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Elementary Statistics for Geographers, Third Edition 3rd edition [Hardback]

4.33/5 (28 ratings by Goodreads)
(University of WisconsinMadison, United States), (Queen's University, Canada),
  • Formāts: Hardback, 653 pages, height x width: 234x156 mm, weight: 1040 g
  • Izdošanas datums: 23-Apr-2009
  • Izdevniecība: Guilford Publications
  • ISBN-10: 1572304847
  • ISBN-13: 9781572304840
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Elementary Statistics for Geographers, Third Edition 3rd editionPalielināt
  • Formāts: Hardback, 653 pages, height x width: 234x156 mm, weight: 1040 g
  • Izdošanas datums: 23-Apr-2009
  • Izdevniecība: Guilford Publications
  • ISBN-10: 1572304847
  • ISBN-13: 9781572304840
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781572304840.html
Keywords:
As with previous editions, Burt (U. of Wisconsin-Madison), Barber (Queens U., Ontario, Canada), and Rigbys (UCLA) updated statistics text for geography students provides an accessible algebra-based introduction with explanations that rely on fundamentals and theoretical underpinnings, to enable students to use statistical methods in their own research, critically evaluate the work of others, and to handle new concepts encountered in advanced classes and later reading. The topics have been extensively reorganized for the third edition to reflect current developments in the field, and a substantial amount of new material has been added, including new chapters covering graphical methods, spatial relationships, analysis of variance, extending regression analysis, spatial analysis, and more worked examples with accompanying graphics. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com) Widely adopted, this uniquely comprehensive text introduces the techniques and concepts of statistics in human and physical geography. Unlike other texts that gloss over the conceptual foundations and focus solely on method, the book explains not only how to apply quantitative tools but also why and how they work. Students gain important skills for utilizing both conventional and spatial statistics in their own research, as well as for critically evaluating the work of others. Most chapters are self-contained in order to provide maximum flexibility in course design. Requiring no math beyond algebra, the book is well suited for undergraduate and beginning graduate-level courses. Helpful features include chapter summaries, suggestions for further reading, and practice problems at the end of each chapter. New to This Edition*Restructured and updated to reflect current developments in the field. *Five entirely new chapters cover graphical methods, spatial relationships, analysis of variance, extending regression analysis, and spatial analysis. *Features even more worked examples, many with accompanying graphics.*The companion website offers datasets and solutions to selected end-of-chapter exercises.

Recenzijas

For more than 15 years, I've used prior editions of this text to teach statistics, because it explains difficult but crucial concepts--such as the central limit theorem--clearly and in depth. I also like the geographical examples and the inclusion of descriptive spatial and temporal statistics, such as moving averages and location quotients. Now, with David Rigby on board and a full-fledged treatment of multiple regression, spatial autocorrelation, and spatial regression, the best book just got even better.--Michael Kuby, School of Geographical Sciences, Arizona State University

A comprehensive introduction to statistical techniques and their appropriate use and application in geographic research. The book is unique in its treatment of both spatial and temporal data-analysis issues, and its methods are grounded in interesting research settings. Statistical concepts are presented in a clear and effective manner, with attention given to the theories and assumptions underpinning the techniques. Instructors will appreciate the structured exercises appearing at the end of each chapter, many of which make use of downloadable datasets.This appealing book is especially well suited as a text for senior undergraduate and beginning graduate geography courses in statistical analysis.--Mark W. Horner, Department of Geography, Florida State University

It is hard to imagine a more comprehensive introductory treatment of geographic statistics. Elementary Statistics for Geographers has an excellent mix of quantitative material, problem-solving techniques, and examples. The examples, both numerical and graphical, clearly demonstrate the utility and limitations of the methods presented. Early chapters on the nature of geographic data, exploratory data analysis, and bivariate statistical relationships provide a strong foundation for the rigorous chapters that follow. The range and depth of the third edition are outstanding, with new sections on ANOVA, spatial statistics, and nonparametric statistics. This book will be equally valuable as a text for advanced undergraduates and beginning graduate students and as a general reference.--Scott M. Robeson, Chair, Department of Geography, Indiana University

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I. INTRODUCTION
Statistics and Geography
3(36)
Statistical Analysis and Geography
8(8)
Data
16(12)
Measurement Evaluation
28(3)
Data and Information
31(2)
Summary
33(6)
II. DESCRIPTIVE STATISTICS
Displaying and Interpreting Data
39(56)
Display and Interpretation of the Distributions of Qualitative Variables
41(5)
Display and Interpretation of the Distributions of Quantitative Variables
46(28)
Displaying and Interpreting Time-Series Data
74(5)
Displaying and Interpreting Spatial Data
79(13)
Summary
92(3)
Describing Data with Statistics
95(61)
Measures of Central Tendency
95(14)
Measures of Dispersion
109(8)
Higher Order Moments or Other Numerical Measures of the Characteristics of Distributions
117(1)
Using Descriptive Statistics with Time-Series Data
118(6)
Descriptive Statistics for Spatial Data
124(23)
Summary
147(9)
Appendix 3a. Review of Sigma Notation
148(2)
Appendix 3b. An Iterative Algorithm for Determining the Weighted or Unweighted Euclidean Median
150(6)
Statistical Relationships
156(45)
Relationships and Dependence
157(1)
Looking for Relationships in Graphs and Tables
158(6)
Introduction to Correlation
164(8)
Introduction to Regression
172(16)
Temporal Autocorrelation
188(3)
Summary
191(10)
Appendix 4a. Review of the Elementary Geometry of a Line
192(2)
Appendix 4b. Least Squares Solution via Elementary Calculus
194(7)
III. INFERENTIAL STATISTICS
Random Variables and Probability Distributions
201(53)
Elementary Probability Theory
201(9)
Concept of a Random Variable
210(10)
Discrete Probability Distribution Models
220(13)
Continuous Probability Distribution Models
233(5)
Bivariate Random Variables
238(8)
Summary
246(8)
Appendix 5a. Counting Rules for Computing Probabilities
246(4)
Appendix 5b. Expected Value and Variance of a Continuous Random Variable
250(4)
Sampling
254(39)
Why Do We Sample?
256(1)
Steps in the Sampling Process
257(3)
Types of Samples
260(2)
Random Sampling and Related Probability Designs
262(9)
Sampling Distributions
271(11)
Geographic Sampling
282(7)
Summary
289(4)
Point and Interval Estimation
293(28)
Statistical Estimation Procedures
294(6)
Point Estimation
300(3)
Interval Estimation
303(12)
Sample Size Determination
315(3)
Summary
318(3)
One-Sample Hypothesis Testing
321(32)
Key Steps in Classical Hypothesis Testing
321(12)
PROB-VALUE Method of Hypothesis Testing
333(5)
Hypothesis Tests Concerning the Population Mean μ and π
338(7)
Relationship between Hypothesis Testing and Confidence Interval Estimation
345(1)
Statistical Significance versus Practical Significance
345(4)
Summary
349(4)
Two-Sample Hypothesis Testing
353(23)
Difference of Means
354(9)
Difference of Means for Paired Observations
363(4)
Difference of Proportions
367(2)
The Equality of Variances
369(4)
Summary
373(3)
Nonparametric Methods
376(56)
Comparison of Parametric and Nonparametric Tests
377(3)
One-and Two-Sample Tests
380(13)
Multisample Kruskal-Wallis Test
393(2)
Goodness-of-Fit Tests
395(10)
Contingency Tables
405(3)
Estimating a Probability Distribution: Kernel Estimates
408(10)
Bootstrapping
418(9)
Summary
427(5)
Analysis of Variance
432(29)
The One-Factor, Completely Randomized Design
434(12)
The Two-Factor, Completely Randomized Design
446(7)
Multiple Comparisons Using the Scheffe Contrast
453(2)
Assumptions of the Analysis of Variance
455(2)
Summary
457(4)
Appendix 11a. Derivation of Equation 11-11 from Equation 11-10
457(4)
Inferential Aspects of Linear Regression
461(37)
Overview of the Steps in a Regression Analysis
461(4)
Assumptions of the Simple Linear Regression Model
465(11)
Inferences in Regression Analysis
476(12)
Graphical Diagnostics for the Linear Regression Model
488(7)
Summary
495(3)
Extending Regression Analysis
498(35)
Multiple Regression Analysis
498(16)
Variable Transformations and the Shape of the Regression Function
514(11)
Validating a Regression Model
525(3)
Summary
528(5)
IV. PATTERNS IN SPACE AND TIME
Spatial Patterns and Relationships
533(44)
Point Pattern Analysis
533(11)
Spatial Autocorrelation
544(15)
Local Indicators of Spatial Association
559(7)
Regression Models with Spatially Autocorrelated Data
566(4)
Geographically Weighted Regression
570(1)
Summary
571(6)
Time Series Analysis
577(44)
Time Series Processes
578(1)
Properties of Stochastic Processes
579(5)
Types of Stochastic Processes
584(4)
Removing Trends: Transformations to Stationarity
588(2)
Model Identification
590(5)
Model Fitting
595(6)
Times Series Models, Running Means, and Filters
601(2)
The Frequency Approach
603(6)
Filter Design
609(7)
Summary
616(5)
Appendix: Statistical Tables 621(22)
Index 643(10)
About the Authors 653
James E. Burt is Professor and former chair of Geography at the University of Wisconsin-Madison. His current research focuses on development of expert system and statistical approaches for quantitative prediction of soils information.

Gerald M. Barber is Associate Professor of Geography and teaches introductory and advanced courses in statistics at Queens University in Kingston, Ontario, Canada. In addition, he is the director of the program in Geographic Information Science and runs the GISLAB. His principal interests are in the application of statistical and optimization models within GIS.

David L. Rigby is Professor of Geography and Statistics at the University of California, Los Angeles. His research interests include regional growth, technological change, evolutionary economic dynamics, and the impacts of globalization and trade on wage inequality.