Linear regression is used for predictive analysis, to put it simply- where a set of predictor variables do a good job in predicting an outcome variable. The model uses the changes in the independent variable or the explanatory variable to predict the change in the dependent variable or the explained variable.

The simplest form of the equation with one dependent and one independent variable is defined by the formula

y = c + b*x

where,

y = estimated dependent

c = constant

b = regression coefficients

x = independent variable.

While making the model, it’s important to keep in mind which variables in particular are significant predictors of the dependent variable and in what way do they–indicated by the magnitude and sign of the beta estimates (i.e. regression co-efficient) impact the dependent variable These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables.