus, if the relationship is nonlinear, alternative modeling techniques may be more appropriate.