INTRODUCTION
Regression analysis can be employed to check whether any relationships exist between a
dependent variable and one or more independent variables or not. Regression analysis can also be
used to evaluate the strength of these relations and can predict their future relationship. Regression
analysis has many applications in finance, forecasting revenues and expense and many more. This
tool has three classifications, namely, linear regression, multiple regression, and non-linear
regression.
Linear regression analysis depicts the relationship between a dependent and an independent
variable, while multiple linear regression deals with the relationship of a dependent and one or
more independent variables. Nevertheless, nonlinear regression analysis handles complex datasets
where a non-linear relationship exists between the dependent and independent variables.
A simple linear model is denoted by Y= a + bX + £, where Y - dependent variable, a – intercept,
b – slope, X – Independent variable and £ - error (residual).
A multiple linear regression can be mathematically represented as Y= a + bX1 + cX2 + dX3
+£, where Y - dependent variable, X1, X2, X3 – Independent variables, a – intercept, b, c, d –
slopes and £ - error (residual).
Here we took a dataset from Kaggle on real estate, which has 8 attributes that includes No.,
transaction rate, house age, distance to the nearest market, number of convenience stores,
latitude, longitude and house price per unit area. Here, multiple regression is used to find the
relationship between various variables to the house price per unit area.