Скачать или смотреть Correlation vs Regression Explained: Association, Causation, and Statistical Significance

In this follow-up lecture, José A. Fiorio, independent researcher in economics and finance, explains the key differences between correlation and regression, with a clear focus on statistical significance, R-squared, and p-values.

Building on my previous video on correlation, this presentation clarifies why:

A correlation coefficient can indicate association but not causation

A regression slope can be negative even when Excel reports a positive “Multiple R”

R-squared measures explanatory power, not predictive certainty

A low p-value is required to claim statistical significance

Statistical results must be interpreted carefully, especially with small samples

Using a practical Excel regression example relating GDP growth and vacancy rates, I show how to interpret:

Correlation coefficients

Regression output in Excel

R-square and adjusted R-square

t-statistics and p-values

Why correlation does not imply causation

This video is intended for:

Students of statistics, econometrics, finance, and economics

Researchers and analysts working with regression models

Anyone seeking a conceptual, intuitive explanation beyond formulas

📌 Previous lecture referenced in this video:
   • Simple Linear Regression Explained – Jose ...  

📘 Topics covered:

Correlation vs regression

Association vs causation

Statistical significance

R-squared explained

P-values in regression

Excel regression output interpretation

Applied economics and data analysis

👤 About the author:
José A. Fiorio is an independent researcher in economics and finance, with research interests in applied financial analysis, investment climate, macroeconomic indicators, and statistical modeling in emerging and developed markets.

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