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## Details

Genre/Form: | Electronic books |
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Additional Physical Format: | Print version: Cox, D.R. (David Roxbee). Principles of applied statistics. Cambridge, UK ; New York : Cambridge University Press, 2011 (DLC) 2011023677 (OCoLC)727702115 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
D R Cox; Christl A Donnelly |

ISBN: | 9781139128797 1139128795 1139115960 9781139115964 9781139118132 1139118137 9781139005036 1139005030 |

OCLC Number: | 768770719 |

Description: | 1 online resource (x, 202 pages) : illustrations |

Contents: | Cover; Title; Copyright; Contents; Preface; 1 Some general concepts; 1.1 Preliminaries; 1.2 Components of investigation; 1.3 Aspects of study design; 1.4 Relationship between design and analysis; 1.5 Experimental and observational studies; 1.6 Principles of measurement; 1.7 Types and phases of analysis; 1.8 Formal analysis; 1.9 Probability models; 1.10 Prediction; 1.11 Synthesis; Notes; 2 Design of studies; 2.1 Introduction; 2.2 Unit of analysis; 2.3 Types of study; 2.4 Avoidance of systematic error; 2.5 Control and estimation of random error; 2.6 Scale of effort; 2.7 Factorial principle. Notes3 Special types of study; 3.1 Preliminaries; 3.2 Sampling a specific population; 3.2.1 Sampling frame; 3.2.2 Precision enhancement; 3.2.3 Multi-stage and temporal sampling; 3.2.4 Less standard sampling methods; 3.3 Experiments; 3.3.1 Primary formulation; 3.3.2 Precision improvement; 3.3.3 Factorial experiments; 3.3.4 Developments; 3.4 Cross-sectional observational study; 3.5 Prospective observational study; 3.6 Retrospective observational study; Notes; 4 Principles of measurement; 4.1 Criteria for measurements; 4.2 Classification of measurements; 4.3 Scale properties. 4.4 Classification by purpose4.5 Censoring; 4.6 Derived variables; 4.7 Latent variables; 4.7.1 Generalities; 4.7.2 Role in model formulation; 4.7.3 Latent structure and latent class models; 4.7.4 Measurement error in regression; Notes; 5 Preliminary analysis; 5.1 Introduction; 5.2 Data auditing; 5.3 Data screening; 5.4 Preliminary graphical analysis; 5.5 Preliminary tabular analysis; 5.6 More specialized measurement; 5.7 Discussion; 6 Model formulation; 6.1 Preliminaries; 6.2 Nature of probability models; 6.3 Types of model; 6.4 Interpretation of probability; 6.5 Empirical models. 6.5.1 Generalities6.5.2 Systematic variation; 6.5.3 Variational structure; 6.5.4 Unit of analysis; Notes; 7 Model choice; 7.1 Criteria for parameters; 7.1.1 Preliminaries; 7.1.2 Parameters of interest; 7.2 Nonspecific effects; 7.2.1 Preliminaries; 7.2.2 Stable treatment effect; 7.2.3 Unstable effect; 7.3 Choice of a specific model; Notes; 8 Techniques of formal inference; 8.1 Preliminaries; 8.2 Confidence limits; 8.3 Posterior distributions; 8.4 Significance tests; 8.4.1 Types of null hypothesis; 8.4.2 Test of atomic null hypothesis; 8.4.3 Application and interpretation of p-values. 8.4.4 Simulation-based procedures8.4.5 Tests of model adequacy; 8.4.6 Tests of model simplification; 8.5 Large numbers of significance tests; 8.5.1 Generalities; 8.5.2 Formulation; 8.5.3 Multi-stage formulation; 8.5.4 Bonferroni correction; 8.5.5 False discovery rate; 8.5.6 Empirical Bayes formulation; 8.6 Estimates and standard errors; 8.6.1 A final assessment; Notes; 9 Interpretation; 9.1 Introduction; 9.2 Statistical causality; 9.2.1 Preliminaries; 9.2.2 Causality and randomized experiments; 9.2.3 Observational parallel; 9.2.4 Qualitative guidelines; 9.2.5 A further notion. |

Responsibility: | D.R. Cox, Christl A. Donnelly. |

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## Reviews

*Editorial reviews*

Publisher Synopsis

"This is an outstanding book in search of a proper audience. ... The authors do a really outstanding job of covering key elements of practice; they illustrate each point with examples (often more than one) nicely offset in gray boxes, and drawn from a huge range of fields. There are insights throughout the book, often ones that are not covered by more typical texts or courses. Despite being a statistical consultant for more than a decade, I learned a good deal and was, perhaps more importantly, given much to think about." Peter Flom, Significance "This book by David Cox and Christl Donnelly, Principles of Applied Statistics, is an extensive coverage of all the necessary steps and precautions one must go through when contemplating applied (i.e. real!) statistics. ... It constitutes a magnificent testimony to the depth and to the spectrum of our field." Christian Robert, Xi'an's Og full review "Principles of Applied Statistics by D. R. Cox and Christl A. Donnelly takes on arguably the most important element of statistical analysis. The authors address the fundamental question of how data analysis methods can be utilized so that when statistical techniques are applied, they yield accurate and useful inferences... Cox and Donnelly have successfully walked a tightrope between being too technical for the beginner and including enough sophistication for the advanced reader. An exceptional feature of their text is the large number of applied and interesting examples, which makes sometimes subtle statistical concepts accessible to a wide audience.... This book will be useful to persons with little experience with statistical methods as a guide to analytic strategies. Equally, for those familiar with statistical methods, the material is a clear and concise reminder of the critical importance of considerations beyond the technical assumptions necessary to apply statistical techniques." Steve Selvin, American Journal of Epidemiology "a valuable distillation of his experience of applied work. It stands as a summary of an entire tradition of using statistics to address scientific problems. If you do not have a few years to spend apprenticed to a master, I can think of few better ways of being initiated into that tradition than reading Principles of Applied Statistics." Cosma Shalizi, American Scientist "Overall this book provides very clear coverage of the non-technical aspects of statistical practice: a superb outline of the meta-level issues of actually analyzing data and answering statistical questions. It would provide an ideal complement to the more traditional courses to which statistics students are exposed. And-I cannot resist-should anyone still require convincing, it demonstrates perfectly that statistics is not merely a branch of mathematics." David J. Hand, Imperial College for International Statistical Review Read more...

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