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Author: Chris Spatz
Publisher:
ISBN: 9780996339223
Category : Quantitative research
Languages : en
Pages : 0
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Book Description
Author: Chris Spatz
Publisher:
ISBN: 9780996339223
Category : Quantitative research
Languages : en
Pages : 0
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Book Description
Author: Måns Thulin
Publisher:
ISBN: 9781032497457
Category : Mathematics
Languages : en
Pages : 0
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Book Description
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Author: Andy Field
Publisher: SAGE
ISBN: 144628915X
Category : Reference
Languages : en
Pages : 994
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Book Description
Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field′s books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you′re doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book′s accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software.
Author: Robert Gould
Publisher:
ISBN: 9780321823656
Category : Statistics
Languages : en
Pages : 736
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Book Description
We live in a data-driven world, and the goal of this Canadian text is to teach students how to access and analyze these data critically. Canadian authors Jim Stallard and Michelle Boué emphasize that learning statistics extends beyond the classroom to an essential life skill, and want Canadian students to develop a "data habit of mind." Regardless of their math backgrounds, students will learn how to think about data and how to reason using data. With a clear, unintimidating writing style and carefully chosen pedagogy, this text makes data analysis accessible to all students. KEY TOPICS: Introduction to Data; Picturing Variation with Graphs; Numerical Summaries of Centre and Variation; Regression Analysis: Exploring Associations between Variables; Modelling Variation with Probability; Modeling Random Events: The Normal and Binomial Models; Survey Sampling and Inference; Hypothesis Testing for Population Proportions; Inferring Population Means; Associations between Categorical Variables; Multiple Comparisons and Analysis of Variance; Experimental Design: Controlling Variation; Inference without Normality;Inference for Regression MARKET: A textbook suitable for all introductory statistics courses
Author: Chris Spatz
Publisher:
ISBN: 9780534506957
Category :
Languages : en
Pages :
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Book Description
Author: David S. Moore
Publisher: W.H. Freeman
ISBN: 9781464103186
Category : Mathematical statistics
Languages : en
Pages :
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Book Description
Author: Andy Field
Publisher: SAGE
ISBN: 152644030X
Category : Social Science
Languages : en
Pages : 2026
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Book Description
With an exciting new look, math diagnostic tool, and a research roadmap to navigate projects, this new edition of Andy Field’s award-winning text offers a unique combination of humor and step-by-step instruction to make learning statistics compelling and accessible to even the most anxious of students. The Fifth Edition takes students from initial theory to regression, factor analysis, and multilevel modeling, fully incorporating IBM SPSS Statistics© version 25 and fascinating examples throughout. SAGE edge offers a robust online environment featuring an impressive array of free tools and resources for review, study, and further exploration, keeping both instructors and students on the cutting edge of teaching and learning. Course cartridges available for Blackboard, Canvas, and Moodle. Andy Field is the award winning author of An Adventure in Statistics: The Reality Enigma and is the recipient of the UK National Teaching Fellowship (2010), British Psychological Society book award (2006), and has been recognized with local and national teaching awards (University of Sussex, 2015, 2016).
Author: Cindy Clements
Publisher:
ISBN:
Category : Fathom
Languages : en
Pages : 0
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Book Description
Author: Pratap Dangeti
Publisher: Packt Publishing Ltd
ISBN: 1788291220
Category : Computers
Languages : en
Pages : 438
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Book Description
Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Author: Damaraju Raghavarao
Publisher: CRC Press
ISBN: 1000103773
Category : Mathematics
Languages : en
Pages : 295
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Book Description
This book provides an overview of the commonly used statistical methodology. It is intended to enable professionals such as medical doctors, engineers, business executives, laboratory technicians, school teachers, and others to understand the basics of statistical thought through self study.
