Geostatistics explained: an introductory guide for earth scientists
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2010
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | XVI, 396 S. Ill., graph. Darst., Kt. 24 cm |
| ISBN: | 9780521746564 9780521763226 |
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| 245 | 1 | 0 | |a Geostatistics explained |b an introductory guide for earth scientists |c Steve McKillup ; Melinda Darby Dyar |
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Datensatz im Suchindex
| _version_ | 1804141229215055872 |
|---|---|
| adam_text | Titel: Geostatistics explained
Autor: McKillup, Stephen
Jahr: 2010
Contents
Preface page xv
1 Introduction 1
1.1 Why do earth scientists need to understand experimental
design and statistics? 1
1.2 What is this book designed to do? 6
2 Doing science : hypotheses, experiments and disproof 8
2.1 Introduction 8
2.2 Basic scientific method 8
2.3 Making a decision about a hypothesis 11
2.4 Why can t a hypothesis or theory ever be proven? 11
2.5 Negative outcomes 12
2.6 Null and alternate hypotheses 12
2.7 Conclusion 13
2.8 Questions 14
3 Collecting and displaying data 15
3.1 Introduction 15
3.2 Variables, sampling units and types of data 15
3.3 Displaying data 17
3.4 Displaying ordinal or nominal scale data 21
3.5 Bivariate data 21
3.6 Data expressed as proportions of a total 25
3.7 Display of geographic direction or orientation 26
3.8 Multivariate data 26
3.9 Conclusion 27
Contents
4 Introductory concepts of experimental design 28
4.1 Introduction 28
4.2 Sampling: mensurative experiments 29
4.3 Manipulative experiments 34
4.4 Sometimes you can only do an unreplicated experiment 40
4.5 Realism 41
4.6 A bit of common sense 42
4.7 Designing a good experiment 43
4.8 Conclusion 44
4.9 Questions 44
5 Doing science responsibly and ethically 45
5.1 Introduction 45
5.2 Dealing fairly with other people s work 45
5.3 Doing the sampling or the experiment 47
5.4 Evaluating and reporting results 48
5.5 Quality control in science 50
5.6 Questions 50
6 Probability helps you make a decision about
your results 51
6.1 Introduction 51
6.2 Statistical tests and significance levels 52
6.3 What has this got to do with making a decision or
statistical testing? 57
6.4 Making the wrong decision 57
6.5 Other probability levels 58
6.6 How are probability values reported? 60
6.7 All statistical tests do the same basic thing 60
6.8 A very simple example: the chi-square test for goodness of fit 60
6.9 What if you get a statistic with a probability of exactly 0.05? 64
6.10 Conclusion 65
6.11 Questions 65
7 Working from samples: data, populations and statistics 66
7.1 Using a sample to infer the characteristics of a population 66
7.2 Statistical tests 66
Contents vii
7.3 The normal distribution 66
7.4 Samples and populations 71
7.5 Your sample mean may not be an accurate estimate of the
population mean 73
7.6 What do you do when you only have data from one
sample? 75
7.7 Why are the statistics that describe the normal
distribution so important? 78
7.8 Distributions that are not normal 80
7.9 Other distributions 80
7.10 Other statistics that describe a distribution 82
7.11 Conclusion 83
7.12 Questions 84
8 Normal distributions: tests for comparing the means
of one and two samples 85
8.1 Introduction 85
8.2 The 95% confidence interval and 95% confidence limits 85
8.3 Using the Z statistic to compare a sample mean and
population mean when population statistics are
known 86
8.4 Comparing a sample mean to an expected value when
population statistics are not known 87
8.5 Comparing the means of two related samples 96
8.6 Comparing the means of two independent samples 98
8.7 Are your data appropriate for a t test? 100
8.8 Distinguishing between data that should be analyzed by
a paired-sample test and a test for two independent
samples 102
8.9 Conclusion 103
8.10 Questions 103
9 Type 1 and Type 2 error, power and sample size 105
9.1 Introduction 105
9.2 Type 1 error 105
9.3 Type 2 error 106
9.4 The power of a test 109
viii Contents
9.5 What sample size do you need to ensure the risk of Type 2
error is not too high? Ill
9.6 Type 1 error, Type 2 error and the concept of risk 113
9.7 Conclusion 113
9.8 Questions 114
10 Single-factor analysis of variance 115
10.1 Introduction 115
10.2 Single-factor analysis of variance 116
10.3 An arithmetic/pictorial example 122
10.4 Unequal sample sizes (unbalanced designs) 128
10.5 An ANOVA does not tell you which particular treatments
appear to be from different populations 128
10.6 Fixed or random effects 128
10.7 Questions 129
11 Multiple comparisons after ANOVA 131
11.1 Introduction 131
11.2 Multiple comparison tests after a Model I ANOVA 131
11.3 An a posteriori Tukey comparison following a significant
result for a single-factor Model I ANOVA 134
11.4 Other a posteriori multiple comparison tests 138
11.5 Planned comparisons 138
11.6 Questions 140
12 Two-factor analysis of variance 142
12.1 Introduction 142
12.2 What does a two-factor ANOVA do? 145
12.3 How does a two-factor ANOVA analyze these data? 146
12.4 How does a two-factor ANOVA separate out the effects of
each factor and interaction? 150
12.5 An example of a two-factor analysis of variance 153
12.6 Some essential cautions and important complications 154
12.7 Unbalanced designs 164
12.8 More complex designs 164
12.9 Questions 165
Contents
13 Important assumptions of analysis of variance,
transformations and a test for equality of
variances 166
13.1 Introduction 166
13.2 Homogeneity of variances 166
13.3 Normally distributed data 167
13.4 Independence 171
13.5 Transformations 171
13.6 Are transformations legitimate? 172
13.7 Tests for heteroscedasticity 174
13.8 Questions 176
14 Two-factor analysis of variance without replication,
and nested analysis of variance 178
14.1 Introduction 178
14.2 Two-factor ANO VA without replication 178
14.3 A posteriori comparison of means after a two-factor
ANO VA without replication 183
14.4 Randomized blocks 184
14.5 Nested ANO VA as a special case of a single-factor
ANOVA 185
14.6 A pictorial explanation of a nested ANOVA 187
14.7 A final comment on ANOVA: this book is only an
introduction 192
14.8 Questions 192
15 Relationships between variables: linear correlation and
linear regression 194
15.1 Introduction 194
15.2 Correlation contrasted with regression 195
15.3 Linear correlation 195
15.4 Calculation of the Pearson r statistic 196
15.5 Is the value of r statistically significant? 202
15.6 Assumptions of linear correlation 202
15.7 Conclusion 202
15.8 Questions 203
Contents
16 Linear regression 204
16.1 Introduction 204
16.2 Linear regression 204
16.3 Calculation of the slope of the regression line 205
16.4 Calculation of the intercept with the Y axis 208
16.5 Testing the significance of the slope and the intercept
of the regression line 211
16.6 An example: school cancellations and snow 217
16.7 Predicting a value of Y from a value of X 219
16.8 Predicting a value of X from a value of Y 219
16.9 The danger of extrapolating beyond the range of data
available 220
16.10 Assumptions of linear regression analysis 220
16.11 Multiple linear regression 223
16.12 Further topics in regression 224
16.13 Questions 225
17 Non-parametric statistics 227
17.1 Introduction 227
17.2 The danger of assuming normality when a population
is grossly non-normal 227
17.3 The value of making a preliminary inspection of the data 229
18 Non-parametric tests for nominal scale data 230
18.1 Introduction 230
18.2 Comparing observed and expected frequencies: the
chi-square test for goodness of fit 231
18.3 Comparing proportions among two or more independent
samples 234
18.4 Bias when there is one degree of freedom 237
18.5 Three-dimensional contingency tables 242
18.6 Inappropriate use of tests for goodness of fit and
heterogeneity 242
18.7 Recommended tests for categorical data 243
18.8 Comparing proportions among two or more related
samples of nominal scale data 243
18.9 Questions 245
Contents
19 Non-parametric tests for ratio, interval or ordinal scale
data 247
19.1 Introduction 247
19.2 A non-parametric comparison between one sample and
an expected distribution 248
19.3 Non-parametric comparisons between two independent
samples 250
19.4 Non-parametric comparisons among more than two
independent samples 256
19.5 Non-parametric comparisons of two related samples 259
19.6 Non-parametric comparisons among three or more
related samples 262
19.7 Analyzing ratio, interval or ordinal data that show gross
differences in variance among treatments and cannot be
satisfactorily transformed 264
19.8 Non-parametric correlation analysis 266
19.9 Other non-parametric tests 268
19.10 Questions 268
20 Introductory concepts of multivariate analysis 270
20.1 Introduction 270
20.2 Simplifying and summarizing multivariate data 271
20.3 An ß-mode analysis: principal components analysis 272
20.4 How does a PCA combine two or more variables into one? 273
20.5 What happens if the variables are not highly correlated? 276
20.6 PCA for more than two variables 277
20.7 The contribution of each variable to the principal
components 279
20.8 An example of the practical use of principal components
analysis 282
20.9 How many principal components should you plot? 282
20.10 How much variation must a PCA explain before it is
useful? 283
20.11 Summary and some cautions and restrictions on use of PCA 283
20.12 Q-mode analyses: multidimensional scaling 284
20.13 How is a univariate measure of dissimilarity among
sampling units extracted from multivariate data? 285
xii Contents
20.14 An example 287
20.15 Stress 289
20.16 Summary and cautions on the use of multidimensional
scaling 290
20.17 Q-mode analyses: cluster analysis 291
20.18 Which multivariate analysis should you use? 295
20.19 Questions 295
21 Introductory concepts of sequence analysis 297
21.1 Introduction 297
21.2 Sequences of ratio, interval or ordinal scale data 298
21.3 Preliminary inspection by graphing 298
21.4 Detection of within-sequence similarity and dissimilarity 299
21.5 Cross-correlation 307
21.6 Regression analysis 308
21.7 Simple linear regression 309
21.8 More complex regression 311
21.9 Simple autoregression 317
21.10 More complex series with a cyclic component 320
21.11 Statistical packages and time series analysis 322
21.12 Some very important limitations and cautions 322
21.13 Sequences of nominal scale data 323
21.14 Records of the repeated occurrence of an event 327
21.15 Conclusion 331
21.16 Questions 332
22 Introductory concepts of spatial analysis 334
22.1 Introduction 334
22.2 Testing whether a spatial distribution occurs at random 335
22.3 Data for the direction of objects 346
22.4 Prediction and interpolation in two dimensions 352
22.5 Conclusion 362
22.6 Questions 362
23 Choosing a test 364
23.1 Introduction 364
Contents xiii
Appendices
Appendix A Critical values of chi-square, tandF 374
Appendix B Answers to questions 380
References 389
Index 391
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| spelling | McKillup, Steve Verfasser (DE-588)141343184 aut Geostatistics explained an introductory guide for earth scientists Steve McKillup ; Melinda Darby Dyar 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2010 XVI, 396 S. Ill., graph. Darst., Kt. 24 cm txt rdacontent n rdamedia nc rdacarrier Includes index Geology / Statistical methods Geologie Geostatistik (DE-588)4020279-3 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Geostatistik (DE-588)4020279-3 s b DE-604 Dyar, Melinda Darby Verfasser (DE-588)141118784 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=019011862&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Geostatistics explained an introductory guide for earth scientists |
| title_auth | Geostatistics explained an introductory guide for earth scientists |
| title_exact_search | Geostatistics explained an introductory guide for earth scientists |
| title_full | Geostatistics explained an introductory guide for earth scientists Steve McKillup ; Melinda Darby Dyar |
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| title_short | Geostatistics explained |
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