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This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
Key Features: Coverage of traditional concepts in statistics includes expected value operators, likelihood functions, maximum likelihood estimation, and least squares estimation, preparing students for concepts they will continue to encounter in more advanced material. Real research on specific antisocial behaviors provides consistent context for answering research questions in an interesting and intuitive way. Discussion of statistical inference in an easy-to-understand manner ensures that students have the foundation they need to avoid misusing hypothesis tests. A detailed presentation of resampling methods and randomization tests for experiments and correlation provides a better way to analyze data when the assumptions of the classical tests are not met. A number of current techniques for data analysis not included in other textbooks are introduced, including quantile plots, quantile-quantile plots, normal quantile plots, analysis of residuals in scatter plots, bootstrap methods, robust estimators, robust regression, and the use of randomization (permutation) tests for experiments and correlation.
Principles of Statistical Data Handling is designed to help readers understand the principles of data handling so that they can make better use of computer data in research or study.
Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters. Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.
This unique intermediate/advanced statistics text uses real research on antisocial behaviors, such as cyberbullying, stereotyping, prejudice, and discrimination, to help readers across the social and behavioral sciences understand the underlying theory behind statistical methods. By presenting examples and principles of statistics within the context of these timely issues, authors Jerome Frieman, Donald A. Saucier, and Stuart S. Miller show how the results of analyses can be used to answer research questions. New techniques for data analysis and a wide range of topics are covered, including how to deal with “messy data” and the importance of engaging in exploratory data analysis.
This text provides a clear discussion of the basic statistical concepts and methods frequently encountered in statistical research. Assuming only a basic level of Mathematics, and with numerous examples and illustrations, this text is a valuable resource for students and researchers in the Sciences and Social Sciences.
This textbook provides a thorough treatment of major statistical methods and techniques for both staticticians and non-statisticians requiring a foundation in applied statistics. There is an emphasis throughout on inference from data, the principle of fitting models by least squares, and careful interpretation of results. The authors employ SAS to produce PC-based statistical graphics and perform some analyses where appropriate. This edition includes updated real-world data sets.
Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demons
Statistics: Principles and Methods, 8th Edition provides students and business professionals with a comprehensive introduction to statistics concepts, terminology, and methods with a wide array of practical applications. Real-world data provides an easily relatable frame of reference, while numerous examples reinforce key ideas and demonstrate critical concepts to help ease student comprehension. Designed for those seeking a highly practical introduction to statistical measurement, reasoning, and analysis, this book requires no specific mathematical background and leaves derivations behind in favor of logic, reasoning, and modern statistics software. Concepts are introduced first in a real-life setting to illustrate immediate relevancy, and are subsequently expanded to relate underlying mechanisms, limitations, and further applications. An emphasis on the relationship between validity and assumptions underscores the importance of critical thinking and the use of appropriate models while instilling thoughtful habits that lead to accuracy in interpretation. Going beyond the typical introductory text to keep the focus on application, this book gives students a deeper understanding of statistics as it is used every day across disciplines and industries.