gistfile1.txt

> pop <- read.csv('popdensitygraph.csv')
> names(pop)
[1] "X" "Y"
> summary(lm(pop$X~pop$Y))

Call:
lm(formula = pop$X ~ pop$Y)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.54671 -0.17203  0.03501  0.14217  0.74129 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.21719    0.01904   11.41  < 2e-16 ***
pop$Y        0.77381    0.19106    4.05  7.3e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.2521 on 202 degrees of freedom
Multiple R-squared: 0.07511,	Adjusted R-squared: 0.07053 
F-statistic:  16.4 on 1 and 202 DF,  p-value: 7.295e-05 

> cor.test(pop$X,pop$Y)

	Pearson's product-moment correlation

data:  pop$X and pop$Y 
t = 4.0501, df = 202, p-value = 7.295e-05
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 0.1420327 0.3965006 
sample estimates:
      cor 
0.2740567 

> c(0.1420327, 0.3965006) ^2
[1] 0.02017329 0.15721273