Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)#

Stage 2 · 🔗 Associations & Correlation · Lesson 07 of 56 · beginner

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Important

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Things that move together#

Much of data analysis begins with a simple question: do two things move together? When ice-cream sales rise with temperature, or umbrella sales with rainfall, we sense an association — a tendency for two variables to vary in step. Making that intuition precise, and measuring how strong it is, is the work of this stage.

Why it’s useful#

Associations are useful in two ways. They explain — revealing which factors relate to an outcome you care about — and they predict — if two variables move together, knowing one helps you guess the other. Spotting the right associations is often the first real insight a dataset yields, and it points to which variables are worth modelling later.

The running example#

This stage grounds the idea in a real dataset used throughout: the City of Chicago taxi trips from September 2022. It is an intuitive place to look for associations — does the fare rise with the distance travelled? with the time taken? The next lesson introduces the dataset in detail; for now, the point is that a familiar, everyday relationship becomes a measurable one once it is in data.

Association is not cause#

One caution to carry from the start: association is not causation. Two variables can move together because one drives the other, because a third factor drives both, or by pure coincidence. Measuring a strong association tells you the variables are related, not why — a distinction that matters the moment anyone tries to act on the finding.

Hint