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:

• Regression Analysis
• Statistical Modeling
• Dependent Variable
• Independent Variable
• Correlation

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.