Reza Hoshmand

Design of Experiments for Agriculture and the Natural Sciences (eBook, PDF)

• Format: PDF

Produktbeschreibung

Useful as a text or reference, this book presents various experimental designs, reasons for selecting each design, and statistical analysis. This second edition features a new chapter on covariance analysis to help readers understand how to reduce the error associated with an experiment and examine covariances among selected variables. It includes expanded material on multiple regression analysis, ANOVA, and ANCOVA. It also provides additional examples, problems, case studies, and a Minitab® guide at the end of several chapters to assist in data analysis. This text remains an ideal introduction to designing experiments in agriculture, environmental science, and natural sciences.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 456
• Erscheinungstermin: 3. Oktober 2018
• Englisch
• ISBN-13: 9781420010640
• Artikelnr.: 54257548

Autorenporträt

Reza Hoshmand (Author)

Inhaltsangabe

The Nature of Agricultural Research. Key Assumptions of Experimental Designs. Designs for Reducing Error. Single-Factor Experimental Designs. Two-Factor Experimental Designs. Three (or More)- Factor Experimental Designs. Treatment Means Comparison. Sample Designs Over Time. Regression and Correlation Analysis. Covariance Analysis. Appendices.

Rezensionen

"... guides readers to think through their problems, to design experiments to answer their questions, to analyze the data accruing from those experiments, and to draw sensible inferences. ... the exercises at the end of the chapter give readers the opportunity to test their understanding. ... a handy companion for agronomists and environmental scientists who need to experiment with treatments they can control."
-R. Webster, Journal of Environmental Quality, Vol. 36, Issue 1, January-February 2007

"...One strength of the text is that there are many actual agricultural and biological examples and data analysis problems. ... This text would be beneficial to those whose backgrounds are in agriculture and biology, those who would like to see basic computational details, and those who prefer the classical test statistic/critical value approach to hypothesis testing."
-Biometrics, December 2006