Books  Data Analysis & Modelling  Data Analysis & Statistics 

Practical Statistics for Geographers and Earth Scientists


By: Nigel Walford

440 pages, illus, figs

John Wiley & Sons

Paperback | Jan 2011 | #156836 | ISBN-13: 9780470849156
Availability: Usually dispatched within 5 days Details
NHBS Price: £24.95 $31/€28 approx
Hardback | Jan 2011 | #156837 | ISBN-13: 9780470849149
Availability: Usually dispatched within 5 days Details
NHBS Price: £80.00 $98/€90 approx

About this book

This is a statistics textbook that all students can work through, regardless of their geography or earth science stream degree pathway and their existing mathematical knowledge. The main rationale for the book is to demystify the mathematical component of statistics and to present these techniques in a digestible fashion. This will be achieved through case studies that illustrate the workings of each technique, through photographs and diagrams that help students visualise some of the processes involved and through a clear explanation of how statistical software packages work.

Designed for students with all levels of math background, this book helps take the angst out of the mathematical part of statistics, and encourages students to gain the competence in statistical procedures needed for independent investigations, fieldwork, and other projects. (Booknews, 1 June 2011)




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.


Further Reading.


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