Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In its simplest form, it involves fitting a straight line to a set of data points on a graph, where the line represents the predicted values of the dependent variable based on the independent variable(s). The equation of the line is typically expressed in the form of (y = mx + b), where (y) is the dependent variable, (x) is the independent variable, (m) is the slope of the line, and (b) is the y-intercept.The primary goal of linear regression is to minimize the difference between the observed values and the values predicted by the linear model, often using a method called least squares. This technique is widely used in various fields such as economics, biology, engineering, and the social sciences to make predictions, analyze relationships, and understand trends in data. Linear regression assumes that there is a linear relationship between the variables, meaning that a change in the independent variable will result in a proportional change in the dependent variable.