Linear regression

From WikiMD's Wellnesspedia

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:

  1. Collect and prepare data
  2. Choose the type of regression to use
  3. Create the model
  4. Check the model fit
  5. Make predictions

Types of Linear Regression[edit | edit source]

There are two types of linear regression:

  1. Simple linear regression: One independent variable and one dependent variable
  2. Multiple linear regression: More than one independent variable and one dependent variable

Assumptions of Linear Regression[edit | edit source]

Linear regression makes several assumptions:

  1. Linearity: The relationship between the independent and dependent variable is linear.
  2. Independence: The observations are independent of each other.
  3. Homoscedasticity: The variance of the errors is constant across all levels of the independent variables.
  4. 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:

  1. Economics: To understand the economic factors affecting business
  2. Finance: To predict stock prices
  3. Healthcare: To predict disease trends
  4. Machine Learning: As a prediction algorithm

See Also[edit | edit source]


Linear regression Resources

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