Manual Of Statistics
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| Author | : Matthew A. Barsalou |
| Publisher | : Quality Press |
| Total Pages | : 371 |
| Release | : 2018-12-19 |
| Genre | : Education |
| ISBN | : 087389975X |
This book was written to provide guidance for those who need to apply statistical methods for practical use. While the book provides detailed guidance on the use of Minitab for calculation, simply entering data into a software program is not sufficient to reliably gain knowledge from data. The software will provide an answer, but the answer may be wrong if the sample was not taken properly, the data was unsuitable for the statistical test that was performed, or the wrong test was selected. It is also possible that the answer will be correct, but misinterpreted. This book provides both guidance in applying the statistical methods described as well as instructions for performing calculations without a statistical software program such as Minitab. One of the authors is a professional statistician who spent nearly 13 years working at Minitab and the other is an experienced and certified Lean Six Sigma Master Black Belt. Together, they strive to present the knowledge of a statistician in a format that can be easily understood and applied by non-statisticians facing real-world problems. Their guidance is provided with the goal of making data analysis accessible and practical. Rather than focusing on theoretical concepts, the book delivers only the information that is critical to success for the practitioner. It is a thorough guide for those who have not yet been exposed to the value of statistics, as well as a reliable reference for those who have been introduced to statistics but are not yet confident in their abilities.
| Author | : David M Lane |
| Publisher | : |
| Total Pages | : 406 |
| Release | : 2014-12-02 |
| Genre | : |
| ISBN | : 9781687894250 |
Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational experience. This part I features the book Front Matter, Chapters 1-10, and the full Glossary. Chapters Include:: I. Introduction, II. Graphing Distributions, III. Summarizing Distributions, IV. Describing Bivariate Data, V. Probability, VI. Research Design, VII. Normal Distributions, VIII. Advanced Graphs, IX. Sampling Distributions, and X. Estimation. Online Statistics Education: A Multimedia Course of Study (http: //onlinestatbook.com/). Project Leader: David M. Lane, Rice University.
| Author | : Janet Peacock |
| Publisher | : Oxford University Press |
| Total Pages | : 540 |
| Release | : 2011 |
| Genre | : Medical |
| ISBN | : 0199551286 |
The majority of medical research involves quantitative methods and so it is essential to be able to understand and interpret statistics. This book shows readers how to develop the skills required to critically appraise research evidence effectively, and how to conduct research and communicate their findings.
| 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 | : 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 | : Michael J. Evans |
| Publisher | : Macmillan |
| Total Pages | : 704 |
| Release | : 2004 |
| Genre | : Mathematics |
| ISBN | : 9780716747420 |
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
| Author | : Gareth James |
| Publisher | : Springer Nature |
| Total Pages | : 617 |
| Release | : 2023-08-01 |
| Genre | : Mathematics |
| ISBN | : 3031387473 |
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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
| Author | : Steven S. Skiena |
| Publisher | : Springer |
| Total Pages | : 456 |
| Release | : 2017-07-01 |
| Genre | : Computers |
| ISBN | : 3319554441 |
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)
| Author | : Michael Sullivan |
| Publisher | : Pearson Education |
| Total Pages | : 0 |
| Release | : 2013 |
| Genre | : Statistics |
| ISBN | : 9780321757272 |
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Michael Sullivan's Statistics: Informed Decisions Using Data, Fourth Edition, connects statistical concepts to students' lives, helping them to think critically, become informed consumers, and make better decisions. Throughout the book, "Putting It Together" features help students visualize the relationships among various statistical concepts. This feature extends to the exercises, providing a consistent vision of the bigger picture of statistics. This book follows the Guidelines for Assessment and Instruction in Statistics Education (GAISE), as recommended by the American Statistical Association, and emphasizes statistical literacy, use of real data and technology, conceptual understanding, and active learning.
| Author | : Robin H. Lock |
| Publisher | : John Wiley & Sons |
| Total Pages | : 866 |
| Release | : 2020-10-13 |
| Genre | : Mathematics |
| ISBN | : 1119682169 |
Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.
