Nigel Walford 
Practical Statistics for Geographers and Earth Scientists 

Practical Statistics for Geographers and Earth Scientists
provides an introductory guide to the principles and application of
statistical analysis in context. This book helps students to gain
the level of competence in statistical procedures necessary for
independent investigations, field-work and other projects. The aim
is to explain statistical techniques using data relating to
relevant geographical, geospatial, earth and environmental science
examples, employing graphics as well as mathematical notation for
maximum clarity. Advice is given on asking the appropriate
preliminary research questions to ensure that the correct data is
collected for the chosen statistical analysis method. The book
offers a practical guide to making the transition from
understanding principles of spatial and non-spatial statistical
techniques to planning a series analyses and generating results
using statistical and spreadsheet computer software.

* Learning outcomes included in each chapter

* International focus

* Explains the underlying mathematical basis of spatial and
non-spatial statistics

* Provides an geographical, geospatial, earth and environmental
science context for the use of statistical methods

* Written in an accessible, user-friendly style

Datasets available on accompanying website at href=’http://www.wiley.com/go/Walford’>www.wiley.com/go/Walford

Inhaltsverzeichnis

Preface xi

Acknowledgements xiii

Glossary xv

Section 1 First principles 1

1 What’s in a number? 3

Learning outcomes

1.1 Introduction to quantitative analysis 4

1.2 Nature of numerical data 9

1.3 Simplifying mathematical notation 14

1.4 Introduction to case studies and structure of the book 19

2 Geographical data: quantity and content 21

Learning outcomes

2.1 Geographical data 21

2.2 Populations and samples 22

2.3 Specifying attributes and variables 43

3 Geographical data: collection and acquisition 57

Learning outcomes

3.1 Originating data 58

3.2 Collection methods 59

3.3 Locating phenomena in geographical space 87

4 Statistical measures (or quantities) 93

Learning outcomes

4.1 Descriptive statistics 93

4.2 Spatial descriptive statistics 96

4.3 Central tendency 100

4.4 Dispersion 118

4.5 Measures of skewness and kurtosis for nonspatial data 124

4.6 Closing comments 129

5 Frequency distributions, probability and hypotheses 131

Learning outcomes

5.1 Frequency distributions 132

5.2 Bivariate and multivariate frequency distributions 137

5.3 Estimation of statistics from frequency distributions 145

5.4 Probability 149

5.5 Inference and hypotheses 165

5.6 Connecting summary measures, frequency distributions and probability 169

Section 2 Testing times 173

6 Parametric tests 175

Learning outcomes

6.1 Introduction to parametric tests 176

6.2 One variable and one sample 177

6.3 Two samples and one variable 201

6.4 Three or more samples and one variable 210

6.5 Confi dence intervals 216

6.6 Closing comments 219

7 Nonparametric tests 221

Learning outcomes

7.1 Introduction to nonparametric tests 222

7.2 One variable and one sample 223

7.3 Two samples and one (or more) variable(s) 245

7.4 Multiple samples and/or multiple variables 256

7.5 Closing comments 264

Section 3 Forming relationships 265

8 Correlation 267

Learning outcomes

8.1 Nature of relationships between variables 268

8.2 Correlation techniques 275

8.3 Concluding remarks 298

9 Regression 299

Learning outcomes

9.1 Specification of linear relationships 300

9.2 Bivariate regression 302

9.3 Concluding remarks 336

10 Correlation and regression of spatial data 341

Learning outcomes

10.1 Issues with correlation and regression of spatial data 342

10.2 Spatial and temporal autocorrelation 345

10.3 Trend surface analysis 378

10.4 Concluding remarks 394

References 397

Further Reading 399

Index 403

Plate section: Statistical Analysis Planner and Checklist falls between pages 172 and 173

Über den Autor

NIGEL WALFORD Kingston University, london, UK