Regression
A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables.
A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables.
It does this by essentially fitting a best-fit line and seeing how the data is dispersed around this line.
Regression helps economists and financial analysts in things ranging from asset valuation to making predictions.
In order for regression results to be properly interpreted, several assumptions about the data and the model itself must hold.
Simple Linear Regression
Simple linear regression is used to estimate the relationship between two quantitative variables. You can use simple linear regression when you want to know:
How strong the relationship is between two variables (e.g., the relationship between rainfall and soil erosion).
The value of the dependent variable at a certain value of the independent variable (e.g., the amount of soil erosion at a certain level of rainfall).
Multiple Linear Regression
Multiple linear regression is used to estimate the relationship between two or more independent variables and one dependent variable. You can use multiple linear regression when you want to know:
How strong the relationship is between two or more independent variables and one dependent variable (e.g. how rainfall, temperature, and amount of fertilizer added affect crop growth).
The value of the dependent variable at a certain value of the independent variables (e.g. the expected yield of a crop at certain levels of rainfall, temperature, and fertilizer addition).
