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Ordinary Least Squares (OLS) Regression — step by step explanation | by Lead Data Scientist & GM, Reliance Jio, | Medium

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Picture this: a bustling classroom, students scribbling away as the clock ticks down the minutes until the next big exam. Some students have been hitting the books for hours, while others have barely glanced at their notes. Intuitively, we understand that those extra hours should translate to better scores, but how can we quantify this relationship? Enter the world of OLS (Ordinary Least Squares) regression, a cornerstone of statistical learning that transforms intuition into quantifiable predictions. Imagine we’re researchers peering through the looking glass at a group of students. We collect data on the number of hours each student studies and their subsequent test scores. Here’s a snippet of our findings: A pattern emerges before our eyes: as study hours increase, so do the scores. It’s a linear dance between effort and reward, and OLS regression is the choreographer that helps us understand this movement in numbers. OLS regression works its magic by finding the perfect coefficient

Ordinary Least Squares (OLS) Regression — step by step explanation Venugopal Adep | AI Product Leader @ Jio 5 min read · Feb 18, 2024 -- Listen Share Picture this: a bustling classroom, students scribbling away as the clock ticks down the minutes until the next big exam. Some students have been hitting the books for hours, while others have barely glanced at their notes. Intuitively, we understand that those extra hours should translate to better scores, but how can we quantify this relationship? Enter the world of OLS (Ordinary Least Squares) regression, a cornerstone of statistical learning

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