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| Preface | |
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| What is Data Analysis? | |
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| Tukey's 1962 paper | |
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| The Path of Statistics | |
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| Strategy Issues in Data Analysis | |
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| Strategy in Data Analysis | |
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| Philosophical issues | |
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| On the theory of data analysis and its teaching | |
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| Science and data analysis | |
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| Economy of forces | |
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| Issues of size | |
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| Strategic planning | |
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| Planning the data collection | |
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| Choice of data and methods. | |
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| Systematic and random errors | |
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| Strategic reserves | |
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| Human factors | |
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| The stages of data analysis | |
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| Inspection | |
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| Error checking | |
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| Modification | |
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| Comparison | |
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| Modeling and Model fitting | |
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| Simulation | |
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| What-if analyses | |
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| Interpretation | |
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| Presentation of conclusions | |
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| Tools required for strategy reasons | |
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| Ad hoc programming | |
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| Graphics | |
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| Record keeping | |
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| Creating and keeping order | |
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| Massive Data Sets | |
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| Introduction | |
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| Disclosure: Personal experiences | |
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| What is massive? A classification of size | |
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| Obstacles to scaling | |
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| Human limitations: visualization | |
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| Human - machine interactions | |
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| Storage requirements | |
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| Computational complexity | |
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| Conclusions | |
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| On the structure of large data sets | |
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| Types of data | |
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| How do data sets grow? | |
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| On data organization | |
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| Derived data sets | |
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| Data base management and related issues | |
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| Data archiving | |
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| The stages of a data analysis | |
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| Planning the data collection | |
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| Actual collection | |
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| Data access | |
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| Initial data checking | |
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| Data analysis proper | |
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| The final product: presentation of arguments and conclusions | |
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| Examples and some thoughts on strategy | |
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| Volume reduction | |
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| Supercomputers and software challenges | |
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| When do we need a Concorde? | |
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| General Purpose Data Analysis and Supercomputers | |
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| Languages, Programming Environments and Data-based Prototyping | |
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| Summary of conclusions | |
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| Languages for Data Analysis | |
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| Goals and purposes | |
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| Natural languages and computing languages | |
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| Natural languages | |
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| Batch languages | |
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| Immediate languages | |
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| Language and literature | |
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| Object orientation and related structural issues | |
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| Extremism and compromises, slogans and reality | |
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| Some conclusions | |
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| Interface issues | |
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| The command line interface | |
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| The menu interface | |
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| The batch interface and programming environments | |
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| Some personal experiences | |
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| Miscellaneous issues | |
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| On building blocks | |
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| On the scope of names | |
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| On notation | |
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| Book-keeping problems | |
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| Requirements for a general purpose immediate language | |
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| Approximate Models | |
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| Models | |
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| Bayesian modeling | |
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| Mathematical statistics and approximate models | |
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| Statistical significance and physical relevance | |
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| Judicious use of a wrong model | |
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| Composite models | |
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| Modeling the length of day | |
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| The role of simulation | |
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| Summary of conclusions | |
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| Pitfalls | |
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| Simpson's paradox | |
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| Missing data | |
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| The Case of the Babylonian Lunar Six | |
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| X-ray crystallography | |
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| Regression of Y on X or of X on Y? | |
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| Create order in data | |
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| General considerations | |
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| Principal component methods | |
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| Principal component methods: Jury data | |
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| Multidimensional scaling | |
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| Multidimensional scaling: the method | |
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| Multidimensional scaling: a synthetic example | |
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| Multidimensional scaling: map reconstruction | |
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| Correspondence analysis | |
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| Correspondence analysis: the method | |
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| K�ltepe eponyms | |
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| Further examples: marketing and Shakespearean plays | |
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| Multidimensional scaling vs. Correspondence analysis | |
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| Hodson's grave data | |
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| Plato data | |
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| More case studies | |
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| A nutshell example | |
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| Shape invariant modeling | |
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| Comparison of point configurations | |
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| The cyclodecane conformation | |
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| The Thomson problem | |
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| Notes on numerical optimization | |
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| References | |
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| Index | |