Statistically Speaking

Statistically Speaking is a book of quotations. It brings together the best expressed thoughts that are especially illuminating and pertinent to the disciplines of probability and statistics. The book is an aid for the individual who loves to quote – and to quote correctly.

Become a stats superstar by using Excel to reveal the powerful secrets of statistics Microsoft Excel offers numerous possibilities for statistical analysis—and you don’t have to be a math wizard to unlock them. In Statistical Analysis with Excel For Dummies, fully updated for the 2021 version of Excel, you’ll hit the ground running with straightforward techniques and practical guidance to unlock the power of statistics in Excel. Bypass unnecessary jargon and skip right to mastering formulas, functions, charts, probabilities, distributions, and correlations. Written for professionals and students without a background in statistics or math, you’ll learn to create, interpret, and translate statistics—and have fun doing it! In this book you’ll find out how to: Understand, describe, and summarize any kind of data, from sports stats to sales figures Confidently draw conclusions from your analyses, make accurate predictions, and calculate correlations Model the probabilities of future outcomes based on past data Perform statistical analysis on any platform: Windows, Mac, or iPad Access additional resources and practice templates through Dummies.com For anyone who’s ever wanted to unleash the full potential of statistical analysis in Excel—and impress your colleagues or classmates along the way—Statistical Analysis with Excel For Dummies walks you through the foundational concepts of analyzing statistics and the step-by-step methods you use to apply them.

Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.

Revised and updated to include the behavioral sciences, the second edition of this introductory statistics book engages students with real-world examples and exercises. To the dismay of many social and behavioral science majors, successfully passing a statistics course in sociology, psychology, and most other social/behavioral science programs is required, and at many institutions statistics is becoming a university-wide requirement. In this newly revised text, the authors continue to make use of their proven stress-busting approach to teaching statistics to self-describe math phobic students. This book uses humorous examples and step-by-step presentations of statistical procedures to illustrate what are often complex and hard-to-grasp statistical concepts. Students and instructors will find this text to be a helpful, easy to interpret and thoroughly comprehensive introduction to social and behavioral statistics. Perfect for social and behavioral sciences upper-level undergrads fearful of that required stats course. It uses stress-busting features like cartoons and real-world examples to illustrate what are often complex and hard-to-grasp statistical concepts. Includes the newest and most necessary tools for students to master statistical skills making handouts or additional books unnecessary and gives instructors and their students a compact and affordable main text for their introductory stats courses.

This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them. This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context. The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts. It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis.

Fiction has a major social impact, not least because it co-shapes the image that society has of various social groups. Drawing on a collection of 170 contemporary Dutch-language novels, Character Constellations presents a range of data-driven, statistical models to study depictions of characters in terms of gender, race, ethnicity, class, age, sexuality, and other identity categories. Incorporating the tools of network analysis, each chapter highlights an aspect of fictional social networks that affects the representation of social groups: their centrality, their communities, and their conflicts. While reading individual novels in light of emerging statistical patterns, combining the formal methods of social network analysis with the interpretive tools of narratology, this study shows how central societal themes such as (in)equality and emancipation, integration and segregation, and social mobility and class struggle are foregrounded, replicated, or distorted in the Dutch novel. Showcasing what character-based critiques of literary representation gain by integrating data-driven methods into the practice of critical close reading, Character Constellations contributes to societal debates on cultural representation and identity and the role