Intuitive introductory statistics

This textbook is designed to give an engaging introduction to statistics and the art of data analysis. The unique scope includes, but also goes beyond, classical methodology associated with the normal distribution. What if the normal model is not valid for a particular data set? This cutting-edge approach provides the alternatives. It is an introduction to the world and possibilities of statistics that uses exercises, computer analyses, and simulations throughout the core lessons. These elementary statistical methods are intuitive. Counting and ranking features prominently in the text. Nonparametric methods, for instance, are often based on counts and ranks and are very easy to integrate into an introductory course. The ease of computation with advanced calculators and statistical software, both of which factor into this text, allows important techniques to be introduced earlier in the study of statistics. This book's novel scope also includes measuring symmetry with Walsh averages, finding a nonparametric regression line, jackknifing, and bootstrapping . Concepts and techniques are explored through practical problems. Quantitative reasoning is at the core of so many professions and academic disciplines, and this book opens the door to the most modern possibilities. Unique approach that includes novel topics for introductory courses including bootstrapping, Walsh averages, and jackknifing Engaging examples and exercises draw readers in and facilitate work by hand and with calculators and statistical software Designed for use inside and outside statistics departments Accompanying data sets, R and Minitab code, and solutions manual make this textbook easy to use for students and instructors Douglas A. Wolfe, PhD, is a Professor Emeritus in the Department of Statistics at The Ohio State Universit y. Much of his current research is in ranked set sampling. He is also the author of a popular textbook on nonparametric statistics. Grant Schneider, PhD, is a Data Scientist at Upstart Network in the San Francisco Bay area. Grant created the accompanying R package and is experienced with statistical programming for research and in the classroom. He received his PhD in Statistics from The Ohio State University.