Applied Plant Science Experimental Design And Statistical Analysis Using The Sas University Edition
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Applied Plant Science Experimental Design and Statistical Analysis Using SAS® OnDemand for Academics
The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analysing and interpreting research can be overwhelming. SAS® is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic institutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research.
Applied Plant Science Experimental Design and Statistical Analysis Using the SAS University Edition
"Presenting the most frequently used statistical methods for plant science in a non-intimidating fashion and teaching the appropriate use of SAS® within the context of plant science research, this book includes numerous examples and is a key resource for students and scientists undertaking statistical analysis and experimental design"--
Experimental Design and Data Analysis for Biologists
Author: Gerald Peter Quinn
language: en
Publisher: Cambridge University Press
Release Date: 2002-03-21
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.