An Introduction to Panel Data Qca in R, Bhattacharya, Preya
Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Halpern, Diane F. Dunn, Dana S. Название: Thought and knowledge ISBN: 1138655171 ISBN-13(EAN): 9781138655171 Издательство: Taylor&Francis Рейтинг: Цена: 10104.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Thought and Knowledge applies theory and research from the learning sciences to teach students the critical thinking skills that they need to succeed in today’s world. The text identifies, defines, discusses, and deconstructs contemporary challenges to critical thinking, from fake news, alternative facts, and deep fakes, to misinformation, disinformation, post-truth, and more. It guides students through the explosion of content on the internet and social media and enables them to become careful and critical evaluators as well as consumers.The text is grounded in psychological science, especially the cognitive sciences, and brought to life through humorous and engaging language and numerous practical and real-world examples and anecdotes. This edition has been streamlined with thoughtful consideration over what content to keep, what to cut, and how much new and current research to add. Critical thinking skills are presented in every chapter, empowering students to learn more efficiently, research more productively, and present logical, critical, and informed arguments. The skills are reviewed at the end of the chapter, and a complete list of skills with definitions and examples are included in the appendix. The text is supported by a companion website that features a robust set of instructor and student resources: www.routledge.com/cw/halpern.Thought and Knowledge can be used as a core text in critical thinking courses offered in departments of psychology, philosophy, English, or across the humanities and social sciences, or as a supplement in any course where critical thinking is emphasized.
Автор: Haigh, John Название: Probability: A Very Short Introduction ISBN: 0199588481 ISBN-13(EAN): 9780199588480 Издательство: Oxford Academ Рейтинг: Цена: 1582.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Making good decisions under conditions of uncertainty requires an appreciation of the way random chance works. In this Very Short Introduction, John Haigh provides a brief account of probability theory; explaining the philosophical approaches, discussing probability distributions, and looking its applications in science and economics.
Автор: Giuseppe Da Prato Название: An Introduction to Infinite-Dimensional Analysis ISBN: 3642421687 ISBN-13(EAN): 9783642421686 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Based on well-known lectures given at Scuola Normale Superiore in Pisa, this book introduces analysis in a separable Hilbert space of infinite dimension. It starts from the definition of Gaussian measures in Hilbert spaces, concepts such as the Cameron-Martin formula, Brownian motion and Wiener integral are introduced in a simple way.
Описание: The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject.
Описание: This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.
Автор: Maurizio Petrelli Название: Introduction to Python in Earth Science Data Analysis ISBN: 3030780546 ISBN-13(EAN): 9783030780548 Издательство: Springer Рейтинг: Цена: 7622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences.
Автор: Vershynin, Roman (university Of Michigan, Ann Arbor) Название: Cambridge series in statistical and probabilistic mathematics ISBN: 1108415199 ISBN-13(EAN): 9781108415194 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The data sciences are moving fast, and probabilistic methods are both the foundation and a driver. This highly motivated text brings beginners up to speed quickly and provides working data scientists with powerful new tools. Ideal for a basic second course in probability with a view to data science applications, it is also suitable for self-study.
Автор: M. Antonia Amaral Turkman, Carlos Daniel Paulino, Peter Muller Название: Computational Bayesian Statistics: An Introduction ISBN: 1108481035 ISBN-13(EAN): 9781108481038 Издательство: Cambridge Academ Рейтинг: Цена: 18058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explains the fundamental ideas of Bayesian analysis, with a focus on computational methods such as MCMC and available software such as R/R-INLA, OpenBUGS, JAGS, Stan, and BayesX. It is suitable as a textbook for a first graduate-level course and as a user`s guide for researchers and graduate students from beyond statistics.
Описание: Real-world data sets are messy and complicated. This book shows students in social science and public management how to prepare these data sets for analysis, with detailed, step-by-step instructions. R and Python code is introduced gradually, providing readers with the tools they need to work with real data.
Описание: Letter Jam is a 2-6 player cooperative word game where players assist each other in composing meaningful words from letters around the table. The trick is holding the letter card so that it`s only visible to other players and not to you.At the start of the game, each player receives a set of face-down letter cards that can be arranged to form an existing word. The setup can be prepared by using a special card scanning app, or by players selecting words for each other. Each player then puts their first card in their stand facing the other players without looking at it, and the game begins.The game is played in turns. Each turn, players simultaneously search other players` letters to see what words they can spell out (telling the others the length of the word they can make up). The player who offers the longest word can then be chosen as the clue giver.The clue giver spells out their clue by putting numbered tokens in front of the other players. Number one goes to the player whose letter comes first in the clue, number two to the second letter etc. They can always use a wild card which can be any letter, but they cannot tell others which letter it represents.Each player with a numbered token (or tokens) in front of them then tries to figure out what their letter is. If they do, they place the card face down before revealing the next letter. At the end of the game, players can then rearrange the cards to try to form an existing word. All players then reveal their cards to see if they were successful or not. The more players who have an existing word in front of them, the bigger their common success.
Автор: Timbers, Tiffany-anne (university Of British Colum Название: Data science ISBN: 0367524686 ISBN-13(EAN): 9780367524685 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
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