.

0 (hodnocen0 x )

(2) Půjčeno:2x 

BK

2nd ed.

London : Sage, 2006

xvi,304 s. : il.

ISBN 1-4129-0796-9 (brož.)

Obsahuje tabulky, předmluvu, dodatky, rejstřík

Bibliografie na s. [297]-300

Geografie - metody statistické - učebnice vysokošk.

000101220

Contents // Preface xii // Preface to the second edition xv // 1 Introduction to Statistical Methods for Geography 1 // 1.1 Introduction 1 // 1.2 The scientific method 1 // 1.3 Exploratory and confirmatory approaches in geography 4 // 1.4 Probability and statistics 4 // 1.4.1 Probability 4 // 1.4.2 Statistics 5 // 1.4.3 Probability paradoxes 6 // 1.4.4 Geographical applications of probability and statistics 8 // 1.5 Descriptive and inferential methods 13 // 1.6 The nature of statistical thinking 14 // 1.7 Special considerations for spatial data 15 // 1.7.1 Modifiable areal unit problem 16 // 1.7.2 Boundary problems 16 // 1.7.3 Spatial sampling procedures 17 // 1.7.4 Spatial autocorrelation 17 // 1.8 Structure of the book 17 // 1.9 Datasets 19 // 1.9.1 Mobile phone signal strength in Erie County, // New York, US 19 // 1.9.2 House sales in Tyne and Wear 20 // 2 Descriptive Statistics 23 // 2.1 Types of data 23 // 2.2 Visual descriptive methods 24 // 2.3 Measures of central tendency 27 // 2.4 Measures of variability 29 // 2.5 Other numerical measures for describing data 30 // 2.5.1 Coefficient of variation 30 // 2.5.2 Skewness 31 // 2.5.3 Kurtosis 31 // 2.5.4 Standard scores 32 // 2.6 Descriptive spatial statistics 32 // 2.6.1 Mean center 32 // 2.6.2 Median center 33 // 2.6.3 Standard distance 34 // 2.6.4 Relative distance 34 // 2.6.5 Illustration of spatial measures of central tendency and dispersion 35 // 2.6.6 Angular data 36 // 2.7 Descriptive statistics in SPSS for Windows 12.0

39 // 2.7.1 Data input 39 // 2.7.2 Descriptive analysis 39 // Exercises 41 // 3 Probability and Discrete Probability Distributions 47 // 3.1 Introduction 47 // 3.2 Sample spaces, random variables, and probabilities 47 // 3.3 Binomial processes and the binomial distribution 49 // 3.4 The geometric distribution 53 // 3.5 The Poisson distribution 55 // 3.6 The hypergeometric distribution 59 // 3.6.1 Application to residential segregation 61 // 3.6.2 Application to the space-time clustering of disease 61 // 3.7 Binomial tests in SPSS for Windows 12.0 63 // Exercises 63 // 4 Continuous Probability Distributions and Probability Models 69 // 4.1 Introduction 69 // 4.2 The uniform or rectangular distribution 69 // 4.3 The normal distribution 72 // 4.4 The exponential distribution 77 // 4.5 Summary of discrete and continuous distributions 82 // 4.6 Probability models 82 // 4.6.1 The intervening opportunities model 84 // 4.6.2 A model of migration 88 // 4.6.3 The future of the human population 89 // Exercises 90 // 5 Inferential Statistics: Confidence Intervals, Hypothesis Testing and Sampling 93 // 5.1 Introduction to inferential statistics 93 // 5.2 Confidence intervals 93 // 5.2.1 Confidence intervals for the mean 93 // 5.2.2 Confidence intervals for the mean when the sample size is small 96 // 5.2.3 Confidence intervals for proportions 97 // 5.3 Hypothesis testing 97 // 5.3.1 Hypothesis testing and one-sample z-tests of the mean 97 // 5.3.2 One-sample t-tests 101 // 5.3.3 One-sample

tests for proportions 103 // 5.3.4 Two-sample tests: differences in means 105 // 5.3.5 Two-sample tests: differences in proportions 108 // 5.4 Distributions of the random variable and distributions of the test statistic 109 // 5.5 Spatial data and the implications of nonindependence 111 // 5.6 Further discussion of the effects of deviations from the assumptions 112 // 5.6.1 One-sample test of proportions: binomial distribution -assumption of constant or equal success probabilities 113 // 5.6.2 One-sample test of proportions: binomial distribution - assumption of independence 114 // 5.6.3 Two-sample difference of means test: assumption of independent observations 115 // 5.6.4 Two-sample difference of means test: assumption of homogeneity 117 // 5.7 Sampling 117 // 5.7.1 Spatial sampling 118 // 5.7.2 Sample size considerations 119 // 5.8 Some tests for spatial measures of central tendency and variability 122 // 5.9 One-sample tests of means in SPSS for Windows 12.0 124 // 5.9.1 Interpretation 124 // 5.10 Two-sample f-tests in SPSS for Windows 12.0 125 // 5.10.1 Data entry 125 // 5.10.2 Running the t-test 125 // 5.11 Two-sample f-tests in Excel 127 // Exercises 128 // 6 Analysis of Variance 132 // 6.1 Introduction 132 // 6.1.1 A note on the use of F-tables 135 // 6.2 Illustrations 135 // 6.2.1 Hypothetical swimming frequency data 135 // 6.2.2 Diurnal variation in precipitation 137 // 6.3 Analysis of variance with two categories 138 // 6.4 Testing the assumptions 138 // 6.5 Consequences

of failure to meet assumptions 138 // 6.6 The nonparametric Kruskal-Wallis test 139 // 6.6.1 Illustration: diurnal variation in precipitation 139 // 6.6.2 More on the Kruskal-Wallis test 140 // 6.7 The nonparametric median test 141 // 6.7.1 Illustration 141 // 6.8 Contrasts 142 // 6.8.1 A priori contrasts 143 // 6.9 Implications for hypothesis tests when assumptions arc not met 144 // 6.9.1 Normality 144 // 6.9.2 Homoscedasticity 144 // 6.9.3 Independence of observations 145 // 6.10 One-way ANOVA in SPSS for Windows 12.0 146 // 6.10.1 Data entry 146 // 6.10.2 Data analysis and interpretation 147 // 6.11 One-way ANOVA in Excel 148 // Exercises 148 // 7 Correlation 154 // 7.1 Introduction and examples of correlation 154 // 7.2 More illustrations 157 // 7.2.1 Mobility and cohort size 157 // 7.2.2 Statewide infant mortality rates and income 157 // 7.3 A significance test for r 160 // 7.3.1 Illustration 160 // 7.4 The correlation coefficient and sample size 160 // 7.5 Spearman’s rank correlation coefficient 162 // 7.6 Additional topics 162 // 7.6.1 The effect of spatial dependence on significance tests for correlation coefficients 162 // 7.6.2 Modifiable area unit problem and spatial aggregation 165 // 7.7 Correlation in SPSS for Windows 12.0 165 // 7.7.1 Illustration 166 // 7.8 Correlation in Excel 167 // Exercises 168 // 8 Introduction to Regression Analysis 170 // 8.1 Introduction 170 // 8.2 Fitting a regression line to a set of bivariate data 173 // 8.2.1 Illustration: income levels

and consumer expenditure 175 // 8.3 Regression in terms of explained and unexplained sums of squares 176 // 8.3.1 Illustration 179 // 8.4 Assumptions of regression 180 // 8.5 Standard error of the estimate 181 // 8.6 Tests for beta 181 // 8.6.1 Illustration 181 // 8.7 Illustration: state aid to secondary schools 182 // 8.8 Linear versus nonlinear models 184 // 8.9 Regression in SPSS for Windows 12.0 186 // 8.9.1 Data input 186 // 8.9.2 Analysis 186 // 8.9.3 Options 186 // 8.9.4 Output 187 // 8.10 Regression in Excel 187 // 8.10.1 Data input 187 // 8.10.2 Analysis 188 // Exercises 188 // 9 More on Regression 192 // 9.1 Multiple regression 192 // 9.1.1 Multicollinearity 193 // 9.1.2 Interpretation of coefficients in multiple regression 194 // 9.2 Misspecification error 194 // 9.3 Dummy variables 196 // 9.3.1 Dummy variable regression in a recreation planning example 198 // 9.4 Multiple regression illustration: species in the Galapagos Islands 200 // 9.4.1 Model 1: The kitchen-sink approach 200 // 9.4.2 Missing values 202 // 9.4.3 Outliers and multicollinearity 204 // 9.4.4 Model 2 204 // 9.4.5 Model 3 206 // 9.4.6 Model 4 206 // 9.5 Variable selection 208 // 9.6 Categorical dependent variable 209 // 9.6.1 Binary response 209 // 9.7 A summary of some problems that can arise in regression analysis 213 // 9.8 Multiple and logistic regression in SPSS for Windows 12.0 213 // 9.8.1 Multiple regression 213 // 9.8.2 Logistic regression 213 // bxercises 218 // 10 Spatial Patterns 222

10.1 Introduction 222 // 10.2 The analysis of point patterns 223 // 10.2.1 Quadrat analysis 224 // 10.2.2 Nearest neighbor analysis 228 // 10.3 Geographic patterns in areal data 231 // 10.3.1 An example using a chi-squared test 231 // 10.3.2 Moran’s I 232 // 10.4 Local statistics 238 // 10.4.1 Introduction 238 // 10.4.2 Local Moran statistic 239 // 10.4.3 Getis’ Gi statistic 240 // 10.5 Finding Moran’s I using SPSSfoi’ Windows 9.0 240 // Exercises 242 // 11 Some Spatial Aspects of Regression Analysis 244 // 11.1 Introduction 244 // 11.2 Addcd-variable plots 245 // 11.3 Spatial regression: autocorrelated errors 246 // 11.4 Spatially varying parameters 247 // 11.4.1 The expansion method 247 // 11.4.2 Geographically weighted regression 248 // 11.5 Illustration 249 // 11.5.1 Ordinary least squares 250 // 11.5.2 Addcd-variable plots 251 // 11.5.3 Spatial regression: autocorrelated errors 253 // 11.5.4 Expansion method 254 // 11.5.5 Geographically weighted regression 255 // Exercises 256 // 12 Data Reduction: Factor Analysis and Cluster Analysis 257 // 12.1 Introduction 257 // 12.2 Factor analysis and principal components analysis 257 // 12.2.1 Illustration: 1990 Census data for Erie County, // New York 258 // 12.2.2 Regression analysis on component scores 262 // 12.3 Cluster analysis 263 // 12.3.1 More on agglomerative methods 266 // 12.3.2 Illustration: 1990 Census data for Erie County, // New York 266 // 12.4 Data reduction methods in SPSS for Windows 12.0 270 // 12.4.1 Factor analysis

270 // 12.4.2 Cluster analysis 271 // Exercises 273 // Epilogue 275 // Appendix A Statistical Tables 277 // Table A.l Random digits 277 // Table A.2 Normal distribution 279 // Table A.3 Student’s r-distribution 280 // Table A.4 Cumulative t-distribution 281 // Table A.5 F-distribution 283 // Table A.6 x2 distribution 286 // Appendix B Mathematical Conventions and Notation 287 // B.l Mathematical conventions 287 // B.2 Mathematical notation 289 // Appendix C Review and Extension of Some Probability Theory 293 // Bibliography 297 // Index // 301