Articles by "Statistics"

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Kevin P. Murphy ... 1104 pages - Publisher: The MIT Press; (August, 2012) ... Language: English - ISBN-10: 0262018020 - ISBN-13: 978-0262018029.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package―PMTK (probabilistic modeling toolkit)―that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Allen B. Downey ... 210 pages - Publisher: O'Reilly Media; (October, 2013) ... Language: English - ISBN-10: 1449370780 - ISBN-13: 978-1449370787.

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics + Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing + Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey + Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

Allen B. Downey ... 226 pages - Publisher: O'Reilly Media; 2nd edition (October, 2014) ... Language: English - AmazonSIN: B00OL084UI.

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.

New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code + Run experiments to test statistical behavior, such as generating samples from several distributions + Use simulations to understand concepts that are hard to grasp mathematically + Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools + Use statistical inference to answer questions about real-world data.

Daniel T. Larose  ... 718 pages - Publisher: Freeman/Worth; 2nd edition (January, 2013) ... Language: English - ASIN: B00HQO0UZI by Amazon - ISBN-10: 1464127182 - ISBN-13: 978-1464127182.

Discovering the Fundamentals of Statistics by Dan Larose is the ideal brief introductory statistics text that balances the teaching of computational skills with conceptual understanding. Written in a concise, accessible style, Discovering the Fundamentals of Statistics helps students develop the quantitative and analytical tools needed to understand statistics in today’s data-saturated world. Dan Larose presents statistical concepts the way instructors teach and the way students learn.

Peter Goos, David Meintrup ... 648 pages - Publisher: Wiley; (April, 2016) ... Language: English - ISBN-10: 1119097150 - ISBN-13: 978-1119097150.

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. + Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). + Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. + Promotes the use of graphs and confidence intervals in addition to p-values. + Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

StataCorp Stata MP v16.0 [Size: 337 MB] ... StataCorp Stata MP 16 for Windows PC also known as Stata/MP provides the most extensive multicore support of any statistics and data management package. Stata/MP is the fastest and largest version of Stata. Almost every computer can take advantage of the advanced multiprocessing capabilities of Stata/MP. Stata/MP lets you analyze data in one-half to two-thirds the time compared with Stata/SE on inexpensive dual-core laptops and in one-quarter to one-half the time on quad-core desktops and laptops. Stata/MP runs even faster on multiprocessor servers. Stata/MP supports up to 64 cores/processors. Stata/SE can analyze up to 2 billion observations. Stata/MP can analyze 10 to 20 billion observations on the largest computers currently available and is ready to analyze up to 1 trillion observations once computer hardware catches up. Stata/MP also allows 120000 variables compared to 32767 variables allowed by Stata/SE. Some procedures are not parallelized and some are inherently sequential, meaning they run the same speed in Stata/MP. For a complete assessment of Stata/MP’s performance, including command-by-command statistics. Stata/MP is the multiprocessor and multicore version of Stata. It’s primary purpose is to run faster. Most of the new features in Stata have been parallelized to run faster on Stata/MP, sometimes much faster.

N. Balakrishnan, Markos V. Koutras, Konstadinos G. Politis ... 620 pages - Publisher: Wiley; (April , 2019) ... Language: English - ASIN: B07QGMBC9F by Amazon.

Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, and discuss the methodology to tackle these problems. A wide-range of topics are covered that include the concepts of probability and conditional probability, univariate discrete distributions, univariate continuous distributions, along with a detailed presentation of the most important probability distributions used in practice, with their main properties and applications.

Designed as a useful guide, the text contains theory of probability, de finitions, charts, examples with solutions, illustrations, self-assessment exercises, computational exercises, problems and a glossary. This important text: • Includes classroom-tested problems and solutions to probability exercises • Highlights real-world exercises designed to make clear the concepts presented • Uses Mathematica software to illustrate the text’s computer exercises • Features applications representing worldwide situations and processes • Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress. Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.

Aileen Nielsen ... 505 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - Amazon SIN: B07Y5WSCV2.

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data + Undertake exploratory time series data analysis + Store temporal data + Simulate time series data + Generate and select features for a time series + Measure error + Forecast and classify time series with machine or deep learning + Evaluate accuracy and performance

Richard J. Larsen, Morris L. Marx ... 752 pages - Publisher: Pearson; 6th edition (January, 2017) ... Language: English - ASIN: B076VG8WHV by Amazon.

Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

Using high-quality, real-world case studies and examples, this introduction to mathematical statistics shows how to use statistical methods and when to use them. This book can be used as a brief introduction to design of experiments. This successful, calculus-based book of probability and statistics, was one of the first to make real-world applications an integral part of motivating discussion. The number of problem sets has increased in all sections. Some sections include almost 50% new problems, while the most popular case studies remain. For anyone needing to develop proficiency with Mathematical Statistics.

Roxy Peck, Tom Short ... 729 pages - Publisher: Cengage Learning; 2nd edition (January, 2018) ... Language: English - ISBN-10: 1337558087 - ISBN-13: 978-1337558082.

Statistics: Learning from Data 2nd Edition helps you learn to think like a statistician. It pays particular attention to areas that students often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps you to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.

Steve McKillup ... 420 pages - Publisher: Cambridge Univ. Press; 2nd edition (November, 2011) ... Language: English - ASIN: B0072J3KAO by Amazon.

An understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material. Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain a useful refresher tool for postgraduates.

Sorin Draghici ... 1036 pages - Publisher: Chapman and Hall/CRC; 2nd edition (April, 2016) ... Language: English - ASIN: B00O5D331Q by Amazon.

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second Edition: Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

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.

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.

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.

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.

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.

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.

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.

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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