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Statistics and Scientific Method: An Introduction for Students and Researchers [Pehme köide]

3.70/5 (12 hinnangut Goodreads-ist)
, (Distinguished University Professor of Statistics, Lancaster University; Adjunct Professor of Biostatistics, Johns Hopkins University School of Public Health; Adjunct Senior Researcher, International Research Institute ofr Climate and Soc)
  • Formaat: Paperback / softback, 190 pages, kõrgus x laius x paksus: 233x167x13 mm, kaal: 298 g, 82 line illustrations, 11 b&w halftones, 4 colour plates
  • Ilmumisaeg: 11-Aug-2011
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199543194
  • ISBN-13: 9780199543199
Teised raamatud teemal:
Statistics and Scientific Method: An Introduction for Students and ResearchersSuurem pilt
  • Formaat: Paperback / softback, 190 pages, kõrgus x laius x paksus: 233x167x13 mm, kaal: 298 g, 82 line illustrations, 11 b&w halftones, 4 colour plates
  • Ilmumisaeg: 11-Aug-2011
  • Kirjastus: Oxford University Press
  • ISBN-10: 0199543194
  • ISBN-13: 9780199543199
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780199543199.html
Märksõnad:
"Most introductory statistics text-books are written either in a highly mathematical style for an intended readership of mathematics undergraduate students, or in a recipe-book style for an intended audience of non-mathematically inclined undergraduate or postgraduate students, typically in a single discipline; hence, "statistics for biologists", "statistics for psychologists", and so on. An antidote to technique-oriented service courses, Statistics and Scientific Method is different. It studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, the text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling and data analysis are integral to the scientific method. Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation. Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice, R, whilst the book's web-page includes downloadable data and R code thatenables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyse their own data if they wish. However, the book is not intended to be a textbook on statistical computing, and all of the material in the book can be understood without using either R or any other computer software"--

"Most introductory statistics text-books are written either in a highly mathematical style for an intended readership of mathematics undergraduate students, or in a recipe-book style for an intended audience of non-mathematically inclined undergraduate or postgraduate students, typically in a single discipline; hence, "statistics for biologists", "statistics for psychologists", and so on. An antidote to technique-oriented service courses, Statistics and Scientific Method is different. It studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, the text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling and data analysis are integral to the scientific method. Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation. Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice, R, whilst the book's web-page includes downloadable data and R code that enables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyse their own data if they wish. However, the book is not intended to be a textbook on statistical computing, and all of the material in the book can be understood without using either R or any other computer software"--

Provided by publisher.

Most introductory statistics text-books are written either in a highly mathematical style for an intended readership of mathematics undergraduate students, or in a recipe-book style for an intended audience of non-mathematically inclined undergraduate or postgraduate students, typically in a single discipline; hence, "statistics for biologists", "statistics for psychologists", and so on.

An antidote to technique-oriented service courses, Statistics and Scientific Method is different. It studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, the text aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling and data analysis are integral to the scientific method.

Aimed primarily at beginning postgraduate students across a range of scientific disciplines (albeit with a bias towards the biological, environmental and health sciences), it therefore assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation.

Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice, R, whilst the book's web-page includes downloadable data and R code that enables the reader to reproduce all of the analyses in the book and, with easy modifications, to adapt the code to analyse their own data if they wish. However, the book is not intended to be a textbook on statistical computing, and all of the material in the book can be understood without using either R or any other computer software.

Arvustused

The authors have a nice writing style and explain all the important concepts well ... reader/student will gain a good understanding of the essential aspects of statistics in scientific research. * Michael R. Chernick, Significance *

1 Introduction
1(4)
1.1 Objectives
1(1)
1.2 Statistics as part of the scientific method
1(1)
1.3 What is in this book, and how should you use it?
2(3)
2 Overview: investigating Newton's law
5(12)
2.1 Newton's laws of motion
5(2)
2.2 Defining the question
7(1)
2.3 Designing the experiment
8(1)
2.4 Exploring the data
9(1)
2.5 Modelling the data
10(2)
2.6 Notational conventions
12(1)
2.7 Making inferences from data
13(2)
2.8 What have we learnt so far?
15(2)
3 Uncertainty: variation, probability and inference
17(16)
3.1 Variation
17(4)
3.2 Probability
21(3)
3.3 Statistical inference
24(3)
3.4 The likelihood function: a principled approach to statistical inference
27(4)
3.5 Further reading
31(2)
4 Exploratory data analysis: gene expression microarrays
33(24)
4.1 Gene expression microarrays
33(3)
4.2 Displaying single batches of data
36(4)
4.3 Comparing multiple batches of data
40(2)
4.4 Displaying relationships between variables
42(3)
4.5 Customized plots for special data types
45(5)
4.5.1 Time series
45(3)
4.5.2 Spatial data
48(1)
4.5.3 Proportions
49(1)
4.6 Graphical design
50(1)
4.7 Numerical summaries
51(6)
4.7.1 Summation notation
51(1)
4.7.2 Summarizing single and multiple batches of data
52(2)
4.7.3 Summarizing relationships
54(3)
5 Experimental design: agricultural field experiments and clinical trials
57(14)
5.1 Agricultural field experiments
57(2)
5.2 Randomization
59(4)
5.3 Blocking
63(2)
5.4 Factorial experiments
65(2)
5.5 Clinical trials
67(1)
5.6 Statistical significance and statistical power
68(2)
5.7 Observational studies
70(1)
6 Simple comparative experiments: comparing drug treatments for chronic asthmatics
71(8)
6.1 Drug treatments for asthma
71(1)
6.2 Comparing two treatments: parallel group and paired designs
71(2)
6.2.1 The parallel group design
72(1)
6.2.2 The paired design
73(1)
6.3 Analysing data from a simple comparative trial
73(4)
6.3.1 Paired design
73(2)
6.3.2 Parallel group design
75(2)
6.4 Crossover designs
77(1)
6.5 Comparing more than two treatments
78(1)
7 Statistical modelling: the effect of trace pollutants on plant growth
79(35)
7.1 Pollution and plant growth
79(1)
7.2 Scientific laws
80(1)
7.3 Turning a scientific theory into a statistical model: mechanistic and empirical models
80(3)
7.4 The simple linear model
83(3)
7.5 Fitting the simple linear model
86(1)
7.6 Extending the simple linear model
87(10)
7.6.1 Transformations
87(3)
7.6.2 More than one explanatory variable
90(1)
7.6.3 Explanatory variables and factors
90(1)
7.6.4 Reanalysis of the asthma trial data
90(2)
7.6.5 Comparing more than two treatments
92(3)
7.6.6 What do these examples tell us?
95(1)
7.6.7 Likelihood-based estimation and testing
95(1)
7.6.8 Fitting a model to the glyphosate data
96(1)
7.7 Checking assumptions
97(6)
7.7.1 Residual diagnostics
99(2)
7.7.2 Checking the model for the root-length data
101(2)
7.8 An exponential growth model
103(4)
7.9 Non-linear models
107(1)
7.10 Generalized linear models
108(4)
7.10.1 The logistic model for binary data
108(2)
7.10.2 The log-linear model for count data
110(1)
7.10.3 Fitting generalized linear models
111(1)
7.11 The statistical modelling cycle: formulate, fit, check, reformulate
112(2)
8 Survival analysis: living with kidney failure
114(13)
8.1 Kidney failure
114(1)
8.2 Estimating a survival curve
115(4)
8.3 How long do you expect to live?
119(3)
8.4 Regression analysis for survival data: proportional hazards
122(1)
8.5 Analysis of the kidney failure data
123(3)
8.6 Discussion and further reading
126(1)
9 Time series analysis: predicting fluctuations in daily maximum temperatures
127(14)
9.1 Weather forecasting
127(1)
9.2 Why do time series data need special treatment?
127(1)
9.3 Trend and seasonal variation
128(2)
9.4 Autocorrelation: what is it and why does it matter?
130(3)
9.5 Prediction
133(5)
9.6 Discussion and further reading
138(3)
10 Spatial statistics: monitoring air pollution
141(17)
10.1 Air pollution
141(3)
10.2 Spatial variation
144(1)
10.3 Exploring spatial variation: the spatial correlogram
144(2)
10.4 Exploring spatial correlation: the variogram
146(1)
10.5 A case-study in spatial prediction: mapping lead pollution in Galicia
147(9)
10.5.1 Galicia lead pollution data
147(1)
10.5.2 Calculating the variogram
147(1)
10.5.3 Mapping the Galicia lead pollution data
148(8)
10.6 Further reading
156(2)
Appendix: The R computing environment
158(5)
A.1 Background material
158(1)
A.2 Installing R
159(1)
A.3 An example of an R session
160(3)
References 163(4)
Index 167
Peter Diggle is Distinguished University Professor of Statistics and Associate Dean for Research in the School of Health and Medicine, Lancaster University, Adjunct Professor in the Department of Biostatistics, Johns Hopkins University School of Public Health and Adjunct Senior Researcher in the International Research Institute for Climate and Society, Columbia University. Between 1974 and 1983 he was a Lecturer, then Reader, in Statistics at the University of Newcastle upon Tyne. Between 1984 and 1988 he was Senior, then Principal, then Chief Research Scientist and Chief of the Division of Mathematics and Statistics at CSIRO, Australia. He has published nine books and around 180 articles on these topics in the open literature. He was awarded the Royal Statistical Society's Guy Medal in Silver in 1997, is a former editor of the Society's Journal, Series B and is a Fellow of the American Statistical Association.



Amanda Chetwynd is Pro-Vice-Chancellor for the Student Experience and Professor of Mathematics and Statistics at Lancaster University. Before joining Lancaster University she held a Post-Doctoral position in the Mathematics Department at the University of Stockholm. She has published three books and around 80 refereed articles. Amanda was awarded a National Teaching Fellowship in 2003 and in 2005 led Lancaster's successful bid for a Postgraduate Statistics Centre of Excellence in Teaching and Learning.