With a growing number of scientists and engineers using JMP software for design of experiments, there is a need for an example-driven book that supports the most widely used textbook on the subject, Design and Analysis of Experiments by Douglas C. Montgomery. Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP meets this need and demonstrates all of the examples from the Montgomery text using JMP. In addition to scientists and engineers, undergraduate and graduate students will benefit greatly from this book. While users need to learn the theory, they also need to learn how to implement this theory efficiently on their academic projects and industry problems. In this first book of its kind using JMP software, Rushing, Karl and Wisnowski demonstrate how to design and analyze experiments for improving the quality, efficiency, and performance of working systems using JMP. Topics include JMP software, two-sample t-test, ANOVA, regression, design of experiments, blocking, factorial designs, fractional-factorial designs, central composite designs, Box-Behnken designs, split-plot designs, optimal designs, mixture designs, and 2 k factorial designs. JMP platforms used include Custom Design, Screening Design, Response Surface Design, Mixture Design, Distribution, Fit Y by X, Matched Pairs, Fit Model, and Profiler. With JMP software, Montgomery's textbook, and Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP, users will be able to fit the design to the problem, instead of fitting the problem to the design.

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

"Numerical Methods using MATLAB, 3e, " is an extensive reference offering hundreds of useful and important numerical algorithms that can be implemented intoMATLAB for a graphical interpretation to help researchers analyze a particular outcome. Many worked examples are given together with exercises and solutions to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization, engineering and science across the board.

"Numerical Methods using MATLAB, 3e, " is an extensive reference offering hundreds of useful and important numerical algorithms that can be implemented intoMATLAB, to help researchers analyze a particular outcome. Many worked examples are given, together with exercises and solutions, to illustrate how numerical methods can be used to study problems that have applications in the biosciences, chaos, optimization, engineering and science. Over 500 numerical algorithms, their fundamental principles, and applicationsGraphs are used extensively to clarify the complexity of problemsIncludes coded genetic algorithmsIncludes the Lagrange multiplier methodUser-friendly and written in a conversational style"

Active learning is a protocol for supervised machine learning in which a learning algorithm sequentially requests the labels of selected data points from a large pool of unlabeled data. This contrasts with passive learning where the labeled data are taken at random. The objective in active learning is to produce a highly-accurate classifier, ideally using fewer labels than the number of random labeled data sufficient for passive learning to achieve the same. Theory of Disagreement-Based Active Learning describes recent advances in our understanding of the theoretical benefits of active learning, and implications for the design of effective active learning algorithms. Much of the monograph focuses on a particular technique, namely disagreement-based active learning, which by now has amassed a mature and coherent literature. It also briefly surveys several alternative approaches from the literature. The emphasis is on theorems regarding the performance of a few general algorithms, including rigorous proofs where appropriate. However, the presentation is intended to be pedagogical, focusing on results that illustrate fundamental ideas rather than obtaining the strongest or most generally known theorems. Theory of Disagreement-Based Active Learning is intended for researchers and advanced graduate students in machine learning and statistics who are interested in gaining a deeper understanding of the recent and ongoing developments in the theory of active learning.

Computational Finance, an exciting new cross-disciplinary research area, depends extensively on the tools and techniques of computer science, statistics, information systems and financial economics for educating the next generation of financial researchers, analysts, risk managers, and financial information technology professionals. This new discipline, sometimes also referred to as "Financial Engineering" or "Quantitative Finance" needs professionals with extensive skills both in finance and mathematics along with specialization in computer science. Soft-Computing in Capital Market hopes to fulfill the need of applications of this offshoot of the technology by providing a diverse collection of cross-disciplinary research. This edited volume covers most of the recent, advanced research and practical areas in computational finance, starting from traditional fundamental analysis using algebraic and geometric tools to the logic of science to explore information from financial data without prejudice. Utilizing various methods, computational finance researchers aim to determine the financial risk with greater precision that certain financial instruments create. In this line of interest, twelve papers dealing with new techniques and/or novel applications related to computational intelligence, such as statistics, econometrics, neural- network, and various numerical algorithms are included in this volume.

A practical tutorial covering how to leverage RStudio functionality to effectively perform R Development, analysis, and reporting with RStudio. The book is aimed at R developers and analysts who wish to do R statistical development while taking advantage of RStudio functionality to ease their development efforts. Familiarity with R is assumed. Those who want to get started with R development using RStudio will also find the book useful. Even if you already use R but want to create reproducible statistical analysis projects or extend R with self-written packages, this book shows how to quickly achieve this using RStudio.

This comprehensive resource provides on-the-job training for statistical programmers who use SAS in the pharmaceutical industry This one-stop resource offers a complete review of what entry- to intermediate-level statistical programmers need to know in order to help with the analysis and reporting of clinical trial data in the pharmaceutical industry. SAS Programming in the Pharmaceutical Industry, Second Edition begins with an introduction to the pharmaceutical industry and the work environment of a statistical programmer. Then it gives a chronological explanation of what you need to know to do the job. It includes information on importing and massaging data into analysis data sets, producing clinical trial output, and exporting data. This edition has been updated for SAS 9.4, and it features new graphics as well as all new examples using CDISC SDTM or ADaM model data structures. Whether you're a novice seeking an introduction to SAS programming in the pharmaceutical industry or a junior-level programmer exploring new approaches to problem solving, this real-world reference guide offers a wealth of practical suggestions to help you sharpen your skills. This book is part of the SAS Press program.

Scientific computing is the study of how to use computers effectively to solve problems that arise from the mathematical modeling of phenomena in science and engineering. It is based on mathematics, numerical and symbolic/algebraic computations and visualization. This book serves as an introduction to both the theory and practice of scientific computing, with each chapter presenting the basic algorithms that serve as the workhorses of many scientific codes; we explain both the theory behind these algorithms and how they must be implemented in order to work reliably in finite-precision arithmetic. The book includes many programs written in Matlab and Maple - Maple is often used to derive numerical algorithms, whereas Matlab is used to implement them. The theory is developed in such a way that students can learn by themselves as they work through the text. Each chapter contains numerous examples and problems to help readers understand the material "hands-on".

Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

Data simulation is a fundamental technique in statistical programming and research. Rick Wicklin's Simulating Data with SAS brings together the most useful algorithms and the best programming techniques for efficient data simulation in an accessible how-to book for practicing statisticians and statistical programmers. This book discusses in detail how to simulate data from common univariate and multivariate distributions, and how to use simulation to evaluate statistical techniques. It also covers simulating correlated data, data for regression models, spatial data, and data with given moments. It provides tips and techniques for beginning programmers, and offers libraries of functions for advanced practitioners. As the first book devoted to simulating data across a range of statistical applications, Simulating Data with SAS is an essential tool for programmers, analysts, researchers, and students who use SAS software.

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Heat Maps in R: How-to is an easy to understand book that starts with a simple heat map and takes you all the way through to advanced heat maps with graphics and data manipulation. Heat Maps in R How-to is the book for you if you want to make use of this free and open source software to get the most out of your data analysis. You need to have at least some experience in using R and know how to run basic scripts from the command line. However, knowledge of other statistical scripting languages such as Octave, S-Plus, or MATLAB will suffice to follow along with the recipes. You need not be from a statistics background.

PROC REPORT by Example: Techniques for Building Professional Reports Using SAS provides real-world examples using PROC REPORT to create a wide variety of professional reports. Written from the point of view of the programmer who produces the reports, this book explains and illustrates creative techniques used to achieve the desired results. Each chapter focuses on a different concrete example, shows an image of the final report, and then takes you through the process of creating that report. You will be able to break each report down to find out how it was produced, including any data manipulation you have to do. The book clarifies solutions to common, everyday programming challenges and typical daily tasks that programmers encounter. For example: * obtaining desired report formats using style templates supplied by SAS and PROC TEMPLATE, PROC REPORT STYLE options, and COMPUTE block features * employing different usage options (DISPLAY, ORDER, GROUP, ANALYSIS, COMPUTED) to create a variety of detail and summary reports * using BREAK statements and COMPUTE blocks to summarize and report key findings * producing reports in various Output Delivery System (ODS) destinations including RTF, PDF, XML, TAGSETS.RTF * embedding images in a report and combining graphical and tabular data with SAS 9.2 and beyond Applicable to SAS users from all disciplines, the real-life scenarios will help elevate your reporting skills learned from other books to the next level. With PROC REPORT by Example: Techniques for Building Professional Reports Using SAS what seemed complex will become a matter of practice

Statisticians and researchers will find "Categorical Data Analysis Using SAS, Third Edition," by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, NPAR1WAY, and CATMOD procedures in a variety of analyses. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, loglinear models, generalized estimating equations, and bioassay analysis.

The third edition updates the use of SAS/STAT software to SAS/STAT 12.1 and incorporates ODS Graphics. Many additional SAS statements and options are employed, and graphs such as effect plots, odds ratio plots, regression diagnostic plots, and agreement plots are discussed. The material has also been revised and reorganized to reflect the evolution of categorical data analysis strategies. Additional techniques include such topics as exact Poisson regression, partial proportional odds models, Newcombe confidence intervals, incidence density ratios, and so on.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

This easy-to-understand guide makes SEM accessible to all users. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

"Applied Data Mining for Forecasting," by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large and identifies the correlation structure between selected candidate inputs and the forecast variable.

This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs.

In "SAS Statistics by Example," Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books.

For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured are correlation, inferential statistics, descriptive statistics, categorical data analysis, simple linear regression, comparing means, multiple regression, logistic regression, non-parametric tests, and power and sample size.

This is not a book that teaches statistics. Rather, "SAS Statistics by Example" is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses.

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's "Logistic Regression Using SAS: Theory and Application, Second Edition," is for you Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing non-linear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).

For anyone who wants to be operating at a high level with the Excel Solver quickly, this is the book for you. Step-By-Step Optimization With Excel Solver is more than 200+ pages of simple yet thorough explanations on how to use the Excel Solver to solve today's most widely known optimization problems. Loaded with screen shots that are coupled with easy-to-follow instructions, this book will simplify many difficult optimization problems and make you a master of the Excel Solver almost immediately. Here are just some of the Solver optimization problems that are solved completely with simple-to-understand instructions and screen shots in this book: The famous "Traveling Salesman" problem using Solver's Alldifferent constraint and the Solver's Evolutionary method to find the shortest path to reach all customers. This also provides an advanced use of the Excel INDEX function. The well-known "Knapsack Problem" which shows how optimize the use of limited space while satisfying numerous other criteria. How to perform nonlinear regression and curve-fitting on the Solver using the Solver's GRG Nonlinear solving method. How to solve the "Cutting Stock Problem" faced by many manufacturing companies who are trying to determine the optimal way to cut sheets of material to minimize waste while satisfying customer orders. Portfolio optimization to maximize return or minimize risk. Venture capital investment selection using the Solver's Binary constraint to maximize Net Present Value of selected cash flows at year 0. Clever use of the If-Then-Else statements makes this a simple problem. How use Solver to minimize the total cost of purchasing and shipping goods from multiple suppliers to multiple locations. How to optimize the selection of different production machine to minimize cost while fulfilling an order. How to optimally allocate a marketing budget to generate the greatest reach and frequency or number of inbound leads at the lowest cost. Step-By-Step Optimization With Excel Solver has complete instructions and numerous tips on every aspect of operating the Excel Solver. You'll fully understand the reports and know exactly how to tweek all of the Solver's settings for total custom use. The book also provides lots of inside advice and guidance on setting up the model in Excel so that it will be as simple and intuitive as possible to work with. All of the optimization problems in this book are solved step-by-step using a 6-step process that works every time. In addition to detailed screen shots and easy-to-follow explanations on how to solve every optimization problem in the book, a link is provided to download an Excel workbook that has all problems completed exactly as they are in this book. Step-By-Step Optimization With Excel Solver is exactly the book you need if you want to be optimizing at an advanced level with the Excel Solver quickly.

Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's "Data Quality for Analytics Using SAS" focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is made up of three parts. The first part, which is conceptual, defines data quality and contains text, definitions, explanations, and examples. The second part shows how the data quality status can be profiled and the ways that data quality can be improved with analytical methods. The final part details the consequences of poor data quality for predictive modeling and time series forecasting.

With this book you will learn how you can use SAS to perform advanced profiling of data quality status and how SAS can help improve your data quality.

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

"Carpenter's Guide to Innovative SAS Techniques" offers advanced SAS programmers an all-in-one programming reference that includes advanced topics not easily found outside the depths of SAS documentation or more advanced training classes. Art Carpenter has written fifteen chapters of advanced tips and techniques, including topics on data summary, data analysis, and data reporting. Special emphasis is placed on DATA step techniques that solve complex data problems. There are numerous examples that illustrate advanced techniques that take advantage of formats, interface with the macro language, and utilize the Output Delivery System. Additional topics include operating system interfaces, table lookup techniques, and the creation of customized reports.

Sanjay Matange and Dan Heath's "Statistical Graphics Procedures by Example: Effective Graphs Using SAS" shows the innumerable capabilities of SAS Statistical Graphics (SG) procedures. The authors begin with a general discussion of the principles of effective graphics, ODS Graphics, and the SG procedures. They then move on to show examples of the procedures' many features. The book is designed so that you can easily flip through it, find the graph you need, and view the code right next to the example.

Among the topics included are how to combine plot statements to create custom graphs; customizing graph axes, legends, and insets; advanced features, such as annotation and attribute maps; tips and tricks for creating the optimal graph for the intended usage; real-world examples from the health and life sciences domain; and ODS styles.

The procedures in "Statistical Graphics Procedures by Example" are specifically designed for the creation of analytical graphs. That makes this book a must-read for analysts and statisticians in the health care, clinical trials, financial, and insurance industries. However, you will find that the examples here apply to all fields.

Provides cutting-edge methods, specialized macros, and proven "best bet" procedures. The specialized macros and dozens of real-world examples illustrate solutions for a broad variety of problems that call for multiple inferences. The book also discusses the pitfalls and advantages of various methods, thereby helping you decide which is the most appropriate for your purposes.

Assuming no knowledge of programming, this book presents both programming concepts and MATLAB's built-in functions, providing a perfect platform for exploiting MATLAB's extensive capabilities for tackling engineering problems. It starts with programming concepts such as variables, assignments, input/output, and selection statements, moves onto loops and then solves problems using both the 'programming concept' and the 'power of MATLAB' side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Ancillaries available with the text: Instructor solution manual (available Aug. 1st); electronic images from the text (available Aug 16th); and, m-files (available Aug 1st). This title presents programming concepts and MATLAB built-in functions side-by-side, giving students the ability to program efficiently and exploit the power of MATLAB to solve problems. It offers in-depth coverage of file input/output, a topic essential for many engineering applications. It features systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning. It includes sections on 'common pitfalls' and 'programming guidelines' that direct students towards best practice. The following are new to this edition: more engineering applications that help the reader learn Matlab in the context of solving technical problems; new and revised end of chapter problems; and, stronger coverage of loops and vectorizing in a new chapter, chapter 5. It is updated to reflect current features and functions of the current release of Matlab.