Statistics Book

Statistics


  • Author : Robin H. Lock
  • Publisher : Wiley Global Education
  • Release Date : 2016-12-01
  • Genre: Mathematics
  • Pages : 590
  • ISBN 10 : 9781119163725

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Statistics Excerpt :

With Wiley’s Enhanced E-Text, you get all the benefits of a downloadable, reflowable eBook with added resources to make your study time more effective, including: • Embedded & Searchable Tables & Figures • Links to Datasets through wiley.com • Video Solutions & Tutorials • Dataset Index embedded including links to datasets by page number Statistics: Unlocking the Power of Data, 2nd Edition continues to utilize these intuitive methods like randomization and bootstrap intervals to introduce the fundamental idea of statistical inference. These methods are brought to life through authentically relevant examples, enabled through easy to use statistical software, and are accessible at very early stages of a course. The program includes the more traditional methods like t-tests, chi-square texts, etc. but only after students have developed a strong intuitive understanding of inference through randomization methods. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The program is driven by real data and real applications.

Statistics Book

Statistics


  • Author : Robin H. Lock
  • Publisher : Wiley Global Education
  • Release Date : 2020-05-07
  • Genre: Mathematics
  • Pages : null
  • ISBN 10 : 9781119715894

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Statistics Excerpt :

Statistics: Unlocking the Power of Data, 2nd Edition continues to utilize these intuitive methods like randomization and bootstrap intervals to introduce the fundamental idea of statistical inference. These methods are brought to life through authentically relevant examples and are accessible at very early stages of the text. Applications are drawn from a wide variety of disciplines, chosen primarily on the basis of perceived interest to students and instructors. Problems and exercises are plentiful and span a wide range of difficulty levels, from very straightforward short answer problems to extended projects.

Why We Sleep Book
Score: 4
From 31 Ratings

Why We Sleep


  • Author : Matthew Walker
  • Publisher : Simon and Schuster
  • Release Date : 2017-10-03
  • Genre: Health & Fitness
  • Pages : 360
  • ISBN 10 : 9781501144318

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Why We Sleep Excerpt :

"Sleep is one of the most important but least understood aspects of our life, wellness, and longevity ... An explosion of scientific discoveries in the last twenty years has shed new light on this fundamental aspect of our lives. Now ... neuroscientist and sleep expert Matthew Walker gives us a new understanding of the vital importance of sleep and dreaming"--Amazon.com.

Statistics Book

Statistics


  • Author : Robin H. Lock
  • Publisher : Wiley
  • Release Date : 2020-11
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : 9781119682165

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Statistics Excerpt :

"This book takes a unique approach of utilizing computer simulation methods to introduce students to the key ideas of statistical inference.Methods such as bootstrap intervals and randomization tests are very intuitive to novice students and capitalize on visual learning skills students bring to the classroom. With proper use of computer support, they are accessible at very early stages of a course with little formal background. Our text introduces statistical inference through these resampling and randomization methods, not only because these methods are becoming increasingly important for statisticians in their own right but also because they are outstanding in building students' conceptual understanding of the key ideas"--

Modern Statistics for Modern Biology Book

Modern Statistics for Modern Biology


  • Author : Susan Holmes
  • Publisher : Cambridge University Press
  • Release Date : 2018-11-30
  • Genre: Uncategoriezed
  • Pages : 400
  • ISBN 10 : 9781108427029

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Modern Statistics for Modern Biology Excerpt :

A far-reaching course in practical advanced statistics for biologists using R/Bioconductor, data exploration, and simulation.

Statistical Analysis and Data Display Book

Statistical Analysis and Data Display


  • Author : Richard M. Heiberger
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-06-29
  • Genre: Mathematics
  • Pages : 730
  • ISBN 10 : 9781475742848

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Statistical Analysis and Data Display Excerpt :

This presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data—showing code, graphics, and accompanying computer listings. They emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how tabular results are used to confirm the visual impressions derived from the graphs. Many of the graphical formats are novel and appear here for the first time in print.

Probability and Computing Book
Score: 4
From 4 Ratings

Probability and Computing


  • Author : Michael Mitzenmacher
  • Publisher : Cambridge University Press
  • Release Date : 2005-01-31
  • Genre: Computers
  • Pages : 352
  • ISBN 10 : 0521835402

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Probability and Computing Excerpt :

"This textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. - It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. - It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications."--Jacket.

Statistical Distributions Book

Statistical Distributions


  • Author : Catherine Forbes
  • Publisher : John Wiley & Sons
  • Release Date : 2011-03-21
  • Genre: Mathematics
  • Pages : 230
  • ISBN 10 : 9781118097823

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Statistical Distributions Excerpt :

A new edition of the trusted guide on commonly used statistical distributions Fully updated to reflect the latest developments on the topic, Statistical Distributions, Fourth Edition continues to serve as an authoritative guide on the application of statistical methods to research across various disciplines. The book provides a concise presentation of popular statistical distributions along with the necessary knowledge for their successful use in data modeling and analysis. Following a basic introduction, forty popular distributions are outlined in individual chapters that are complete with related facts and formulas. Reflecting the latest changes and trends in statistical distribution theory, the Fourth Edition features: A new chapter on queuing formulas that discusses standard formulas that often arise from simple queuing systems Methods for extending independent modeling schemes to the dependent case, covering techniques for generating complex distributions from simple distributions New coverage of conditional probability, including conditional expectations and joint and marginal distributions Commonly used tables associated with the normal (Gaussian), student-t, F and chi-square distributions Additional reviewing methods for the estimation of unknown parameters, such as the method of percentiles, the method of moments, maximum likelihood inference, and Bayesian inference Statistical Distributions, Fourth Edition is an excellent supplement for upper-undergraduate and graduate level courses on the topic. It is also a valuable reference for researchers and practitioners in the fields of engineering, economics, operations research, and the social sciences who conduct statistical analyses.

Practical Statistics for Data Scientists Book
Score: 3
From 1 Ratings

Practical Statistics for Data Scientists


  • Author : Peter Bruce
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2017-05-10
  • Genre: Computers
  • Pages : 318
  • ISBN 10 : 9781491952917

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Practical Statistics for Data Scientists Excerpt :

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

Statistics Explained Book
Score: 5
From 1 Ratings

Statistics Explained


  • Author : Steve McKillup
  • Publisher : Cambridge University Press
  • Release Date : 2011-11-03
  • Genre: Medical
  • Pages : null
  • ISBN 10 : 9781139502948

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Statistics Explained Excerpt :

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.

Graph Representation Learning Book

Graph Representation Learning


  • Author : William L. Hamilton
  • Publisher : Morgan & Claypool Publishers
  • Release Date : 2020-09-16
  • Genre: Computers
  • Pages : 159
  • ISBN 10 : 9781681739649

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Graph Representation Learning Excerpt :

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Introduction to Statistical Investigations Book

Introduction to Statistical Investigations


  • Author : Nathan Tintle
  • Publisher : Wiley Global Education
  • Release Date : 2015-12-17
  • Genre: Mathematics
  • Pages : 696
  • ISBN 10 : 9781119154310

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Introduction to Statistical Investigations Excerpt :

Introduction to Statistical Investigations leads students to learn about the process of conducting statistical investigations from data collection, to exploring data, to statistical inference, to drawing appropriate conclusions. The text is designed for a one-semester introductory statistics course. It focuses on genuine research studies, active learning, and effective use of technology. Simulations and randomization tests introduce statistical inference, yielding a strong conceptual foundation that bridges students to theory-based inference approaches. Repetition allows students to see the logic and scope of inference. This implementation follows the GAISE recommendations endorsed by the American Statistical Association.

Data Visualization Book

Data Visualization


  • Author : Robert Grant
  • Publisher : CRC Press
  • Release Date : 2018-12-07
  • Genre: Mathematics
  • Pages : 222
  • ISBN 10 : 9781351781749

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Data Visualization Excerpt :

This is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia. Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data. Features: Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images. Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods. Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).