tumor.lst<-scan(what=list(dose=0,time=0,y=0))
  0 11 0 0  104 0  300  8 0  300   104 1  600   11 0  600   104 1
I other data omitted for space considerations I

  tumor.df<-data.frame(tumor.lst)

  tumor.fit<-glm(y~dose+time,
                family=binomial(link=logit),data=tumor.df)
  summary(tumor.fit)


  Deviance Residuals:
         Min         1Q     Median          3Q      Max
   -1.097395 -0.8206817 -0.5886815 -0.06352017 2.348401

  Coefficients:
                     Value   Std. Error   t value
  (Intercept) -7.400518981 2.8509479454 -2.595810
         dose  0.002413828 0.0008839569  2.730708
         time  0.055395038 0.0277976033  1.992799

  Residual Deviance: 137.8663 on 147 degrees of freedom

  anova(tumor.fit,test="Chisq")

  Analysis of Deviance Table
  Binomial model
  Response: y
  Terms added sequentially (first to last)
       Df Deviance Resid. Df Resid. Dev     Pr(Chi)
  NULL                   149   160.5651
  dose  1  7.05758       148   153.5076 0.007893063
  time  1 15.64132       147   137.8663 0.000076563


  tumor.fit2<-glm(y~time+dose,family=binomial(link=logit),
                  data=tumor.df)

  anova(tumor.fit2,test="Chisq")

.i(.Analysis of deviance table:in S-plus anova;
  Analysis of Deviance Table
  Binomial model
  Response: y

  Terms added sequentially (first to last)
       Df Deviance Resid. Df Resid. Dev     Pr(Chi)
  NULL                   149   160.5651
  time  1 14.66734       148   145.8978 0.000128249
  dose  1  8.03156       147   137.8663 0.004596931