Analysis of Quantal Response DataThis book takes the standard methods as the starting point, and then describes a wide range of relatively new approaches and procedures designed to deal with more complicated data and experiments - including much recent research in the area. Throughout mention is given to the computing requirements - facilities available in large computing packages like BMDP, SAS and SPSS are also described. |
Contents
Extensions and alternatives | 3 |
Maximumlikelihood fitting of simple models | 41 |
22 | 64 |
Describing time to response | 190 |
Overdispersion | 234 |
Nonparametric and robust methods | 303 |
Design and sequential methods | 340 |
Appendices | 370 |
F Useful addresses | 387 |
References | 439 |
| 485 | |
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Common terms and phrases
algorithm analysis approach approximation asymptotic beta-binomial model binary binomial distribution binomial model bioassay Biometrics Biometrika Chapter comparison confidence interval considered correlation corresponding cumulative distribution function d₁ data of Table data sets denote described deviance discussion dosage dose levels dose-response evaluated example Exercise experiment extended models Figure Finney flour-beetle GENSTAT given GLIM goodness-of-fit illustration individuals insects iteration likelihood estimation likelihood-ratio likelihood-ratio test linear models link function litter log-likelihood logistic regression logit model matrix maximum maximum-likelihood estimate menarche method mice MINITAB mixture model Morgan natural mortality Nelder normal observed obtain optimization over-dispersion P₁ parameter estimates plot Poisson Pregibon probability of response probit procedure proportions quantal assay data quantal response data quasi-likelihood random variable Ridout robust sample score test sequential simulation Spearman-Kärber estimate standard errors Statist substances survival analysis symmetric tolerance distribution toxicity transformation values variance