Analysis of Binomial Data Obtained From Plant-Tissue-Culture Studies Using Generalized Linear Models
In biology, the objective of research scientists is to gain an understanding of biological rela-tionships through experimentation. Statistical analyses are an essential part of biological research. Statistical methods used in biological research range from the very simple to the extremely complex. Consequently, caution should be taken when selecting an appropriate statistical method. Data obtained from plant-cell and tissue-culture studies can generally be placed into one of two or more groups. In other words, a dependent variable consists of placing an observation into a category. The purpose of this paper was to present statistical methods suitable for the analysis of binomial data obtained from plant-tissue culture. First, there is an explanation of how the generalized linear models are obtained and then an analysis of somatic-embryo germination data together with an interpretation of the results.
Anahtar Kelimeler:
Logistic models, generalized linear models, plant-tis
Analysis of Binomial Data Obtained From Plant-Tissue-Culture Studies Using Generalized Linear Models
In biology, the objective of research scientists is to gain an understanding of biological rela-tionships through experimentation. Statistical analyses are an essential part of biological research. Statistical methods used in biological research range from the very simple to the extremely complex. Consequently, caution should be taken when selecting an appropriate statistical method. Data obtained from plant-cell and tissue-culture studies can generally be placed into one of two or more groups. In other words, a dependent variable consists of placing an observation into a category. The purpose of this paper was to present statistical methods suitable for the analysis of binomial data obtained from plant-tissue culture. First, there is an explanation of how the generalized linear models are obtained and then an analysis of somatic-embryo germination data together with an interpretation of the results.
Keywords:
Logistic models, generalized linear models, plant-tis,
- ISSN: 1300-0152
- Yayın Aralığı: 6
- Yayıncı: TÜBİTAK