Statistics Fundamentals Succinctly
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Author | : Katie Kormanik |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 104 |
Release | : 2017-02-01 |
Genre | : |
ISBN | : 9781542809498 |
Statistics is the foundation of intelligent data analysis. Statistics Fundamentals Succinctly by Katie Kormanik provides the foundational bricks and mortar needed to master the theories and methodologies behind statistical procedures. In less than 100 pages, you'll understand how to better gather and interpret all the information at your fingertips.
Author | : Barbara Illowsky |
Publisher | : |
Total Pages | : 2106 |
Release | : 2023-12-13 |
Genre | : Mathematics |
ISBN | : |
Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Author | : Larry Wasserman |
Publisher | : Springer Science & Business Media |
Total Pages | : 446 |
Release | : 2013-12-11 |
Genre | : Mathematics |
ISBN | : 0387217363 |
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Author | : N.B. Singh |
Publisher | : N.B. Singh |
Total Pages | : 149 |
Release | : |
Genre | : Mathematics |
ISBN | : |
"A Handbook of Statistics Fundamentals" is a comprehensive guide designed for absolute beginners seeking to navigate the intricate landscape of statistics with ease and confidence. This accessible handbook provides a clear and concise introduction to fundamental statistical concepts, from basic principles to advanced techniques, catering to learners of all backgrounds. With an emphasis on simplicity and clarity, the book covers essential topics such as descriptive statistics, probability theory, hypothesis testing, regression analysis, and more, presenting complex ideas in an understandable and engaging manner. Whether you're a student embarking on your statistical journey or a professional looking to refresh your understanding, this book equips you with the knowledge and tools necessary to tackle statistical challenges with proficiency and assurance.
Author | : F.M. Dekking |
Publisher | : Springer Science & Business Media |
Total Pages | : 485 |
Release | : 2006-03-30 |
Genre | : Mathematics |
ISBN | : 1846281687 |
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Author | : Michael J. Crawley |
Publisher | : John Wiley & Sons |
Total Pages | : 348 |
Release | : 2005-05-06 |
Genre | : Mathematics |
ISBN | : 9780470022986 |
Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing. * Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. * Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data. * The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing. * Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. * Includes numerous worked examples and exercises within each chapter. * Accompanied by a website featuring worked examples, data sets, exercises and solutions: http://www.imperial.ac.uk/bio/research/crawley/statistics Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.
Author | : Michael H. Herzog |
Publisher | : Springer |
Total Pages | : 146 |
Release | : 2019-08-13 |
Genre | : Science |
ISBN | : 3030034992 |
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
Author | : Mr.Desidi Narsimha Reddy |
Publisher | : Leilani Katie Publication |
Total Pages | : 205 |
Release | : 2024-09-05 |
Genre | : Computers |
ISBN | : 9363489698 |
Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. Lova Naga Babu Ramisetti, EPM Consultant, Department of Information Technology, MiniSoft Empowering Techonolgy, 10333 Harwin Dr. #375e, Houston, TX 77036, USA. Mr.Harikrishna Pathipati, EPM Manager, Department of Information Technology, ITG Technologies, 10998 S Wilcrest Dr, Houston, TX 77099, USA.
Author | : Randall Schumacker |
Publisher | : Springer Science & Business Media |
Total Pages | : 298 |
Release | : 2013-01-24 |
Genre | : Computers |
ISBN | : 1461462274 |
This book was written to provide resource materials for teachers to use in their introductory or intermediate statistics class. The chapter content is ordered along the lines of many popular statistics books so it should be easy to supplement the content and exercises with class lecture materials. The book contains R script programs to demonstrate important topics and concepts covered in a statistics course, including probability, random sampling, population distribution types, role of the Central Limit Theorem, creation of sampling distributions for statistics, and more. The chapters contain T/F quizzes to test basic knowledge of the topics covered. In addition, the book chapters contain numerous exercises with answers or solutions to the exercises provided. The chapter exercises reinforce an understanding of the statistical concepts presented in the chapters. An instructor can select any of the supplemental materials to enhance lectures and/or provide additional coverage of concepts and topics in their statistics book.
Author | : Avrim Blum |
Publisher | : Cambridge University Press |
Total Pages | : 433 |
Release | : 2020-01-23 |
Genre | : Computers |
ISBN | : 1108617360 |
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.