Regression analysis uses to study .....

The correct answer is - The causal relationship between two or more variables

Key Points

• Regression analysis
  • It is a statistical tool used to analyze the relationship between variables.
  • The primary goal of regression analysis is to determine whether there is a causal relationship between one dependent variable and one or more independent variables.
  • This technique helps in understanding how the dependent variable changes when the independent variables are varied.
• Key focus on causality
  • Unlike correlation, regression analysis aims to establish a cause-and-effect relationship.
  • For instance, determining how changes in advertising expenditure (independent variable) affect sales (dependent variable).

Additional Information

• Types of Regression Analysis
  • Simple Linear Regression
    • Examines the relationship between one dependent variable and one independent variable.
    • Example: Studying how temperature (independent variable) affects ice cream sales (dependent variable).
  • Multiple Linear Regression
    • Involves one dependent variable and two or more independent variables.
    • Example: Analyzing how factors like advertising budget, price, and product quality influence sales.
  • Logistic Regression
    • Used when the dependent variable is categorical (e.g., binary outcomes like "yes" or "no").
    • Example: Predicting whether a customer will make a purchase based on their browsing history.
• Key Assumptions of Regression Analysis
  • The relationship between the dependent and independent variables is linear.
  • The residuals (errors) are normally distributed and independent.
  • No significant multicollinearity exists among the independent variables.
• Applications of Regression Analysis
  • Used in economics to forecast trends like GDP growth or inflation rates.
  • In marketing, it helps determine the impact of various factors on sales or customer behavior.
  • In healthcare, it is used to study the relationship between risk factors and diseases.