Linear Regression [Sup] {Polynomial}
Description
Linear regression is used to identify the relationship between a dependent variable and one or more independent variables and is typically leveraged to make predictions about future outcomes. When there is only one independent variable and one dependent variable, it is known as simple linear regression. As the number of independent variables increases, it is referred to as multiple linear regression. For each type of linear regression, it seeks to plot a line of best fit, which is calculated through the method of least squares. However, unlike other regression models, this line is straight when plotted on a graph.
Varieties
Standard Linear Regression models the relationship between variables as a straight line, using the least squares method to find the best fit.
Polynomial Regression is a form of Linear regression known as a special case of Multiple linear regression which estimates the relationship as an nth degree polynomial. It is sensitive to outliers so the presence of one or two outliers can also badly affect the performance.
Formula
