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This is a user-friendly guide to statistics using SAS(R) 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(R) 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; and examines key topics such as merging data frames, multivariate analysis and linear regression.
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Edward F. Durner received his BS in Horticulture from the University of Maryland, his MS in Horticulture from Virginia Tech, and his Ph.D. in Horticultural Science from North Carolina State University. He joined the Horticulture Department at Rutgers in 1986 and conducted tree fruit physiology research, including peach flower bud cold hardiness, rootstock - scion interactions in peach and apple, and consumer acceptance of disease resistant apple cultivars. From the late 1990's through 2005 his research focused on photoperiod and temperature conditioning of strawberry plug plants for off-season production. From 2006 - 2016 my research program was placed on hiatus to serve as Director of the Student Sustainable Farm at Rutgers, a student-run 5-acre CSA (Community Supported Agriculture) farm with 50 to 175 shareholders that provided experiential learning of organic vegetable, herb and flower production for students at Rutgers - The State University of New Jersey. In 2016 he returned to his roles in teaching and research, investigating alternative strawberry production systems and plant conditioning and goldenberry (Physalis peruviana) as a new fruit crop for North America. He teaches or has taught Applied Plant Science Statistics, Applied Physiology of Hort Crops, Agroecology Practicum, Fruit Production, Seminars in Horticulture, and Perspectives on Agriculture and the Environment.
Riassunto
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.
