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The 5 Things You Need to Know about Statistics provides an accessible introduction to statistical thinking for anthropologists and other social scientists who feel some mixture of dread and loathing when it comes to quantification and data analysis. It is not so much an introduction to statistics as a primer on how to think statistically in order to do precise ethnographic studies. Readers will be empowered by the realization that statistics is not an arcane, enigmatical science but a set of tools for learning about the world in which we live. Unlike other books on statistics for beginners, this book-guides readers through the underlying logic of the major statistical methods before applying those methods in interpreting ethnographic research, thus emphasizing understanding of quantitative methods;-uses a single data set in explaining each method, allowing readers to grasp how different methods offer varying interpretations of the data;-discusses increasingly complex techniques in plain, easy-to-understand language intended for beginning students.;-covers five central ideas: central tendency, dispersion, Chi-square, ANOVA, correlation;-shows readers how to use these quantitative statistical methods in doing real-life ethnographic fieldwork.
The 5 Things You Need to Know about Statistics provides an accessible introduction to statistical thinking for anthropologists and other social scientists who feel some mixture of dread and loathing when it comes to quantification and data analysis. It is not so much an introduction to statistics as a primer on how to think statistically in order to do precise ethnographic studies. Readers will be empowered by the realization that statistics is not an arcane, enigmatical science but a set of tools for learning about the world in which we live. Unlike other books on statistics for beginners, this book-guides readers through the underlying logic of the major statistical methods before applying those methods in interpreting ethnographic research, thus emphasizing understanding of quantitative methods;-uses a single data set in explaining each method, allowing readers to grasp how different methods offer varying interpretations of the data;-discusses increasingly complex techniques in plain, easy-to-understand language intended for beginning students.;-covers five central ideas: central tendency, dispersion, Chi-square, ANOVA, correlation;-shows readers how to use these quantitative statistical methods in doing real-life ethnographic fieldwork.
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.
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
This critical case study exposes the educational realities of Latinos in K-12 public schools in the Western United States from the students’ own perspectives. Issues that are often over simplified and commonly misunderstood are brought to life. Their accounts are then compared with the viewpoints of a range of K-12 teachers on matters of community, learning, race, culture, and school politics.
Modern insights into international trade and commerce reveal new landscapes shaped by technological advancements, shifting economic power, and complex global trade. As the world becomes interconnected, digital transformation, including e-commerce and blockchain technology, is revolutionizing how goods and services are exchanged across borders, streamlining transactions and enhancing transparency. Rising markets and changing political landscapes are reshaping traditional trade routes and strategies. A nuanced understanding of new trade policies and economic agreements is necessary to leverage data analytics and adapt to evolving consumer preference. Modern Insights in International Trade and Commerce offers theoretical knowledge and practical insights into international trade and commerce. By integrating case studies, empirical data, and expert analyses, it provides a rich resource for further academic exploration into global business, market entry strategies, and cross-cultural management. This book covers topics such as, and is a useful resource for academicians, researchers, business owners, consultants, strategists, and economists.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Being able to make and receive payments is an essential facet of modern life. It is integral to the banking and finance systems, and it touches all global citizens. In some areas, payment systems are rapidly evolving – moving swiftly from paper payment instruments, to electronic, to real-time – but in others, underdeveloped payment systems hold back economic and social development. This book is intended to assist the reader in navigating the payments landscape. The author explores highly topical areas, such as the role of payment systems in enabling commerce to contribute to the development of emerging economies, the evolution of payment systems from paper instruments to computerization, the role of cryptocurrencies, and the slow decline of plastic credit and debit cards owing to alternative forms of payment being introduced. Altogether, this book provides a comprehensive overview of the evolution of payment and offers projections for the future, encouraging readers to explore their own predictions, using the framework that the book has provided. It is vital reading for technologists, marketers, executives and investors in the FinTech sector, as well as academics teaching business and technology courses.
When you took statistics in school, your instructor gave you specially prepared datasets, told you what analyses to perform, and checked your work to see if it was correct. Once you left the class, though, you were on your own. Did you know how to create and prepare a dataset for analysis? Did you know how to select and generate appropriate graphics and statistics? Did you wonder why you were forced to take the class and when you would ever use what you learned? That's where Stats with Cats can help you out. The book will show you: How to decide what you should put in your dataset and how to arrange the data. How to decide what graphs and statistics to produce for your data. How you can create a statistical model to answer your data analysis questions. The book also provides enough feline support to minimize any stress you may experience. Charles Kufs has been crunching numbers for over thirty years, first as a hydrogeologist, and since the 1990s as a statistician. He is certified as a Six Sigma Green Belt by the American Society for Quality. He currently works as a statistician for the federal government and he is here to help you.
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.