Description

Book Synopsis
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.

Trade Review

“Overall, this is potentially a very useful, reader-friendly book for its target audience.” (Soil Use and Management, 1 December 2013)



Table of Contents

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

Practical Statistics for Geographers and Earth

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    Order before 4pm today for delivery by Mon 17 Aug 2026.

    A Hardback by Nigel Walford

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      Publisher: John Wiley & Sons Inc
      Publication Date: Publication Date: 07/01/2011
      ISBN13: 9780470849149, 978-0470849149
      ISBN10: 0470849142

      Description

      Book Synopsis
      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.

      Trade Review

      “Overall, this is potentially a very useful, reader-friendly book for its target audience.” (Soil Use and Management, 1 December 2013)



      Table of Contents

      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

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