The regression estimates are practiced to demonstrate the relationship between a dependent variable and one or more independent variables. Linear regression is a commonly used type of predictive analysis and is often used to model non-linear relationships. The prediction done by Linear regression is limited to numeric output. The main point of regression is to inspect two things:
- Does a set of predictor variables do a good job in predicting an outcome variable?
- Which variables are meaningful predictors of the outcome variable, and in what way do they influence the outcome variable?
A linear regression line has an equation of the form of Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).