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Full-Text Articles in Life Sciences
Mapping Qtl With Covariates, Cherie A. Ochsenfeld, Kristofer Jennings, R. W. Doerge
Mapping Qtl With Covariates, Cherie A. Ochsenfeld, Kristofer Jennings, R. W. Doerge
Conference on Applied Statistics in Agriculture
Quantitative trait loci (QTL) analysis is an effective tool for locating regions of the genome associated with a trait. Quantitative trait data are complex, and when statistically testing for the location of a QTL, the distribution of the test statistic is typically unknown. Historically, asymptotic thresholds have been difficult to derive for QTL analysis. Permutation testing has successfully provided significance thresholds for QTL analysis, but the need for exchangeability among the observations limits these empirically derived thresholds to simple linear models and does not permit the inclusion of important covariates in the model. We address the limitation of permutation theory …
Path Analysis In Agricultural Research, K. Bondari
Path Analysis In Agricultural Research, K. Bondari
Conference on Applied Statistics in Agriculture
Path analysis introduced by Wright in 1921 as "correlation and causation" has been extensively used in agriculture, sociology, and epidemiology, among many other fields. This study will review path diagrams, algorithms, and the relationship to standardized and mUltivariate regression analyses. Basic assumptions underlying path analysis (e.g., cause and effect relationship, linearity of regression, complete additivity) will also be discussed. Several research examples will be presented to better acquaint statisticians invol ved in agricultural research wi th the methodology and application of path analysis suitable for agricultural data. The method of path coefficient is simple, easy to use, and if "tracing …
A Combined Analysis Of Experiments When Treatments Differ Among Experiments, Paul N. Hinz, Mario R. Pareja
A Combined Analysis Of Experiments When Treatments Differ Among Experiments, Paul N. Hinz, Mario R. Pareja
Conference on Applied Statistics in Agriculture
The advantages of repeating experiments in several locations and years are discussed and standard methods of analysis are reviewed. The methods assume that the same treatments are used in each experiment. This paper discusses a method used for a combined analysis when the treatments represent levels of a quantitative factor but differ among experiments. The method makes use of multiple regression analysis in which a continuous variable represents treatment levels, classification variables represent experiments, and products of the continuous and classification variables represent differences among experiments. The method is illustrated on data from a series of experiments designed to study …