Statistical Design (Springer Texts in Statistics) 2008th Edition by George Casella (PDF)

Description

Statistical design is one of the fundamentals of our subject, being at the core of the growth of statistics during the previous century. In this book the basic theoretical underpinnings are covered. It describes the principles that drive good designs and good statistics.Design played a key role in agricultural statistics and set down principles of good practice, principles that still apply today. Statistical design is all about understanding where the variance comes from, and making sure that is where the replication is. Indeed, it is probably correct to say that these principles are even more important today.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I took a class in “Experimental Design” and our professor (actually a former colleague of Casella at Cornell) chose this book after having a brief look on different books dealing with this topic. However, he and also the whole class suffered from this choice.Although Casella provides a nice view on some aspects (distinction between “Treatment” and “Experiment” Design), this is the first edition of the book and it is full of mistakes! Of course the spelling and grammar mistakes are negliable, but the data-and formula mistakes are not. After reading the book carefully, we recognized that the amount of these mistakes is incredible and lost our trust in the book.Considering this: Definitely not recommendable.

⭐I got my PhD in biostatistics and thought I knew pretty well about experimental design until I read this book.If you are looking for a cookbook style information, this is not for you and you will find that the coverage of this book is far from being extensive. However, if you want to UNDERSTAND the principles of statistical design and apply it without rummaging the exactly same design whenever you meet one, you would not find any better book.

⭐Below is abstract of my critique of the book that will appear as a Forum piece in Agronomy Journal in early 2013.Abstract: In many disciplines, basic and applied, a high frequency of errors of statistical analysis have been documented in numerous reviews over the decades. One insufficiently appreciated source of this has been the failure of statisticians, individually and collectively, to provide clear definitions for many of the terms they use – and failure to stick with those definitions over time and across disciplines. The field of experimental design is one area where such problems have become acute. I analyze the terminology used in a recent text in that field, Statistical Design by G. Casella, to document the phenomenon, but the problems identified are widespread and of ancient lineage. I show the availability of a clearer, more consistent terminology, most of it well-established more than half a century ago. Key issues commented on are the tripartite structure of the design of an experiment, the need for experimental units to be physically independent of each other, the definition of pseudoreplication, and confusion over the meaning of split unit designs. Problems identified seem to reflect a longstanding conflict between the classical, experiment-oriented approach to design and the model-oriented approach to that topic. Aficionados of the latter have tended to stray from the classical terminology of experimental design, redefining terms in a somewhat casual fashion and thereby considerably confusing non-statisticians in particular.

Keywords

Free Download Statistical Design (Springer Texts in Statistics) 2008th Edition in PDF format
Statistical Design (Springer Texts in Statistics) 2008th Edition PDF Free Download
Download Statistical Design (Springer Texts in Statistics) 2008th Edition 2008 PDF Free
Statistical Design (Springer Texts in Statistics) 2008th Edition 2008 PDF Free Download
Download Statistical Design (Springer Texts in Statistics) 2008th Edition PDF
Free Download Ebook Statistical Design (Springer Texts in Statistics) 2008th Edition