Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Spatial Autocorrelation
Autocorrelation occurs whenever a variable exhibits a regular pattern over space, when its values at a set of locations depend on values of the same variables at other locations.Odland introduces spatial autocorrelation to the reader in a concise and readable fashion, and describes the statistical p.
Spatial Autocorrelation
Monograph on statistical analysis methodology for measurement of geographic distribution - develops the theory of spatial autocorrelation together with sample procedures, and considers their use in the presentation and evaluation of maps showing statistical tables in the fields of economic geography and human geography. Bibliography pp. 173 to 176 and graphs.
Automatic Autocorrelation and Spectral Analysis
Spectral analysis requires subjective decisions which influence the final estimate and mean that different analysts can obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that is only acceptable if it is close to the best attainable accuracy for most types of stationary data. This book describes a method which fulfils the above near-optimal-solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.
Elementary Statistics for Geographers
Introduces the techniques and concepts of statistics in human and physical geography. This book explains not only how to apply quantitative tools but also why and how they work. It helps students gain important skills for utilizing conventional and spatial statistics in their own research, as well as for critically evaluating the work of others.
Applied Spatial Statistics for Public Health Data
While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical information systems (GISs). This is the first thorough overview to integrate spatial statistics with data management and the display capabilities of GIS. It describes methods for assessing the likelihood of observed patterns and quantifying the link between exposures and outcomes in spatially correlated data. This introductory text is designed to serve as both an introduction for the novice and a reference for practitioners in the field Requires only minimal background in public health and only some knowledge of statistics through multiple regression Touches upon some advanced topics, such as random effects, hierarchical models and spatial point processes, but does not require prior exposure Includes lavish use of figures/illustrations throughout the volume as well as analyses of several data sets (in the form of "data breaks") Exercises based on data analyses reinforce concepts
Applied Linear Statistical Models
Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.
Encyclopedia of Measurement and Statistics
Publisher Description
Quantitative Analysis
Written in a lecture format with solved problems at the end of each chapter, this book surveys quantitative modeling and decision analysis techniques. It serves to familiarize the reader with quantitative techniques utilized in planning and optimizing complex systems, as well as students experiencing the subject for the first time. It can be used by students of business and public administration without a background in calculus as well as engineers with significant scientific training. It allows the reader to comprehend the material through examples and problems and also demonstrates the value and shortcomings of many methods. Quantitative Analysis: An introduction developed out of the author's experience teaching the material to students at the University of California Los Angeles, California State University, Northridge, and the University of Southern California, Los Angeles.
