Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The dependent variable is usually a continuous variable, such as a price or a quantity, while the independent variables can be continuous or categorical variables.
In linear regression, we assume that the relationship between the dependent variable and the independent variables is linear, which means that a change in the independent variables will result in a proportional change in the dependent variable. The goal of linear regression is to find the best fitting line that describes the relationship between the variables. This line is called the regression line or the best-fit line.
To find the regression line, we use a mathematical equation that describes the relationship between the dependent variable and the independent variables. This equation is typically of the form:
y = b0 + b1x1 + b2x2 + ... + bn*xn
where y is the dependent variable, x1, x2, ..., xn are the independent variables, and b0, b1, b2, ..., bn are the coefficients of the regression equation.
The coefficients are estimated using a method called least squares regression, which minimizes the sum of the squared differences between the actual values of the dependent variable and the predicted values based on the independent variables.
Linear regression is widely used in many applications, such as forecasting, trend analysis, and modeling the relationship between two or more variables. It is a simple and effective method for modeling linear relationships between variables, but it may not be suitable for more complex relationships.