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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