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Covariance And Correlation Made Simple (and Why You Should Care)

When we think about probability, statistics, and distributions, we often focus on modeling a single variable. Doing so, however, can make us miss how variables interact — and these interactions can completely change our decisions and insights. In this video, we explore covariance and correlation, showing why looking only at individual (marginal) distributions can be misleading. You’ll see how the joint behavior of variables reveals insights you wouldn’t get from marginals alone, with applications in fields like finance. We’ll cover: Marginal distributions and expected values Joint distributions, covariance, and correlation A realistic finance example By the end, you’ll understand how considering relationships between variables can reveal insights and guide better decisions that you would otherwise miss. 0:00 Introduction 0:30 Coin Flip Bet, Marginal Distributions, and Expected Values 1:49 Joint Distributions 2:57 Covariance Intuition 4:29 Positive and Negative Covariance 6:19 Covariance and Dispersion 7:05 Correlation 8:24 Correlation and Risk 10:22 Conclusion #covariance #correlation #probability #datascience #SoME4 Credits: Huge thank you to Raihan_DesignPX (https://rive.app/marketplace/8358-160...) for the character animation, remixed under a CC BY license. About Me: I’m a professor sharing educational resources on probability, statistics, optimization methods, algorithms, and programming. I'm currently based in Vancouver, Canada at the University of British Columbia. Contact: [email protected] LinkedIn:   / omar-swei