Linear Regression is where it all starts - it is the basic statistical model approach which can be used fro building a basic predictive model for predicting a continuous target eg, sale price , salary , etc.
Simple Linear Regression
Simple linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable. It looks for statistical relationship but not deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degree Celsius it is possible to accurately predict Fahrenheit. Statistical relationship is not accurate in determining relationship between two variables. For example, relationship between height and weight.
In regression we define two type of variables : X – independent variables or Predictor variables Y – Dependent variables or Target variables.
Using X variables we try to predict our Y or target variable.
The regression line follows the equation of the straight line :
Y=B0 + B1X1 + B2X2 + B3X3… BnXn
Here , Y is the dependent/target variable that we have to predict , B0 is the intercept of the line. X1,X2 …Xn are independent variables. B0,B1,B2….Bn are the coefficients independent variables.