In statistical modeling, regression analysis is used to estimate the relationships between two or
more variables:
-Dependent variable (aka criterion variable) is the main factor you are trying to understand and
predict.
-Independent variables (aka explanatory variables, or predictors) are the factors that might
influence the dependent variable
Regression analysis helps you understand how the dependent variable changes when one of the
independent variables varies and allows to mathematically determine which of those variables
really has an impact.
Technically, a regression analysis model is based on the sum of squares, which is a
mathematical way to find the dispersion of data points. The goal of a model is to get the smallest
possible sum of squares and draw a line that comes closest to the data
In statistics, they differentiate between a simple and multiple linear regression. Simple linear
regression models the relationship between a dependent variable and one independent variables
using a linear function. If you use two or more explanatory variables to predict the dependent
variable, you deal with multiple linear regression. If the dependent variable is modeled as a
non-linear function because the data relationships do not follow a straight line, use nonlinear
regression instead. The focus of this tutorial will be on a simple linear regression.
As an example, let's take sales numbers for umbrellas for the last 24 months and find out the
average monthly rainfall for the same period. Plot this information on a chart, and the regression
line will demonstrate the relationship between the independent variable (rainfall) and dependent
variable (umbrella sales):
