Welcome back! Last time you learned how regression measures relationships between economic variables. But when economists publish their findings, how do you read their results?
Today we'll decode actual regression output that you see in economic research papers and reports.
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What's the first thing you'd want to know about a regression result?
Regression output shows three key numbers for each variable: the coefficient, its sign (positive or negative), and something called statistical significance.
Think of this as the "report card" for each economic relationship the researcher tested.
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If a coefficient is -0.75, what does the negative sign immediately tell you?
Statistical significance tells us whether the relationship is reliable or might just be random noise. Economists use "p-values" to measure this.
A p-value under 0.05 means we can trust the relationship. Above 0.05 means it might be coincidence.
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Why might economists care whether a relationship is statistically significant?
In research papers, you'll see asterisks (*) next to coefficients. One asterisk means significant at 10% level, two at 5%, and three at 1%.
More asterisks = more confidence the relationship is real, not random.
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What would three asterisks next to a coefficient suggest about that relationship?
R-squared measures how much of the variation in your outcome variable the regression explains. It ranges from 0% to 100%.
An R-squared of 0.65 means the model explains 65% of the variation in the data.
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If income regression has R-squared of 0.40, what does this tell you?
