Welcome back! You've mastered spotting correlations in economic data. But economists need to go deeper: how much does one variable affect another?

This is where regression analysis becomes our superpower. It measures relationships between economic variables quantitatively.

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In one sentence, how does regression go beyond simple correlation?

Think of regression as finding the "best fit line" through scattered data points. Imagine plotting income versus education years - dots would scatter around a general upward trend.

Regression draws the single straight line that best represents this relationship.

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If you plotted height versus weight, what direction would the best fit line slope?

The slope of this best fit line tells us everything. A steep upward slope means the variables have a strong positive relationship. A gentle slope shows a weak relationship.

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In economics, we call this slope the "coefficient" - it measures how much Y changes when X increases by one unit.

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What would a negative slope between price and quantity demanded tell us?

Here's where regression gets powerful: coefficients show marginal effects. If education's coefficient is 2000, then each additional year of schooling increases income by $2000 on average.

This "per unit change" interpretation makes regression incredibly useful for economic policy decisions.

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How might policymakers use a coefficient linking minimum wage to employment?

Regression coefficients answer "how much?" questions that correlation cannot. Correlation only tells us variables move together. Regression tells us by how much.