Linear Regression is one of the Statistical concept used in Regression analysis of Data Science to analyse the data. In this article, I’m not going deal with the mathematical part of the concept rather I will explain you the core concept of the topic.
Before explaining about Linear regression, let me give you a basic idea about Regression and Regression analysis.
Regression is a most commonly used method of predicting an output of dependent variable with the corresponding independent variable. There are distinct types of regression techniques available to make forecasts. These techniques are mostly driven by the following three metrics
- No.of independent variables
- Shape of regression line
- Type of dependent variable
According to the statistical modelling, Regression analysis is a process of estimating the relationships among different variables statically. It includes many techniques for modeling and analyzing several variables, when the emphasis is on the relationship between a dependent variable and one or more independent variables.
More precisely, Regression analysis helps you to understand how a dependent variable will vary when an independent variable is changed keeping the other independent variables fixed.
There are several benefits of using this analysis. Some of them are as follows
- It indicates significant relationships between dependent and independent variable.
- It suggests the strength of impact of multiple independent variables on a dependent variable.
As mentioned above, many techniques are involved in this. Here I’m listing out 7 mostly used techniques
- Linear Regression
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
Linear regression in data science is a technique used to express the relationship between a dependent variable(scalar) and one or more independent variables (explanatory variables).
If the equation has only one explanatory variable, it is called “Linear Regression (also called Simple Linear Regression)” and if more than one explanatory variable, it is called “Multiple Linear Regression (not to confused with Multivariate Linear Regression)”.
Numerous procedures have been developed for parameter estimation and inference in Linear Regression. These procedures differ in the process of calculation to obtain the desired results.
The frequently used method for calculating Linear Regression is “Least Squares method”. In this article, I’m not going to explain you about Least Squares method as it becomes a different subject if I explains it here,instead I suggest you to refer Google for more info.
In Data Science, it is one of the mostly used techniques and is the one where people loves first in learning Regression analysis techniques.
Model image for Linear Regression: