Spatial Statistical Methods For Geography
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Spatial Statistical Methods for Geography
This accessible new textbook offers a straightforward introduction to doing spatial statistics in the context of unique considerations that apply with geographic data. Grounded in real world examples, it shows you how to extend traditional statistical methods for use with spatial data.
Statistical Methods for Geography
How do beginning students of statistics for geography learn to fully understand the key concepts and apply the principal techniques? This text, now in its Fourth Edition, provides exactly that resource. Accessibly written, and focussed on student learning, it’s a statistics 101 that includes definitions, examples, and exercise throughout. Now fully integrated with online self-assessment exercises and video navigation, it explains everything required to get full credits for any undergraduate statistics module: Descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis. Exercises in the text are complemented with online exercise and prompts that test the understanding of concepts and techniques, additional online exercises review understanding of the entire chapter, relating concepts and techniques. Completely revised and updated for accessibility, including new material (on measures of distance, statistical power, sample size selection, and basic probability) with related exercises and downloadable datasets. It is the only text required for undergraduate modules in statistical analysis, statistical methods, and quantitative geography.
Statistical Methods for Geography
Statistical Methods for Geography is a systematic introduction to the essential techniques that all students must understand to complete a module in statistical analysis. It provides an integrated overview of spatial data and spatial data analysis. Key features include: - An applied focus on methods for geographers: the text presents a detailed discussion of regression analysis and autocorrelation - the principal methods of statistical inference used in geography. - An introduction to the principles of spatial analysis and spatial patterns, including probability and probability models; hypothesis testing and sampling; analysis of variance; point patterns; and patterns in areal data.