The Mata Book: A Book for Serious Programmers and Those Who Want to Be is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities. The book is serious in that it covers those advanced features, and teaches them. The reader is assumed to have programming experience, but only some programming experience. That experience could be with Stata's ado language, or with Python, Java, C++, Fortran, or other languages like them. As the book says, "being serious is a matter of attitude, not current skill level or knowledge". The author of the book is William Gould, who is also the designer and original programmer of Mata, of Stata, and who also happens to be the president of StataCorp.

A friendly, straightforward guide that does not assume knowledge of programming, this book helps new R users hit the ground running. Eric L. Einspruch provides an overview of the software and shows how to download and install R, RStudio, and R packages. Featuring example code, screenshots, tips, learning exercises, and worked-through examples of statistical techniques, the book demonstrates the capabilities and nuances of these powerful free statistical analysis and data visualization tools. Fundamental aspects of data wrangling, analysis, visualization, and reporting are introduced, using both Base R and Tidyverse approaches. Einspruch emphasizes processes that support research reproducibility, such as use of comments to document R code and use of R Markdown capabilities. The book also helps readers navigate the vast array of R resources available to further develop their skills.

"This would be an excellent book for undergraduate, graduate and beyond....The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data.... having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives - and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions." Michelle Pantoya, Texas Tech University Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches. Features: 1. Extensive use of modern open source software (R). 2. Many code examples are provided. 3. The uncertainty analyses conform to accepted professional standards (ASME). 4. The book is self-contained and includes all necessary material including chapters on statistics and programming in R. Benjamin D. Shaw is a professor in the Mechanical and Aerospace Engineering Department at the University of California, Davis. His research interests are primarily in experimental and theoretical aspects of combustion. Along with other courses, he has taught undergraduate and graduate courses on engineering experimentation and uncertainty analysis. He has published widely in archival journals and became an ASME Fellow in 2003.

Many professional, high-quality surveys collect data on people's behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics. You will learn how to: Create a robust research question and design that suits secondary analysis Locate, access and explore data online Understand data documentation Check and 'clean' secondary data Manage and analyse your data to produce meaningful results Replicate analyses of data in published articles and books Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you'll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book's companion website give you an opportunity to practice, check your understanding and work hands on with real data as you're learning.

Essentials of Programming in Mathematica (R) provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica (R) programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author's tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.

This supplementary book for the social, behavioral, and health sciences helps readers with no prior knowledge of IBM (R) SPSS (R) Statistics, statistics, or mathematics learn the basics of SPSS. Designed to reduce fear and build confidence, the book guides readers through point-and-click sequences using clear examples from real scientific research and invites them to replicate the findings. Relevant outcomes are provided for reference, and exercises at the end of Chapters 2 - 5 provide additional practice. After reading the book and using the program, readers will come away with a basic knowledge of the most commonly used procedures in statistics.

In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.

Introductory Statistics for Health & Nursing using SPSS is an impressive introductory statistics text ideal for all health science and nursing students. Health and nursing students can be anxious and lacking in confidence when it comes to handling statistics. This book has been developed with this readership in mind. This accessible text eschews long and off-putting statistical formulae in favour of non-daunting practical and SPSS-based examples. What's more, its content will fit ideally with the common course content of stats courses in the field. Introductory Statistics for Health & Nursing using SPSS is also accompanied by a companion website containing data-sets and examples for use by lecturers with their students. The inclusion of real-world data and a host of health-related examples should make this an ideal core text for any introductory statistics course in the field.

SPSS syntax is the command language used by SPSS to carry out all of its commands and functions. In this book, Jacqueline Collier introduces the use of syntax to those who have not used it before, or who are taking their first steps in using syntax. Without requiring any knowledge of programming, the text outlines: - how to become familiar with the syntax commands; - how to create and manage the SPSS journal and syntax files; - and how to use them throughout the data entry, management and analysis process. Collier covers all aspects of data management from data entry through to data analysis, including managing the errors and the error messages created by SPSS. Syntax commands are clearly explained and the value of syntax is demonstrated through examples. This book also supports the use of SPSS syntax alongside the usual button and menu-driven graphical interface (GIF) using the two methods together, in a complementary way. The book is written in such a way as to enable you to pick and choose how much you rely on one method over the other, encouraging you to use them side-by-side, with a gradual increase in use of syntax as your knowledge, skills and confidence develop. This book is ideal for all those carrying out quantitative research in the health and social sciences who can benefit from SPSS syntax's capacity to save time, reduce errors and allow a data audit trail.

Accessibly written and easy to use, Applied Statistics Using SPSS is an all-in-one self-study guide to SPSS and do-it-yourself guide to statistics. Based around the needs of undergraduate students embarking on their own research project, the text's self-help style is designed to boost the skills and confidence of those that will need to use SPSS in the course of doing their research project. The book is pedagogically well developed and contains many screen dumps and exercises, glossary terms and worked examples. Divided into two parts, Applied Statistics Using SPSS covers : 1. A self-study guide for learning how to use SPSS. 2. A reference guide for selecting the appropriate statistical technique and a stepwise do-it-yourself guide for analysing data and interpreting the results. 3. Readers of the book can download the SPSS data file that is used for most of the examples throughout the book. Geared explicitly for undergraduate needs, this is an easy to follow SPSS book that should provide a step-by-step guide to research design and data analysis using SPSS.

SPSS for Windows is the most widely used computer package for analyzing quantitative data. In a clear, readable, non-technical style, this book teaches beginners how to use the program, input and manipulate data, use descriptive analyses and inferential techniques, including: t-tests, analysis of variance, correlation and regression, nonparametric techniques, and reliability analysis and factor analysis.

The author provides an overview of statistical analysis, and then shows in a simple step-by-step method how to set up an SPSS file in order to run an analysis as well as how to graph and display data. He explains how to use SPSS for all the main statistical approaches you would expect to find in an introductory statistics course.

The book is written for users of Versions 6 and 6.1, but will be equally valuable to users of later versions.

In this second edition of An Introduction to Stata Programming, the author introduces concepts by providing the background and importance for the topic, presents common uses and examples, then concludes with larger, more applied examples referred to as "cookbook recipes." This is a great reference for anyone who wants to learn Stata programming. For those learning, the author assumes familiarity with Stata and gradually introduces more advanced programming tools. For the more advanced Stata programmer, the book introduces Stata's Mata programming language and optimization routines.

Il libro contiene in forma compatta il programma svolto negli insegnamenti introduttivi di Statistica e tratta alcuni argomenti indispensabili per l'attivita di ricerca, come le tecniche di simulazione Monte Carlo, i metodi di inferenza statistica, di best fit e di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Il testo e rivolto agli studenti universitari dei corsi ad indirizzo scientifico e a tutti quei ricercatori che devono risolvere problemi concreti che coinvolgono l'analisi dei dati e le tecniche di simulazione. In questa edizione, completamente rivista e corretta, sono stati aggiunti alcuni importanti argomenti sul test d'ipotesi (a cui e stato dedicato un capitolo interamente nuovo) e sul trattamento degli errori sistematici. Per la prima volta e stato adottato il software R, con una ricca libreria di programmi originali accessibile al lettore.

Advanced Engineering Mathematics with MATLAB, Fourth Edition builds upon three successful previous editions. It is written for today's STEM (science, technology, engineering, and mathematics) student. Three assumptions under lie its structure: (1) All students need a firm grasp of the traditional disciplines of ordinary and partial differential equations, vector calculus and linear algebra. (2) The modern student must have a strong foundation in transform methods because they provide the mathematical basis for electrical and communication studies. (3) The biological revolution requires an understanding of stochastic (random) processes. The chapter on Complex Variables, positioned as the first chapter in previous editions, is now moved to Chapter 10. The author employs MATLAB to reinforce concepts and solve problems that require heavy computation. Along with several updates and changes from the third edition, the text continues to evolve to meet the needs of today's instructors and students. Features: Complex Variables, formerly Chapter 1, is now Chapter 10. A new Chapter 18: Ito's Stochastic Calculus. Implements numerical methods using MATLAB, updated and expanded Takes into account the increasing use of probabilistic methods in engineering and the physical sciences Includes many updated examples, exercises, and projects drawn from the scientific and engineering literature Draws on the author's many years of experience as a practitioner and instructor Gives answers to odd-numbered problems in the back of the book Offers downloadable MATLAB code at www.crcpress.com

Ideal for those already familiar with basic Excel features, this updated Third Edition of Neil J. Salkind's Excel Statistics: A Quick Guide shows readers how to utilize Microsoft (R) Excel's functions and Analysis ToolPak to answer simple and complex questions about data. Part I explores 35 Excel functions, while Part II contains 20 Analysis ToolPak tools. To make it easy to see what each function or tool looks like when applied, at-a-glance two-page spreads describe each function and its use with corresponding screenshots. In addition, actual data files used in the examples are readily available online at an open-access Student Study Site.

Sage est un logiciel libre de calcul mathematique s'appuyant sur le langage de programmation Python. Ses auteurs, une communaute internationale de centaines d'enseignants et de chercheurs, se sont donne pour mission de fournir une alternative viable aux logiciels Magma, Maple, Mathematica et Matlab. Sage fait appel pour cela a de multiples logiciels libres existants, comme GAP, Maxima, PARI et diverses bibliotheques scientifiques pour Python, auxquels il ajoute des milliers de nouvelles fonctions. Il est disponible gratuitement et fonctionne sur les systemes d'exploitation usuels. Pour les lyceens, Sage est une formidable calculatrice scientifique et graphique. Il assiste efficacement l'etudiant de premier cycle universitaire dans ses calculs en analyse, en algebre lineaire, etc. Pour la suite du parcours universitaire, ainsi que pour les chercheurs et les ingenieurs, Sage propose les algorithmes les plus recents dans diverses branches des mathematiques. De ce fait, de nombreuses universites enseignent Sage des le premier cycle pour les travaux pratiques et les projets. Ce livre est le premier ouvrage generaliste sur Sage, toutes langues confondues. Coecrit par des enseignants et chercheurs intervenant a tous les niveaux (IUT, classes preparatoires, licence, master, doctorat), il met l'accent sur les mathematiques sous-jacentes a une bonne comprehension du logiciel. En cela, il correspond plus a un cours de mathematiques effectives illustre par des exemples avec Sage qu'a un mode d'emploi ou un manuel de reference. La premiere partie est accessible aux eleves de licence. Le contenu des parties suivantes s'inspire du programme de l'epreuve de modelisation de l'agregation de mathematiques. Ce livre est diffuse sous licence libre Creative Commons. Il peut etre telecharge gratuitement depuis http: //sagebook.gforge.inria.fr/.

Designed for anyone who needs a comprehensive introduction to the principles of statistical methods and their applications, this text is written in a practical, non-threatening style. Step-by-step worked examples are used to illustrate the use of statistical techniques in solving practical problems while self-study exercises test students' knowledge. The use of Excel and MINITAB is fully integrated throughout the book to demonstrate the application of computer packages to solve a wide range of statistical problems. Presented alongside manual methods, these computer solutions include detailed instructions and annotated print outs where appropriate. The second edition retains the straightforward writing style and practical illustration of manual and computer methods which made the previous book successful for a wide range of courses.