Introduction | |
Statistics and Geography | p. 3 |
Statistical Analysis and Geography | p. 8 |
Data | p. 16 |
Measurement Evaluation | p. 28 |
Data and Information | p. 31 |
Summary | p. 33 |
Descriptive Statistics | |
Displaying and Interpreting Data | p. 39 |
Displaying and Interpretation of the Distributions of Qualitative Variables | p. 41 |
Display and Interpretation of the Distributions of Quantitative Variables | p. 46 |
Displaying and Interpreting Time-Series Data | p. 74 |
Displaying and Interpreting Spatial Data | p. 79 |
Summary | p. 92 |
Describing Data with Statistics | p. 95 |
Measures of Central Tendency | p. 95 |
Measures of Dispersion | p. 109 |
Higher Order Moments or Other Numerical Measures of the Characteristics of Distributions | p. 117 |
Using Descriptive Statistics with Time-Series Data | p. 118 |
Descriptive Statistics for Spatial Data | p. 124 |
Summary | p. 147 |
Review of Sigma Notation | p. 148 |
An Iterative Algorithm for Determining the Weighted or Unweighted Euclidean Median | p. 150 |
Statistical Relationships | p. 156 |
Relationships and Dependence | p. 157 |
Looking for Relationships in Graphs and Tables | p. 158 |
Introduction to Correlation | p. 164 |
Introduction to Regression | p. 172 |
Temporal Autocorrelation | p. 188 |
Summary | p. 191 |
Review of the Elementary Geometry of a Line | p. 192 |
Least Squares Solution via Elementary Calculus | p. 194 |
Inferential Statistics | |
Random Variables and Probability Distributions | p. 201 |
Elementary Probability Theory | p. 201 |
Concept of a Random Variable | p. 210 |
Discrete Probability Distribution Models | p. 220 |
Continuous Probability Distribution Models | p. 233 |
Bivariate Random Variables | p. 238 |
Summary | p. 246 |
Counting Rules for Computing Probabilities | p. 246 |
Expected Value and Variance of a Continuous Random Variable | p. 250 |
Sampling | p. 254 |
Why Do We Sample? | p. 256 |
Steps in the Sampling Process | p. 257 |
Types of Samples | p. 260 |
Random Sampling and Related Probability Designs | p. 262 |
Sampling Distributions | p. 271 |
Geographic Sampling | p. 282 |
Summary | p. 289 |
Point and Interval Estimation | p. 293 |
Statistical Estimation Procedures | p. 294 |
Point Estimation | p. 300 |
Interval Estimation | p. 303 |
Sample Size Determination | p. 315 |
Summary | p. 318 |
One-Sample Hypothesis Testing | p. 321 |
Key Steps in Classical Hypothesis Testing | p. 321 |
prob-value Method of Hypothesis Testing | p. 333 |
Hypothesis Tests Concerning the Population Mean m and p<$$$> | p. 338 |
Relationship between Hypothesis Testing and Confidence Interval Estimation | p. 345 |
Statistical Significance versus Practical Significance | p. 345 |
Summary | p. 349 |
Two-Sample Hypothesis Testing | p. 353 |
Difference of Means | p. 354 |
Difference of Means for Paired Observations | p. 363 |
Difference of Proportions | p. 367 |
The Equality of Variances | p. 369 |
Summary | p. 373 |
Nonparametric Methods | p. 376 |
Comparison of Parametric and Nonparametric Tests | p. 377 |
One- and Two-Sample Tests | p. 380 |
Multisample Kruskal-Wallis Test | p. 393 |
Goodness-of-Fit Tests | p. 395 |
Contingency Tables | p. 405 |
Estimating a Probability Distribution: Kernel Estimates | p. 408 |
Bootstrapping | p. 418 |
Summary | p. 427 |
Analysis of Variance | p. 432 |
The One-Factor, Completely Randomized Design | p. 434 |
The Two-Factor, Completely Randomized Design | p. 446 |
Multiple Comparisons Using the Scheffe Contrast | p. 453 |
Assumptions of the Analysis of Variance | p. 455 |
Summary | p. 457 |
Derivation of Equation 11-11 from Equation 11-10 | p. 457 |
Inferential Aspects of Linear Regression | p. 461 |
Overview of the Steps in a Regression Analysis | p. 461 |
Assumptions of the Simple Linear Regression Model | p. 465 |
Inferences in Regression Analysis | p. 476 |
Graphical Diagnostics for the Linear Regression Model | p. 488 |
Summary | p. 495 |
Extending Regression Analysis | p. 498 |
Multiple Regression Analysis | p. 498 |
Variable Transformations and the Shape of the Regression Function | p. 514 |
Validating a Regression Model | p. 525 |
Summary | p. 528 |
Patterns in Space and Time | |
Spatial Patterns and Relationships | p. 533 |
Point Pattern Analysis | p. 533 |
Spatial Autocorrelation | p. 544 |
Local Indicators of Spatial Association | p. 559 |
Regression Models with Spatially Autocorrelated Data | p. 566 |
Geographically Weighted Regression | p. 570 |
Summary | p. 571 |
Time Series Analysis | p. 577 |
Time Series Processes | p. 578 |
Properties of Stochastic Processes | p. 579 |
Types of Stochastic Processes | p. 584 |
Removing Trends: Transformations to Stationarity | p. 588 |
Model Identification | p. 590 |
Model Fitting | p. 595 |
Times Series Models, Running Means, and Filters | p. 601 |
The Frequency Approach | p. 603 |
Filter Design | p. 609 |
Summary | p. 616 |
Appendix: Statistical Tables | p. 621 |
Index | p. 643 |
About the Authors | p. 653 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.