Need help with Statistics? Want a quick review or refresher for class? This is the book for you! REA's Statistics Super Review gives you everything you need to know!This Super Review can be used as a supplement to your high school or college textbook, or as a handy guide for anyone who needs a fast review of the subject.- Comprehensive, yet concise coverage - review covers the material that students must know about statistics. Each topic is presented in a clear and easy-to-understand format that makes learning easier.- Questions and answers for each topic - let you practice what you've learned and build your statistics skills.- End-of-chapter quizzes - gauge your understanding of the important information you need to know, so you'll be ready for any homework assignment, quiz, or test.Whether you need a quick refresher on the subject, or are prepping for your next exam, we think you'll agree that REA's Super Review provides all you need to know!

Need help with Statistics? Want a quick review or refresher for class? This is the book for you! REA’s Statistics Super Review gives you everything you need to know!This Super Review can be used as a supplement to your high school or college textbook, or as a handy guide for anyone who needs a fast review of the subject.• Comprehensive, yet concise coverage – review covers the material that students must know about statistics. Each topic is presented in a clear and easy-to-understand format that makes learning easier.• Questions and answers for each topic – let you practice what you’ve learned and build your statistics skills.• End-of-chapter quizzes – gauge your understanding of the important information you need to know, so you’ll be ready for any homework assignment, quiz, or test.Whether you need a quick refresher on the subject, or are prepping for your next exam, we think you’ll agree that REA’s Super Review provides all you need to know!

Inside the Book: Graphic displays Numerical measures Probability Sampling Principles of testing Univariate inferential tests Bivariate relationships Review questions Resource center Glossary Common mistakes Tables Why CliffsNotes? Go with the name you know and trust Get the information you need-fast! CliffsNotes Quick Review guides give you a clear, concise, easy-to-use review of the basics. Introducing each topic, defining key terms, and carefully walking you through sample problems, this guide helps you grasp and understand the important concepts needed to succeed. Access 500 additional practice questions at www.cliffsnotes.com/go/quiz/statistics Master the Basics–Fast Complete coverage of core concepts Easy topic-by-topic organization Access hundreds of practice problems at www.cliffsnotes.com/go/quiz/statistics

Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

Get all you need to know with Super Reviews! Each Super Review is packed with in-depth, student-friendly topic reviews that fully explain everything about the subject. The Statistics Super Review includes frequency distributions, numerical methods of describing data, measures of variability, probability, distributions, sampling theory, statistical inference, general linear model inferences, experimental design, the chi-square test, and time series. Take the Super Review quizzes to see how much you've learned - and where you need more study. Makes an excellent study aid and textbook companion. Great for self-study! DETAILS - From cover to cover, each in-depth topic review is easy-to-follow and easy-to-grasp - Perfect when preparing for homework, quizzes, and exams! - Review questions after each topic that highlight and reinforce key areas and concepts - Student-friendly language for easy reading and comprehension - Includes quizzes that test your understanding of the subject

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data