Articles by "Data Analysis"

Showing posts with label Data Analysis. Show all posts

Wednesday, February 14, 2018 , , , ,
GUIs (also known as graphical user interfaces or UIs) provide point-and-click control of software applications, eliminating the need to learn a language or type commands in order to run the application. MATLAB apps are self-contained MATLAB programs with GUI front ends that automate a task or calculation. The GUI typically contains controls such as menus, toolbars, buttons, and sliders. Many MATLAB products, such as Curve Fitting Toolbox, Signal Processing Toolbox, and Control System Toolbox include apps with custom user interfaces. You can also create your own custom apps, including their corresponding UIs, for others to use.
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Tuesday, February 06, 2018 , ,
Florin Gorunescu ... 360 pages - Publisher: Springer; (June, 2011) ... Language: English - ISBN-10: 3642197205 - ISBN-13: 978-3642197208 ...

The knowledge discovery process is as old as Homo sapiens. Until some time ago this process was solely based on the ‘natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since “knowledge is power”. The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be applied in real-world situations. Accordingly, it is meant for all those who wish to learn how to explore and analysis of large quantities of data in order to discover the hidden nugget of information.
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Sunday, February 04, 2018 , , ,
Stephen Satchell ... 328 pages - Publisher: Academic Press; 1st edition (October, 2009) ... Language: English - ISBN-10: 0750633611 - ISBN-13: 978-0750633611 ...

The practical aspects of optimization rarely receive global, balanced examinations. Stephen Satchell’s nuanced assembly of technical presentations about optimization packages (by their developers) and about current optimization practice and theory (by academic researchers) makes available highly practical solutions to our post-liquidity bubble environment. The commercial chapters emphasize algorithmic elements without becoming sales pitches, and the academic chapters create context and explore development opportunities. Together they offer an incisive perspective that stretches toward new products, new techniques, and new answers in quantitative finance. Presents a unique "confrontation" between software engineers and academics: * Highlights a global view of common optimization issues. * Emphasizes the research and market challenges of optimization software while avoiding sales pitches. * Accentuates real applications, not laboratory results.
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Saturday, January 20, 2018 , , ,
Jorge Magalhães-Mendes, David Greiner ... 127 pages - Publisher: Springer; 1st edition (November, 2016) ... Language: English - ISBN-10: 3319373218 - ISBN-13: 978-3319373218 ...

This book focuses on civil and structural engineering and construction management applications. The contributions constitute modified, extended and improved versions of research presented at the minisymposium organized by the editors at the ECCOMAS conference on this topic in Barcelona 2014.
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Monday, January 15, 2018 , ,
James M. Curran ... 331 pages - Publisher: CRC Press; 1st edition (July, 2010) ... Language: English - ISBN-10: 1420088262 - ISBN-13: 978-1420088267 ...

 Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to R + Considerations and techniques for the visual display of data through graphics + An overview of statistical hypothesis tests and the reasoning behind them A comprehensive guide to the use of the linear model, the foundation of most statistics encountered. + An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression. + Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist. Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.
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Tuesday, January 09, 2018 , , ,
Richard McElreath ... 487 pages - Publisher: Chapman and Hall/CRC; (December, 2015) ... Language: English - ISBN-10: 1482253445 - ISBN-13: 978-1482253443.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource: The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
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Saturday, December 23, 2017 , , ,
Xiangyu Kong,‎ Changhua Hu,‎ Zhansheng Duan ... 323 pages - Publisher: Springer; 1st edition (January 9, 2017) ... Language: English - ISBN-10: 981102913X - ISBN-13: 978-9811029134 ...

 This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
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Thursday, December 14, 2017 , ,
Sanjeev Kulkarni,‎ Gilbert Harman ... 232 pages - Publisher: Wiley; 1st edition (August, 2011) ... Language: English - ISBN-10: 0470641835 - ISBN-13: 978-0470641835 ...

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning: A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
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Tuesday, December 05, 2017 , ,
Luca Gaetano Amaru ... 156 pages - Publisher: Springer; 1st edition (August, 2016) ... Language: English - ASIN: B01JL45ZLE by Amazon Digital Services ...

This book introduces new logic primitives for electronic design automation tools. The author approaches fundamental EDA problems from a different, unconventional perspective, in order to demonstrate the key role of rethinking EDA solutions in overcoming technological limitations of present and future technologies. The author discusses techniques that improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. Readers will be enabled to accelerate formal methods by studying core properties of logic circuits and developing new frameworks for logic reasoning engines.
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Tuesday, November 28, 2017 , , ,
Stat-Ease Design-Expert v10.0.7.0 for x64 [Size: 38 MB] ... Stat-Ease, Inc. welcomes you to version 10 of Design-Expert software (DX10) for design of experiments (DOE). Use this Windows®-based program to optimize your product or process. It provides many powerful statistical tools, such as: Two-level factorial screening designs: Identify the vital factors that affect your process or product so you can make breakthrough improvements. + General factorial studies: Discover the best combination of categorical factors, such as source versus type of raw material supply. + Response surface methods (RSM): Find the optimal process settings to achieve peak performance. + Mixture design techniques: Discover the ideal recipe for your product formulation. + Combinations of process factors, mixture components, and categorical factors: Mix your cake (with different ingredients) and bake it too! Your Design-Expert program offers rotatable 3D plots to easily view response surfaces from all angles. Use your mouse to set flags and explore the contours on interactive 2D graphs. Our numerical optimization function finds maximum desirability for dozens of responses simultaneously! You'll find a wealth of statistical details within the program itself via various Help screens. Take advantage of this information gold mine that is literally at your fingertips. Also, do not overlook the helpful annotations provided on all reports. For a helpful collection of checklists and "cheat sheets," see the Handbook for Experimenters. It's free to all registered users. Furthermore, for quick primers on the principles of design and analysis, we recommend you read the following two soft-cover books from Stat-Ease Principals Mark Anderson and Pat Whitcomb,  published by Productivity Press of New York city: DOE Simplified: Practical Tools for Effective Experimentation,3rd Edition + RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments.
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Wednesday, November 22, 2017 , , , ,
Syouji Nakamura, Toshio Nakagawa ... 316 pages - Publisher: World Scientific Publishing Company (November, 2009) ... Language: English - ISBN-10: 9814277436 - ISBN-13: 978-9814277433 ...

Reliability theory and applications become major concerns of engineers and managers engaged in making high quality products and designing highly reliable systems. This book aims to survey new research topics in reliability theory and useful applied techniques in reliability engineering. Our research group in Nagoya, Japan has continued to study reliability theory and applications for more than twenty years, and has presented and published many good papers at international conferences and in journals. This book focuses mainly on how to apply the results of reliability theory to practical models. Theoretical results of coherent, inspection, and damage systems are summarized methodically, using the techniques of stochastic processes. There exist optimization problems in computer and management sciences and engineering. It is shown that such problems as computer, information and network systems are solved by using the techniques of reliability. Furthermore, some useful techniques applied to the analysis of stochastic models in management science and plants are shown. The reader will learn new topics and techniques, and how to apply reliability models to actual ones. The book will serve as an essential guide to a subject of study for graduate students and researchers and as a useful guide for reliability engineers engaged not only in maintenance work but also in management and computer works.
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Tuesday, November 21, 2017 , , ,
Wes McKinney ... 544 pages - Publisher: O'Reilly Media; 2nd edition (October, 2017) ... Language: English - ISBN-10: 1491957662 - ISBN-13: 978-1491957660 ... 

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing * Learn basic and advanced features in NumPy (Numerical Python) * Get started with data analysis tools in the pandas library * Use flexible tools to load, clean, transform, merge, and reshape data * Create informative visualizations with matplotlib * Apply the pandas groupby facility to slice, dice, and summarize datasets * Analyze and manipulate regular and irregular time series data * Learn how to solve real-world data analysis problems with thorough, detailed examples.
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Monday, November 20, 2017 , ,
Alan Agresti ... 400 pages - Publisher: Wiley-Interscience; 2nd edition (March, 2007) ... Language: English - ISBN-10: 0471226181 - ISBN-13: 978-0471226185 ...

The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition features: Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models * A unified perspective based on generalized linear models * An emphasis on logistic regression modeling * An appendix that demonstrates the use of SAS(r) for all methods * An entertaining historical perspective on the development of the methods * Specialized methods for ordinal data, small samples, multicategory data, and matched pairs * More than 100 analyses of real data sets and nearly 300 exercises. Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.
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Monday, November 20, 2017 , , , ,
Hector Guerrero ... 338 pages - Publisher: Springer; (March, 2010) ... Language: English - ISBN-10: 3642108342 - ISBN-13: 978-3642108341 ...

Why does the World Need―Excel Data Analysis, Modeling, and Simulation? When spreadsheets ?rst became widely available in the early 1980s, it spawned a revolution in teaching. What previously could only be done with arcane software and large scale computing was now available to the common-man, on a desktop. Also, before spreadsheets, most substantial analytical work was done outside the classroom where the tools were; spreadsheets and personal computers moved the work into the classroom. Not only did it change how the analysis curriculum was taught, but it also empowered students to venture out on their own to explore new ways to use the tools. I can’t tell you how many phone calls, of?ce visits, and/or emails I have received in my teaching career from ecstatic students crowing about what they have just done with a spreadsheet model. I have been teaching courses related to spreadsheet based analysis and modeling for about 25 years and I have watched and participated in the spreadsheet revolution.
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Friday, November 17, 2017 , , ,
R. Lyman Ott, Micheal T. Longnecker ... 1297 pages - Publisher: Brooks Cole; 6th edition (December, 2008) ... Language: English - ISBN-10: 0495017582 - ISBN-13: 978-0495017585 ...

Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.
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Wednesday, November 15, 2017 , , ,
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ... 426 pages - Publisher: Springer; (September, 2017) ... Language: English - ISBN-10: 1461471370 - ISBN-13: 978-1461471370.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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Tuesday, November 14, 2017 , , ,
Brian Steele, John Chandler, Swarna Reddy ... 430 pages - Publisher: Springer; 1st edition (January, 2017) ... Language: English - ISBN-10: 3319457950 - ISBN-13: 978-3319457956 ...

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts: (a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter. (b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System. (c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Monday, November 13, 2017 , , ,
Mohssen Mohammed,‎ Muhammad Badruddin Khan,‎ Eihab Bashier Mohammed Bashier ... 226 pages - Publisher: CRC Press; 1st edition (July, 2016) ... Language: English - ISBN-10: 1498705383 - ISBN-13: 978-1498705387 ...

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.
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Thursday, November 09, 2017 , , , ,
IBM SPSS Statistics v25 [Size: 722 MB] ... IBM SPSS Statistics Subscription delivers the power of SPSS Statistics predictive analytics capabilities with a flexible subscription payment option, including an easier purchasing, management and licensing experience. With the introduction of SPSS Statistics Subscription, organizations, groups and individuals can take advantage of predictive analytics capabilities to deliver the maximum amount of value to the user. Propel research & analysis with a fast and powerful solution: A comprehensive set of statistical tools - Work inside a single, integrated interface to run descriptive statistics, regression, advanced statistics and many more. Create publication ready charts, tables, and decision trees in one tool. - Integration with Open Source - Enhance the SPSS Syntax with R and Python through specialized extensions. Leverage the 130+ extensions available on our Extension Hub, or build your own and share with your peers to create a customized solution. - Easy statistical analysis - Use a simple drag and drop interface to access a wide range of capabilities and work across multiple data sources. Plus, flexible deployment options make purchasing and managing your software easy. Subscription Base: Data Preparation - IBM SPSS Data Preparation is a fully integrated module of IBM SPSS Statistics. - Bootstrapping - Bootstrapping is a useful technique for testing model stability. Custom Tables and Advanced Statistics Add-On: Advanced Statistics - Improve the accuracy of your analysis and the dependability of your conclusions with statistics designed for complex relationships. - Regression - IBM SPSS Regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. - Custom Tables: IBM SPSS Custom Tables enables you to summarize SPSS Statistics data, and display your analyses as presentation-quality, production-ready tables.
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Sunday, November 05, 2017 , , ,
Bradley P. Carlin, Thomas A. Louis ... 552 pages - Publisher: Chapman and Hall/CRC; 3rd edition (June, 2008) ... Language: English - ISBN-10: 1584886978 - ISBN-13: 978-1584886976 ...

Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition: New data examples, corresponding R and WinBUGS code, and homework problems - Explicit descriptions and illustrations of hierarchical modeling―now commonplace in Bayesian data analysis - A new chapter on Bayesian design that emphasizes Bayesian clinical trials - A completely revised and expanded section on ranking and histogram estimation - A new case study on infectious disease modeling and the 1918 flu epidemic - A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem―available both electronically and in print. Ideal for Anyone Performing Statistical Analyses: Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.
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