Model 1 Call: lm(formula = age ~ proportion.black) Residuals:

Model 1
Call:
lm(formula = age ~ proportion.black)
Residuals:
Min 1Q Median 3Q Max
-2.5449 -1.1117 -0.5285 0.9635 4.3421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8790 0.5688 1.545 0.133
proportion.black 10.6471 1.5095 7.053 7.68e-08 ***
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.669 on 30 degrees of freedom
Multiple R-squared: 0.6238, Adjusted R-squared: 0.6113
F-statistic: 49.75 on 1 and 30 DF, p-value: 7.677e-08
Model 2
Call:
lm(formula = `log(age)` ~ `log(proportion.black)`)
Residuals:

Min 1Q Median 3Q Max
-0.43995 -0.10603 -0.01366 0.12853 0.35434
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.97275 0.07462 13.036 6.85e-14 ***
`log(proportion.black)` 0.72504 0.11901 6.092 1.08e-06 ***
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1761 on 30 degrees of freedom
Multiple R-squared: 0.553, Adjusted R-squared: 0.5381
F-statistic: 37.12 on 1 and 30 DF, p-value: 1.077e-06