Statistics for Earth and Environmental Scientists

Name: Statistics for Earth and Environmental Scientists
Price: 184.71 USD
Availability: InStock
ISBN: 9780470584699

ISBN-10: 0470584696

ISBN-13: 9780470584699

Edition: 2011

Authors: John H. Schuenemeyer, Lawrence J. Drew

List price: $108.00

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Description:

The goal of this book is to present skills and knowledge of important statistical concepts through data analytic tools and then apply them to real world problems in the environmental sciences. Since there is no single approach that works best in all research circumstances, the authors introduce models using the frequentist approach, but also discuss Bayesian, nonparametric, and computer intensive methods. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, sampling all of which are explored in the context of case studies, which use real data from earth science applications. Subsequent chapters focus on general principles of…

Book details

List price: $108.00
Copyright year: 2011
Publisher: John Wiley & Sons, Limited
Publication date: 1/4/2011
Binding: Hardcover
Pages: 420
Size: 6.00" wide x 10.00" long x 1.25" tall
Weight: 1.716
Language: English



Preface



Role of Statistics and Data Analysis



Introduction



Case Studies



Data



Samples Versus the Population: Some Notation



Vector and Matrix Notation



Frequency Distributions and Histograms



Distribution as a Model



Sample Moments



Normal (Gaussian) Distribution



Exploratory Data Analysis



Estimation



Bias



Causes of Variance



About Data



Reasons to Conduct Statistically Based Studies



Data Mining



Modeling



Transformations



Statistical Concepts



Statistics Paradigms



Summary


Exercises



Modeling Concepts



Introduction



Why Construct a Model?



What Does a Statistical Model Do?



Steps in Modeling



Is a Model a Unique Solution to a Problem?



Model Assumptions



Designed Experiments



Replication



Summary


Exercises



Estimation and Hypothesis Testing on Means and Other Statistics



Introduction



Independence of Observations



Central Limit Theorem



Sampling Distributions



Confidence Interval Estimate on a Mean



Confidence Interval on the Difference Between Means



Hypothesis Testing on Means



Bayesian Hypothesis Testing



Nonparametric Hypothesis Testing



Bootstrap Hypothesis Testing on Means



Testing Multiple Means via Analysis of Variance



Multiple Comparisons of Means



Nonparametric ANOVA



Paired Data



Kolmogorov-Smirnov Goodness-of-Fit Test



Comments on Hypothesis Testing



Summary


Exercises



Regression



Introduction



Pittsburgh Coal Quality Case Study



Correlation and Covariance



Simple Linear Regression



Multiple Regression



Other Regression Procedures



Nonlinear Models



Summary


Exercises



Time Series



Introduction



Time Domain



Frequency Domain



Wavelets



Summary


Exercises



Spatial Statistics



Introduction



Data



Three-Dimensional Data Visualization



Spatial Association



Effect of Trend



Semivariogram Models



Kriging



Space-Time Models



Summary


Exercises



Multivariate Analysis



Introduction



Multivariate Graphics



Principal Components Analysis



Factor Analysis



Cluster Analysis



Multidimensional Scaling



Discriminant Analysis



Tree-Based Modeling



Summary


Exercises



Discrete Data Analysis and Point Processes



Introduction



Discrete Process and Distributions



Point Processes



Lattice Data and Models



Proportions



Contingency Tables



Generalized Linear Models



Summary


Exercises



Design of Experiments



Introduction



Sampling Designs



Design of Experiments



Comments on Field Studies and Design



Missing Data



Summary


Exercises



Directional Data



Introduction



Circular Data



Spherical Data



Summary


Exercises


References


Index