Linear vs. Multiple Regression

Regression Analysis

• Linear Regression:
  • Involves a single independent variable (predictor) to explain the dependent variable (response)
  • Goal is to create a linear equation that best predicts the value of the dependent variable based on the independent variable
  • Equations take the form of Y = β0 + β1X + ε, where Y is the dependent variable, X is the independent variable, β0 is the intercept, β1 is the slope, and ε is the error term
• Multiple Regression:
  • Involves more than one independent variable (predictors) to explain the dependent variable (response)
  • Goal is to create a linear equation that best predicts the value of the dependent variable based on multiple independent variables
  • Equations take the form of Y = β0 + β1X1 + β2X2 + … + βnXn + ε, where Y is the dependent variable, X1, X2, …, Xn are the independent variables, β0 is the intercept, β1, β2, …, βn are the slopes, and ε is the error term
• Key differences:
  • Number of independent variables (1 in linear regression, >1 in multiple regression)
  • Complexity of the equation (simpler in linear regression, more complex in multiple regression)

This quiz covers the key differences between linear regression, which involves predicting a continuous outcome using a single predictor variable, and multiple regression, which involves predicting the same outcome using multiple predictor variables simultaneously.