Applied Statistics for Environmental Science with R

Applied Statistics for Environmental Science with R
Author: Abbas F. M. Al-Karkhi,Wasin A. A. Alqaraghuli
Publsiher: Elsevier
Total Pages: 240
Release: 2019-09-13
ISBN 10: 0128186232
ISBN 13: 9780128186237
Language: EN, FR, DE, ES & NL

Applied Statistics for Environmental Science with R Book Review:

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems. Includes step-by-step tutorials to aid in understanding the process and implementation of unique data Presents statistical theory in a simple way without complex mathematical proofs Shows how to analyze data using R software and provides R scripts for all examples and figures

Statistical Data Analysis Explained

Statistical Data Analysis Explained
Author: Clemens Reimann,Peter Filzmoser,Robert Garrett,Rudolf Dutter
Publsiher: John Wiley & Sons
Total Pages: 362
Release: 2011-08-31
ISBN 10: 1119965284
ISBN 13: 9781119965282
Language: EN, FR, DE, ES & NL

Statistical Data Analysis Explained Book Review:

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

Applied Statistics in Agricultural Biological and Environmental Sciences

Applied Statistics in Agricultural  Biological  and Environmental Sciences
Author: Barry Glaz,Kathleen M. Yeater
Publsiher: John Wiley & Sons
Total Pages: 672
Release: 2020-01-22
ISBN 10: 0891183590
ISBN 13: 9780891183594
Language: EN, FR, DE, ES & NL

Applied Statistics in Agricultural Biological and Environmental Sciences Book Review:

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Foundational and Applied Statistics for Biologists Using R

Foundational and Applied Statistics for Biologists Using R
Author: Ken A. Aho
Publsiher: CRC Press
Total Pages: 618
Release: 2016-03-09
ISBN 10: 1439873399
ISBN 13: 9781439873397
Language: EN, FR, DE, ES & NL

Foundational and Applied Statistics for Biologists Using R Book Review:

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complica

Environmental and Ecological Statistics with R

Environmental and Ecological Statistics with R
Author: Song S. Qian
Publsiher: CRC Press
Total Pages: 536
Release: 2016-11-03
ISBN 10: 1498728731
ISBN 13: 9781498728737
Language: EN, FR, DE, ES & NL

Environmental and Ecological Statistics with R Book Review:

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

Statistical Methods in Water Resources

Statistical Methods in Water Resources
Author: D.R. Helsel,R.M. Hirsch
Publsiher: Elsevier
Total Pages: 546
Release: 1993-03-03
ISBN 10: 9780080875088
ISBN 13: 0080875084
Language: EN, FR, DE, ES & NL

Statistical Methods in Water Resources Book Review:

Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies. The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

Learn R for Applied Statistics

Learn R for Applied Statistics
Author: Eric Goh Ming Hui
Publsiher: Apress
Total Pages: 243
Release: 2018-11-30
ISBN 10: 1484242009
ISBN 13: 9781484242001
Language: EN, FR, DE, ES & NL

Learn R for Applied Statistics Book Review:

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

Statistics for Linguists An Introduction Using R

Statistics for Linguists  An Introduction Using R
Author: Bodo Winter
Publsiher: Routledge
Total Pages: 310
Release: 2019-10-30
ISBN 10: 1351677438
ISBN 13: 9781351677431
Language: EN, FR, DE, ES & NL

Statistics for Linguists An Introduction Using R Book Review:

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.

An R Companion for Applied Statistics I

An R Companion for Applied Statistics I
Author: Danney Rasco
Publsiher: SAGE Publications
Total Pages: 256
Release: 2020-01-28
ISBN 10: 1071806300
ISBN 13: 9781071806302
Language: EN, FR, DE, ES & NL

An R Companion for Applied Statistics I Book Review:

An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provides one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner′s Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.

Applied Statistics in Agricultural Biological and Environmental Sciences

Applied Statistics in Agricultural  Biological  and Environmental Sciences
Author: Barry Glaz,Kathleen M. Yeater
Publsiher: John Wiley & Sons
Total Pages: 672
Release: 2020-01-22
ISBN 10: 0891183590
ISBN 13: 9780891183594
Language: EN, FR, DE, ES & NL

Applied Statistics in Agricultural Biological and Environmental Sciences Book Review:

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors cover fundamental concepts and methodologies of experimental design and analysis, and also delve into advanced statistical topics, all explored by analyzing real agronomic data with practical and creative approaches using available software tools. IN PRESS! This book is being published according to the “Just Published” model, with more chapters to be published online as they are completed.

Quantitative Social Science Data with R

Quantitative Social Science Data with R
Author: Brian J. Fogarty
Publsiher: SAGE
Total Pages: 328
Release: 2018-11-24
ISBN 10: 1526459728
ISBN 13: 9781526459725
Language: EN, FR, DE, ES & NL

Quantitative Social Science Data with R Book Review:

Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs. Gradually introducing applied statistics and the language and functionality of R and R Studio software, it uses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to: - Understand and use quantitative data to answer questions - Approach surrounding ethical issues - Collect quantitative data - Manage, write about, and share the data effectively Supported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned.

Statistical Methods for Environmental Epidemiology with R

Statistical Methods for Environmental Epidemiology with R
Author: Roger D. Peng,Francesca Dominici
Publsiher: Springer Science & Business Media
Total Pages: 144
Release: 2008-12-15
ISBN 10: 9780387781679
ISBN 13: 0387781676
Language: EN, FR, DE, ES & NL

Statistical Methods for Environmental Epidemiology with R Book Review:

As an area of statistical application, environmental epidemiology and more speci cally, the estimation of health risk associated with the exposure to - vironmental agents, has led to the development of several statistical methods and software that can then be applied to other scienti c areas. The stat- tical analyses aimed at addressing questions in environmental epidemiology have the following characteristics. Often the signal-to-noise ratio in the data is low and the targets of inference are inherently small risks. These constraints typically lead to the development and use of more sophisticated (and pot- tially less transparent) statistical models and the integration of large hi- dimensional databases. New technologies and the widespread availability of powerful computing are also adding to the complexities of scienti c inves- gation by allowing researchers to t large numbers of models and search over many sets of variables. As the number of variables measured increases, so do the degrees of freedom for in uencing the association between a risk factor and an outcome of interest. We have written this book, in part, to describe our experiences developing and applying statistical methods for the estimation for air pollution health e ects. Our experience has convinced us that the application of modern s- tistical methodology in a reproducible manner can bring to bear subst- tial bene ts to policy-makers and scientists in this area. We believe that the methods described in this book are applicable to other areas of environmental epidemiology, particularly those areas involving spatial{temporal exposures.

Discovering Statistics Using R

Discovering Statistics Using R
Author: Andy Field,Jeremy Miles,Zoë Field
Publsiher: SAGE
Total Pages: 992
Release: 2012-03-07
ISBN 10: 144628915X
ISBN 13: 9781446289150
Language: EN, FR, DE, ES & NL

Discovering Statistics Using R Book Review:

Lecturers - request an e-inspection copy of this text or contact your local SAGE representative to discuss your course needs. Watch Andy Field's introductory video to Discovering Statistics Using R Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.

Modern Applied Statistics with S PLUS

Modern Applied Statistics with S PLUS
Author: William N. Venables,Brian D. Ripley
Publsiher: Springer Science & Business Media
Total Pages: 549
Release: 2013-11-11
ISBN 10: 1475727194
ISBN 13: 9781475727197
Language: EN, FR, DE, ES & NL

Modern Applied Statistics with S PLUS Book Review:

A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets, with many of the methods discussed being modern approaches to topics such as linear and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, spatial statistics, and classification. This second edition is intended for users of S-PLUS 3.3, or later, and covers both Windows and UNIX. It treats the recent developments in graphics and new statistical functionality, including bootstraping, mixed effects linear and non-linear models, factor analysis, and regression with autocorrelated errors. The authors have written several software libraries which enhance S-PLUS, and these, plus all the datasets used, are available on the Internet.

Bayesian Statistical Methods

Bayesian Statistical Methods
Author: Brian J. Reich,Sujit K. Ghosh
Publsiher: CRC Press
Total Pages: 275
Release: 2019-04-12
ISBN 10: 0429510918
ISBN 13: 9780429510915
Language: EN, FR, DE, ES & NL

Bayesian Statistical Methods Book Review:

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

Easy Statistics for Food Science with R

Easy Statistics for Food Science with R
Author: Abbas F. M. Alkarkhi,Wasin A. A. Alqaraghuli
Publsiher: Academic Press
Total Pages: 228
Release: 2018-09-18
ISBN 10: 0128142634
ISBN 13: 9780128142639
Language: EN, FR, DE, ES & NL

Easy Statistics for Food Science with R Book Review:

Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. Contains numerous step-by-step tutorials help the reader to learn quickly Covers the theory and application of the statistical techniques Shows how to analyze data using R software Provides R scripts for all examples and figures

Applied Spatial Data Analysis with R

Applied Spatial Data Analysis with R
Author: Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
Publsiher: Springer Science & Business Media
Total Pages: 405
Release: 2013-06-21
ISBN 10: 1461476186
ISBN 13: 9781461476184
Language: EN, FR, DE, ES & NL

Applied Spatial Data Analysis with R Book Review:

Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Partnerships Governance and Sustainable Development

Partnerships  Governance and Sustainable Development
Author: P. Glasbergen,Frank Biermann,A. P. J. Mol
Publsiher: Edward Elgar Publishing
Total Pages: 314
Release: 2007
ISBN 10: 1847208665
ISBN 13: 9781847208668
Language: EN, FR, DE, ES & NL

Partnerships Governance and Sustainable Development Book Review:

. . . this is a book to read for anybody who wants a good overview of ongoing research on environmental partnerships in public administration, business administration, political science and sociology. Thomas Sikor, Journal of Integrative Environmental Sciences The profit of this book is the well-proportioned mixture of theoretical reflections . . . and empirical findings, mostly presented in the form of case studies. . . the volume offers a well-structured and recommendable account of the current state of governance and partnerships in the field of sustainable development. Thomas Krumm, Political Studies Review This well-structured volume brings together a group of leading experts on an important emerging topic of global and local environmental policy. The book is highly recommended for every student and scholar in the field of environmental governance. Martin Jänicke, Freie Universität Berlin, Germany Partnerships have emerged as a critical best practice in the pursuit of sustainability. Glasbergen, Biermann and Mol s book explores the partnership issue from a variety of empirical and theoretical perspectives highlighting how to understand them and what (not) to do. Highly recommended. Daniel C. Esty, Yale University, US This significant study discusses the emergence of partnerships for sustainable development as an innovative, and potentially influential, new type of governance. With contributions from leading experts in the field, the partnership paradigm is discussed and the contributors explore the process, extent and circumstances under which partnerships can improve the legitimacy and effectiveness of governance for sustainable development. Scientific research on partnerships within the context of governance theory is fairly new, and there is a clear need to systemize a knowledge base to further define the international research agenda. In addition, there is an urgent demand from governments and international organizations, as well as from non-governmental actors, for strategic insights to build upon their activities in this field. This book is designed to address the questions, debates and agendas related to this new mode of governance. This multi-disciplinary book brings together unique perspectives from organizational theory, policy science, sociology and political science. As such, it will be warmly welcomed by academics of environmental policy and politics as well as scholars and researchers interested in governance for sustainable development. It will also appeal to public policy scholars.

Statistical Methods for Environmental and Agricultural Sciences

Statistical Methods for Environmental and Agricultural Sciences
Author: Reza Hoshmand
Publsiher: CRC Press
Total Pages: 464
Release: 1997-08-26
ISBN 10: 9780849331527
ISBN 13: 0849331528
Language: EN, FR, DE, ES & NL

Statistical Methods for Environmental and Agricultural Sciences Book Review:

The first edition of this book, popular around the world, is surpassed only by this new Second Edition. Improvements such as new and revised exercises, a broad range of practical and relevant case studies, and expanded theoretical concepts make this even better for users of statistics. The book emphasizes the practical application of statistics and provides examples in various fields of environmental and agriculture sciences. Because it uses simple, non-mathematical language to present statistical techniques, the reader requires only a familiarity with elementary algebra and mathematical notations to understand and apply the concepts described. This logically organized book covers the following topics: Part 1 introduces statistical concepts as they apply to different fields of environmental and agriculture sciences and provides descriptive measures of central tendency and variability; Part 2 covers probability and sampling concepts used in inferential statistics; Part 3 presents parametric methods in hypothesis testing, which include research designs; Part 4 discusses a number of nonparametric techniques; Part 5 explains tests of association and prediction; and lastly, analysis of change over time is detailed in Part 6. The appendices contain statistical tables for reference purposes.

Encyclopedia of Supramolecular Chemistry

Encyclopedia of Supramolecular Chemistry
Author: J. L. Atwood,University Research Professor Department of Chemistry Jerry L Atwood,Jonathan W. Steed
Publsiher: CRC Press
Total Pages: 1670
Release: 2004
ISBN 10: 9780824747244
ISBN 13: 0824747240
Language: EN, FR, DE, ES & NL

Encyclopedia of Supramolecular Chemistry Book Review:

Covers the fundamentals of supramolecular chemistry; supramolecular advancements and methods in the areas of chemistry, biochemistry, biology, environmental and materials science and engineering, physics, computer science, and applied mathematics.