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Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software

Advanced R Solutions (Paperback): Malte Grosser, Henning Bumann, Hadley Wickham Advanced R Solutions (Paperback)
Malte Grosser, Henning Bumann, Hadley Wickham
R1,273 Discovery Miles 12 730 Ships in 12 - 17 working days

*When R creates copies, and how it affects memory usage and code performance *Everything you could ever want to know about functions *The differences between calling and exiting handlers *How to employ functional programming to solve modular tasks *The motivation, mechanics, usage, and limitations of R's highly pragmatic S3 OO system *The R6 OO system, which is more like OO programming in other languages *The rules that R uses to parse and evaluate expressions *How to use metaprogramming to generate HTML or LaTeX with elegant R code *How to identify and resolve performance bottlenecks

Tolerance Analysis of Electronic Circuits Using MATHCAD (Paperback): Robert Boyd Tolerance Analysis of Electronic Circuits Using MATHCAD (Paperback)
Robert Boyd
R2,144 Discovery Miles 21 440 Ships in 12 - 17 working days

Written for the practicing electronics professional, Tolerance Analysis of Electronic Circuits Using MATHCADä offers a comprehensive, step-by-step treatment of methods used to perform analyses essential to the design process of circuit cards and systems of cards, including:
· worst-case analysis,
· limits for production testing,
· component stress analysis,
· determining if a design meets specification limits, and
· manufacturing yield analysis
Using a practical approach that allows engineers and technicians to put the techniques directly into practice, the author presents the mathematical procedures used to determine performance limits. The topics and techniques discussed include extreme value and root-sum-square analysis using symmetric and asymmetric tolerance, Monte Carlo analysis using normal and uniform distributions, sensitivity formulas, tolerance analyses of opamp offsets, and anomalies of high-Q ac circuits.

Quantitative Investigations in the Biosciences using MINITAB (Paperback): John Eddison Quantitative Investigations in the Biosciences using MINITAB (Paperback)
John Eddison
R1,887 Discovery Miles 18 870 Ships in 12 - 17 working days

Until recently, acquiring a background in the basic methodological principles that apply to most types of investigations meant struggling to obtain results through laborious calculations. The advent of statistical software packages has removed much of the tedium and many of the errors of manual calculations and allowed a marked increase in the depth and sophistication of analyses. Although most statistics classes now incorporate some instruction in using a statistics package, most introductory texts do not. Quantitative Investigations in the Biosciences using MINITAB fills this void by providing an introduction to investigative methods that, in addition to outlining statistical principles and describing methods of calculations, also presents essential commands and interprets output from the statistics package MINITAB. The author introduces the three basic elements of investigations-design, analysis, and reporting-using an extremely accessible approach that keeps mathematical detail to a minimum. He groups statistical tests according to the type of problem they are used to examine, such as comparisons, sequential relationships, and associations. Quantitative Investigations in the Biosciences using MINITAB draws techniques and examples from a variety of subjects, ranging from physiology and biochemistry through to ecology, behavioral sciences, medicine, agriculture and horticulture, and complements the mathematical results with formal conclusions for all of the worked examples. It thus provides an ideal handbook for anyone in virtually any field who wants to apply statistical techniques to their investigations.

Data Driven Statistical Methods (Hardcover, 1st ed): Jim Zidek Data Driven Statistical Methods (Hardcover, 1st ed)
Jim Zidek; Peter Sprent
R4,509 Discovery Miles 45 090 Ships in 12 - 17 working days

Data Driven Statistical Methods is designed for use either as a text book at the undergraduate level, as a source book providing material and suggestions for teachers wishing to incorporate some of its features into more general courses, and also as a self-instruction manual for applied statisticians seeking a simple introduction to many important practical concepts that use the 'data driven' rather than the 'model driven' approach.

Techniques for Evaluating the Differences in Multiregional Input-Output Databases - A Comparative Evaluation of CO2... Techniques for Evaluating the Differences in Multiregional Input-Output Databases - A Comparative Evaluation of CO2 Consumption-Based Accounts Calculated Using Eora, GTAP and WIOD (Paperback, Softcover reprint of the original 1st ed. 2017)
Anne Owen
R3,395 Discovery Miles 33 950 Ships in 10 - 15 working days

This book introduces the Eora, Global Trade Analysis Project (GTAP) and World Input-Output (WIOD) databases and provides detailed metadata on the data sources, database structures and construction techniques used to build each system. It offers a detailed account of how multi-regional input-output (MRIO) databases are used to calculate consumption-based accounts - guiding the reader through each mathematical step and explaining the associated equations. It demonstrates that different MRIO databases calculate different national level consumption-based CO2 accounts. If these results are to be used as evidence in climate policy-making, analysts need to be confident about the accuracy of the databases and understand why the results differ. It carefully explains the mathematical equations behind each technique and provides a link to a repository where the reader can access specially prepared MATLAB functions associated with the techniques. To make meaningful comparisons between the three MRIO databases, each is mapped to a consistent classification system comprising 40 countries and 17 sectors. Further, readers can access the aggregated databases using the link provided. The effect of this aggregation is shown to be minimal, so readers can be confident that the aggregated versions of each database reflect the full-sized versions. The book concludes by making recommendations as to how future MRIO databases could be accurately and consistently constructed and how they should be used in policy-making in light of the findings. Endorsement Anne Owen did a splendid job in comparing the most important Global Multiregional Input Output databases (GMRIO) available. She probably is the first to develop a good understanding what factors in GMRIO construction really contribute to uncertainties in carbon footprint analysis. A great achievement, packed in easy to understand language!Prof. Arnold Tukker Scientific director CML Leiden, NL With its thorough investigations and clear explanations of the differences in MRIO databases and outcomes, this book is compulsory reading for both skilled practitioners and scholars when starting any input-output analysis in a multiregional context. Dr. Harry C. Wilting Senior researcher, environment and economics PBL Netherlands Environmental Assessment Agency, NL A novel exposition on understanding some of the biggest datasets underpinning current global sustainability assessments. Dr. Owen straddles technical incision with practitioner relevant insights and includes a range of methodological advances that will ensure the continued relevance of this work for many years to come. Prof. Richard Wood Industrial Ecology Program NTNU, Norway

R for Marketing Research and Analytics (Paperback): Chris Chapman, Elea McDonnell Feit R for Marketing Research and Analytics (Paperback)
Chris Chapman, Elea McDonnell Feit
R2,229 Discovery Miles 22 290 Ships in 9 - 15 working days

This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.

Advanced R, Second Edition (Hardcover, 2nd edition): Hadley Wickham Advanced R, Second Edition (Hardcover, 2nd edition)
Hadley Wickham
R5,292 Discovery Miles 52 920 Ships in 12 - 17 working days

Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code. By reading this book, you will learn: The difference between an object and its name, and why the distinction is important The important vector data structures, how they fit together, and how you can pull them apart using subsetting The fine details of functions and environments The condition system, which powers messages, warnings, and errors The powerful functional programming paradigm, which can replace many for loops The three most important OO systems: S3, S4, and R6 The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation Effective debugging techniques that you can deploy, regardless of how your code is run How to find and remove performance bottlenecks The second edition is a comprehensive update: New foundational chapters: "Names and values," "Control flow," and "Conditions" comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them Much deeper coverage of metaprogramming, including the new tidy evaluation framework use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming Use of color in code chunks and figures Hadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.

Modelling Covariances and Latent Variables Using EQS (Paperback): G. Dunn Modelling Covariances and Latent Variables Using EQS (Paperback)
G. Dunn
R3,171 Discovery Miles 31 710 Ships in 12 - 17 working days

This primer has been designed as a self-instructional text which serves to introduce the reader to both the principles of statistical modelling of covariance structures and to the use of the EQS software package. It is divided into three parts - the first covering the basic ideas and language of covariance structure modelling together with an introduction to the EQS package. The second section covers a wide variety of models suitable for cross-sectional and longitudinal data and the final section discusses a wide variety of practical problems. This book should be of interest to researchers in psychology, sociology and medicine who use the EQS software; applied and consultant statisticians.

Discovering Statistics Using IBM SPSS Statistics (Hardcover, 5th Revised edition): Andy Field Discovering Statistics Using IBM SPSS Statistics (Hardcover, 5th Revised edition)
Andy Field
R3,916 Discovery Miles 39 160 Ships in 12 - 17 working days

With an exciting new look, new characters to meet, and its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities. What's brand new: A radical new design with original illustrations and even more colour A maths diagnostic tool to help students establish what areas they need to revise and improve on. A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills New sections on replication, open science and Bayesian thinking Now fully up to date with latest versions of IBM SPSS Statistics (c). All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment. Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics.

Computational Actuarial Science with R (Hardcover): Arthur Charpentier Computational Actuarial Science with R (Hardcover)
Arthur Charpentier
R4,123 Discovery Miles 41 230 Ships in 12 - 17 working days

A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).

A Tour of Data Science - Learn R and Python in Parallel (Paperback): Nailong Zhang A Tour of Data Science - Learn R and Python in Parallel (Paperback)
Nailong Zhang
R1,529 Discovery Miles 15 290 Ships in 12 - 17 working days

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

Geocomputation with R (Hardcover): Robin Lovelace, Jakub Nowosad, Jannes Muenchow Geocomputation with R (Hardcover)
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
R2,305 Discovery Miles 23 050 Ships in 9 - 15 working days

Discusses sf, a new foundational package that defines a new set of classes for working with spatial data in R Developed as an open source project on Github Written at an introductory level

Handbook of SAS (R) DATA Step Programming (Hardcover, New): Arthur Li Handbook of SAS (R) DATA Step Programming (Hardcover, New)
Arthur Li
R4,506 Discovery Miles 45 060 Ships in 12 - 17 working days

To write an accomplished program in the DATA step of SAS (R), programmers must understand programming logic and know how to implement and even create their own programming algorithm. Handbook of SAS (R) DATA Step Programming shows readers how best to manage and manipulate data by using the DATA step. The book helps novices avoid common mistakes resulting from a lack of understanding fundamental and unique SAS programming concepts. It explains that learning syntax does not solve all problems; rather, a thorough comprehension of SAS processing is needed for successful programming. The author also guides readers through a programming task. In most of the examples, the author first presents strategies and steps for solving the problem, then offers a solution, and finally gives a more detailed explanation of the solution. Understanding the DATA steps, particularly the program data vector (PDV), is critical to proper data manipulation and management in SAS. This book helps SAS programmers thoroughly grasp the concept of DATA step processing and write accurate programs in the DATA step. Numerous supporting materials, including data sets and programs used in the text, are available on the book's CRC Press web page.

Numerical Methods for Chemical Engineers Using Excel, VBA, and MATLAB (Hardcover, New): Victor J Law Numerical Methods for Chemical Engineers Using Excel, VBA, and MATLAB (Hardcover, New)
Victor J Law
R2,542 Discovery Miles 25 420 Ships in 12 - 17 working days

While teaching the Numerical Methods for Engineers course over the last 15 years, the author found a need for a new textbook, one that was less elementary, provided applications and problems better suited for chemical engineers, and contained instruction in Visual Basic (R) for Applications (VBA). This led to six years of developing teaching notes that have been enhanced to create the current textbook, Numerical Methods for Chemical Engineers Using Excel (R), VBA, and MATLAB (R). Focusing on Excel gives the advantage of it being generally available, since it is present on every computer-PC and Mac-that has Microsoft Office installed. The VBA programming environment comes with Excel and greatly enhances the capabilities of Excel spreadsheets. While there is no perfect programming system, teaching this combination offers knowledge in a widely available program that is commonly used (Excel) as well as a popular academic software package (MATLAB). Chapters cover nonlinear equations, Visual Basic, linear algebra, ordinary differential equations, regression analysis, partial differential equations, and mathematical programming methods. Each chapter contains examples that show in detail how a particular numerical method or programming methodology can be implemented in Excel and/or VBA (or MATLAB in chapter 10). Most of the examples and problems presented in the text are related to chemical and biomolecular engineering and cover a broad range of application areas including thermodynamics, fluid flow, heat transfer, mass transfer, reaction kinetics, reactor design, process design, and process control. The chapters feature "Did You Know" boxes, used to remind readers of Excel features. They also contain end-of-chapter exercises, with solutions provided.

The New Statistics with R - An Introduction for Biologists (Paperback, 2nd Revised edition): Andy Hector The New Statistics with R - An Introduction for Biologists (Paperback, 2nd Revised edition)
Andy Hector 1
R1,209 Discovery Miles 12 090 Ships in 9 - 15 working days

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria. This new edition includes the latest advances in R and related software and has been thoroughly "road-tested" over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis. The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.

Complex Survey Data Analysis with SAS (Hardcover): Taylor H. Lewis Complex Survey Data Analysis with SAS (Hardcover)
Taylor H. Lewis
R2,556 Discovery Miles 25 560 Ships in 12 - 17 working days

Complex Survey Data Analysis with SAS (R) is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips readers with the knowledge to confidently account for them during the estimation and inference process by employing the SURVEY family of SAS/STAT (R) procedures. The book offers comprehensive coverage of the most essential topics, including: Drawing random samples Descriptive statistics for continuous and categorical variables Fitting and interpreting linear and logistic regression models Survival analysis Domain estimation Replication variance estimation methods Weight adjustment and imputation methods for handling missing data The easy-to-follow examples are drawn from real-world survey data sets spanning multiple disciplines, all of which can be downloaded for free along with syntax files from the author's website: http://mason.gmu.edu/~tlewis18/. While other books may touch on some of the same issues and nuances of complex survey data analysis, none features SAS exclusively and as exhaustively. Another unique aspect of this book is its abundance of handy workarounds for certain techniques not yet supported as of SAS Version 9.4, such as the ratio estimator for a total and the bootstrap for variance estimation. Taylor H. Lewis is a PhD graduate of the Joint Program in Survey Methodology at the University of Maryland, College Park, and an adjunct professor in the George Mason University Department of Statistics. An avid SAS user for 15 years, he is a SAS Certified Advanced programmer and a nationally recognized SAS educator who has produced dozens of papers and workshops illustrating how to efficiently and effectively conduct statistical analyses using SAS.

Introduction to Data Science - Data Analysis and Prediction Algorithms with R (Hardcover): Martina Topic Introduction to Data Science - Data Analysis and Prediction Algorithms with R (Hardcover)
Martina Topic
R2,637 Discovery Miles 26 370 Ships in 9 - 15 working days

Covers the basics of R and the tidyverse Demonstrate how to use ggplot2 to generate graphs and describe important Data Visualization principles Introduces Data Wranglin topics such as web scrapping, using regular expressions, and joining and reshaping data tables using the tidyverse tools Illustrates the importance of statistics in data analysis using case studies Uses the caret package to build prediction algorithms including K-nearest Neighbors and Random Forests Includes tools used on a day-to-day basis in data science projects including RStudio, UNIX/Linux shell, Git and GitHub, and knitr and R Markdown

Python Programming and Numerical Methods - A Guide for Engineers and Scientists (Paperback): Qingkai Kong, Timmy Siauw,... Python Programming and Numerical Methods - A Guide for Engineers and Scientists (Paperback)
Qingkai Kong, Timmy Siauw, Alexandre Bayen
R1,825 Discovery Miles 18 250 Ships in 12 - 17 working days

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings.

Independent Random Sampling Methods (Hardcover, 1st ed. 2018): Luca Martino, David Luengo, Joaquin Miguez Independent Random Sampling Methods (Hardcover, 1st ed. 2018)
Luca Martino, David Luengo, Joaquin Miguez
R3,210 R2,953 Discovery Miles 29 530 Save R257 (8%) Ships in 9 - 15 working days

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.

Multi-Level Bayesian Models for Environment Perception (Hardcover, 1st ed. 2022): Csaba Benedek Multi-Level Bayesian Models for Environment Perception (Hardcover, 1st ed. 2022)
Csaba Benedek
R2,821 Discovery Miles 28 210 Ships in 12 - 17 working days

This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and also provides a detailed state-of-the-art overview of existing techniques for automated, multi-level interpretation of the observed static or dynamic environment. To ensure a sound theoretical basis of the new models, the surveys and algorithmic developments are performed in well-established Bayesian frameworks. Low level scene understanding functions are formulated as various image segmentation problems, where the advantages of probabilistic inference techniques such as Markov Random Fields (MRF) or Mixed Markov Models are considered. For the object level scene analysis, the book mainly relies on the literature of Marked Point Process (MPP) approaches, which consider strong geometric and prior interaction constraints in object population modeling. In particular, key developments are introduced in the spatial hierarchical decomposition of the observed scenarios, and in the temporal extension of complex MRF and MPP models. Apart from utilizing conventional optical sensors, case studies are provided on passive radar (ISAR) and Lidar-based Bayesian environment perception tasks. It is shown, via several experiments, that the proposed contributions embedded into a strict mathematical toolkit can significantly improve the results in real world 2D/3D test images and videos, for applications in video surveillance, smart city monitoring, autonomous driving, remote sensing, and optical industrial inspection.

Practical Numerical and Scientific Computing with MATLAB (R) and Python (Hardcover): Eihab B. M. Bashier Practical Numerical and Scientific Computing with MATLAB (R) and Python (Hardcover)
Eihab B. M. Bashier
R2,556 Discovery Miles 25 560 Ships in 12 - 17 working days

Practical Numerical and Scientific Computing with MATLAB (R) and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.

Data Visualization - A Practical Introduction (Paperback): Kieran Healy Data Visualization - A Practical Introduction (Paperback)
Kieran Healy
R1,033 R959 Discovery Miles 9 590 Save R74 (7%) Ships in 12 - 17 working days

An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions

Applied Statistics with R - A Practical Guide for the Life Sciences (Paperback): Justin C. Touchon Applied Statistics with R - A Practical Guide for the Life Sciences (Paperback)
Justin C. Touchon
R1,395 Discovery Miles 13 950 Ships in 10 - 15 working days

The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them. The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Discovering Statistics Using R (Mixed media product): Zoe Field Discovering Statistics Using R (Mixed media product)
Zoe Field
R2,079 R1,624 Discovery Miles 16 240 Save R455 (22%) Ships in 12 - 17 working days

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.

Spatial Data Analysis in Ecology and Agriculture Using R (Hardcover, 2nd edition): Richard E. Plant Spatial Data Analysis in Ecology and Agriculture Using R (Hardcover, 2nd edition)
Richard E. Plant
R3,395 Discovery Miles 33 950 Ships in 12 - 17 working days

Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data. Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods Updates its coverage of R software including newly introduced packages Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.

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