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What is included with this book?
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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