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Experimental Design and Analysis for Tree Improvement provides a set of practical procedures to follow when planning, designing and analysing tree improvement trials. Using many fully-worked examples, it outlines how to: design field, glasshouse and laboratory trials; efficiently collect data and construct electronic data files; pre-process data, screening for data quality and outliers; analyse data from single and across-site trials using either GenStat or SAS; and interpret the results from statistical analyses. The authors address the many practical issues often faced in forest tree improvement trials and describe techniques that will give conclusive results with the minimum expense. The techniques provided are applicable to the improvement of not only trees, but to crops in general. Building on the success of the first edition, the second edition covers commercially-available software packages for design generation (CycDesigN) and data pre-processing and automated generation of programs for statistical analysis (DataPlus). For analysis, it provides both GenStat and SAS programs as generated by DataPlus.
We developed the first edition of this book because we perceived a need for a compilation on study design with application to studies of the ecology, conser- tion, and management of wildlife. We felt that the need for coverage of study design in one source was strong, and although a few books and monographs existed on some of the topics that we covered, no single work attempted to synthesize the many facets of wildlife study design. We decided to develop this second edition because our original goal – synthesis of study design – remains strong, and because we each gathered a substantial body of new material with which we could update and expand each chapter. Several of us also used the first edition as the basis for workshops and graduate teaching, which provided us with many valuable suggestions from readers on how to improve the text. In particular, Morrison received a detailed review from the graduate s- dents in his “Wildlife Study Design” course at Texas A&M University. We also paid heed to the reviews of the first edition that appeared in the literature.
Experimental Design and Analysis for Tree Improvement provides a set of practical procedures to follow when planning, designing and analysing tree improvement trials. Using examples, it outlines how to: design field, glasshouse and laboratory trials efficiently collect and construct electronic data files pre-process data, screening for data quality and outliers analyse data from single and across-site trials interpret the results from statistical analyses. The authors address the many practical issues often faced in forest tree improvement trials and describe techniques that will give meaningful results. The techniques provided are applicable to the improvement of not only trees, but to crops in general. This fully revised third edition includes the construction of p-rep and spatial designs using the commercially available software package for design generation (CycDesigN). For analysis of the examples, it provides online Genstat and SAS programs and a link to R programs.
This new edition of a successful title offers procedures involved in preparing, designing, analyzing and interpreting forestry trials, primarily for tree introduction and improvement
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.