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Principles of applied statistics /
"Applied statistics is more than data analysis, but it is easy to lose sight of the big picture. David Cox and Christl Donnelly distil decades of scientific experience into usable principles for the successful application of statistics, showing how good statistical strategy shapes every stage o...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.