Types of Linear Regression

A brief overview of Simple and Multiple Linear Regression

Types of Linear Regression

In this blog, I’m going to provide a brief overview of the different types of Linear Regression with their applications to some real-world problems.

Linear Regression is generally classified into two types:

1. Simple Linear Regression
2. Multiple Linear Regression

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1. Simple

In Simple Linear Regression, we try to find the relationship between a single independent variable (input) and a corresponding dependent variable (output). This can be expressed in the form of a straight line.

The same equation of a line can be re-written as:

1. Y represents the output or dependent variable.
2. β0 and β1 are two unknown constants that represent the intercept and coefficient (slope) respectively.
3. ε (Epsilon) is the error term.

The following is a sample graph of a Simple Linear Regression Model :

Graph of Simple Linear Regression Model

Applications of Simple Linear Regression include :

1. Predicting crop yields based on the amount of rainfall: Yield is dependent variable while the amount of rainfall is independent variable.
2. Marks scored by student based on number of hours studied (ideally) : Here marks scored is dependent and number of hours studied is independent.
3. Predicting the Salary of a person based on years of experience : Thus Experience become the independent variable while Salary becomes the dependent variable.

2. Multiple Linear Regression