Which model fits the data better?  Let's look at the predicted values for y
(the fitted values) for each model and compare them to the observed values 

 MTB > print c2 c4 c16

	c2 = y, the observed values
	c4 = y-hat or predicted values for linear model
	c16 = y-hat or predicted values for model with x-squared term added
  ROW     C2        C4       C16
 
    1   2.81   1.50032   2.42585
    2   1.82   1.50032   2.42585
    3   1.80   1.50032   2.42585
    4   2.25   1.72756   1.68649
    5   2.19   1.72756   1.68649
    6   2.02   1.72756   1.68649
    7   1.57   1.72756   1.68649
    8   1.40   1.72756   1.68649
    9   1.94   1.95479   1.32322
   10   1.40   1.95479   1.32322
   11   1.18   1.95479   1.32322
   12   1.63   2.18203   1.33602
   13   1.26   2.18203   1.33602
   14   0.93   2.18203   1.33602
   15   2.22   2.63651   2.48986
   16   1.97   2.63651   2.48986
   17   1.74   2.63651   2.48986
   18   4.49   2.86375   3.63090
   19   4.33   2.86375   3.63090
   20   3.10   2.86375   3.63090
 
The model with the x-squared term is a better fit to the data than the linear 
model. You can see that the predicted values for this model(c16) are much
closer to the observed values(c2) than the predicted values for the linear
model(c4).
