Book contents
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- Chapter 1 Introduction to scientific data analysis
- Chapter 2 Excel and data analysis
- Chapter 3 Data distributions I
- Chapter 4 Data distributions II
- Chapter 5 Measurement, error and uncertainty
- Chapter 6 Least squares I
- Chapter 7 Least squares II
- Chapter 8 Non-linear least squares
- Chapter 9 Tests of significance
- Chapter 10 Data Analysis tools in Excel and the Analysis ToolPak
- Appendix 1 Statistical tables
- Appendix 2 Propagation of uncertainties
- Appendix 3 Least squares and the principle of maximum likelihood
- Appendix 4 Standard uncertainties in mean, intercept and slope
- Appendix 5 Introduction to matrices for least squares analysis
- Appendix 6 Useful formulae
- Answers to exercises and end of chapter problems
- References
- Index
Preface to the first edition
Published online by Cambridge University Press: 05 March 2012
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- Chapter 1 Introduction to scientific data analysis
- Chapter 2 Excel and data analysis
- Chapter 3 Data distributions I
- Chapter 4 Data distributions II
- Chapter 5 Measurement, error and uncertainty
- Chapter 6 Least squares I
- Chapter 7 Least squares II
- Chapter 8 Non-linear least squares
- Chapter 9 Tests of significance
- Chapter 10 Data Analysis tools in Excel and the Analysis ToolPak
- Appendix 1 Statistical tables
- Appendix 2 Propagation of uncertainties
- Appendix 3 Least squares and the principle of maximum likelihood
- Appendix 4 Standard uncertainties in mean, intercept and slope
- Appendix 5 Introduction to matrices for least squares analysis
- Appendix 6 Useful formulae
- Answers to exercises and end of chapter problems
- References
- Index
Summary
Preface to the first edition
Experiments and experimentation have central roles to play in the education of scientists. For many destined to participate in scientific enquiry through laboratory or field based studies, the ability to apply ‘experimental methods’ is a key skill that they rely upon throughout their professional careers. For others whose interests and circumstances take them into other fields upon completion of their studies, the experience of ‘wrestling with nature’ so often encountered in experimental work, offers enduring rewards: Skills developed in the process of planning, executing and deliberating upon experiments are of lasting value in a world in which some talents become rapidly redundant.
Laboratory and field based experimentation are core activities in the physical sciences. Good experimentation is a blend of insight, imagination, skill, perseverance and occasionally luck. Vital to experimentation is data analysis. This is rightly so, as careful analysis of data can tease out features and relationships not apparent at a first glance at the ‘numbers’ emerging from an experiment. This, in turn, may suggest a new direction for the experiment that might offer further insight into a phenomenon or effect being studied. Equally importantly, after details of an experiment are long forgotten, facility gained in applying data analysis methods remains as a highly valued and transferable skill.
- Type
- Chapter
- Information
- Data Analysis for Physical ScientistsFeaturing Excel®, pp. xiii - xviPublisher: Cambridge University PressPrint publication year: 2012