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.

"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

Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs. Senior Contributing Authors Barbara Illowsky, De Anza College Susan Dean, De Anza College Contributing Authors Daniel Birmajer, Nazareth College Bryan Blount, Kentucky Wesleyan College Sheri Boyd, Rollins College Matthew Einsohn, Prescott College James Helmreich, Marist College Lynette Kenyon, Collin County Community College Sheldon Lee, Viterbo University Jeff Taub, Maine Maritime Academy

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

"Statistics and Probability, things you should know, questions and answers" is a thoughtfully crafted practice book designed to enhance your understanding and mastery of statistical analysis and probability theory. Whether you're a student looking to improve your academic performance, a professional seeking to strengthen your analytical skills, or an avid learner eager to delve into the fascinating world of statistics and probability, this book is an indispensable resource. Inside these pages, you'll find a vast collection of carefully selected exercises spanning various topics, ranging from the fundamentals to more advanced concepts. Each exercise is designed to reinforce key principles, challenge your problem-solving abilities, and provide valuable hands-on experience in applying statistical and probabilistic techniques. The book covers a wide range of topics, including: · Descriptive statistics: Learn how to summarize and interpret data using measures of central tendency, variability, and graphical representations. · Probability theory: Explore the foundations of probability, including basic concepts, conditional probability, independence, and the laws of probability. · Statistical inference: Develop skills in hypothesis testing, confidence intervals, and understanding the concept of p-values. · Regression analysis: Discover how to build regression models, interpret coefficients, and make predictions based on data. · Experimental design: Gain insights into designing experiments, sampling techniques, and the importance of randomization. · Multivariate analysis: Dive into techniques such as factor analysis, cluster analysis, and principal component analysis. Each exercise is accompanied by detailed solutions and explanations, enabling you to reinforce your knowledge and rectify any misconceptions. The comprehensive nature of the book ensures that you'll encounter a wide variety of scenarios, preparing you to tackle real-world problems with confidence. "Statistics and Probability Exercises" is not just a book of problems—it's a companion that guides you through the intricacies of statistics and probability, offering valuable insights and practical wisdom. Whether you're studying on your own or in a classroom setting, this book will help you develop a strong foundation in statistical analysis and probability theory. Embark on your journey to statistical proficiency today and let "Statistics and Probability, things you should know, questions and answers" be your trusted companion along the way.

Whether you're a student, researcher, or just curious about the world around you, understanding the basics of statistics is essential. In Statistics, things you should know (Questions and Answers) you'll learn everything you need to know to get started with statistics. With the basics of data collection and measurement, you'll learn about statistical distributions, hypothesis testing, correlation and regression analysis, and more therefore, this book is the perfect place to start.

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.

If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy. The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more—all explained in simple, clear, and yes, funny illustrations. Never again will you order the Poisson Distribution in a French restaurant!

This book is about helping you to choose and use the right statistical technique to analyze your data and write about your results and findings convincingly. It provides a guide to the essential statistical skills needed for success in your assignment, project or dissertation. Berman Brown and Saunders concentrate on particular statistical tests and their three Ws--what, why, and when. They provide you with the tools to choose the graphs and statistics that are suitable for your data, and to understand what the statistical results actually mean. In addition, the book explains why it is impossible to avoid using statistics in analysing data; describes the language of statistics to make it easier to understand the various terms used for statistical techniques; deals with using tables and charts to present data so that they are easy to understand; and explains the statistics used to describe data used to inferdifferences and relationships. The book also includes a handy alphabet of statistics as well as a glossary of key statistical terms. --From publisher's description.