"This completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. The authors show how all analysis of variance and multiple regression can be accomplished within this framework." "Intended for advanced undergraduate or graduate courses on data analysis, statistics, and/or quantitative methods taught in psychology, education, or other behavioral and social science departments, this book also appeals to researchers who analyze data. A protected website featuring additional examples and problems with data sets, lecture notes, PowerPoint presentations, and class-tested exam questions is available to adopters. This material uses SAS but can easily be adapted to other programs. A working knowledge of basic algebra and any multiple regression program is assumed."--BOOK JACKET.
This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful.
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin. Murray, J. S., Dunson, D. B., Carin, L., and Lucas, J. E. (2013). ... Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo ...
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This book is a guide to the practical application of statistics to data analysis in the physical sciences.
The author understands your peculiar concerns and has therefore written this book in a clear and concise manner. The work is also thorough, relevant, and up-to-date. You are not required to be an experienced analyst to read this book.
Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other.
You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python.
This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research.
Each chapter of the book quickly introduces a key ‘theme’ of Data Analysis, before immersing you in the practical aspects of each theme.
The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social ...