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 |

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