This classic reference details methods for effectively analyzing non-standard or messy data sets. The authors introduce each topic with examples, follow up with a theoretical discussion, and conclude with a case study. They emphasize the distinction between design structure and the structure of treatments and focus on using the techniques with several statistical packages, including SAS, BMDP, and SPSS.
Spotlight customer reviews:
Customer Rating: Summary: first in a series of books by these authors on messy data Comment: Messy data is data that does fit into the structure to be directly analyzed by the standard methods. In the case of linear models based on designed experiments this can be due to missing data or unbalanced data. The authors do a fabulous job of laying out the various situations that involve methods for handling such problems. By separating the material into three volumes the authors can concentrate on the nitty gritty details as they do here in this volume, the longest of the three. Customer Rating: Summary: excellent book to keep Comment: This book is a classical book in data analysis. It provides techniques and methods for effectively analyzing non-standard or messy data sets that arise from experimental design situations. You can always be benefit from the book for your whole life
