In diesem anwendungsorientierten Lehrbuch werden kompakt alle elementaren statistischen Verfahren fur die OEkonomie anschaulich erklart. Der leicht verstandliche Text ist mit vielen Beispielen und UEbungen erganzt. Die praxisnahe Darstellung der Methoden wird durch die Erklarung und Anwendung der Statistikprogramme R (Open-Source-Progamm) und SPSS vervollstandigt. Im Text sind fur beide Programme viele Programmanweisungen enthalten. Zielgruppe sind insbesondere wirtschaftswissenschaftlich orientierte Studierende. Fur die 4. Auflage wurde das Buch uberarbeitet und erganzt. Leser des gedruckten Buchs erhalten nun in der Springer Nature Flashcards-App zusatzlich kostenfreien Zugriff auf 99 exklusive Lernfragen, mit denen sie ihr Wissen uberprufen koennen.

Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15-18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, sensor signals, and time series.

The 18th Conference of IASC-ERS, COMPSTAT'2008,is held in Porto,P- tugal,fromAugust24thtoAugust29th2008,locallyorganisedbytheFaculty of Economics of the University of Porto. COMPSTAT is an initiative of the European Regional Section of the Int- national Association for Statistical Computing (IASC-ERS), a section of the International Statistical Institute (ISI). COMPSTAT conferences started in 1974 in Wien; previous editions of COMPSTAT were held in Berlin (2002), Prague (2004) and Rome (2006). It is one of the most prestigious world conferences in Computational Statistics, regularly attracting hundreds of - searchers and practitioners, and has gained a reputation as an ideal forum for presenting top qualitytheoretical and applied work,promoting interdis- plinary researchand establishing contacts amongstresearcherswith common interests. COMPSTAT'2008 is the ?rst edition of COMPSTAT to be hosted by a Portuguese institution. Keynote lectures are addressed by Peter Hall (Department of Mathematics and Statistics, The University of Melbourne), Heikki Mannila (Department of Computer Science, Faculty of Science, University of Helsinki) and Timo Ter. asvirta (School of Economics and Management, University of Aarhus). The conference program includes two tutorials: "Computational Methods in Finance"byJamesGentle(DepartmentofComputationalandDataSciences, George Mason University) and "Writing R Packages" by Friedrich Leisch (Institut fur .. Statistik, Ludwig-Maximilians-Universit. at). Each COMPSTAT meeting is organised with a number of topics highlighted, which lead to - vited Sessions. The Conference program includes also contributed sessions in di?erent topics (both oral communications and posters).

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.

Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

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.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

Some probability problems are so difficult that they stump the smartest mathematicians. But even the hardest of these problems can often be solved with a computer and a Monte Carlo simulation, in which a random-number generator simulates a physical process, such as a million rolls of a pair of dice. This is what "Digital Dice" is all about: how to get numerical answers to difficult probability problems without having to solve complicated mathematical equations.

Popular-math writer Paul Nahin challenges readers to solve twenty-one difficult but fun problems, from determining the odds of coin-flipping games to figuring out the behavior of elevators. Problems build from relatively easy (deciding whether a dishwasher who breaks most of the dishes at a restaurant during a given week is clumsy or just the victim of randomness) to the very difficult (tackling branching processes of the kind that had to be solved by Manhattan Project mathematician Stanislaw Ulam). In his characteristic style, Nahin brings the problems to life with interesting and odd historical anecdotes. Readers learn, for example, not just how to determine the optimal stopping point in any selection process but that astronomer Johannes Kepler selected his second wife by interviewing eleven women.

The book shows readers how to write elementary computer codes using any common programming language, and provides solutions and line-by-line walk-throughs of a MATLAB code for each problem.

"Digital Dice" will appeal to anyone who enjoys popular math or computer science. In a new preface, Nahin wittily addresses some of the responses he received to the first edition.

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: * Addresses the theoretical aspects of the field * Advises on how to prepare and perform phylogenetic analysis * Helps with interpreting analyses and visualisation of phylogenies * Describes the software architecture * Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users - from those using phylogenetic tools, to computational biologists and Bayesian statisticians.

This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embedded throughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embedded throughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.

Designed for engineers, computer scientists, and physicists or for use as a textbook in computational courses, Applied Linear Algebra & Optimization Using MATLAB, provides the reader with numerous applications, m-files, and practical examples to solve problems. Balancing theoretical concepts with computational speed and accuracy, the book includes numerous short programs in MATLAB that can be used to solve problems involving systems of linear equations, matrices, vectors, computer graphics, and more. The book is accompanied by a CD-ROM with all of the figures, m-files for all of the programs, and MATLAB simulations from industry. Complete solutions and Microsoft PowerPoint slides are available to instructors for use as a textbook.Brief Table of Contents: 1. Matrices and Linear Systems. 2. Iterative Methods for Linear Systems. 3. The Eigenvalue Problems. 4. Numerical Computation of Eigenvalues. 5. Interpolation and Approximation. 6. Linear Programming. 7. Nonlinear Programming. Appendices. About the CD-ROM

Typische Argumentationen der Mathematischen Statistik werden exemplarisch erlautert: Warum kann aus den Ergebnissen einer Stichprobenuntersuchung auf die Gesamtheit geschlossen werden? Welche Ungenauigkeiten und Unsicherheiten sind dabei moeglich? Wie und warum koennen zufallsbedingte Abweichungen mit mathematischen Methoden analysiert werden? Das Buch ist nicht im klassischen Satz-Beweis-Stil geschrieben. Aufgaben und Schaubilder verdeutlichen die moeglichst weitgehend verbal beschriebenen Gedankengange. Symbol-Graber gibt es nicht. Wichtige Sachverhalte werden mehrfach wiederholt. Einfuhrende Motivationen und abschliessende Resumees runden die Darstellungen ab.

Dieses Buch richtet sich an Studierende verschiedener Fachrichtungen, die das Softwarepaket Octave als kostenfreien und praktischen Lernassistenten nutzen moechten. Es stellt dar, wie sich Octave zur Loesung mathematischer Probleme aus technischen und ingenieurwissenschaftlichen Anwendungen einsetzen lasst. Nebenbei koennen mit diesem Buch elementare Programmierkenntnisse erlernt oder aufgefrischt werden. Da Octave Parallelen zu dem kostenpflichtigen, haufig auf Rechnerarbeitsplatzen in Hochschulen und forschungsorientierten Einrichtungen installierten Softwarepaket MATLAB aufweist, lassen sich die in diesem Buch besprochenen Inhalte und Methoden bequem in die Hochschule und daruber hinaus in die spatere Berufspraxis ubertragen. Das Buch eignet sich damit auch fur Anwender, die in ihrem Berufsleben mathematische Probleme mit Octave oder MATLAB zu loesen haben. Behandelt werden die wichtigsten Grundlagen und Methoden von Octave: elementare Rechnungen mit reellen und komplexen Zahlen, die besonders wichtige Arbeit mit Matrizen und Vektoren, die Arbeit mit Zeichenketten, die Loesung von linearen Gleichungssystemen, die Erstellung von Grafiken mit und ohne animierten Inhalten, die Nutzung und die eigene Programmierung von Octave-Skripten und Octave-Funktionen. Lernenden wird an ausgewahlten Beispielen aus den Bereichen Lineare Algebra, Analysis und numerische Mathematik erlautert, wie Octave zur UEberprufung und Korrektur von Rechenergebnissen bzw. Rechenwegen sowie zum Verstehen und Entdecken von mathematischen Sachverhalten eingesetzt werden kann. Ausserdem werden die Loesung linearer und nichtlinearer Optimierungsprobleme, die Approximation von Daten und Funktionen (Methode der kleinsten Quadrate, Interpolation mit Polynomen und Splines), die Loesung nichtlinearer Gleichungssysteme sowie ausgewahlte Grundlagen der beschreibenden Statistik und Wahrscheinlichkeitsrechnung behandelt. UEbungsaufgaben laden zum Mitmachen ein und helfen, die besprochenen Inhalte zu verstehen, anzuwenden und auf die Aufgaben und Probleme aus den eigenen Mathematikvorlesungen zu ubertragen. Zu jeder Aufgabe gibt es mehr oder weniger ausfuhrliche Musterloesungen. Zusatzmaterialien zum Download erganzen das Buch, wobei die enthaltenen Skripte und Funktionen von den Lesern als Ausgangspunkt fur eigene Programmiertatigkeiten genutzt werden koennen und sollen.

Dieses Lehrbuch liefert einen Einstieg in die mathematische Statistik und baut systematisch eine Brucke zum maschinellen Lernen. Dabei werden sowohl klassische und bis heute wichtige Verfahren untersucht als auch moderne Klassifikationsmethoden des statistischen Lernens. Diese werden mathematisch prazise analysiert und anhand von lebensnahen Beispielen illustriert. Das Buch verschafft den Leserinnen und Lesern einen UEberblick uber statistische Methoden der Datenanalyse und deren mathematischen Grundprinzipien. Der Fokus auf nicht-asymptotische Resultate erlaubt den Zugang zu modernen Anwendungen und fuhrt an aktuelle Forschungsfragen heran. Aufgaben am Kapitelende runden das Buch ab.

R is a statistical computer program used and developed by statisticians around the world. It is probably the leading statistical program, at least among statisticians, and it is freely available. This book is intended for the newcomer who wants to do statistical analysis with R and needs a guide to get started. The book focuses on statistical data problems that are often encountered within the biosceinces. It puts special emphasis on linear models and analysis of repeated measurements data, but also deals with binary data and survival data, among others. Problems are presented and solutions -- along with the corresponding OR code and output -- are provided. The guide is divided into two parts: the first part on R basics and the second part on the statistical analyses using R. Various datasets are used for illustration and they are all available in the R package Guide1data.

Building SPSS Graphs to Understand Data is for anyone needing to understand large or small amounts of data. It describes how to build and interpret graphs, showing how understanding data means that the graph must clearly and succinctly answer questions about the data. In 16 of the 19 chapters research questions are presented, and the reader builds the appropriate graph needed to answer the questions. This handy guide can be used in conjunction with any introductory or intermediate statistics book where the focus is on in-depth presentation of how graphs are used. This book will also useful for graduate students doing research at the masters or doctoral level. The book also contains a chapter designed to address many of the ways that graphs can be used to mislead the graph reader.

In the last decade, the boundary between physics and computer science has become a hotbed of interdisciplinary collaboration. Every passing year shows that physicists and computer scientists have a great deal to say to each other, sharing metaphors, intuitions, and mathematical techniques. In this book, two leading researchers in this area introduce the reader to the fundamental concepts of computational complexity. They go beyond the usual discussion of P, NP and NP-completeness to explain the deep meaning of the P vs. NP question, and explain many recent results which have not yet appeared in any textbook. They then give in-depth explorations of the major interfaces between computer science and physics: phase transitions in NP-complete problems, Monte Carlo algorithms, and quantum computing. The entire book is written in an informal style that gives depth with a minimum of mathematical formalism, exposing the heart of the matter without belabouring technical details. The only mathematical prerequisites are linear algebra, complex numbers, and Fourier analysis (and most chapters can be understood without even these). It can be used as a textbook for graduate students or advanced undergraduates, and will be enjoyed by anyone who is interested in understanding the rapidly changing field of theoretical computer science and its relationship with other sciences.

Dieses Buch bietet eine kompakte Einfuhrung in die Datenauswertung mit der freien Statistikumgebung R. Ziel ist es dabei, einen UEberblick uber die Funktionalitat von R zu liefern und einen schnellen Einstieg in die deskriptive Datenauswertung sowie in die Umsetzung der wichtigsten statistischen Tests zu ermoeglichen. Zudem deckt das Buch die vielfaltigen Moeglichkeiten ab, Diagramme zu erstellen, Daten mit anderen Programmen auszutauschen und R durch Zusatzpakete zu erweitern. Das Buch ist damit fur Leser geeignet, die R kennenlernen und rasch in konkreten Aufgabenstellungen einsetzen moechten. Fur die 3. Auflage wurde das Buch grundlegend uberarbeitet und auf Neuerungen der R Version 4.1.0 sowie der aktuellen Landschaft der Zusatzpakete abgestimmt. Mit einer starkeren Ausrichtung auf Data Science Anwendungen stellt das Buch nun ausfuhrlich die Pakete dplyr zur Datenaufbereitung und ggplot2 fur Diagramme vor. Daruber hinaus enthalt das Buch eine Darstellung von dynamischen R Markdown Dokumenten zur Unterstutzung reproduzierbarer Auswertungen.

Dieses Buch zeigt Ihnen, wie Sie mit Excel beinahe muhelos Informationen aus Daten gewinnen und Datensatze systematisch analysieren koennen. Beides ist (k)eine Kunst! Die statistischen Methoden werden anhand eines einzigen Datensatzes vorgestellt und diskutiert. So wird deutlich, wie die Methoden aufeinander aufbauen und nach und nach immer mehr Informationen aus den Daten entnommen werden koennen. Die verwendeten Funktionen von Excel werden dabei ausfuhrlich erklart - die Vorgehensweise lasst sich daher leicht auf andere Datensatze ubertragen. Verschiedene didaktische Elemente erleichtern die Orientierung und das Arbeiten mit dem Buch: An den Checkpoints sind die wichtigsten Aspekte aus jedem Kapitel kurz zusammengefasst. In der Rubrik Freak-Wissen werden weiterfuhrende Aspekte angesprochen, um Lust auf mehr zu machen. Alle Beispiele werden mit Hand und Excel gerechnet. Zahlreiche Anwendungen und Loesungen sowie weitere Datensatze stehen auf der Internetplattform des Autors zur Verfugung. Passende Foliensatze sind fur Lehrende auf der Verlagsseite des Buchs abrufbar. Fur die zweite Auflage wurde das Buch vollstandig auf Excel 2019 umgestellt und aktualisiert. Daruber hinaus wurden Abschnitte zu Preis- und Mengenindizes, Teststarke sowie ein Kapitel zu Varianzanalyse erganzt.

Employ the essential and hands-on tools and functions of MATLAB's ordinary differential equation (ODE) and partial differential equation (PDE) packages, which are explained and demonstrated via interactive examples and case studies. This book contains dozens of simulations and solved problems via m-files/scripts and Simulink models which help you to learn programming and modeling of more difficult, complex problems that involve the use of ODEs and PDEs. You'll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving more complex engineering and scientific computing problems that require and use differential equations. Practical MATLAB Modeling with Simulink explains various practical issues of programming and modelling. After reading and using this book, you'll be proficient at using MATLAB and applying the source code from the book's examples as templates for your own projects in data science or engineering. What You Will Learn Model complex problems using MATLAB and Simulink Gain the programming and modeling essentials of MATLAB using ODEs and PDEs Use numerical methods to solve 1st and 2nd order ODEs Solve stiff, higher order, coupled, and implicit ODEs Employ numerical methods to solve 1st and 2nd order linear PDEs Solve stiff, higher order, coupled, and implicit PDEs Who This Book Is For Engineers, programmers, data scientists, and students majoring in engineering, applied/industrial math, data science, and scientific computing. This book continues where Apress' Beginning MATLAB and Simulink leaves off.