Statistics for Data Science and Business Analysis - The Multiple Linear Regression Model

The video tutorial introduces multivariate regression, highlighting its importance in modeling complex real-world scenarios where multiple factors influence outcomes. It explains the population multivariate regression model, emphasizing the role of multiple independent variables and their coefficients. The tutorial discusses the shift from finding the best fitting line to the best fitting model, focusing on minimizing the sum of squared errors. It concludes by explaining how adding variables can increase a model's explanatory power, setting the stage for determining the optimal number of variables in future lessons.

What is the relationship between SSE and SSR as mentioned in the text?

How does increasing the number of variables affect the explanatory power of a model?