AI Terms

Linear Regression

Definition: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.  

Detailed Description:

Linear regression assumes a linear relationship between the dependent variable and the independent variables. It is used to make predictions, estimate relationships, and analyze trends.

The equation for simple linear regression is:

y = mx + b

where:

  • y is the dependent variable
  • x is the independent variable
  • m is the slope of the line 
  • b is the y-intercept 

Multiple linear regression extends this equation to include multiple independent variables.

Linear regression is used in various applications, such as:

  • Predictive modeling: Predicting future values of a variable.
  • Statistical analysis: Understanding relationships between variables.
  • Data visualization: Creating scatter plots to visualize the relationship between variables.

Related Terms:

FAQs:

  1. What is the difference between simple and multiple linear regression?

    • Simple linear regression models the relationship between a dependent variable and a single independent variable, while multiple linear regression models the relationship between a dependent variable and multiple independent variables. 
  2. How is linear regression used in predictive modeling?

    • Linear regression can be used to predict future values of a variable based on past data.
  3. What are the assumptions of linear regression?

    • Linearity, independence, homoscedasticity, and normality of residuals.

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