Testing is a surefire way to dramatically improve your website’s conversion rate and increase revenue. When you run experiments with changes to design or content, you’ll quickly discover which changes better motivate your users to take action. This book shows how to learn from your customers’ behavior and decisions, and how their responses reveal the strengths and weaknesses of your site. It will show you how to make websites that work harder and convert better. Learn how to approach experiments to improve conversion Understand the various methods of testing including A/B and multivariate Discover experiment ideas, and go beyond optimization to innovation Recognize the UX and design implications of experimenting Learn to analyze data and deliver results Experimenting changes the way you think about design and the way you work. It helps prevent the loudest voice from deciding direction; instead, through an experiment, you’ll ask the most important voices--your customers--“What do you think?”

Computational Colour Science Using MATLAB 2nd Edition offers a practical, problem-based approach to colour physics. The book focuses on the key issues encountered in modern colour engineering, including efficient representation of colour information, Fourier analysis of reflectance spectra and advanced colorimetric computation. Emphasis is placed on the practical applications rather than the techniques themselves, with material structured around key topics. These topics include colour calibration of visual displays, computer recipe prediction and models for colour-appearance prediction. Each topic is carefully introduced at three levels to aid student understanding. First, theoretical ideas and background information are discussed, then explanations of mathematical solutions follow and finally practical solutions are presented using MATLAB. The content includes: A compendium of equations and numerical data required by the modern colour and imaging scientist. Numerous examples of solutions and algorithms for a wide-range of computational problems in colour science. Example scripts using the MATLAB programming language. This 2nd edition contains substantial new and revised material, including three innovative chapters on colour imaging, psychophysical methods, and physiological colour spaces; the MATLAB toolbox has been extended with a professional, optimized, toolbox to go alongside the current teaching toolbox; and a java toolbox has been added which will interest users who are writing web applications and/or applets or mobile phone applications. Computational Colour Science Using MATLAB 2nd Edition is an invaluable resource for students taking courses in colour science, colour chemistry and colour physics as well as technicians and researchers working in the area. In addition, it acts a useful reference for professionals and researchers working in colour dependent industries such as textiles, paints, print & electronic imaging. Review from First Edition: "...highly recommended as a concise introduction to the practicalities of colour science..." (Color Technology, 2004)

Experiments are the most effective way to learn about the world. By cleverly interfering with something to see how it reacts we are able to find out how it works. In contrast to passive observation, experimenting provides us with data relevant to our research and thus less time and effort is spent separating relevant from irrelevant information. The art of experimentation is often learnt by doing, so an intuitive understanding of the experimental method usually evolves gradually through years of trial and error. This book speeds up the journey for the reader to becoming a proficient experimenter. Organized in two parts, this unique text begins by providing a general introduction to the scientific approach to experimentation. It then describes the processes and tools required, including the relevant statistical and experimental methods. Towards the end of the book a methodology is presented, which leads the reader through the three phases of an experiment: 'Planning', 'Data Collection', and 'Analysis and Synthesis'. Experiment! Provides an excellent introduction to the methodology and implementation of experimentation in the natural, engineering and medical sciences Puts practical tools into scientific context Features a number of selected actual experiments to explore what are the key characteristics of good experiments Includes examples and exercises in every chapter This book focuses on general research skills, such as adopting a scientific mindset, learning how to plan meaningful experiments and understanding the fundamentals of collecting and interpreting data. It is directed to anyone engaged in experiments, especially Ph.D. and masters students just starting to create and develop their own experiments.

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describes exactly how regression models are developed and evaluated —where real data is used, instead of contrived textbook-like problems. Completing this book will allow you to understand and build basic business/economic models using regression analysis. You will be able to interpret the output of those models and you will be able to evaluate the models for accuracy and shortcomings. Even if you never build a model yourself, at some point in your career it is likely that you will find it necessary to interpret one; this book will make that possible. Included are instructions for using Microsoft Excel to build business/economic models using regression analysis with an appendix using screen shots and step-by-step instructions.

Applied Data Mining for Forecasting Using SAS, 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.

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.

This book is part of the SAS Press program.

When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field.

Business Intelligence Cookbook: A Project Lifecycle Approach Using Oracle Technology is an expert guide for enhancing your data warehousing and business intelligence skills, specifically targeted at Oracle Database 11g. With over 80 advanced step-by-step recipes, this book walks you through creating, optimizing, and managing actionable business intelligence solutions. What this Book will help me do Understand practical project management approaches specific to business intelligence and data warehousing. Learn to effectively estimate efforts for DW/BI projects using structured methodologies. Model data using Oracle Database and Oracle SQL Data Modeler while aligning it with business requirements. Discover best practices for transitioning BI solutions from development to deployment. Master techniques to secure organizational data as a critical asset. Author(s) John Heaton is an experienced IT professional specializing in data warehousing and business intelligence projects. With extensive knowledge of Oracle technologies, John brings deep technical insights along with a clear writing style, making complex concepts accessible to readers. He is passionate about helping professionals in the IT industry enhance their skills. Who is it for? This book is ideal for IT professionals, data warehouse developers, and project managers working with Oracle Database who seek to advance their expertise in business intelligence. If you have foundational knowledge of DW/BI concepts and want to professionally manage complete lifecycle projects leveraging Oracle tools, this guide is tailored for you.

Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

In JMP, you choose from a wide repertoire of interactive graphs to help your data best tell its story. Delve into JMP 10 Basic Analysis and Graphing to find the ideal graph for your data, from basic distributions, bubble plots, scatterplots, and parallel plots to geographic maps and tree maps.

Still trying to wrap your head around R? With more than two million users, R is the open-source programming language standard for data analysis and statistical modeling. R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming for even the most seasoned statisticians. This easy-to-follow guide explains how to use R for data processing and statistical analysis, and then, shows you how to present your data using compelling and informative graphics. You'll gain practical experience using R in a variety of settings and delve deeper into R's feature-rich toolset. Includes tips for the initial installation of R Demonstrates how to easily perform calculations on vectors, arrays, and lists of data Shows how to effectively visualize data using R's powerful graphics packages Gives pointers on how to find, install, and use add-on packages created by the R community Provides tips on getting additional help from R mailing lists and websites Whether you're just starting out with statistical analysis or are a procedural programming pro, R For Dummies is the book you need to get the most out of R.

A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causality. To ensure a thorough understanding of all key concepts, Statistical Inference provides numerous examples and solutions along with complete and precise answers to many fundamental questions, including: How do we determine that a given dataset is actually a random sample? With what level of precision and reliability can a population sample be estimated? How are probabilities determined and are they the same thing as odds? How can we predict the level of one variable from that of another? What is the strength of the relationship between two variables? The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution. Each chapter includes appendices and exercises, allowing readers to test their comprehension of the presented material. Statistical Inference: A Short Course is an excellent book for courses on probability, mathematical statistics, and statistical inference at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and practitioners who would like to develop further insights into essential statistical tools.

Learn how to create beautiful, interactive, browser-based data visualizations with the D3 JavaScript library. This hands-on book shows you how to use a combination of JavaScript and SVG to build everything from simple bar charts to complex infographics. You’ll learn how to use basic D3 tools by building visualizations based on real data from the New York Metropolitan Transit Authority. Using historical tables, geographical information, and other data, you’ll graph bus breakdowns and accidents and the percentage of subway trains running on time, among other examples. By the end of the book, you’ll be prepared to build your own web-based data visualizations with D3. Join a dataset with elements of a webpage, and modify the elements based on the data Map data values onto pixels and colors with D3’s scale objects Apply axis and line generators to simplify aspects of building visualizations Create a simple UI that allows users to investigate and compare data Use D3 transitions in your UI to animate important aspects of the data Get an introduction to D3 layout tools for building more sophisticated visualizations If you can code and manipulate data, and know how to work with JavaScript and SVG, this book is for you.

"A great book, one that I will certainly add to my personal library." --Paul J. Nahin, Professor Emeritus of Electrical Engineering, University of New Hampshire Classic Problems of Probability presents a lively account of the most intriguing aspects of statistics. The book features a large collection of more than thirty classic probability problems which have been carefully selected for their interesting history, the way they have shaped the field, and their counterintuitive nature. From Cardano's 1564 Games of Chance to Jacob Bernoulli's 1713 Golden Theorem to Parrondo's 1996 Perplexing Paradox, the book clearly outlines the puzzles and problems of probability, interweaving the discussion with rich historical detail and the story of how the mathematicians involved arrived at their solutions. Each problem is given an in-depth treatment, including detailed and rigorous mathematical proofs as needed. Some of the fascinating topics discussed by the author include: Buffon's Needle problem and its ingenious treatment by Joseph Barbier, culminating into a discussion of invariance Various paradoxes raised by Joseph Bertrand Classic problems in decision theory, including Pascal's Wager, Kraitchik's Neckties, and Newcomb's problem The Bayesian paradigm and various philosophies of probability Coverage of both elementary and more complex problems, including the Chevalier de Méré problems, Fisher and the lady testing tea, the birthday problem and its various extensions, and the Borel-Kolmogorov paradox Classic Problems of Probability is an eye-opening, one-of-a-kind reference for researchers and professionals interested in the history of probability and the varied problem-solving strategies employed throughout the ages. The book also serves as an insightful supplement for courses on mathematical probability and introductory probability and statistics at the undergraduate level.

An easily accessible, real-world approach to probability and stochastic processes Introduction to Probability and Stochastic Processes with Applications presents a clear, easy-to-understand treatment of probability and stochastic processes, providing readers with a solid foundation they can build upon throughout their careers. With an emphasis on applications in engineering, applied sciences, business and finance, statistics, mathematics, and operations research, the book features numerous real-world examples that illustrate how random phenomena occur in nature and how to use probabilistic techniques to accurately model these phenomena. The authors discuss a broad range of topics, from the basic concepts of probability to advanced topics for further study, including Itô integrals, martingales, and sigma algebras. Additional topical coverage includes: Distributions of discrete and continuous random variables frequently used in applications Random vectors, conditional probability, expectation, and multivariate normal distributions The laws of large numbers, limit theorems, and convergence of sequences of random variables Stochastic processes and related applications, particularly in queueing systems Financial mathematics, including pricing methods such as risk-neutral valuation and the Black-Scholes formula Extensive appendices containing a review of the requisite mathematics and tables of standard distributions for use in applications are provided, and plentiful exercises, problems, and solutions are found throughout. Also, a related website features additional exercises with solutions and supplementary material for classroom use. Introduction to Probability and Stochastic Processes with Applications is an ideal book for probability courses at the upper-undergraduate level. The book is also a valuable reference for researchers and practitioners in the fields of engineering, operations research, and computer science who conduct data analysis to make decisions in their everyday work.

A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.

The Seventh Edition of Introduction to Statistical Quality Control provides a comprehensive treatment of the major aspects of using statistical methodology for quality control and improvement. Both traditional and modern methods are presented, including state-of-the-art techniques for statistical process monitoring and control and statistically designed experiments for process characterization, optimization, and process robustness studies. The seventh edition continues to focus on DMAIC (define, measure, analyze, improve, and control--the problem-solving strategy of six sigma) including a chapter on the implementation process. Additionally, the text includes new examples, exercises, problems, and techniques. Statistical Quality Control is best suited for upper-division students in engineering, statistics, business and management science or students in graduate courses.

Today, audiovisual archives and libraries have become very popular especially in the field of collecting, preserving and transmitting cultural heritage. However, the data in these archives or libraries - videos, images, soundtracks, etc. - constitute as such only potential cognitive resources for a given public (or "target community"). One of the most crucial issues of digital audiovisual libraries is indeed to enable users to actively appropriate audiovisual resources for their own concern (in research, education or any other professional or non-professional context). This means, an adaptation of the audiovisual data to the specific needs of a user or user group can be represented by small and closed "communities" as well as by networks of open communities around the globe. "Active appropriation" is, basically speaking, the use of existing digital audiovisual resources by users or user communities according to their expectations, needs, interests or desires. This process presupposes: 1) the definition and development of models or "scenarios" of cognitive processing of videos by the user; 2) the availability of tools necessary for defining, developing, reusing and sharing meta-linguistic resources such as thesauruses, ontologies or description models by users or user communities. Both aspects are central to the so-called semiotic turn in dealing with digital (audiovisual) texts, corpora of texts or again entire (audiovisual) archives and libraries. They demonstrate practically and theoretically the well-known "from data to metadata" or "from (simple) information to (relevant) knowledge" problem, which obviously directly influences the effective use, social impact and relevancy, and therefore also the future, of digital knowledge archives. This book offers a systematic, comprehensive approach to these questions from a theoretical as well as practical point of view. Contents Part 1. The Practical, Technical and Theoretical Context 1. Analysis of an Audiovisual Resource. 2. The Audiovisual Semiotic Workshop (ASW) Studio - A Brief Presentation. 3. A Concrete Example of a Model for Describing Audiovisual Content. 4. Model of Description and Task of Analysis. Part 2. Tasks in Analyzing an Audiovisual Corpus 5. The Analytical Task of "Describing the Knowledge Object". 6. The Analytical Task of "Contextualizing the Domain of Knowledge". 7. The Analytical Task of "Analyzing the Discourse Production around a Subject". Part 3. Procedures of Description 8. Definition of the Domain of Knowledge and Configuration of the Topical Structure. 9. The Procedure of Free Description of an Audiovisual Corpus. 10. The Procedure of Controlled Description of an Audiovisual Corpus. Part 4. The ASW System of Metalinguistic Resources 11. An Overview of the ASW Metalinguistic Resources. 12. The Meta-lexicon Representing the ASW Universe of Discourse.

Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming. R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression Provides beginning programming instruction for those who want to write their own scripts Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.

Unlock your brain's potential using mind mapping Mind mapping is a popular technique that can be applied in a variety of situations and settings. Students can make sense of complex topics and structure their revision with mind mapping; business people can manage projects and collaborate with colleagues using mind maps, and any creative process can be supported by using a mind map to explore ideas and build upon them. Mind maps allow for greater creativity when recording ideas and information whatever the topic, and enable the note-taker to associate words with visual representations. Mind Mapping For Dummies explains how mind mapping works, why it's so successful, and the many ways it can be used. It takes you through the wide range of approaches to mind mapping, looks at the available mind mapping software options, and investigates advanced mind mapping techniques for a range of purposes, including studying for exams, improving memory, project management, and maximizing creativity. Suitable for students of all ages and study levels An excellent resource for people working on creative projects who wish to use mind mapping to develop their ideas Shows businesspeople how to maximize their efficiency, manage projects, and brainstorm effectively If you're a student, artist, writer, or businessperson, Mind Mapping For Dummies shows you how to unlock your brain's potential.