Nigel Walford
Practical Statistics for Geographers and Earth Scientists (eBook, ePUB)
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Nigel Walford
Practical Statistics for Geographers and Earth Scientists (eBook, ePUB)
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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…mehr
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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
Produktdetails
- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 440
- Erscheinungstermin: 5. Juli 2011
- Englisch
- ISBN-13: 9781119957027
- Artikelnr.: 38245089
- Verlag: John Wiley & Sons
- Seitenzahl: 440
- Erscheinungstermin: 5. Juli 2011
- Englisch
- ISBN-13: 9781119957027
- Artikelnr.: 38245089
NIGEL WALFORD Kingston University, london, UK
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
Preface. Acknowledgements. Glossary. Section 1 First principles. 1 What's
in a number? Learning outcomes. 1.1 Introduction to quantitative analysis.
1.2 Nature of numerical data. 1.3 Simplifying mathematical notation. 1.4
Introduction to case studies and structure of the book. 2 Geographical
data: quantity and content. Learning outcomes. 2.1 Geographical data. 2.2
Populations and samples. 2.3 Specifying attributes and variables. 3
Geographical data: collection and acquisition. Learning outcomes. 3.1
Originating data. 3.2 Collection methods. 3.3 Locating phenomena in
geographical space. 4 Statistical measures (or quantities). Learning
outcomes. 4.1 Descriptive statistics. 4.2 Spatial descriptive statistics.
4.3 Central tendency. 4.4 Dispersion. 4.5 Measures of skewness and kurtosis
for nonspatial data. 4.6 Closing comments. 5 Frequency distributions,
probability and hypotheses. Learning outcomes. 5.1 Frequency distributions.
5.2 Bivariate and multivariate frequency distributions. 5.3 Estimation of
statistics from frequency distributions. 5.4 Probability. 5.5 Inference and
hypotheses. 5.6 Connecting summary measures, frequency distributions and
probability. Section 2 Testing times. 6 Parametric tests. Learning
outcomes. 6.1 Introduction to parametric tests. 6.2 One variable and one
sample. 6.3 Two samples and one variable. 6.4 Three or more samples and one
variable. 6.5 Con3 dence intervals. 6.6 Closing comments. 7 Nonparametric
tests. Learning outcomes. 7.1 Introduction to nonparametric tests. 7.2 One
variable and one sample. 7.3 Two samples and one (or more) variable(s). 7.4
Multiple samples and/or multiple variables. 7.5 Closing comments. Section 3
Forming relationships. 8 Correlation. Learning outcomes. 8.1 Nature of
relationships between variables. 8.2 Correlation techniques. 8.3 Concluding
remarks. 9 Regression. Learning outcomes. 9.1 Specifcation of linear
relationships. 9.2 Bivariate regression. 9.3 Concluding remarks. 10
Correlation and regression of spatial data. Learning outcomes. 10.1 Issues
with correlation and regression of spatial data. 10.2 Spatial and temporal
autocorrelation. 10.3 Trend surface analysis. 10.4 Concluding remarks.
References. Further Reading. Index. Plate section: Statistical Analysis
Planner and Checklist falls between pages 172 and 173.
in a number? Learning outcomes. 1.1 Introduction to quantitative analysis.
1.2 Nature of numerical data. 1.3 Simplifying mathematical notation. 1.4
Introduction to case studies and structure of the book. 2 Geographical
data: quantity and content. Learning outcomes. 2.1 Geographical data. 2.2
Populations and samples. 2.3 Specifying attributes and variables. 3
Geographical data: collection and acquisition. Learning outcomes. 3.1
Originating data. 3.2 Collection methods. 3.3 Locating phenomena in
geographical space. 4 Statistical measures (or quantities). Learning
outcomes. 4.1 Descriptive statistics. 4.2 Spatial descriptive statistics.
4.3 Central tendency. 4.4 Dispersion. 4.5 Measures of skewness and kurtosis
for nonspatial data. 4.6 Closing comments. 5 Frequency distributions,
probability and hypotheses. Learning outcomes. 5.1 Frequency distributions.
5.2 Bivariate and multivariate frequency distributions. 5.3 Estimation of
statistics from frequency distributions. 5.4 Probability. 5.5 Inference and
hypotheses. 5.6 Connecting summary measures, frequency distributions and
probability. Section 2 Testing times. 6 Parametric tests. Learning
outcomes. 6.1 Introduction to parametric tests. 6.2 One variable and one
sample. 6.3 Two samples and one variable. 6.4 Three or more samples and one
variable. 6.5 Con3 dence intervals. 6.6 Closing comments. 7 Nonparametric
tests. Learning outcomes. 7.1 Introduction to nonparametric tests. 7.2 One
variable and one sample. 7.3 Two samples and one (or more) variable(s). 7.4
Multiple samples and/or multiple variables. 7.5 Closing comments. Section 3
Forming relationships. 8 Correlation. Learning outcomes. 8.1 Nature of
relationships between variables. 8.2 Correlation techniques. 8.3 Concluding
remarks. 9 Regression. Learning outcomes. 9.1 Specifcation of linear
relationships. 9.2 Bivariate regression. 9.3 Concluding remarks. 10
Correlation and regression of spatial data. Learning outcomes. 10.1 Issues
with correlation and regression of spatial data. 10.2 Spatial and temporal
autocorrelation. 10.3 Trend surface analysis. 10.4 Concluding remarks.
References. Further Reading. Index. Plate section: Statistical Analysis
Planner and Checklist falls between pages 172 and 173.
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
Preface. Acknowledgements. Glossary. Section 1 First principles. 1 What's
in a number? Learning outcomes. 1.1 Introduction to quantitative analysis.
1.2 Nature of numerical data. 1.3 Simplifying mathematical notation. 1.4
Introduction to case studies and structure of the book. 2 Geographical
data: quantity and content. Learning outcomes. 2.1 Geographical data. 2.2
Populations and samples. 2.3 Specifying attributes and variables. 3
Geographical data: collection and acquisition. Learning outcomes. 3.1
Originating data. 3.2 Collection methods. 3.3 Locating phenomena in
geographical space. 4 Statistical measures (or quantities). Learning
outcomes. 4.1 Descriptive statistics. 4.2 Spatial descriptive statistics.
4.3 Central tendency. 4.4 Dispersion. 4.5 Measures of skewness and kurtosis
for nonspatial data. 4.6 Closing comments. 5 Frequency distributions,
probability and hypotheses. Learning outcomes. 5.1 Frequency distributions.
5.2 Bivariate and multivariate frequency distributions. 5.3 Estimation of
statistics from frequency distributions. 5.4 Probability. 5.5 Inference and
hypotheses. 5.6 Connecting summary measures, frequency distributions and
probability. Section 2 Testing times. 6 Parametric tests. Learning
outcomes. 6.1 Introduction to parametric tests. 6.2 One variable and one
sample. 6.3 Two samples and one variable. 6.4 Three or more samples and one
variable. 6.5 Con3 dence intervals. 6.6 Closing comments. 7 Nonparametric
tests. Learning outcomes. 7.1 Introduction to nonparametric tests. 7.2 One
variable and one sample. 7.3 Two samples and one (or more) variable(s). 7.4
Multiple samples and/or multiple variables. 7.5 Closing comments. Section 3
Forming relationships. 8 Correlation. Learning outcomes. 8.1 Nature of
relationships between variables. 8.2 Correlation techniques. 8.3 Concluding
remarks. 9 Regression. Learning outcomes. 9.1 Specifcation of linear
relationships. 9.2 Bivariate regression. 9.3 Concluding remarks. 10
Correlation and regression of spatial data. Learning outcomes. 10.1 Issues
with correlation and regression of spatial data. 10.2 Spatial and temporal
autocorrelation. 10.3 Trend surface analysis. 10.4 Concluding remarks.
References. Further Reading. Index. Plate section: Statistical Analysis
Planner and Checklist falls between pages 172 and 173.
in a number? Learning outcomes. 1.1 Introduction to quantitative analysis.
1.2 Nature of numerical data. 1.3 Simplifying mathematical notation. 1.4
Introduction to case studies and structure of the book. 2 Geographical
data: quantity and content. Learning outcomes. 2.1 Geographical data. 2.2
Populations and samples. 2.3 Specifying attributes and variables. 3
Geographical data: collection and acquisition. Learning outcomes. 3.1
Originating data. 3.2 Collection methods. 3.3 Locating phenomena in
geographical space. 4 Statistical measures (or quantities). Learning
outcomes. 4.1 Descriptive statistics. 4.2 Spatial descriptive statistics.
4.3 Central tendency. 4.4 Dispersion. 4.5 Measures of skewness and kurtosis
for nonspatial data. 4.6 Closing comments. 5 Frequency distributions,
probability and hypotheses. Learning outcomes. 5.1 Frequency distributions.
5.2 Bivariate and multivariate frequency distributions. 5.3 Estimation of
statistics from frequency distributions. 5.4 Probability. 5.5 Inference and
hypotheses. 5.6 Connecting summary measures, frequency distributions and
probability. Section 2 Testing times. 6 Parametric tests. Learning
outcomes. 6.1 Introduction to parametric tests. 6.2 One variable and one
sample. 6.3 Two samples and one variable. 6.4 Three or more samples and one
variable. 6.5 Con3 dence intervals. 6.6 Closing comments. 7 Nonparametric
tests. Learning outcomes. 7.1 Introduction to nonparametric tests. 7.2 One
variable and one sample. 7.3 Two samples and one (or more) variable(s). 7.4
Multiple samples and/or multiple variables. 7.5 Closing comments. Section 3
Forming relationships. 8 Correlation. Learning outcomes. 8.1 Nature of
relationships between variables. 8.2 Correlation techniques. 8.3 Concluding
remarks. 9 Regression. Learning outcomes. 9.1 Specifcation of linear
relationships. 9.2 Bivariate regression. 9.3 Concluding remarks. 10
Correlation and regression of spatial data. Learning outcomes. 10.1 Issues
with correlation and regression of spatial data. 10.2 Spatial and temporal
autocorrelation. 10.3 Trend surface analysis. 10.4 Concluding remarks.
References. Further Reading. Index. Plate section: Statistical Analysis
Planner and Checklist falls between pages 172 and 173.