Completely up to date and extremely student friendly, A SIMPLE GUIDE TO IBM SPSS: FOR VERSION 23.0, Fourteenth Edition, equips you with everything you need to know about the newest version of SPSS (R) for Windows (R) so you can effectively use the program in your statistics class. The guide's straightforward style frees you to concentrate on learning basic statistical concepts, while still developing familiarity with SPSS (R). Its clear, step-by-step instruction quickly gets you up to speed, enabling you to confidently use SPSS (R) to do homework problems and conduct statistical analyses for research projects.

This handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will Learn Discover the modes and classes of R objects and how to use them Use both packaged and user-created functions in R Import/export data and create new data objects in R Create descriptive functions and manipulate objects in R Take advantage of flow control and conditional statements Work with packages such as base, stats, and graphics Who This Book Is For Those with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organised by CASE, Center of Applied Statistics and Eco nomics at Humboldt-Universitat zu Berlin in cooperation with F'reie Universitat Berlin and University of Potsdam. The topics of COMPSTAT include methodological applications, innovative soft ware and mathematical developments, especially in the following fields: statistical risk management, multivariate and robust analysis, Markov Chain Monte Carlo Methods, statistics of E-commerce, new strategies in teaching (Multimedia, In ternet), computerbased sampling/questionnaires, analysis of large databases (with emphasis on computing in memory), graphical tools for data analysis, classification and clustering, new statistical software and historical development of software."

Commonly there is no natural place in a traditional curriculum for mathematics or statistics, where a bridge between theory and practice fits into. On the other hand, the demand for an education designed to supplement theoretical training by practial experience has been rapidly increasing.

There exists, consequently, a bit of a dichotomy between theoretical and applied statistics, and this book tries to straddle that gap. It links up the theory of a selection of statistical procedures used in general practice with their application to real world data sets using the statistical software package SAS (Statistical Analysis System). These applications are intended to illustrate the theory and to provide, simultaneously, the ability to use the knowledge effectively and readily in execution.

This book constitutes the thoroughly refereed post-proceedings of the 9th International Symposium on Graph Drawing, GD 2001, held in Vienna, Austria, in September 2001.The 32 revised full papers presented were carefully reviewed and selected from 66 paper submissions. Also included are a corrected version of a paper from the predecessor volume, short reports on the software systems exhibition, two papers of the special session on graph exchange formats, and a report on the annual graph drawing contests. The papers are organized in topical sections on hierarchical drawing, planarity, crossing theory, compaction, planar graphs, symmetries, interactive drawing, representations, aesthetics, 2D- and 3D-embeddings, data visualization, floor planning, and planar drawing.

¿The best book on Maple just got better. This lively book is bursting with clear descriptions, revealing examples and top tips. It is gentle enough to act as an introduction and yet sufficiently comprehensive and well organised to serve as a reference manual. Maple Release 7 is significantly different to earlier releases, so this book will appeal even to hardened users who want to catch up fast.¿ ¿Des Higham, University of Strathclyde, UK This book provides an accelerated introduction to Maple for scientific programmers who already have experience in other computer languages (such as C, Pascal, or FORTRAN). It gives an overview of the most commonly used constructs and provides an elementary introduction to Maple programming. This edition of the book has been extensively updated for Maple Release 7 with future releases in mind. This has involved a substantial update of all programs, examples and exercises. Extensive new material has also been added, including an appendix on complex variables in a computer algebra context.

The modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.

Quantum Methods with Mathematica, the first book of its kind, has achieved worldwide success and critical acclaim.

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

This book offers a detailed application guide to XploRe - an interactive statistical computing environment. As a guide it contains case studies of real data analysis situations. It helps the beginner in statistical data analysis to learn how XploRe works in real life applications. Many examples from practice are discussed and analysed in full length. Great emphasis is put on a graphic based understanding of the data interrelations. The case studies include: Survival modelling with Cox's proportional hazard regression, Vitamin C data analysis with Quantile Regression, and many others.

This book covers the needs of scientists - be they mathematicians, physicists, chemists or engineers - in terms of symbolic computation, and allows them to locate quickly, via a detailed table of contents and index, the method they require for the precise problem they are adressing.It requires no prior experience of symbolic computation, nor specialized mathematical knowledge, and provides quick access to the practical use of symbolic computation software. The organization of the book in mutually independent chapters, each focusing on a specific topic, allows the user to select what is of interest without necessarily reading everything.

IBM SPSS for Introductory Statistics is designed to help students learn how to analyze and interpret research. In easy-to-understand language, the authors show readers how to choose the appropriate statistic based on the design, and to interpret outputs appropriately. There is such a wide variety of options and statistics in SPSS, that knowing which ones to use and how to interpret the outputs can be difficult. This book assists students with these challenges. Comprehensive and user-friendly, the book prepares readers for each step in the research process: design, entering and checking data, testing assumptions, assessing reliability and validity, computing descriptive and inferential parametric and nonparametric statistics, and writing about results. Dialog windows and SPSS syntax, along with the output, are provided. Several realistic data sets, available online, are used to solve the chapter problems. This new edition includes updated screenshots and instructions for IBM SPSS 25, as well as updated pedagogy, such as callout boxes for each chapter indicating crucial elements of APA style and referencing outputs. IBM SPSS for Introductory Statistics is an invaluable supplemental (or lab text) book for students. In addition, this book and its companion, IBM SPSS for Intermediate Statistics, are useful as guides/reminders to faculty and professionals regarding the specific steps to take to use SPSS and/or how to use and interpret parts of SPSS with which they are unfamiliar.

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.

The most widely used statistical method in seasonal adjustment is without doubt that implemented in the X-11 Variant of the Census Method II Seasonal Adjustment Program. Developed at the US Bureau of the Census in the 1950's and 1960's, this computer program has undergone numerous modifications and improvements, leading especially to the X-11-ARIMA software packages in 1975 and 1988 and X-12-ARIMA, the first beta version of which is dated 1998. While these software packages integrate, to varying degrees, parametric methods, and especially the ARIMA models popularized by Box and Jenkins, they remain in essence very close to the initial X-11 method, and it is this "core" that Seasonal Adjustment with the X-11 Method focuses on. With a Preface by Allan Young, the authors document the seasonal adjustment method implemented in the X-11 based software. It will be an important reference for government agencies, macroeconomists, and other serious users of economic data. After some historical notes, the authors outline the X-11 methodology. One chapter is devoted to the study of moving averages with an emphasis on those used by X-11. Readers will also find a complete example of seasonal adjustment, and have a detailed picture of all the calculations. The linear regression models used for trading-day effects and the process of detecting and correcting extreme values are studied in the example. The estimation of the Easter effect is dealt with in a separate chapter insofar as the models used in X-11-ARIMA and X-12-ARIMA are appreciably different. Dominique Ladiray is an Administrateur at the French Institut National de la Statistique et des Etudes Economiques. He is also a Professor at the Ecole Nationale de la Statistique et de l'Administration Economique, and at the Ecole Nationale de la Statistique et de l'Analyse de l'Information. He currently works on short-term economic analysis. Benoît Quenneville is a methodologist with Statistics Canada Time Series Research and Analysis Centre. He holds a Ph.D. from the University of Western Ontario. His research interests are in time series analysis with an emphasis on official statistics.

Get started with an accelerated introduction to the R ecosystem, programming language, and tools including R script and RStudio. Utilizing many examples and projects, this book teaches you how to get data into R and how to work with that data using R. Once grounded in the fundamentals, the rest of Practical R 4 dives into specific projects and examples starting with running and analyzing a survey using R and LimeSurvey. Next, you'll carry out advanced statistical analysis using R and MouselabWeb. Then, you'll see how R can work for you without statistics, including how R can be used to automate data formatting, manipulation, reporting, and custom functions. The final part of this book discusses using R on a server; you'll build a script with R that can run an RStudio Server and monitor a report source for changes to alert the user when something has changed. This project includes both regular email alerting and push notification. And, finally, you'll use R to create a customized daily rundown report of a person's most important information such as a weather report, daily calendar, to-do's and more. This demonstrates how to automate such a process so that every morning, the user navigates to the same web page and gets the updated report. What You Will Learn Set up and run an R script, including installation on a new machine and downloading and configuring R Turn any machine into a powerful data analytics platform accessible from anywhere with RStudio Server Write basic R scripts and modify existing scripts to suit your own needs Create basic HTML reports in R, inserting information as needed Build a basic R package and distribute it Who This Book Is For Some prior exposure to statistics, programming, and maybe SAS is recommended but not required.

MATLAB , a software package developed by Math Works, Inc. is powerful, versatile and interactive software for scientific and technical computations including simulations. Specialised toolboxes provided with several built-in functions are a special feature of MATLAB .

This book titled System Design through MATLAB Control Toolbox and SIMULINK aims at getting the reader started with computations and simulations in system engineering quickly and easily and then proceeds to build concepts for advanced computations and simulations that includes the control and compensation of systems. Simulation through SIMULINK has also been described to allow the reader to get the feel of the real world situation.

This book is appropriate for undergraduate students undergoing final semester of their project work, postgraduate students who have MATLAB integrated in their course or wish to take up simulation problem in the area of system engineering for their dissertation work and research scholars for whom MATLAB along with toolboxes and SIMULINK is an indispensable tool for simulation.

To download interactive simulations of various systems (523Kb) described in the book, go to http//www.springer.co.uk/matlab.htm

It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. The prerequisites for XploRe - the statistical computing environment - are an introductory course in statistics or mathematics. The reader of this book should be familiar with basic elements of matrix algebra and the use of HTML browsers. This guide is designed to help students to XploRe their data, to learn (via data interaction) about statistical methods and to disseminate their findings via the HTML outlet. The XploRe APSS (Auto Pilot Support System) is a powerful tool for finding the appropriate statistical technique (quantlet) for the data under analysis. Homogeneous quantlets are combined in XploRe into quantlibs. The XploRe language is intuitive and users with prior experience of other sta tistical programs will find it easy to reproduce the examples explained in this guide. The quantlets in this guide are available on the CD-ROM as well as on the Internet. The statistical operations that the student is guided into range from basic one-dimensional data analysis to more complicated tasks such as time series analysis, multivariate graphics construction, microeconometrics, panel data analysis, etc. The guide starts with a simple data analysis of pullover sales data, then in troduces graphics. The graphics are interactive and cover a wide range of dis plays of statistical data."

This book contains the proceedings of the 12th International Conference on TheoremProvinginHigherOrderLogics(TPHOLs 99), whichwasheldinNice at the University of Nice-Sophia Antipolis, September 14{17, 1999. Thirty- ve papers were submitted as completed research, and each of them was refereed by at least three reviewers appointed by the program committee. Twenty papers were selected for publication in this volume. Followingawell-establishedtraditioninthisseriesofconferences, anumberof researchers also came to discuss work in progress, using short talks and displays at a poster session. These papers are included in a supplementary proceedings volume. These supplementary proceedings take the form of a book published by INRIA in its series of research reports, under the following title: Theorem ProvinginHigherOrderLogics: EmergingTrends1999. The organizers were pleased that Dominique Bolignano, Arjeh Cohen, and Thomas Kropf accepted invitations to be guest speakers for TPHOLs 99. For several years, D. Bolignano has been the leader of the VIP team in the Dyade consortium between INRIA and Bull and is now at the head of a company Trusted Logic. His team has been concentrating on the use of formal methods for the e ective veri cationof securityproperties for protocols used in electronic commerce. A. Cohen has had a key in?uence on the development of computer algebra in The Netherlands and his contribution has been of particular imp- tance to researchersinterested in combining the severalknown methods of using computers to perform mathematical investigations. T. Kropf is an important actor in the Europe-wide project PROSPER, which aims to deliver the be- ts of mechanized formal analysis to system builders in industry."

Increasing the designer's con dence that a piece of software or hardwareis c- pliant with its speci cation has become a key objective in the design process for software and hardware systems. Many approaches to reaching this goal have been developed, including rigorous speci cation, formal veri cation, automated validation, and testing. Finite-state model checking, as it is supported by the explicit-state model checkerSPIN, is enjoying a constantly increasingpopularity in automated property validation of concurrent, message based systems. SPIN has been in large parts implemented and is being maintained by Gerard Ho- mann, and is freely available via ftp fromnetlib.bell-labs.comor from URL http: //cm.bell-labs.com/cm/cs/what/spin/Man/README.html. The beauty of nite-state model checking lies in the possibility of building \push-button" validation tools. When the state space is nite, the state-space traversal will eventually terminate with a de nite verdict on the property that is being validated. Equally helpful is the fact that in case the property is inv- idated the model checker will return a counterexample, a feature that greatly facilitates fault identi cation. On the downside, the time it takes to obtain a verdict may be very long if the state space is large and the type of properties that can be validated is restricted to a logic of rather limited expressiveness.

This compact introduction to Mathematicaaccessible to beginners at all levelspresents the basic elements of the latest version 3 (front End.txt.Int.:, kernel, standard packages). Using examples and exercises not specific to a scientific area, it teaches readers how to effectively solve problems in their own field. The cross-platform CD-ROM contains the entire book in the form of Mathematica notebooks, including color graphics, animations, and hyperlinks, plus the program MathReader.

This unusual introduction to Maple shows readers how Maple or any other computer algebra system fits naturally into a mathematically oriented work environment. Designed for mathematicians, engineers, econometricians, and other scientists, this book shows how computer algebra can enhance their theoretical work. A CD-ROM contains all the Maple worksheets presented in the book.

This upper-division laboratory supplement for courses in abstract algebra consists of several Mathematica packages programmed as a foundation for group and ring theory. Additionally, the "user's guide" illustrates the functionality of the underlying code, while the lab portion of the book reflects the contents of the Mathematica-based electronic notebooks. Students interact with both the printed and electronic versions of the material in the laboratory, and can look up details and reference information in the user's guide. Exercises occur in the stream of the text of the lab, which provides a context within which to answer, and the questions are designed to be either written into the electronic notebook, or on paper. The notebooks are available in both 2.2 and 3.0 versions of Mathematica, and run across all platforms for which Mathematica exits. A very timely and unique addition to the undergraduate abstract algebra curriculum, filling a tremendous void in the literature.

This Volume contains the Keynote, Invited and Full Contributed papers presented at COMPSTAT'98. A companion volume (Payne & Lane, 1998) contains papers describing the Short Communications and Posters. COMPSTAT is a one-week conference held every two years under the auspices of the International Association of Statistical Computing, a section of the International Statistical Institute. COMPSTAT'98 is organised by IACR-Rothamsted, IACR-Long Ashton, the University of Bristol Department of Mathematics and the University of Bath Department of Mathematical Sciences. It is taking place from 24-28 August 1998 at University of Bristol. Previous COMPSTATs (from 1974-1996) were in Vienna, Berlin, Leiden, Edinburgh, Toulouse, Prague, Rome, Copenhagen, Dubrovnik, Neuchatel, Vienna and Barcelona. The conference is the main European forum for developments at the interface between statistics and computing. This was encapsulated as follows in the COMPSTAT'98 Call for Papers. Statistical computing provides the link between statistical theory and applied statistics. The scientific programme of COMPSTAT ranges over all aspects of this link, from the development and implementation of new computer-based statistical methodology through to innovative applications and software evaluation. The programme should appeal to anyone working in statistics and using computers, whether in universities, industrial companies, research institutes or as software developers.

This companion to The New Statistical Analysis of Data by Anderson and Finn provides a hands-on guide to data analysis using SPSS. Included with this guide are instructions for obtaining the data sets to be analysed via the World Wide Web. First, the authors provide a brief review of using SPSS, and then, corresponding to the organisation of The New Statistical Analysis of Data, readers participate in analysing many of the data sets discussed in the book. In so doing, students both learn how to conduct reasonably sophisticated statistical analyses using SPSS whilst at the same time gaining an insight into the nature and purpose of statistical investigation.