Foundation of Mathematics-Simple Linear Regression

Simply put, simple linear regression is a method used to identify and quantify the relationship between two variables. It allows us to predict the value of one variable based on the value of another. Before we dive in, let’s talk about the assumptions that must be met for simple linear regression to be valid. First, there must be a linear relationship between the two variables. Second, the errors in the model must be normally distributed and have constant variance. And third, there should be no multicollinearity or outliers in the data. To understand simple linear regression, we need to define the independent and dependent variables. The independent variable is the variable that we can control or manipulate, while the dependent variable is the variable that we want to predict or explain. <a href="https://medium.com/@klaudiatsai/simple-linear-regression-5bbcf1c5864d">Website</a>