Articles by "Statistics"

Showing posts with label Statistics. Show all posts

Thursday, April 09, 2020 , , ,
John MacInnes ... 334 pages - Publisher: SAGE Publications Ltd; (December, 2016) ... Language: English - ASIN: B01JZ7IRCG by Amazon.

Many professional, high-quality surveys collect data on people's behaviour, experiences, lifestyles and attitudes. The data they produce is more accessible than ever before. This book provides students with a comprehensive introduction to using this data, as well as transactional data and big data sources, in their own research projects. Here you will find all you need to know about locating, accessing, preparing and analysing secondary data, along with step-by-step instructions for using IBM SPSS Statistics.

You will learn how to: Create a robust research question and design that suits secondary analysis + Locate, access and explore data online + Understand data documentation + Check and 'clean' secondary data + Manage and analyse your data to produce meaningful results + Replicate analyses of data in published articles and books. Using case studies and video animations to illustrate each step of your research, this book provides you with the quantitative analysis skills you'll need to pass your course, complete your research project and compete in the job market. Exercises throughout the book and on the book's companion website give you an opportunity to practice, check your understanding and work hands on with real data as you're learning.
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Thursday, March 19, 2020 , , , ,
Norman Matloff ... 444 pages - Publisher: Routledge; (June, 2019) ... Language: English - ISBN-10: 1138393290 - ISBN-13: 978-1138393295.

Probability and Statistics for Data Science: Math + R + Data covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming.
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Monday, January 20, 2020 , , ,
Sanjiv Jaggia, Alison Kelly ... 587 pages - Publisher: McGraw-Hill Education; 2nd edition (February, 2019) ... Language: English - ISBN-10: 1260547655 - ISBN-13: 978-1260547658.

Essentials of Business Statistics: Communicating with Numbers is a core statistics textbook that sparks student interest and bridges the gap between how statistics is taught and how practitioners think about and apply statistical methods. Throughout the text, the emphasis is on communicating with numbers rather than on number crunching. By incorporating the perspective of professional users, the subject matter is more relevant and the presentation of material more straightforward for students. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, and how they need it, so that your class time is more engaging and effective.
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Monday, January 13, 2020 , ,
Ke-Lin Du, M. N. S. Swamy ... 824 pages - Publisher: Springer; (December, 2013) ... Language: English - ISBN-10: 144715570X - ISBN-13: 978-1447155706.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence and data mining.
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Friday, January 10, 2020 , , , ,
Bruce Ratner ... 724 pages - Publisher: Chapman and Hall/CRC; 3rd edition (June, 2017) ... Language: English - ISBN-10: 9781498797603 - ISBN-13: 978-1498797603. 

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition: The current chapters have been completely rewritten. + The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. + Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). + Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.
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Tuesday, December 31, 2019 , , ,
Soumya D. Mohanty ... 136 pages - Publisher: Chapman and Hall/CRC; (December, 2018) ... Language: English - ASIN: B07LCWSVMD by Amazon.

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis.

Features: Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory + Focuses on methodology and results rather than formal proofs + Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) + Uses concrete and realistic data analysis examples to guide the reader + Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges.
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Thursday, December 19, 2019 , , ,
Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li ... 316 pages - Publisher: Springer; (May, 2011) ... Language: English - ASIN: B00F5QT36Q by Amazon.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
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Tuesday, December 03, 2019 , ,
Bryan F.J. Manly, Jorge A. Navarro Alberto ... 269 pages - Publisher: Routledge; 4th edition (October, 2016) ... Language: English - ISBN-10: 1498728960 - ISBN-13: 978-1498728966.

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.
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Wednesday, November 06, 2019 , ,
Per Christian Hansen, Víctor Pereyra, Godela Scherer ... 324 pages - Publisher: JHUP; (December, 2012) ... Language: English - ASIN: B00AXYQU5M by Amazon.

As one of the classical statistical regression techniques, and often the first to be taught to new students, least squares fitting can be a very effective tool in data analysis. Given measured data, we establish a relationship between independent and dependent variables so that we can use the data predictively. The main concern of Least Squares Data Fitting with Applications is how to do this on a computer with efficient and robust computational methods for linear and nonlinear relationships. The presentation also establishes a link between the statistical setting and the computational issues. In a number of applications, the accuracy and efficiency of the least squares fit is central, and Per Christian Hansen, Víctor Pereyra, and Godela Scherer survey modern computational methods and illustrate them in fields ranging from engineering and environmental sciences to geophysics. Anyone working with problems of linear and nonlinear least squares fitting will find this book invaluable as a hands-on guide, with accessible text and carefully explained problems. Included are• an overview of computational methods together with their properties and advantages• topics from statistical regression analysis that help readers to understand and evaluate the computed solutions• many examples that illustrate the techniques and algorithms. Least Squares Data Fitting with Applications can be used as a textbook for advanced undergraduate or graduate courses and professionals in the sciences and in engineering.
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Saturday, October 19, 2019 , , ,
Peter Bruce, Andrew Bruce ... 320 pages - Publisher: O'Reilly; (May, 2017) ... Language: English - ISBN-10: 1491952962 - ISBN-13: 978-1491952962. 

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science - How random sampling can reduce bias and yield a higher quality dataset, even with big data - How the principles of experimental design yield definitive answers to questions - How to use regression to estimate outcomes and detect anomalies - Key classification techniques for predicting which categories a record belongs to - Statistical machine learning methods that “learn” from data - Unsupervised learning methods for extracting meaning from unlabeled data.
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Tuesday, October 01, 2019 , , ,
Charles M. Judd, Gary H. McClelland, Carey S. Ryan ... 378 pages - Publisher: Routledge; 3rd edition (May, 2017) ... Language: English - ISBN-10: 1138819832 - ISBN-13: 978-1138819832.

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.

Highlights of the third edition include: A new chapter on logistic regression; Expanded treatment of mixed models for data with multiple random factors; Updated examples; An enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
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Thursday, September 26, 2019 , , , ,
William Menke, Joshua Menke ... 342 pages - Publisher: Academic Press; 2nd edition (March, 2016) ... Language: English - ISBN-10: 0128044888 - ISBN-13: 978-0128044889.

Environmental Data Analysis with MatLab is a new edition that expands fundamentally on the original with an expanded tutorial approach, new crib sheets, and problem sets providing a clear learning path for students and researchers working to analyze real data sets in the environmental sciences. Since publication of the bestselling Environmental Data Analysis with MATLAB®, many advances have been made in environmental data analysis. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often noisy data drawn from a broad range of sources. The work teaches the basics of the underlying theory of data analysis and then reinforces that knowledge with carefully chosen, realistic scenarios.
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Thursday, September 19, 2019 , , , ,
MoonJung Cho, Wendy L. Martinez ... 286 pages - Publisher: Chapman and Hall/CRC; (December, 2014) ... Language: English - ISBN-10: 1466596562 - ISBN-13: 978-1466596566

Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book: Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB * Presents examples of how MATLAB can be used to analyze data * Offers access to a companion website with data sets and additional examples * Contains figures and visual aids to assist in application of the software *Explains how to determine what method should be used for analysis. Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.
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Sunday, September 08, 2019 , , ,
Patrick Laurie Davies ... 320 pages - Publisher: Chapman and Hall/CRC; (July, 2014) ... Language: English - ASIN: B00L2LDVM0 by Amazon

The First Detailed Account of Statistical Analysis That Treats Models as Approximations: The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data. Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth.

The author develops a concept of approximation for probability models with applications to: Discrete data + Location scale + Analysis of variance (ANOVA) + Nonparametric regression, image analysis, and densities + Time series + Model choice. The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.
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Saturday, September 07, 2019 , , , ,
Daniel J. Denis ... 224 pages - Publisher: Wiley; (September, 2018) ... Language: English - ISBN-10: 9781119465812 - ISBN-13: 978-1119465812

This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts. Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research.
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Friday, September 06, 2019 , , ,
Conrad Carlberg ... 568 pages - Publisher: Que Publishing; (December, 2017) ... Language: English - ISBN-10: 0789759055 - ISBN-13: 978-0789759054

Nationally recognized Excel expert Conrad Carlberg shows you how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples and downloadable workbooks, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, Carlberg offers insightful coverage of crucial topics ranging from experimental design to the statistical power of F tests. Updated for Excel 2016, this guide covers both modern consistency functions and legacy compatibility functions. Becoming an expert with Excel statistics has never been easier! In this book, you’ll find crystal-clear instructions, insider insights, and complete step-by-step guidance.
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Tuesday, September 03, 2019 , , ,
Practical Statistics For Data And Business Analysis [Size: 6.34 GB] ... This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and Google. After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics. In no time with simple and easy way you will learn and love statistics. We stress in this course to make it very spontaneous to make all students love statistics .
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Monday, July 08, 2019 , , , ,
Keith McCormick, Jesus Salcedo, Jon Peck, Andrew Wheeler ... 528 pages - Publisher: Wiley; 1st edition (May, 2017) ... Language: English - ISBN-10: 1119003555 - ISBN-13: 978-1119003557

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization: SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis - Display complex relationships and create better visualizations - Model complex interactions and master predictive analytics - Integrate R and Python with SPSS Statistics for more efficient, more powerful code. These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.
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Thursday, March 21, 2019 , , , ,
John Fox, Sanford Weisberg ... 608 pages - Publisher: SAGE Publications; 3rd edition (October, 2018) ... Language: English - ISBN-10: 1544336470 - ISBN-13: 978-1544336473 ...

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the carand effects packages for R for this new edition, and include coverage of RStudio and R Markdown.
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Sunday, March 03, 2019 , , ,
Jacek Welc, Pedro J. R. Esquerdo ... 286 pages - Publisher: Springer; (December, 2017) ... Language: English - ASIN: B078Q1RQDV by Amazon...

This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies. Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.
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