Scientific inference: learning from data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2013
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | XIV, 224 S. |
| ISBN: | 9781107607590 9781107024823 |
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Datensatz im Suchindex
| _version_ | 1804150626969452544 |
|---|---|
| adam_text | Contents
For the student page
χ
For the instructor
xii
1
Science and statistical data analysis
1
1
.1
Scientific method
1
1.2
Inference
3
1.3
Scientific inference
6
1.4
Data analysis in a nutshell
7
1.5
Random samples
8
1.6
Know your data
10
1.7
Language
11
1.8
Statistical computing using
R
12
1.9
How to use this book
12
2
Statistical summaries of data
14
2.1
Plotting data
14
2.2
Plotting univariate data
16
2.3
Centre of data: sample mean, median and mode
18
2.4
Dispersion in data: variance and standard deviation
21
2.5
Min, max,
quantiles and the five-number summary
24
2.6
Error bars, standard errors and precision
25
2.7
Plots of
Divariate data
28
2.8
The sample correlation coefficient
36
2.9
Plotting multivariate data
38
2.10
Good practice in statistical graphics
43
2ΛΧ
Chapter summary
44
3
Simple statistical inferences
46
3.1
Inference about the mean of a sample
46
VII
viii Contents
3.2
Difference in means from two samples
49
3.3
Straight line fits
51
3.4
Linear regression in practice
56
3.5
Residuals: what lies beneath
58
3.6
Case study: regression of Reynolds data
59
3.7
Chapter summary
63
4
Probability theory
64
4.1
Experiments, outcomes and events
64
4.2
Probability
69
4.3
The rules of the probability calculus
72
4.4
Random variables
82
4.5
The visual perception of randomness
89
4.6
The meaning of probability and random
89
4.7
Chapter summary
92
5
Random variables
94
5.1
Properties of random variables
94
5.2
Discrete random variables
100
5.3
Continuous random variables
110
5.4
Change of variables
116
5.5
Approximate variance relations (or the propagation
of errors)
120
5.6
Chapter summary
122
6
Estimation and maximum likelihood
124
6.1
Models
124
6.2
Case study: Rutherford
& Geiger
data
125
6.3
Maximum likelihood estimation
129
6.4
Weighted least squares
133
6.5
Case study:
pion
scattering data
139
6.6
Chapter summary
140
7
Significance tests and confidence intervals
142
7.1
A thought experiment
142
7.2
Significance testing and test statistics
143
7.3
Pearson s
χ2
test
146
7.4
Fixed-level tests and decisions
153
7.5
Interpreting test results
156
7.6
Confidence intervals on MLEs
159
7.7
Chapter summary
166
Contents ix
8 Monte Carlo
methods
169
8.1
Generating pseudo-random numbers
169
8.2
Estimating sampling distributions by Monte Carlo
175
8.3
Computing confidence by bootstrap
181
8.4
The power of Monte Carlo
183
8.5
Further reading
184
8.6
Chapter summary
184
Appendix A Getting started with statistical computation
185
A.I What is R?
185
A.2 A first
R
session
185
A.3 Entering data
187
A.4 Quitting
R
188
A.5 More mathematics
188
A.6 Writing your own
R
scripts
189
A.7 Producing graphics in
R
190
A.8 Saving graphics in
R
192
A.9 Good practice with
R
193
Appendix
В
Data case studies
195
B.I Michelson s speed of light data
195
B.2 Rutherford-Geiger radioactive decay
196
В
. 3
A study of fluid flow
198
B.4 The HR diagram
199
B.5 A particle physics experiment
202
B.6 Atmospheric conditions in New York City
205
Appendix
С
Combinations and permutations
207
C.I Permutations
207
C.2 Combinations
208
C.3 Probability of combinations
209
Appendix
D
More on confidence intervals
210
Appendix
E
Glossary
214
Appendix
F
Notation
219
References
221
Index
223
Scientific Inference
Providing the knowledge and practical experience to begin
analysing scientific data, this book is ideal for physical sciences
students wishing to improve their data handling skills.
The book focuses on explaining and developing the practice and
understanding of basic statistical analysis, concentrating on a few
core ideas, such as the visual display of information, modelling using
the likelihood function, and simulating random data.
Key concepts are developed through a combination of graphical
explanations, worked examples, example computer code and case
studies using real data. Students will develop an understanding of
the ideas behind statistical methods and gain experience in applying
them in practice.
Simon Vaughan is a Reader in the Department of Physics and
Astronomy, University of Leicester, where he has developed and
runs a highly regarded course for final year physics students on the
subject of statistics and data analysis.
Online Resources
www.cambr idge.org/9781
107607590
Ы
Data files for the case studies so students can practise
analysing data
У
Exercises to test students understanding.
|
| any_adam_object | 1 |
| author | Vaughan, Simon |
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| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften Geographie |
| edition | 1. publ. |
| format | Book |
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| language | English |
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| spelling | Vaughan, Simon Verfasser aut Scientific inference learning from data Simon Vaughan 1. publ. Cambridge Cambridge Univ. Press 2013 XIV, 224 S. txt rdacontent n rdamedia nc rdacarrier R Programm (DE-588)4705956-4 gnd rswk-swf Schlussfolgern (DE-588)4251178-1 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Datenaufbereitung (DE-588)4148865-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s DE-604 R Programm (DE-588)4705956-4 s Datenaufbereitung (DE-588)4148865-9 s Datenverarbeitung (DE-588)4011152-0 s Schlussfolgern (DE-588)4251178-1 s 1\p DE-604 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026175588&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026175588&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Vaughan, Simon Scientific inference learning from data R Programm (DE-588)4705956-4 gnd Schlussfolgern (DE-588)4251178-1 gnd Datenverarbeitung (DE-588)4011152-0 gnd Datenanalyse (DE-588)4123037-1 gnd Datenaufbereitung (DE-588)4148865-9 gnd Statistik (DE-588)4056995-0 gnd |
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| title | Scientific inference learning from data |
| title_auth | Scientific inference learning from data |
| title_exact_search | Scientific inference learning from data |
| title_full | Scientific inference learning from data Simon Vaughan |
| title_fullStr | Scientific inference learning from data Simon Vaughan |
| title_full_unstemmed | Scientific inference learning from data Simon Vaughan |
| title_short | Scientific inference |
| title_sort | scientific inference learning from data |
| title_sub | learning from data |
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| topic_facet | R Programm Schlussfolgern Datenverarbeitung Datenanalyse Datenaufbereitung Statistik Einführung |
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