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by Afroz ChakureAugust 9th, 2019

Linear Regression is generally classified into two types:

- Simple Linear Regression
- Multiple Linear Regression

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:

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

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

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

In Multiple Linear Regression, we try to find relationship between **2 or more independent variables (inputs)** and corresponding dependent variable (output). The independent variables can be continuous or categorical .

The equation that describes how the predicted values of y is related to **p independent variables** is called as **Multiple Linear Regression equation:**

Below is the graph for Multiple Linear Regression Model, applied on the **iris** data set:

- It helps us
**predict trends**and**future values**. The multiple linear regression analysis can be used to get**point estimates**. - It can be used to
**forecast effects**or impacts of changes. That is, multiple linear regression analysis can help to understand**how much will the dependent variable change when we change the independent variables**. - It can be used to
**identify the strength of the effect that the independent variables have on a dependent variable**.

L O A D I N G

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