Elementary Statistics for Geographers, Third Edition

ISBN-10: 1572304847

ISBN-13: 9781572304840

Edition: 3rd 2009 (Revised)

Authors: Lawrence A. Pervin, Oliver P. John, Gerald M. Barber, David L. Rigby, James E. Burt

List price: $99.00
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Book details

List price: $99.00
Edition: 3rd
Copyright year: 2009
Publisher: Guilford Publications
Publication date: 3/19/2009
Binding: Hardcover
Pages: 653
Size: 6.50" wide x 9.25" long x 1.25" tall
Weight: 2.596
Language: English

Lawrence A. Pervin is Professor of Psychology at Rutgers University. Following an undergraduate education at Brooklyn College and Queens College (City University of New York), he obtained his doctorate from Harvard University in 1962. After six years at Princeton University he went to Rutgers as an Associate Dean to help develop a new, experimental college. The author of many journal articles, including a Citation Classic, and invited book chapters, he also is the author of two leading personality texts, «MDUL»Personality: Theory and Research«MDNM» (Fifth Edition) and «MDUL»Current Controversies and Issues in Personality«MDNM» (Second Edition). Dr. Pervin has served on the editorial board of «MDUL»Psychological Review«MDNM» and the «MDUL»Journal of Personality and Social Psychology«MDNM», and is founding editor of «MDUL»Psychological Inquiry«MDNM», a new international journal of peer commentary and review.

(UPDATED BY KL, 10/21/03) James E. Burt is Professor of Geography at the University of Wisconsin/n-/Madison. Gerald M. Barber currently teaches in the Department of Geography at Queen's University in Canada and maintains an independent consulting practice.

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. nbsp; Gerald M. Barber is Associate Professor of Geography and teaches introductory and advanced courses in statistics at Queen’s University in Kingston, Ontario, Canada.nbsp;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.

Statistics and Geography
Statistical Analysis and Geography
Measurement Evaluation
Data and Information
Descriptive Statistics
Displaying and Interpreting Data
Displaying and Interpretation of the Distributions of Qualitative Variables
Display and Interpretation of the Distributions of Quantitative Variables
Displaying and Interpreting Time-Series Data
Displaying and Interpreting Spatial Data
Describing Data with Statistics
Measures of Central Tendency
Measures of Dispersion
Higher Order Moments or Other Numerical Measures of the Characteristics of Distributions
Using Descriptive Statistics with Time-Series Data
Descriptive Statistics for Spatial Data
Review of Sigma Notation
An Iterative Algorithm for Determining the Weighted or Unweighted Euclidean Median
Statistical Relationships
Relationships and Dependence
Looking for Relationships in Graphs and Tables
Introduction to Correlation
Introduction to Regression
Temporal Autocorrelation
Review of the Elementary Geometry of a Line
Least Squares Solution via Elementary Calculus
Inferential Statistics
Random Variables and Probability Distributions
Elementary Probability Theory
Concept of a Random Variable
Discrete Probability Distribution Models
Continuous Probability Distribution Models
Bivariate Random Variables
Counting Rules for Computing Probabilities
Expected Value and Variance of a Continuous Random Variable
Why Do We Sample?
Steps in the Sampling Process
Types of Samples
Random Sampling and Related Probability Designs
Sampling Distributions
Geographic Sampling
Point and Interval Estimation
Statistical Estimation Procedures
Point Estimation
Interval Estimation
Sample Size Determination
One-Sample Hypothesis Testing
Key Steps in Classical Hypothesis Testing
prob-value Method of Hypothesis Testing
Hypothesis Tests Concerning the Population Mean m and p<$$$>
Relationship between Hypothesis Testing and Confidence Interval Estimation
Statistical Significance versus Practical Significance
Two-Sample Hypothesis Testing
Difference of Means
Difference of Means for Paired Observations
Difference of Proportions
The Equality of Variances
Nonparametric Methods
Comparison of Parametric and Nonparametric Tests
One- and Two-Sample Tests
Multisample Kruskal-Wallis Test
Goodness-of-Fit Tests
Contingency Tables
Estimating a Probability Distribution: Kernel Estimates
Analysis of Variance
The One-Factor, Completely Randomized Design
The Two-Factor, Completely Randomized Design
Multiple Comparisons Using the Scheffe Contrast
Assumptions of the Analysis of Variance
Derivation of Equation 11-11 from Equation 11-10
Inferential Aspects of Linear Regression
Overview of the Steps in a Regression Analysis
Assumptions of the Simple Linear Regression Model
Inferences in Regression Analysis
Graphical Diagnostics for the Linear Regression Model
Extending Regression Analysis
Multiple Regression Analysis
Variable Transformations and the Shape of the Regression Function
Validating a Regression Model
Patterns in Space and Time
Spatial Patterns and Relationships
Point Pattern Analysis
Spatial Autocorrelation
Local Indicators of Spatial Association
Regression Models with Spatially Autocorrelated Data
Geographically Weighted Regression
Time Series Analysis
Time Series Processes
Properties of Stochastic Processes
Types of Stochastic Processes
Removing Trends: Transformations to Stationarity
Model Identification
Model Fitting
Times Series Models, Running Means, and Filters
The Frequency Approach
Filter Design
Appendix: Statistical Tables
About the Authors
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