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[null,"1.","Record Nr.",null,"UNINA9910148716503321"]
[null,null,"Autore",null,"Burt James E"]
[null,null,"Titolo",null,"Elementary Statistics for Geographers, Third Edition"]
[null,null,"Pubbl/distr/stampa",null,"New York : , : Guilford Publications, , 2009"]
[{"$nulls":4},"©2009"]
[null,null,"Edizione",null,"[3rd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (669 pages)"]
[null,null,"Disciplina",null,"519.502491"]
[null,null,"Soggetti",null,"Geography - Statistical methods"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Half Title Page -- Title Page -- Copyright Page -- Preface -- Contents -- I. INTRODUCTION -- 1. Statistics and Geography -- 1.1. Statistical Analysis and Geography -- 1.2. Data -- 1.3. Measurement Evaluation -- 1.4. Data and Information -- 1.5. Summary -- II. DESCRIPTIVE STATISTICS -- 2. Displaying and Interpreting Data -- 2.1. Display and Interpretation of the Distributions of Qualitative Variables -- 2.2. Display and Interpretation of the Distributions of Quantitative Variables -- 2.3. Displaying and Interpreting Time-Series Data -- 2.4. Displaying and Interpreting Spatial Data -- 2.5. Summary -- 3. Describing Data with Statistics -- 3.1. Measures of Central Tendency -- 3.2. Measures of Dispersion -- 3.3. Higher Order Moments or Other Numerical Measures of the Characteristics of Distributions -- 3.4. Using Descriptive Statistics with Time-Series Data -- 3.5. Descriptive Statistics for Spatial Data -- 3.6. Summary -- Appendix 3a. Review of Sigma Notation -- Appendix 3b. An Iterative Algorithm for Determining the Weighted or Unweighted Euclidean Median -- 4. Statistical Relationships -- 4.1. Relationships and Dependence -- 4.2. Looking for Relationships in Graphs and Tables -- 4.3. Introduction to Correlation -- 4.4. Introduction to Regression -- 4.5. Temporal Autocorrelation -- 4.6. Summary -- Appendix 4a. Review of the Elementary Geometry of a Line -- Appendix 4b. Least Squares Solution via Elementary Calculus -- III. INFERENTIAL STATISTICS -- 5. Random Variables and Probability Distributions -- 5.1. Elementary Probability Theory -- 5.2. Concept of a Random Variable -- 5.3. Discrete Probability Distribution Models -- 5.4. Continuous Probability Distribution Models -- 5.5. Bivariate Random Variables -- 5.6. Summary -- Appendix 5a. Counting Rules for "]
[{"$nulls":4},"Computing Probabilities."]
[{"$nulls":4},"Appendix 5b. Expected Value and Variance of a Continuous Random Variable -- 6. Sampling -- 6.1. Why Do We Sample? -- 6.2. Steps in the Sampling Process -- 6.3. Types of Samples -- 6.4. Random Sampling and Related Probability Designs -- 6.5. Sampling Distributions -- 6.6. Geographic Sampling -- 6.7. Summary -- 7. Point and Interval Estimation -- 7.1. Statistical Estimation Procedures -- 7.2. Point Estimation -- 7.3. Interval Estimation -- 7.4. Sample Size Determination -- 7.5. Summary -- 8. One-Sample Hypothesis Testing -- 8.1. Key Steps in Classical Hypothesis Testing -- 8.2. PROB-VALUE Method of Hypothesis Testing -- 8.3. Hypothesis Tests Concerning the Population Mean μ and π -- 8.4. Relationship between Hypothesis Testing and Confidence Interval Estimation -- 8.5. Statistical Significance versus Practical Significance -- 8.6. Summary -- 9. Two-Sample Hypothesis Testing -- 9.1. Difference of Means -- 9.2. Difference of Means for Paired Observations -- 9.3. Difference of Proportions -- 9.4. The Equality of Variances -- 9.5. Summary -- 10. Nonparametric Methods -- 10.1. Comparison of Parametric and Nonparametric Tests -- 10.2. One- and Two-Sample Tests -- 10.3. Multisample Kruskal-Wallis Test -- 10.4. Goodness-of-Fit Tests -- 10.5. Contingency Tables -- 10.6. Estimating a Probability Distribution: Kernel Estimates -- 10.7. Bootstrapping -- 10.8. Summary -- 11. Analysis of Variance -- 11.1. The One-Factor, Completely Randomized Design -- 11.2. The Two-Factor, Completely Randomized Design -- 11.3. Multiple Comparisons Using the Scheffé Contrast -- 11.4. Assumptions of the Analysis of Variance -- 11.5. Summary -- Appendix 11a. Derivation of Equation 11-11 from Equation 11-10 -- 12. Inferential Aspects of Linear Regression -- 12.1. Overview of the Steps in a Regression Analysis -- 12.2. Assumptions of the Simple Linear Regression Model."]
[{"$nulls":4},"12.3. Inferences in Regression Analysis -- 12.4. Graphical Diagnostics for the Linear Regression Model -- 12.5. Summary -- 13. Extending Regression Analysis -- 13.1. Multiple Regression Analysis -- 13.2. Variable Transformations and the Shape of the Regression Function -- 13.3. Validating a Regression Model -- 13.4. Summary -- IV. PATTERNS IN SPACE AND TIME -- 14. Spatial Patterns and Relationships -- 14.1. Point Pattern Analysis -- 14.2. Spatial Autocorrelation -- 14.3. Local Indicators of Spatial Association -- 14.4. Regression Models with Spatially Autocorrelated Data -- 14.5. Geographically Weighted Regression -- 14.6. Summary -- 15. Time Series Analysis -- 15.1. Time Series Processes -- 15.2. Properties of Stochastic Processes -- 15.3. Types of Stochastic Processes -- 15.4. Removing Trends: Transformations to Stationarity -- 15.5. Model Identification -- 15.6. Model Fitting -- 15.7. Times Series Models, Running Means, and Filters -- 15.8. The Frequency Approach -- 15.9. Filter Design -- 15.10. Summary -- Appendix: Statistical Tables -- Index -- About the Authors."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]