Linear regression
Linear regression is a statistical analysis technique used to understand the relationship between two variables. It is a fundamental tool in statistics, machine learning, and data science.
Overview[edit | edit source]
Linear regression models the relationship between two variables by fitting a linear equation to observed data. The steps to perform linear regression are:
- Collect and prepare data
- Choose the type of regression to use
- Create the model
- Check the model fit
- Make predictions
Types of Linear Regression[edit | edit source]
There are two types of linear regression:
- Simple linear regression: One independent variable and one dependent variable
- Multiple linear regression: More than one independent variable and one dependent variable
Assumptions of Linear Regression[edit | edit source]
Linear regression makes several assumptions:
- Linearity: The relationship between the independent and dependent variable is linear.
- Independence: The observations are independent of each other.
- Homoscedasticity: The variance of the errors is constant across all levels of the independent variables.
- Normality: The errors of the prediction will follow a normal distribution.
Applications of Linear Regression[edit | edit source]
Linear regression is used in various fields including:
- Economics: To understand the economic factors affecting business
- Finance: To predict stock prices
- Healthcare: To predict disease trends
- Machine Learning: As a prediction algorithm
See Also[edit | edit source]
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Contributors: Prab R. Tumpati, MD