e Y b0 b1X plotX e xlabrate ylabresiduals pch20 ablineh0 col8 25 30

# E y b0 b1x plotx e xlabrate ylabresiduals pch20

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> e <- Y - (b0 + b1*X) > plot(X, e, xlab="rate", ylab="residuals", pch=20) > abline(h=0, col=8, lwd=2) 1.5 2.0 2.5 3.0 3.5 -50 0 50 100 rate residuals I What does this plot of e i versus X i say about the fit? 29
How does labor supply change with pay? We’ve estimated the linear model: hours = 1913 + 81 × rate + e It increases: Every dollar extra per hour leads to an increase of 81 expected annual hours worked. 30
Questions remain ... 1. Does this apply today? 2. Does the same hold for those who make more money? 3. How good is this estimate? 4. Is pay rate the only variable of importance? 5. Do higher wages cause more work? 31
What’s next? The goal for the next couple of weeks in class is to answer the following general questions: I How do we select a best fitting line? I What is the accuracy of our predictions? I How do we validate and criticize the model? I How can we use regression analysis to answer relevant science/decision questions? 32

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