> ## Table 9.3 and 9.4
> dat=read.table("table9_3.dat", h=F)
> dat
  V1 V2        V3    V4
1  1 -1        -1 65.60
2  2 -1         1 45.59
3  3  1        -1 78.72
4  4  1         1 62.96
5  5  0         0 64.78
6  6  0         0 64.33
7  7  2 2(-1.173) 89.73
8  8  4 4(-1.173) 93.04
9  9  6 6(-1.173) 75.06
> time=dat[,2]
> temp=c(-1, 1, -1, 1, 0, 0, -1.173*2, -1.173*4, -1.173*6)
> yield=dat[,4]
> g=lm(yield ~ time + temp + time:temp + I(time^2), subset=1:6)  # model 
(9.12) using the first 6 runs only
> summary(g)  # Table 9.4 

Call:
lm(formula = yield ~ time + temp + time:temp + I(time^2), subset = 1:6)

Residuals:
         1          2          3          4          5          6 
 1.184e-17 -2.296e-17 -2.367e-17  1.222e-17  2.250e-01 -2.250e-01 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  64.5550     0.2250 286.911  0.00222 **
time          7.6225     0.1591  47.910  0.01329 * 
temp         -8.9425     0.1591 -56.207  0.01133 * 
I(time^2)    -1.3375     0.2756  -4.854  0.12935   
time:temp     1.0625     0.1591   6.678  0.09462 . 
---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 

Residual standard error: 0.3182 on 1 degrees of freedom
Multiple R-Squared: 0.9998,	Adjusted R-squared: 0.9991 
F-statistic:  1381 on 4 and 1 DF,  p-value: 0.02018