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Probit and Survival Analysis

  • Linda J. Young
  • Jerry H. Young

Abstract

In previous chapters, we have estimated survival rates for a given time period. Here, we want to return to the issue of survival. We will be concentrating on two aspects of survival. First, we will look at estimating survival as a function of some dose. The dose may be used in controlling the population such as applying insecticide or herbicide to reduce the insect or weed populations, respectively. From both the economic and environmental perspectives, we want to keep the number of applications to a minimum, leading us to concentrate on distributions and sampling (see Chapters 1 to 6). However, once it has been determined that control is needed, the question arises, “How much is enough?” The statistical approach to interpreting bioassay results has historically been probit analysis. More recently, logistic regression has become popular. Both probit analysis and logistic regression are considered here. We should also note that the same methods of estimating the probability of survival as a function of some dose are appropriate even though the dose may not have been purposefully applied. It could be the result of contamination in the environment. The question then becomes, “How much can the individuals tolerate?” Although the question differs the statistical approach to the problem is the same. Finney (1973) is the classical work in this area. Morgan (1992) provides a comprehensive treatment of quantal response data.

Keywords

Probit Model Probit Analysis Nest Success Nest Survival Daily Survival Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Linda J. Young
    • 1
  • Jerry H. Young
    • 2
  1. 1.Biometry DepartmentUniversity of NebraskaLincolnUSA
  2. 2.Entomology Department (Emeritus)Oklahoma State UniversityUSA

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