Incorporating Additional Variables in Regression Analysis

Requirements Clarification & Assessment

1. Understanding the Problem Context

   • Determine the primary objective of the regression model. Is it for prediction, inference, or both?
   • Identify the dependent variable and the existing independent variables (m variables).

2. Data Exploration for New Variables

   • Analyze the source and nature of the additional n variables.
   • Evaluate the frequency and accuracy of data collection for these variables.
   • Assess the completeness and quality of the data.

3. Relevance and Theoretical Justification

   • Ensure the new variables have theoretical backing or empirical evidence supporting their inclusion.
   • Determine how these variables relate to the dependent variable and existing variables.

4. Preliminary Data Analysis

   • Perform exploratory data analysis (EDA) to understand distributions, outliers, and potential relationships.
   • Check for missing values and plan for imputation if necessary.