Statistical Methods in Agriculture and Experimental Biology (Statistical Methods in Agriculture & Experimental Biology)

by RogerMead (Author), RobertN.Curnow (Author), Roger Mead (Author), Robert N. Curnow (Contributor), Anne M. Hasted (Contributor), Anne M . Hasted (Author)

• New

  Paperback
  2002

  $112.59

  The third edition of this popular introductory text maintains the character that won worldwide respect for its predecessors but features a number of enhancements that broaden its scope, increase its utility, and bring the treatment thoroughly up to date. It provides complete coverage of the statistical ideas and methods essential to students in agriculture or experimental biology. In addition to covering fundamental methodology, this treatment also includes more advanced topics that the authors believe help develop an appreciation of the breadth of statistical methodology now available. The emphasis is not on mathematical detail, but on ensuring students understand why and when various methods should be used. New in the Third Edition: * A chapter on the two simplest yet most important methods of multivariate analysis * Increased emphasis on modern computer applications * Discussions on a wider range of data types and the graphical display of data * Analysis of mixed cropping experiments and on-farm experiments

• Used

  Paperback
  1993

  $7.09

  An introductory text for scientists working in agriculture and experimental biology, Statistical Methods in Agriculture and Experimental Biology includes all the basic statistical methods relevant to their work. Undergraduate and postgraduate majors in those subjects will find its information most essential to their studies.
  Material on more advanced topics- not usually discussed in an introductory text-focuses on multiple regression, incomplete block experimental design, confounded and split-plot experimental designs, non-linear and log-linear models, and repeated measurements. The authors believe that research scientists should be aware of the potential benefits of those more advanced methods in their work.
  Particular emphasis is placed upon the assumptions implicit in statistical methods: a full chapter is devoted to that important aspect. It also stresses the importance of designing experiments properly, particularly in using small, natural blocks and factorial treatment structure, and of using available resources efficiently, and extracting all information from the data.