Description: It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line.Â
Linear Regression is of mainly two types: Simple Linear Regression and Multiple Linear Regression.
Simple Linear Regression is characterized by one independent variable.
Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables.
While finding best fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression.
Algorithm: This best fit line is known as regression line and represented by a linear equation Y= a *X + b. n this equation: Y – Dependent Variable, a – Slope, X – Independent variable, b – Intercept. Â
These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line.