> xy    # print the xy dataframe
      x     y
1  29.7  16.6
2  68.4  49.1
3 120.7 121.7
4 217.2 219.6
5 313.5 375.5
6 419.1 570.8
7 535.9 648.2
8 641.5 755.6
> 
> xy.reg <- lm(y~x)    # Fit the regression (lm=linear model)
> summary(xy.reg)      # Print out estimates, t-tests

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-31.052 -14.738  -4.175   5.488  66.428 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -27.56895   19.84220  -1.389    0.214    
x             1.26925    0.05503  23.063 4.36e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 32.65 on 6 degrees of freedom
Multiple R-squared: 0.9888,     Adjusted R-squared: 0.987 
F-statistic: 531.9 on 1 and 6 DF,  p-value: 4.355e-07 

> anova(xy.reg)        # Print out ANOVA table
Analysis of Variance Table

Response: y
          Df Sum Sq Mean Sq F value    Pr(>F)    
x          1 567033  567033   531.9 4.355e-07 ***
Residuals  6   6396    1066                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
> 
> 
> xy.reg0 <- lm(y~x -1)  #  Fits regression thru Origin
> summary(xy.reg0)      # Print out estimates, t-tests

Call:
lm(formula = y ~ x - 1)

Residuals:
    Min      1Q  Median      3Q     Max 
-42.572 -26.359 -18.987  -1.848  64.924 

Coefficients:
  Estimate Std. Error t value Pr(>|t|)    
x  1.20705    0.03408   35.42 3.71e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 34.75 on 7 degrees of freedom
Multiple R-squared: 0.9945,     Adjusted R-squared: 0.9937 
F-statistic:  1255 on 1 and 7 DF,  p-value: 3.712e-09 

> anova(xy.reg0)        # Print out ANOVA table
Analysis of Variance Table

Response: y
          Df  Sum Sq Mean Sq F value    Pr(>F)    
x          1 1515175 1515175  1254.5 3.712e-09 ***
Residuals  7    8454    1208                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
>