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E-raamat: Statistical Analysis of Geographical Data: An Introduction

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(University of Oxford)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 14-Mar-2017
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118525111
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Statistical Analysis of Geographical Data: An IntroductionSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 14-Mar-2017
  • Kirjastus: Wiley-Blackwell
  • Keel: eng
  • ISBN-13: 9781118525111
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Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the readers understanding.
Preface xi
1 Dealing with data
1(12)
1.1 The role of statistics in geography
1(2)
1.1.1 Why do geographers need to use statistics?
1(2)
1.2 About this book
3(1)
1.3 Data and measurement error
3(10)
1.3.1 Types of geographical data: nominal, ordinal, interval, and ratio
3(2)
1.3.2 Spatial data types
5(1)
1.3.3 Measurement error, accuracy and precision
6(1)
1.3.4 Reporting data and uncertainties
7(2)
1.3.5 Significant figures
9(1)
1.3.6 Scientific notation (standard form)
10(1)
1.3.7 Calculations in scientific notation
11(1)
Exercises
12(1)
2 Collecting and summarizing data
13(24)
2.1 Sampling methods
13(4)
2.1.1 Research design
13(2)
2.1.2 Random sampling
15(1)
2.1.3 Systematic sampling
16(1)
2.1 A Stratified sampling
17(1)
2.2 Graphical summaries
17(7)
2.2.1 Frequency distributions and histograms
17(4)
2.2.2 Time series plots
21(1)
2.2.3 Scatter plots
22(2)
2.3 Summarizing data numerically
24(13)
2.3.1 Measures of central tendency: mean, median and mode
24(1)
2.3.2 Mean
24(1)
2.3.3 Median
25(1)
2.3.4 Mode
25(3)
2.3.5 Measures of dispersion
28(1)
2.3.6 Variance
29(1)
2.3.7 Standard deviation
30(1)
2.3.8 Coefficient of variation
30(3)
2.3.9 Skewness and kurtosis
33(1)
Exercises
33(4)
3 Probability and sampling distributions
37(12)
3.1 Probability
37(2)
3.1.1 Probability, statistics and random variables
37(1)
3.1.2 The properties of the normal distribution
38(1)
3.2 Probability and the normal distribution: z-scores
39(4)
3.3 Sampling distributions and the central limit theorem
43(6)
Exercises
47(2)
4 Estimating parameters with confidence intervals
49(6)
4.1 Confidence intervals on the mean of a normal distribution: the basics
49(1)
4.2 Confidence intervals in practice: the t-distribution
50(3)
4.3 Sample size
53(1)
4.4 Confidence intervals for a proportion
53(2)
Exercises
54(1)
5 Comparing datasets
55(26)
5.1 Hypothesis testing with one sample: general principles
55(6)
5.1.1 Comparing means: one-sample z-test
56(4)
5.1.2 p-values
60(1)
5.1.3 General procedure for hypothesis testing
61(1)
5.2 Comparing means from small samples: one-sample t-test
61(2)
5.3 Comparing proportions for one sample
63(1)
5.4 Comparing two samples
64(11)
5.4.1 Independent samples
64(1)
5.4.2 Comparing means: t-test with unknown population variances assumed equal
64(4)
5.4.3 Comparing means: t-test with unknown population variances assumed unequal
68(3)
5.4.4 t-test for use with paired samples (paired t-test)
71(3)
5.4.5 Comparing variances: F-test
74(1)
5.5 Non-parametric hypothesis testing
75(6)
5.5.1 Parametric and non-parametric tests
75(1)
5.5.2 Mann-whitney U-test
75(4)
Exercises
79(2)
6 Comparing distributions: the Chi-squared test
81(8)
6.1 Chi-squared test with one sample
81(3)
6.2 Chi-squared test for two samples
84(5)
Exercises
87(2)
7 Analysis of variance
89(20)
7.1 One-way analysis of variance
90(9)
7.2 Assumptions and diagnostics
99(2)
7.3 Multiple comparison tests after analysis of variance
101(4)
7.4 Non-parametric methods in the analysis of variance
105(1)
7.5 Summary and further applications
106(3)
Exercises
107(2)
8 Correlation
109(12)
8.1 Correlation analysis
109(1)
8.2 Pearson's product-moment correlation coefficient
110(2)
8.3 Significance tests of correlation coefficient
112(2)
8.4 Spearman's rank correlation coefficient
114(2)
8.5 Correlation and causality
116(5)
Exercises
117(4)
9 Linear regression
121(24)
9.1 Least-squares linear regression
121(1)
9.2 Scatter plots
122(2)
9.3 Choosing the line of best fit: the `least-squares' procedure
124(4)
9.4 Analysis of residuals
128(2)
9.5 Assumptions and caveats with regression
130(1)
9.6 Is the regression significant?
131(4)
9.7 Coefficient of determination
135(2)
9.8 Confidence intervals and hypothesis tests concerning regression parameters
137(3)
9.8.1 Standard error of the regression parameters
137(1)
9.8.2 Tests on the regression parameters
138(1)
9.8.3 Confidence intervals on the regression parameters
139(1)
9.8.4 Confidence interval about the regression line
140(1)
9.9 Reduced major axis regression
140(2)
9.10 Summary
142(3)
Exercises
142(3)
10 Spatial statistics
145(28)
10.1 Spatial data
145(12)
10.1.1 Types of spatial data
145(1)
10.1.2 Spatial data structures
146(3)
10.1.3 Map projections
149(8)
10.2 Summarizing spatial data
157(2)
10.2.1 Mean centre
157(1)
10.2.2 Weighted mean centre
157(1)
10.2.3 Density estimation
158(1)
10.3 Identifying clusters
159(3)
10.3.1 Quadrat test
159(3)
10.3.2 Nearest neighbour statistics
162(1)
10.4 Interpolation and plotting contour maps
162(1)
10.5 Spatial relationships
163(10)
10.5.1 Spatial autocorrelation
163(1)
10.5.2 Join counts
164(7)
Exercises
171(2)
11 Time series analysis
173(20)
11.1 Time series in geographical research
173(1)
11.2 Analysing time series
174(16)
11.2.1 Describing time series: definitions
174(1)
11.2.2 Plotting time series
175(4)
11.2.3 Decomposing time series: trends, seasonality and irregular fluctuations
179(1)
11.2.4 Analysing trends
180(6)
11.2.5 Removing trends (`detrending' data)
186(1)
11.2.6 Quantifying seasonal variation
187(2)
11.2.7 Autocorrelation
189(1)
11.3 Summary
190(3)
Exercises
190(3)
Appendix A Introduction to the R Package 193(12)
Appendix B Statistical tables 205(36)
References 241(2)
Index 243
Simon J. Dadson is Associate Professor of Physical Geography at Oxford University and Tutor in Geography at Christ Church.
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