**ERRATA for Piegorsch-Bailer (1997) 1st ed. (updated 07-Jul-2005)**

Figure | Description |

Fig. 2.1 |
SAS program to find c2 P-value. |

Fig. 2.3 | S-Plus function for generating bootstrap confidence intervals for a binomial p. |

Fig. 4.1 | SAS Proc FREQ program for Fisher's Exact test of data in Table 4.2. |

Fig. 4.3 | SAS Proc TTEST code for Freeman-Tukey transformed data from Table 4.3. |

Fig. 4.5 | SAS Proc NPAR1WAY code for calculating the rank-sum statistic with data from Table 4.4. |

Fig. 5.2 | SAS Proc GLM code for comparing temperature condition data from Table 5.1. |

Fig. 5.4 | SAS program to find multivariate-t critical point. |

Fig. 6.1 | SAS program to find t(3) P-value. |

Fig. 6.2 | SAS Proc REG code to fit LS regression line and obtain a test of linear trend for data in Table 6.1. |

Fig. 6.7 | SAS program to obtain Cochran-Armitage trend statistic, applied to data from Table 6.4. |

Fig. 6.10 | SAS program to obtain Bailer-Portier time-at-risk adjusted trend test statistic, applied to data from Table 6.6. |

Fig. 6.13 | S-Plus program to detect overdispersion in teratology data from Table 6.8. |

Fig. 6.14 | S-Plus function for calculating the overdispersion GEE trend test statistic for data in Table 6.8. |

Fig. 6.15 | S-Plus program to detect overdispersion in count data from Table 6.2. |

Fig. 6.16 | S-Plus program to calculate the QL trend test statistic for data in Table 6.2. |

Fig. 7.1 | SAS Proc REG program for quadratic regression fit and linear regression fit to data in Exercise 6.7. |

Fig. 7.5 | SAS Proc NLIN program for threshold segmented regression fit to mercury toxicity data in Table 7.1. |

BONUS | S-Plus nls threshold segmented regression fit to mercury
toxicity data in Table 7.1. |

Fig. 7.7 | SAS Proc NLIN program for bilinear segmented regression fit to mercury toxicity data in Table 7.1. |

Fig. 7.9 | SAS Proc NLIN program for power transformed regression fit to data in Table 7.2. |

Fig. 7.11 | SAS Proc LOGISTIC program for logistic regression fit to data in Example 6.5. |

Fig. 7.13 | SAS Proc GENMOD program for log-linear regression fit for data in Example 6.2. |

Fig. 7.19 | SAS Proc PROBIT program for logistic regression fit to minnow mortality data. |

Fig. 7.21 | S-Plus glm program for logistic regression fit to minnow
mortality data. |

Fig. 7.22 | S-Plus function for estimating an ED50 along with confidence limits based on Fieller's Theorem |

Fig. 7.24 | SAS Proc GENMOD program for logistic regression fit to the two-group minnow mortality data. |

Fig. 8.1 | SAS Proc GENMOD program for quadratic regression fit to data in Exercise 6.7. |

Fig. 8.3 | SAS Proc GENMOD program for gamma GLiM fit to data in Table 8.2. |

Fig. 8.5 | SAS Proc GENMOD program for logistic fit to data in Table 8.3. |

Fig. 8.8 | Output (edited) from S-Plus glm logistic fit to data in Table
8.3. |

Fig. 8.10 | Output (edited) from S-Plus glm logistic GLiM fit to data in
Table 8.4. |

Fig. 8.12 | SAS Proc GENMOD program for two-factor log-linear GLiM with interaction. |

Fig. 9.1 | SAS Proc FREQ program for testing dose X sex independence in A. tenuiremis. |

Fig. 9.3 | Output (edited) from S-Plus crosstabs function for testing dose
X sex independence in A. tenuiremis |

Fig. 9.4 | User-defined S-Plus power divergence function and associated output
(edited) applied to testing dose X sex independence in A.
tenuiremis. |

Fig. 9.5 | Output (edited) from S-Plus crosstabs program for testing the
homogeneity of p53 mutant spectra in Table 9.5. |

Fig. 9.6 | S-Plus Zd statistic function. |

Fig. 9.7 | Output (edited) from S-Plus Zd function for testing the
homogeneity of p53 mutant spectra in Table 9.5 |

Fig. 9.10 | SAS Proc GENMOD complementary log program for evaluating simple independent action between sodium azide and chromium-VI in Table 9.8. |

Fig. 10.1 | S-Plus Wc statistic
function. |

Fig. 10.2 | Output (edited) from S-Plus Wc.stat function for testing
carcinogenicity of benzene |

Fig. 11.1 | S-Plus survfit code and output for product-limit estimates with
data in Table 11.1. |

Fig. 11.2 | SAS LIFETEST program to generate product-limit estimates for data in Table 11.1. |

Fig.
11.6 | S-Plus code to calculate the (negative) log- likelihood under a Weibull parent distribution and perform ML estimation with acute toxicity data in Table 11.1. |

Fig. 11.8 | SAS Proc LIFETEST and Proc LIFEREG code for calculating event time quantiles for acute toxicity data in Table 11.1. |

Fig. 11.9 | SAS Proc LIFETEST code for nonparametric tests of the equality of survivor functions for spider departure data. |

Fig. 11.11 | SAS Proc LIFEREG code for fitting an AFT Weibull model to acute toxicity data in Table 11.1 |

BONUS | S-Plus program for fitting an AFT Weibull model to acute toxicity data in Table 11.1. |

Fig. 11.12 | SAS Proc LIFEREG code for fitting an AFT model to data in Example 11.3. |

Fig.
11.13 | SAS Proc PHREG code for fitting a Cox regression model to spider departure data. |

BONUS | S-Plus coxph fit of the Cox model to the spider departure time
data. |

NOTE: The authors, the Department of Mathematics & Statistics at Miami
University, the Department of Statistics at the University of South Carolina
provide this material as a free service. No guarantee, implicit or expressed, is
provided for its use, nor is any responsibility accepted for consequences
thereof. Unauthorized use is prohibited.