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E-raamat: Statistical Methods for Geography: A Student's Guide

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  • Formaat: 432 pages
  • Ilmumisaeg: 04-Dec-2019
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529700237
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Statistical Methods for Geography: A Student's GuideSuurem pilt
  • Formaat: 432 pages
  • Ilmumisaeg: 04-Dec-2019
  • Kirjastus: Sage Publications Ltd
  • Keel: eng
  • ISBN-13: 9781529700237
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Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques.



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.

 

 


Arvustused

This book has become the gold standard for teaching statistical methods to geographers. With a friendly and accessible manner, the author covers introductory statistics while revealing the quirkiness of spatial data. It is suitable for a one-year undergraduate class in geography, and there is no better reference for students transitioning to graduate studies. While always including rich examples form human geography, this new edition includes more examples from physical geography that will appeal to a wider audience. -- Nicholas Nagle Absolutely fabulous resource that connects the utility of statistics for addressing geographic problems and issues. I have long used this text in teaching and research, beginning with the first edition in 2001. The continued revision and updating make this the premier text for introductory quantitative geographical inquiry. -- Alan Murray

About the Author xiii
Preface to the First Edition xiv
Preface to the Second Edition xvii
Preface to the Third Edition xix
Preface to the Fourth Edition xx
Preface to the Fifth Edition xxi
Online Resources xxii
1 Introduction To Statistical Methods For Geography 1(26)
1.1 Introduction
1(1)
1.2 The scientific method
2(2)
1.3 Exploratory and confirmatory approaches in geography
4(1)
1.4 Probability and statistics
5(10)
1.4.1 Probability
5(1)
1.4.2 Statistics
6(1)
1.4.3 Probability paradoxes
7(3)
1.4.4 Geographical applications of probability and statistics
10(5)
1.5 Descriptive and inferential methods
15(2)
1.6 The nature of statistical thinking
17(1)
1.7 Special considerations for spatial data
18(2)
1.7.1 The modifiable areal unit problem
18(1)
1.7.2 Boundary problems
19(1)
1.7.3 Spatial sampling procedures
20(1)
1.7.4 Spatial autocorrelation
20(1)
1.8 The structure of the book
20(2)
1.9 Datasets
22(5)
1.9.1 Introduction
22(1)
1.9.2 Home sales in Milwaukee, Wisconsin, USA in 2012
22(1)
1.9.3 Singapore census data for 2010
23(1)
1.9.4 Hypothetical UK housing prices
23(2)
1.9.5 1990 Census Data for Erie County, New York
25(1)
1.9.6 Monthly rain gauge accumulations for Seattle
25(1)
1.9.7 PM2.5 Particulate matter data for Buffalo, New York (2018)
25(2)
2 Descriptive Statistics 27(34)
2.1 Types of data
27(1)
2.2 Visual descriptive methods
28(4)
2.3 Measures of central tendency
32(3)
2.4 Measures of variability
35(1)
2.5 Other numerical measures for describing data
36(3)
2.5.1 Coefficient of variation
36(1)
2.5.2 Skewness
37(1)
2.5.3 Kurtosis
38(1)
2.5.4 Standard scores
39(1)
2.6 Descriptive spatial statistics
39(12)
2.6.1 The measurement of distance
39(2)
2.6.2 Mean center
41(1)
2.6.3 Median center
42(2)
2.6.4 Standard distance
44(1)
2.6.5 Relative distance
45(1)
2.6.6 Illustration of spatial measures of central tendency and dispersion
46(1)
2.6.7 Angular data
47(4)
2.7 Descriptive statistics in SPSS 25 for Windows
51(1)
2.7.1 Data input
51(1)
2.7.2 Descriptive analysis
51(1)
Solved exercises
52(2)
Exercises
54(7)
3 Probability And Discrete Probability Distributions 61(35)
3.1 Introduction
61(1)
3.2 Sample spaces, random variables, and probabilities
62(5)
3.3 Binomial processes and the binomial distribution
67(4)
3.4 The geometric distribution
71(2)
3.5 The Poisson distribution
73(7)
3.6 The hypergeometric distribution
80(4)
3.6.1 Application to residential segregation
81(1)
3.6.2 Application to the space-time clustering of disease
82(2)
3.7 Binomial tests in SPSS 25 for Windows
84(1)
Solved exercises
84(5)
Exercises
89(7)
4 Continuous Probability Distributions And Probability Models 96(30)
4.1 Introduction
96(1)
4.2 The uniform or rectangular distribution
97(2)
4.3 The normal distribution
99(6)
4.4 The exponential distribution
105(6)
4.5 Summary of discrete and continuous distributions
111(2)
4.6 Probability models
113(7)
4.6.1 The intervening opportunities model
113(5)
4.6.2 A model of migration
118(1)
4.6.3 The future of the human population
119(1)
Solved exercises
120(3)
Exercises
123(3)
5 Inferential Statistics: Confidence Intervals, Hypothesis Testing, And Sampling 126(61)
5.1 Introduction to inferential statistics
126(1)
5.2 Confidence intervals
127(6)
5.2.1 Confidence intervals for the mean
127(3)
5.2.2 Confidence intervals for the mean when the sample size is small
130(1)
5.2.3 Confidence intervals for the difference between two means
130(2)
5.2.4 Confidence intervals for proportions
132(1)
5.3 Hypothesis testing
133(18)
5.3.1 Hypothesis testing and one-sample z-tests of the mean
133(5)
5.3.2 One-sample t-tests
138(2)
5.3.3 One-sample tests for proportions
140(3)
5.3.4 Two-sample tests: differences in means
143(4)
5.3.5 Two-sample tests: differences in proportions
147(2)
5.3.6 Type II errors and statistical power
149(2)
5.4 Distributions of the random variable and distributions of the test statistic
151(2)
5.5 Spatial data and the implications of nonindependence
153(2)
5.6 Further discussion of the effects of deviations from the assumptions
155(5)
5.6.1 One-sample test of proportions: binomial distribution - assumption of constant or equal success probabilities
155(1)
5.6.2 One-sample test of proportions: binomial distribution - assumption of independence
156(2)
5.6.3 Two-sample difference of means test: assumption of independent observations
158(2)
5.6.4 Two-sample difference of means test: assumption of homogeneity
160(1)
5.7 Sampling
160(6)
5.7.1 Spatial sampling
162(1)
5.7.2 Sample size considerations
163(3)
5.8 Some tests for spatial measures of central tendency and variability
166(2)
5.9 One-sample tests of means in SPSS 25 for Windows
168(1)
5.9.1 Interpretation
168(1)
5.10 Two-sample t-tests in SPSS 25 for Windows
169(3)
5.10.1 Data entry
169(1)
5.10.2 Running the t-test
170(2)
5.11 Two-sample t-tests in Excel
172(1)
Solved exercises
173(9)
Exercises
182(5)
6 Analysis Of Variance 187(29)
6.1 Introduction
187(4)
6.1.1 A note on the use of F-tables
190(1)
6.2 Illustrations
191(3)
6.2.1 Hypothetical swimming frequency data
191(2)
6.2.2 Diurnal variation in precipitation
193(1)
6.3 Analysis of variance with two categories
194(1)
6.4 Testing the assumptions
195(1)
6.5 Consequences of failure to meet assumptions
195(3)
6.5.1 Normality
195(1)
6.5.2 Homoscedasticity
196(1)
6.5.3 Independence of observations
196(2)
6.6 The nonparametric Kruskal-Wallis test
198(2)
6.6.1 Illustration: diurnal variation in precipitation
198(1)
6.6.2 More on the Kruskal-Wallis test
199(1)
6.7 The nonparametric median test
200(2)
6.7.1 Illustration
200(2)
6.8 Contrasts
202(1)
6.8.1 A priori contrasts
203(1)
6.9 One-way ANOVA in SPSS 25 for Windows
203(3)
6.9.1 Data entry
203(1)
6.9.2 Data analysis and interpretation
204(2)
6.10 One-way ANOVA in Excel
206(1)
Solved exercises
207(2)
Exercises
209(7)
7 Correlation 216(23)
7.1 Introduction and examples of correlation
216(3)
7.2 More illustrations
219(4)
7.2.1 Mobility and cohort size
219(2)
7.2.2 Statewide infant mortality rates and income
221(2)
7.3 A significance test for r
223(1)
7.3.1 Illustration
223(1)
7.4 The correlation coefficient and sample size
224(2)
7.5 Spearman's rank correlation coefficient
226(2)
7.6 Additional topics
228(2)
7.6.1 The effect of spatial dependence on significance tests for correlation coefficients
228(2)
7.6.2 The modifiable area unit problem and spatial aggregation
230(1)
7.7 Correlation in SPSS 25 for Windows
230(4)
7.7.1 Illustration
231(3)
7.8 Correlation in Excel
234(1)
Solved exercises
234(1)
Exercises
235(4)
8 Data Reduction: Factor Analysis And Cluster Analysis 239(22)
8.1 Introduction
239(1)
8.2 Factor analysis and principal components analysis
240(7)
8.2.1 Illustration: 1990 Census data for Erie County, New York
241(6)
8.3 Cluster analysis
247(9)
8.3.1 More on agglomerative methods
248(1)
8.3.2 Illustration: 1990 Census data for Erie County, New York
249(7)
8.4 Data reduction methods in SPSS 25 for Windows
256(1)
8.4.1 Factor analysis
256(1)
8.4.2 Cluster analysis
256(1)
Exercises
257(4)
9 Introduction To Regression Analysis 261(28)
9.1 Introduction
261(3)
9.2 Fitting a regression line to a set of bivariate data
264(5)
9.2.1 Illustration: income levels and consumer expenditure
267(2)
9.3 Regression in terms of explained and unexplained sums of squares
269(4)
9.3.1 Illustration
272(1)
9.4 Assumptions of regression
273(1)
9.5 Standard error of the estimate
273(1)
9.6 Tests for p
274(1)
9.6.1 Illustration
274(1)
9.7 Illustration: state aid to secondary schools
275(2)
9.8 Linear versus nonlinear models
277(2)
9.9 Regression in SPSS 25 for Windows
279(2)
9.9.1 Data input
279(1)
9.9.2 Analysis
280(1)
9.9.3 Options
280(1)
9.9.4 Output
280(1)
9.10 Regression in Excel
281(1)
9.10.1 Data input
281(1)
9.10.2 Analysis
281(1)
Solved exercises
282(3)
Exercises
285(4)
10 More On Regression 289(39)
10.1 Multiple regression
289(3)
10.1.1 Multicollinearity
291(1)
10.1.2 Interpretation of coefficients in multiple regression
291(1)
10.1.3 Adjusted r2
292(1)
10.2 Misspecification error
292(2)
10.3 Dummy variables
294(5)
10.3.1 Dummy variable regression in a recreation planning example
297(2)
10.4 Multiple regression illustration: species in the Galapagos Islands
299(11)
10.4.1 Model 1: The kitchen-sink approach
300(3)
10.4.2 Missing values
303(1)
10.4.3 Outliers and multicollinearity
304(1)
10.4.4 Model 2
304(3)
10.4.5 Model 3
307(2)
10.4.6 Model 4
309(1)
10.5 Variable selection
310(1)
10.6 Regression analysis on component scores
311(1)
10.7 Categorical dependent variable
311(5)
10.7.1 Binary response: logistic regression
312(4)
10.8 A summary of some problems that can arise in regression analysis
316(2)
10.9 Multiple and logistic regression in SPSS 25 for Windows
318(4)
10.9.1 Multiple regression
318(1)
10.9.2 Logistic regression
318(4)
Exercises
322(6)
11 Introduction To Spatial Patterns And Spatial Regression 328(30)
11.1 Introduction
328(1)
11.2 The analysis of point patterns
329(6)
11.2.1 Quadrat analysis
331(4)
11.3 Geographic patterns in real data: Moran's I
335(6)
11.4 Local statistics
341(1)
11.4.1 Introduction
341(1)
11.4.2 Local Moran statistic
341(1)
11.5 Introduction to spatial aspects of regression
342(1)
11.6 Spatial lag model and neighborhood-based explanatory variables
343(1)
11.7 Spatial regression: autocorrelated errors
344(1)
11.8 Geographically weighted regression
345(1)
11.9 Illustration
346(5)
11.9.1 Ordinary least squares
347(3)
11.9.2 Spatial regression: autocorrelated errors
350(1)
11.9.3 Geographically weighted regression
351(1)
11.10 Finding Moran's I using SPSS 25 for Windows
351(1)
11.11 Finding Moran's I using GeoDa
352(2)
11.12 Spatial Regression with GeoDa 1.4.6
354(2)
Exercises
356(2)
Epilogue 358(3)
Answers For Selected Exercises 361(8)
Appendix A: Statistical Tables 369(17)
Table A.1 Random digits
369(4)
Table A.2 Normal distribution
373(1)
Table A.3 Student's t-distribution
374(2)
Table A.4 Cumulative distribution of student's t-distribution
376(6)
Table A.5 F-distribution
382(3)
Table A.6 x2 distribution
385(1)
Appendix B: Mathematical Conventions And Notation 386(6)
B.1 Mathematical conventions
386(2)
B.2 Mathematical notation
388(4)
B.2.1 More examples
391(1)
Bibliography 392(7)
Index 399
Peter A. Rogerson is SUNY (State University of New York) Distinguished Professor in the Department of Geography at the University at Buffalo, Buffalo, New York, USA. He also holds an adjunct appointment in the Department of Biostatistics.
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