Linear Regression Notes

How to Replicate Example 10.1 in Statistics... by Mendenhall et al, 5th Ed, in R:

Y <- c(1,1,2,2,4)
X <- c(1,2,3,4,5)
fit <- lm(Y ~ X)
plot(X,Y,xlim=c(0,5),ylim=c(-1,4))
abline(fit)

> lm(Y ~ X)

Call:
lm(formula = Y ~ X)

Coefficients:
(Intercept)            X 
       -0.1          0.7

> plot(X,Y,xlim=c(0,5),ylim=c(-1,4))
> abline(fit)



> summary(fit)

Call:
lm(formula = Y ~ X)

Residuals:
         1          2          3          4          5
 4.000e-01 -3.000e-01 -6.891e-17 -7.000e-01  6.000e-01

Coefficients:
            Estimate Std. Error t value Pr(>|t|) 
(Intercept)  -0.1000     0.6351  -0.157   0.8849 
X             0.7000     0.1915   3.656   0.0354 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6055 on 3 degrees of freedom
Multiple R-squared: 0.8167,    Adjusted R-squared: 0.7556
F-statistic: 13.36 on 1 and 3 DF,  p-value: 0.03535

Link used for reference:
Using R for Linear Regression<http://www.montefiore.ulg.ac.be/~kvansteen/GBIO0009-1/ac20092010/Class8/Using%20R%20for%20linear%20regression.pdf>
<http://www.statmethods.net/advgraphs/axes.html>
<http://www.dummies.com/programming/r/how-to-add-variables-to-a-data-frame-in-r/>