What Is Linear Regression?

Linear regression is a method for making predictions or estimates. Using a supervised learning algorithm, a linear relationship is determined between a dependent variable and one or more explanatory variables. It can be applied to various fields of study, commercial or academic in particular.

Linear Regression: A Definition

Linear regression is a statistical technique for modeling the relationships between different variables (dependent and independent). Used to describe and analyze values ​​or data, linear regression aims to make predictions or forecasts.

How Linear Regression Works

Linear regression uses a chosen estimation technique, a dependent variable, and one or more explanatory variables to form a linear equation estimating the values ​​of the dependent variable. This is assuming that there is a causal relationship between the two variables.

Example Of Linear Regression

For example: you want to determine how your advertising investments affect the level of your sales. To do this, we will use a linear regression to examine the relationship between the two variables (investments and sales). It will serve as a forecast if this relationship is clearly represented.

The Main Purposes Of Linear Regressions

• Identify the explanatory variables that are associated with the dependent variable
• Understand the relationship between dependent and explanatory variables
• make predictions

Applications And Types Of Linear Regression

Application Examples

• The modeling of traffic accidents according to speed, road conditions and others to inform traffic police services.
• Modeling high school retention rates to better understand the factors that contribute to school dropout.
• The modeling of real estate losses by fire as a function of variables: the degree of involvement of firefighters, reaction time or securities.

The Different Types Of Regression

• Simple regression
• Multiple regression
• Linear regression
• Non-linear regression

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