R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-apple-darwin10.7.0/x86_64 (64-bit)

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> x <- c(1,2,3,4,5,6)   # Create ordered collection (vector)
> y <- x^2              # Square the elements of x
> print(y)              # print (vector) y
[1]  1  4  9 16 25 36
> mean(y)               # Calculate average (arithmetic mean) of (vector) y; result is scalar
[1] 15.16667
> var(y)                # Calculate sample variance
[1] 178.9667
> lm_1 <- lm(y ~ x)     # Fit a linear regression model "y = f(x)" or "y = B0 + (B1 * x)" 
>                       # store the results as lm_1
> print(lm_1)           # Print the model from the (linear model object) lm_1

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
     -9.333        7.000  

> summary(lm_1)         # Compute and print statistics for the fit of the (linear model object) lm_1 par(mfrow=c(2, 2))    # Request 2x2 plot layout

Call:
lm(formula = y ~ x)

Residuals:
      1       2       3       4       5       6 
 3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -9.3333     2.8441  -3.282 0.030453 *  
x             7.0000     0.7303   9.585 0.000662 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583,	Adjusted R-squared: 0.9478 
F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662 

> q()                   # quit