
Applied Plant Science Experimental Design and Statistical Analysis Using SAS® OnDemand for Academics
- 392 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Applied Plant Science Experimental Design and Statistical Analysis Using SAS® OnDemand for Academics
About this book
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. This book: - Covers experimental designs and data analysis protocols- Is presented as a how-to guide with many examples- Includes freely downloadable data sets- Examines key topics such as merging data frames, multivariate analysis and linear regressionAuthored by an experienced teacher of applied plant science statistics, this book assumes no prior background in statistics and guides users through the appropriate methodologies in research. It is an invaluable tool for advanced undergraduate and graduate students, in addition to researchers, extension consultants, faculty and technicians.
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Information
Table of contents
- Cover
- Half Title
- Title
- Copyright
- Contents
- Online Resources
- 1 Introduction to Plant Science Research and Experimentation
- 2 An Introduction to SAS® OnDemand for Academics
- 3 A Review of Basic Statistics and Terms
- 4 Some Basic Experimental Design Principles
- 5 Variation and the Analysis of Variance (ANOVA)
- 6 The Completely Random Design
- 7 Factorials
- 8 Contrasts
- 9 Simple Linear Regression
- 10 Regression Diagnostics
- 11 Mean Separation (Multiple Comparison) Techniques
- 12 The Randomized Complete Block Design
- 13 The Latin Square Design
- 14 The Split-plot and Split-split-plot Designs
- 15 Expected Mean Squares
- 16 Analyzing a Series of Experiments
- 17 The Analysis of Covariance
- 18 Procedures for Non-normal Data
- 19 Sampling
- 20 When Data Doesn’t Behave
- 21 Presentation of Research Results
- Index
- Back