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Statistical Methods for Geography
A Student’s Guide

- Peter A. Rogerson - State University of New York, Buffalo, USA

** Statistical Methods for Geography** is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module.

The fifth edition of this bestselling text includes:

· Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis.

· New examples from physical geography and additional real-world examples.

· Updated in-text and online exercises along with downloadable datasets.

This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.

**Peter A. Rogerson** is SUNY Distinguished Professor in the Department of Geography at the University at Buffalo, USA.

1.1 Introduction |

1.2 The scientific method |

1.3 Exploratory and confirmatory approaches in geography |

1.4 Probability and statistics |

1.5 Descriptive and inferential methods |

1.6 The nature of statistical thinking |

1.7 Special considerations for spatial data |

1.8 The structure of the book |

1.9 Datasets |

2.1 Types of data |

2.2 Visual descriptive methods |

2.3 Measures of central tendency |

2.4 Measures of variability |

2.5 Other numerical measures for describing data |

2.6 Descriptive spatial statistics |

2.7 Descriptive statistics in SPSS 25 for Windows |

Solved exercises |

Exercises |

3.1 Introduction |

3.2 Sample spaces, random variables, and probabilities |

3.3 Binomial processes and the binomial distribution |

3.4 The geometric distribution |

3.5 The Poisson distribution |

3.6 The hypergeometric distribution |

3.7 Binomial tests in SPSS 25 for Windows |

Solved exercises |

Exercises |

4.1 Introduction |

4.2 The uniform or rectangular distribution |

4.3 The normal distribution |

4.4 The exponential distribution |

4.5 Summary of discrete and continuous distributions |

4.6 Probability models |

Solved exercises |

Exercises |

5.1 Introduction to inferential statistics |

5.2 Confidence intervals |

5.3 Hypothesis testing |

5.4 Distributions of the random variable and distributions of the test statistic |

5.5 Spatial data and the implications of nonindependence |

5.6 Further discussion of the effects of deviations from the assumptions |

5.7 Sampling |

5.8 Some tests for spatial measures of central tendency and variability |

5.9 One-sample tests of means in SPSS 25 for Windows |

5.10 Two-sample t-tests in SPSS 25 for Windows |

Solved exercises |

Exercises |

6.1 Introduction |

6.2 Illustrations |

6.3 Analysis of variance with two categories |

6.4 Testing the assumptions |

6.5 Consequences of failure to meet assumptions |

6.6 The nonparametric Kruskal–Wallis test |

6.7 The nonparametric median test |

6.8 Contrasts |

6.9 One-way ANOVA in SPSS 25 for Windows |

6.10 One-way ANOVA in Excel |

Solved exercises |

Exercises |

7 CORRELATION |

7.1 Introduction and examples of correlation |

7.2 More illustrations |

7.3 A significance test for r |

7.4 The correlation coefficient and sample size |

7.5 Spearman’s rank correlation coefficient |

7.6 Additional topics |

7.7 Correlation in SPSS 25 for Windows |

7.8 Correlation in Excel |

Solved exercises |

Exercises |

8.1 Introduction |

8.2 Factor analysis and principal components analysis |

8.3 Cluster analysis |

8.4 Data reduction methods in SPSS 25 for Windows |

Exercises |

9.1 Introduction |

9.2 Fitting a regression line to a set of bivariate data |

9.3 Regression in terms of explained and unexplained sums of squares |

9.4 Assumptions of regression |

9.5 Standard error of the estimate |

9.6 Tests for ß |

9.7 Illustration: state aid to secondary schools |

9.8 Linear versus nonlinear models |

9.9 Regression in SPSS 25 for Windows |

9.10 Regression in Excel |

Solved exercises |

Exercises |

10.1 Multiple regression |

10.2 Misspecification error |

10.3 Dummy variables |

10.4 Multiple regression illustration: species in the Galápagos Islands |

10.5 Variable selection |

10.6 Regression analysis on component scores |

10.7 Categorical dependent variable |

10.8 A summary of some problems that can arise in regression analysis |

10.9 Multiple and logistic regression in SPSS 25 for Windows |

Exercises |

11.1 Introduction |

11.2 The analysis of point patterns |

11.3 Geographic patterns in areal data |

11.4 Local statistics |

11.5 Introduction to spatial aspects of regression |

11.6 Spatial lag model and neighborhood-based explanatory variables |

11.7 Spatial regression: autocorrelated errors |

11.8 Geographically weighted regression |

11.9 Illustration |

11.10 Finding Moran’s I using SPSS 25 for Windows |

11.11 Finding Moran’s I using GeoDa |

11.12 Spatial Regression with GeoDa 1.4.6 |

Exercises |