Load, wrangle, and analyze your data using the world's most powerful statistical programming language About This Book * Load, manipulate and analyze data from different sources * Gain a deeper understanding of fundamentals of applied statistics * A practical guide to performing data analysis in practice Who This Book Is For Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn * Navigate the R environment * Describe and visualize the behavior of data and relationships between data * Gain a thorough understanding of statistical reasoning and sampling * Employ hypothesis tests to draw inferences from your data * Learn Bayesian methods for estimating parameters * Perform regression to predict continuous variables * Apply powerful classification methods to predict categorical data * Handle missing data gracefully using multiple imputation * Identify and manage problematic data points * Employ parallelization and Rcpp to scale your analyses to larger data * Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with "messy data", large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach.

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.

This practical cookbook covers a broad range of topics in an easy to understand manner. step by step instructions guide you through even the most complicated of tools in Minitab. This book is great for anyone who is familiar with statistics and who wants to learn how Minitab works. Whilst you do not need to be an expert in all areas of statistics, you should understand the basics of the chapters you are interested in.

Explore the big data field and learn how to perform data analytics and predictive modelling in STATA About This Book * Visualize and analyse data in STATA to devise a business strategy * Learn STATA programming and predictive modeling * Discover how you can become a data scientist with the power of STATA Who This Book Is For This book is for all the professionals and students who want to learn STATA programming and apply predictive modelling concepts. This book is also very helpful for experienced STATA programmers as it provides advanced statistical modelling concepts and their application. What You Will Learn * Perform important statistical tests to become a STATA data scientist * Be guided through how to program in STATA * Implement logistic and linear regression models * Visualize and program the data in STATA * Analyse survey data, time series data, and survival data * Perform database management in STATA In Detail STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA. Style and approach This book is a hands-onguide to STATA programming and statistical modelling providing many STATA code examples and taking. You through the working of the code in detail.

DESCRIPTION The TI-84 Plus series graphing calculators is are the de facto standard for graphing calculators used by students in grades 6 through college. With so many features and functions, the TI-84 Plus graphing calculator can be a little intimidating. Using the TI-84 Plus is an easy-to-follow guide to using these calculators for class and for the SAT and ACT. It starts with a hands-on orientation to the calculator so readers will be comfortable with its menus, buttons, and the special vocabulary it uses. Then, it explores key features while tackling problems just like the ones seen in math and sciences classes. TI-84 Plus calculators are permitted on most standardized tests, so the book provides specific guidance for SAT and ACT math. Along the way, easy-to-find reference sidebars offer skills in a nutshell for those times when just a quick reminder is needed. KEY SELLING POINTS Includes coverage of the brand-new TI-84 Plus CE For TI-83 Plus and TI-84 Plus series of graphing calculators The "missing manual" for the TI-84 Plus calculators Gets readers up and running on calculators fast Fun, engaging, and approachable examples Easy hands-on learn by doing approach AUDIENCE This book is written for students, teachers-anyone who wants to use the TI-84 Plus or TI-83 Plus of graphing calculators. No prior experience is needed and it assumes no advanced knowledge of math and science. ABOUT THE TECHNOLOGY The TI-84 Plus series is the de facto standard for graphing calculators used by students in grades 6 through college and for standardized tests. These calculators can do everything from basic arithmetic through graphing, pre-calculus, calculus, statistics, and probability, and are even great tools for learning programming.

Gain sharp insights into your data and solve real-world data science problems with R-from data munging to modeling and visualization About This Book * Handle your data with precision and care for optimal business intelligence * Restructure and transform your data to inform decision-making * Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn * Connect to and load data from R's range of powerful databases * Successfully fetch and parse structured and unstructured data * Transform and restructure your data with efficient R packages * Define and build complex statistical models with glm * Develop and train machine learning algorithms * Visualize social networks and graph data * Deploy supervised and unsupervised classification algorithms * Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Learn the art of building robust and powerful recommendation engines using R About This Book * Learn to exploit various data mining techniques * Understand some of the most popular recommendation techniques * This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn * Get to grips with the most important branches of recommendation * Understand various data processing and data mining techniques * Evaluate and optimize the recommendation algorithms * Prepare and structure the data before building models * Discover different recommender systems along with their implementation in R * Explore various evaluation techniques used in recommender systems * Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

Written in a friendly, Beginner's Guide format, showing the user how to use the digital media aspects of Matlab (image, video, sound) in a practical, tutorial-based style. This is great for novice programmers in any language who would like to use Matlab as a tool for their image and video processing needs, and also comes in handy for photographers or video editors with even less programming experience wanting to find an all-in-one tool for their tasks.

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.

This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software a working installation of R is all that is necessary to begin.

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.

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 dashboard is a collection of data visualization tools that provide the means to quickly get an overview of how an organization or a section of an organization is performing. Industries such as sales and manufacturing use dashboards extensively, but dashboards are quickly being adapted across all types of profit and non-profit organizations. THE DESIGN OF INFORMATION DASHBOARDS USING SAS is a nuts and bolts guide to building information dashboards using SAS software. The primary audience for this book is SAS programmers charged with developing dashboards for their organization. This audience would include data managers, report writers, and business analysts. A secondary audience includes business mangers and non-programmers who are just hoping to learn a little more about the potential of the technology. The first four chapters provide background on the science of dashboards and related concepts. The remaining chapters cover coding and design of dashboard elements using SAS software. By providing clear, well-structured examples, the volume shows the reader how to quickly and easily construct basic dashboards that are suitable to their unique needs and environment. SAS users familiar with the basics of SAS and the fundamentals of SAS/GRAPH software will be able to make small changes to the sample code contained in the book to design simple dashboards. Advanced users with more extensive knowledge of SAS/GRAPH and the annotate facility will be able to more fully customize the sample code to fit a variety of needs. CHAPTER DESCRIPTIONS Chapter I. AN INTRODUCTION TO DASHBOARDS The first chapter defines precisely what dashboards are and their common characteristics. Following a brief history of information dashboards, the chapter discusses their value, as well as some negatives, and describes current use and trends. Finally, the value that SAS contributes to producing the medium is introduced. Chapter II. SEVEN STEPS TO CREATING A DASHBOARD The development of a dashboard often requires a substantial investment of time and money, so designers should do it thoughtfully. The goal of this chapter is to guide the reader through the dashboard development process. The chapter provides an overview of the major steps involved, including preparation, design, construction, and maintenance of dashboards. Chapter III. ESSENTIAL ELEMENTS OF A DASHBOARD When you create your dashboard, several essential elements should be present on the interface to make the dashboard maximally effective. The third chapter covers these essential components of a dashboard. Chapter IV. BEST PRACTICES IN DASHBOARD VISUAL DESIGN This chapter covers the foundations of good dashboard design and addresses the contributions of Edward Tufte and Stephen Few to the area. The chapter delves into the science of visual perception and how to apply them to good dashboard design. Chapter V. CREATING DASHBOARD KEY PERFORMANCE INDICATORS USING SAS The fifth chapter presents a library of effective dashboard display media and discusses how to produce them using SAS coding. Programmers will be able to pick and choose those chart types that are most appropriate for their particular dashboard. Strengths and weaknesses of the various chart types are discussed. This chapter will also introduces new SAS procedures such as PROC GKPI. Chapter VI. ASSEMBLING AND DISTRIBUTING SAS DASHBOARDS This chapter describes how to bring all the visual components together to produce a single dashboard display. PROC GREPLAY, ODSLAYOUT, and ODS TAGSETS are described as the methods of choice. Methods of distributing this output are described. Chapter VII. DESIGING DASHBOARDS USING SAS BI DASHBOARDS The final chapter briefly describes the design of dashboards using SAS BI Dashboards business intelligence software. For a limited time use the following code for 10% off your purchase on this site: F46FRNCS This title is also available for purchase on Amazon.com.

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.

Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with "JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition."

Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way suitable for publication, perform a wide range of JMP analyses, interpret the results, and more.

Topics include screening data for errors selecting subsets computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale performing bivariate analyses for all types of variables performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA) Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation.

This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

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

"Finite Difference Fundamentals in MATLAB" is devoted to the solution of numerical problems employing basic finite difference (FD) methods in MATLAB platform. FD is one momentous tool of numerical analysis on science and engineering problems. Advent of faster speed computer processors and user-friendliness of MATLAB have marvelously facilitated FD solution obtaining what is demonstrated in every chapter. Another aspect of the text is juxtaposition on computing and graphing features. The coverage narrates key executional MATLAB style of FD terminologies without arithmetic complexity. Self-training illustrations and end-of-chapter exercises inspire the reader a checkup on thorough understanding. The comprehensive introduction will benefit science and engineering undergraduates studying numerical analysis issues ranging archetype to advanced.