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.

Click on the Supplements tab above for further details on the different versions of SPSS programs.Making statistics-and statistical software-accessible and rewarding This book provides readers with step-by-step guidance on running a wide variety of statistical analyses in IBM(R) SPSS(R) Statistics, Stata, and other programs. Author David Kremelberg begins his user-friendly text by covering charts and graphs through regression, time-series analysis, and factor analysis. He provides a background of the method, then explains how to run these tests in IBM SPSS and Stata. He then progresses to more advanced kinds of statistics such as HLM and SEM, where he describes the tests and explains how to run these tests in their appropriate software including HLM and AMOS. This is an invaluable guide for upper-level undergraduate and graduate students across the social and behavioral sciences who need assistance in understanding the various statistical packages.

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.

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.

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.

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.

This book offers an introduction to computer programming, numerical analysis, and other mathematical ideas that extend the basic topics learned in calculus. It illustrates how mathematicians and scientists write computer programs, covering the general building blocks of programming languages and a description of how these concepts fit together to allow computers to produce the results they do. Topics explored here include binary arithmetic, algorithms for rendering graphics, the smooth interpolation of discrete data, and the numerical approximation of non-elementary integrals. The book uses an open-source computer algebra system called Maxima. Using Maxima, first-time programmers can perform familiar tasks, such as graphing functions or solving equations, and learn the basic structures of programming before moving on to other popular programming languages. The epilogue provides some simple examples of how this process works in practice. The book will particularly appeal to students who have finished their calculus sequence.

The Statistical Imagination, a basic social science statistics text with illustrations and exercises for sociology, social work, political science, and criminal justice courses, teaches readers that statistics is not just a mathematical exercise; it is a way of analyzing and understanding the social world. Praised for a writing style that takes the anxiety out of statistics courses, the author explains basic statistical principles through a variety of engaging exercises, each designed to illuminate the unique theme of examining society both creatively and logically. In an effort to make the study of statistics relevant to students of the social sciences, the author encourages readers to interpret the results of calculations in the context of more substantive social issues, while continuing to value precise and accurate research. Ritchey begins by introducing students to the essentials of learning statistics; fractions, proportions, percentages, standard deviation, sampling error and sampling distribution, along with other math hurdles, are clearly explained to fill in any math gaps students may bring to the classroom. Treating statistics as a skill learned best by doing, the author supplies a range of student-friendly questions and exercises to both demystify the calculation process, and to encourage the kind of proportional thinking needed to master the subject. In addition to pencil-and-paper exercises, The Statistical Imagination includes computer-based assignments for use with the free Student Version SPSS 9.0 CD-ROM that accompanies each new copy of the book.