The linear regression equation is a one-degree equation with the most basic form being Y = mX + C where m is the slope of the line and C is the standard error. It is used when the response variable is continuous in nature for example height, weight, and the number of hours. It can be a simple linear regression if it involves continuous dependent variable with one independent variable and a multiple linear regression if it has multiple independent variables.
Linear regression is a standard statistical practice to calculate the best fit line passing through the data points when plotted. The best fit line is chosen in such a way so that the distance of each data point is minimum from the line which reduces the overall error of the system. Linear regression assumes that the various features in the data are linearly related to the target. It is often used in predictive analytics for calculating estimates in the foreseeable future.
