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 www.wiley.com/go/Walford

InhaltsangabePreface. 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.

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