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Articles by "Data Analysis"
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R for Data Analysis in Easy Steps
The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, the book describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.
Introduction to Probability with Statistical Applications 2nd Edition
Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises. * Expanded section on the algebra of sets. * Expanded chapters on probabilities to include more classical examples. * New section on regression. * Online instructors' manual containing solutions to all exercises. Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications.
Artificial Neural Networks (ANNs): Training Courses/Lessons
Artificial Neural Networks: New Research
This current book provides new research on artificial neural networks (ANNs). Topics discussed include the application of ANNs in chemistry and chemical engineering fields; the application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents; the use of ANNs for solar radiation estimation; the use of in silico methods to design and evaluate skin UV filters; a practical model based on the multilayer perceptron neural network (MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut and exit cut, of a milling tool; parameter extraction of small-signal and noise models of microwave transistors based on ANNs; and the application of ANNs to deep-learning and predictive analysis in semantic TCM telemedicine systems.
Intelligent Techniques for Data Science
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions. The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
SPSS12 Made Simple
SPSS 12 Made Simple provides a step-by-step coverage of every aspect of data analysis with SPSS from data entry to interpretation of the output. As well as advice on data entry and checking, there is guidance on the best ways of describing a data set and the choice of an appropriate statistical technique. Finally, the output is fully explained, with reference to fully annotated SPSS output. Extensive illustrations show exactly what is on the screen at every stage of the process, helping the reader to avoid common pitfalls and check their progress along the way. Most chapters end with practical exercises to illustrate the main points raised and allow the reader to test their understanding; but there is a final general revision section with further exercises on a range of topics. SPSS 12 Made Simple: * Covers a wide range of statistical tests including t-tests, ANOVA, correlation, regression, multi-way frequency analysis, discriminant analysis, logistic regression and factor analysis. * Shows you how to get as much out of your data as possible. * Gives advice (with appropriate cautions and caveats) on choosing a statistical test. * Makes extensive use of annotated screen snapshots of SPSS output, windows and dialog boxes. * Includes both chapter-specific and general exercises.
Neural Networks: Methodology and Applications
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Introduction to DATA ANALYTICS with MATLAB
This book initiates the reader into the techniques of data analysis with MATLAB. The major contents are as follows: Data Processing Importing and Exporting Data Plotting Data Missing Data Representing Missing Data Values Working with NaNs Interpolating Missing Data Inconsistent Data Filtering Data Detrending Data Differencing Data Interactive Data Exploration Marking Up Graphs with Data Brushing Making Graphs Responsive with Data Linking Interacting with Graphed Data Data Brushing with the Variables Editor Using Data Tips to Explore Graphs Descriptive Statistics Functions for Calculating Descriptive Statistics Regression Analysis Linear Correlation Linear Regression Fitting Data with Curve Fitting Toolbox Functions Interactive Fitting Programmatic Fitting MATLAB Functions for Polynomial Models Linear Model with Nonpolynomial Terms Multiple Regression Time Series Analysis Time Series Objects Time Series Data Sample Time Series Constructor Time Series Tools Importing and Exporting Data Plotting Time Series Selecting Data for Analysis Editing Data, Time, Attributes, and Events Processing and Manipulating Time Series
MatLab GUI: Training Courses/Lessons
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.
Data Mining: Concepts, Models and Techniques
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.
Optimizing Optimization: The Next Generation of Opt. Applications
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.
Evolutionary Algorithms and Metaheuristics in Civil Engineering
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.
Introduction to Data Analysis with R for Forensic Scientists
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.
Statistical Rethinking: A Bayesian Course
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.
Principal Component Analysis Networks and Algorithms
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.
An Elementary Introduction to Statistical Learning Theory
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.
New Data Structures and Algorithms for Logic Synthesis...
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.
Stat-Ease Design-Expert
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.Stochastic Reliability Modeling, Optimization and Applications
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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