Buch, Englisch, 452 Seiten, Format (B × H): 160 mm x 243 mm, Gewicht: 776 g
Buch, Englisch, 452 Seiten, Format (B × H): 160 mm x 243 mm, Gewicht: 776 g
ISBN: 978-1-58488-538-2
Verlag: Taylor & Francis Inc
Written to meet the needs of both students and applied researchers, Design of Experiments for Agriculture and the Natural Sciences, Second Edition serves as an introductory guide to experimental design and analysis. Like the popular original, this thorough text provides an understanding of the logical underpinnings of design and analysis by selecting and discussing only those carefully chosen designs that offer the greatest utility. However, it improves on the first edition by adhering to a step-by-step process that greatly improves accessibility and understanding. Real problems from different areas of agriculture and science are presented throughout to show how practical issues of design and analysis are best handled.
Completely revised to greatly enhance readability, this new edition includes:
- A new chapter on covariance analysis to help readers reduce errors, while enhancing their ability to examine covariances among selected variables
- Expanded material on multiple regression and variance analysis
- Additional examples, problems, and case studies
- A step-by-step Minitab® guide to help with data analysis
Intended for those in the agriculture, environmental, and natural science fields as well as statisticians, this text requires no previous exposure to analysis of variance, although some familiarity with basic statistical fundamentals is assumed. In keeping with the book's practical orientation, numerous workable problems are presented throughout to reinforce the reader's ability to creatively apply the principles and concepts in any given situation.
Zielgruppe
Undergraduate
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
