Simon James Dadson

Statistical Analysis of Geographical Data

An Introduction

• Gebundenes Buch

Produktbeschreibung

Presents an introduction to the application of statistical analysis. This title covers a range of topics, including uncertainty analysis, graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression.

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Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader's understanding.

Produktdetails

• Verlag: John Wiley & Sons / Wiley
• Seitenzahl: 264
• Erscheinungstermin: 21. März 2016
• Englisch
• Abmessung: 218mm x 140mm x 18mm
• Gewicht: 476g
• ISBN-13: 9780470977033
• ISBN-10: 0470977035
• Artikelnr.: 39558822

Autorenporträt

Simon J. Dadson is Associate Professor of Physical Geography at Oxford University and Tutor in Geography at Christ Church.

Inhaltsangabe

Preface xi 1 Dealing with data 1 1.1 The role of statistics in geography 1
1.2 About this book 3 1.3 Data and measurement error 3 2 Collecting and
summarizing data 13 2.1 Sampling methods 13 2.2 Graphicalsummaries 17 2.3
Summarizing data numerically 24 3 Probability and sampling distributions 37
3.1 Probability 37 3.2 Probability and the normal distribution: z?]scores
39 3.3 Sampling distributions and the central limit theorem 43 4 Estimating
parameters with confidence intervals 49 4.1 Confidence intervals on the
mean of a normal distribution: the basics 49 4.2 Confidence intervals in
practice: the t?]distribution 50 4.3 Sample size 53 4.4 Confidence
intervals for a proportion 53 5 Comparing datasets 55 5.1 Hypothesis
testing with one sample: general principles 55 5.2 Comparing means from
small samples: one?]sample t?]test 61 5.3 Comparing proportions for one
sample 63 5.4 Comparing two samples 64 5.5 Non?]parametric hypothesis
testing 75 6 Comparing distributions: the Chi?]squared test 81 6.1
Chi?]squared test with one sample 81 6.2 Chi?]squared test for two samples
84 7 Analysis of variance 89 7.1 Oneway analysis of variance 90 7.2
Assumptions and diagnostics 99 7.3 Multiple comparison tests after analysis
of variance 101 7.4 Non?]parametric methods in the analysis of variance 105
7.5 Summary and further applications 106 8 Correlation 109 8.1 Correlation
analysis 109 8.2 Pearson's product?]moment correlation coefficient 110 8.3
Significance tests of correlation coefficient 112 8.4 Spearman's rank
correlation coefficient 114 8.5 Correlation and causality 116 9 Linear
regression 121 9.1 Least?]squares linear regression 121 9.2 Scatter plots
122 9.3 Choosing the line of best fit: the 'least?]squares'procedure 124
9.4 Analysis of residuals 128 9.5 Assumptions and caveats with regression
130 9.6 Is the regression significant? 131 9.7 Coefficient of determination
135 9.8 Confidence intervals and hypothesis tests concerning regression
parameters 137 9.9 Reduced major axis regression 140 10 Spatial statistics
145 10.1 Spatial data 145 10.2 Summarizing spatial data 157 10.3
Identifying clusters 159 10.4 Interpolation and plotting contour maps 162
10.5 Spatial relationships 163 11 Time series analysis 173 11.1 Time series
in geographical research 173 11.2 Analysing time series 174 Appendix A:
Introduction to the R package 193 Appendix B: Statistical tables 205
References 241 Index 243