Assumption 7| Classical Linear Regression Model | Observations gerater than Parameters Explained

Описание: In this video, I explain a key assumption of the Classical Linear Regression Model (CLRM): that the number of observations must exceed the number of parameters. This assumption ensures that the regression model is identifiable and provides meaningful results. Understanding this fundamental requirement is essential for anyone working with regression analysis.

🔍 What You Will Learn:

Why the number of observations must be greater than the number of parameters
The role of this assumption in ensuring model stability and reliability
Problems that arise when this assumption is violated (e.g., overfitting or underidentification)
Practical examples to clarify the concept
How this assumption affects model estimation and interpretation
📊 Key Highlights:

Clear explanation of the "observations greater than parameters" assumption
Real-world examples of models with sufficient and insufficient observations
Tips for ensuring your dataset meets this assumption
implications for econometric modeling