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Statistical Models
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  • Cited by 244
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Vipulanandan, C. and Mohammed, A. 2020. Magnetic Field Strength and Temperature Effects on the Behavior of Oil Well Cement Slurry Modified with Iron Oxide Nanoparticles and Quantified with Vipulanandan Models. Journal of Testing and Evaluation, Vol. 48, Issue. 6, p. 20180107.

    Wood, Amy E. and Mattson, Christopher A. 2019. Quantifying the effects of various factors on the utility of design ethnography in the developing world. Research in Engineering Design,

    Sharifi-Malvajerdi, Saeed Zhu, Feiyu Fogarty, Colin B. Fay, Michael P. Fairhurst, Rick M. Flegg, Jennifer A. Stepniewska, Kasia and Small, Dylan S. 2019. Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model. Malaria Journal, Vol. 18, Issue. 1,

    Bradley, Patrick E. 2019. Methodology for the sequence analysis of building stocks. Building Research & Information, Vol. 47, Issue. 2, p. 141.

    Ahmad, Amreen Ahmad, Tanvir and Bhatt, Abhishek 2019. Data and Communication Networks. Vol. 847, Issue. , p. 227.

    Machado Santos, Simone Cabral Neto, João and Mendonça Silva, Maisa 2019. Forecasting model to assess the potential of secondary lead production from lead acid battery scrap. Environmental Science and Pollution Research,

    Bugdol, Monika N. Mitas, Andrzej W. Lipowicz, Anna M. Bugdol, Marcin D. and Bieńkowska, Maria J. 2019. Information Technology in Biomedicine. Vol. 762, Issue. , p. 251.

    Moore, Kat S. and 't Hoen, Peter A. C. 2019. Computational approaches for the analysis of RNA–protein interactions: A primer for biologists. Journal of Biological Chemistry, Vol. 294, Issue. 1, p. 1.

    Joss, Lisa and Müller, Erich A. 2019. Machine Learning for Fluid Property Correlations: Classroom Examples with MATLAB. Journal of Chemical Education,

    Elsadek, Sanaa Fekry Abed Makhlouf, Mohamed Abd Allah and Aldeen, Mohamed Amal 2019. Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. Vol. 845, Issue. , p. 198.

    Yi, Dewei Su, Jinya Liu, Cunjia and Chen, Wen-Hua 2019. New Driver Workload Prediction Using Clustering-Aided Approaches. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 49, Issue. 1, p. 64.

    Lowery, Patrick G. 2019. Plea Bargains Among Serious and Violent Girls: An Intersectional Approach Exploring Race in the Juvenile Court. Feminist Criminology, Vol. 14, Issue. 1, p. 115.

    2018. Optimization Techniques and Applications with Examples. p. 165.

    Welc, Jacek and Esquerdo, Pedro J. Rodriguez 2018. Applied Regression Analysis for Business. p. 213.

    El-Kassabi, Hadeel Serhani, Mohamed Adel Bouhaddioui, Chafik and Dssouli, Rachida 2018. Emerging Technologies for Developing Countries. Vol. 206, Issue. , p. 99.

    Jiang, Peng Sellers, William R. and Liu, X. Shirley 2018. Big Data Approaches for Modeling Response and Resistance to Cancer Drugs. Annual Review of Biomedical Data Science, Vol. 1, Issue. 1, p. 1.

    Fazzolari, Michela Petrocchi, Marinella and Spognardi, Angelo 2018. Intelligent Data Engineering and Automated Learning – IDEAL 2018. Vol. 11314, Issue. , p. 698.

    Olson, Mark E. Soriano, Diana Rosell, Julieta A. Anfodillo, Tommaso Donoghue, Michael J. Edwards, Erika J. León-Gómez, Calixto Dawson, Todd Camarero Martínez, J. Julio Castorena, Matiss Echeverría, Alberto Espinosa, Carlos I. Fajardo, Alex Gazol, Antonio Isnard, Sandrine Lima, Rivete S. Marcati, Carmen R. and Méndez-Alonzo, Rodrigo 2018. Plant height and hydraulic vulnerability to drought and cold. Proceedings of the National Academy of Sciences, Vol. 115, Issue. 29, p. 7551.

    Roberts, Charlotte A. and Steckel, Richard H. 2018. The Backbone of Europe. p. 325.

    Kumari, Khushbu and Yadav, Suniti 2018. Linear regression analysis study. Journal of the Practice of Cardiovascular Sciences, Vol. 4, Issue. 1, p. 33.

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Book description

This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.

Reviews

‘At last, a second course in statistics that is serious, correct, and interesting. The book teaches regression, causal modeling, maximum likelihood, and the bootstrap. Everyone who analyzes real data should read this book.’

Persi Diaconis - Stanford University

‘This book is outstanding for the clarity of its thought and writing. It prepares readers for a critical assessment of the technical literature in the social and health sciences, and provides a welcome antidote to the standard formulaic approach to statistics.’

Erich L. Lehmann - University of California, Berkeley

‘In Statistical Models, David Freedman explains the main statistical techniques used in causal modeling - and where the skeletons are buried. Complex statistical ideas are clearly presented and vividly illustrated with interesting examples. Both newcomers and practitioners will benefit from reading this book.’

Alan Krueger - Princeton University

‘Regression techniques are often applied to observational data with the intent of drawing causal conclusions. In what circumstances is this justified? What are the assumptions underlying the analysis? Statistical Models answers these questions. The book is essential reading for anybody who uses regression to do more than summarize data. The treatment is original, and extremely well written. Critical discussions of research papers from the social sciences are most insightful. I highly recommend this book to anybody who engages in statistical modeling, or teaches regression, and most certainly to all of my students.’

Aad van der Vaart - Vrije Universiteit Amsterdam

‘A pleasure to read, Statistical Models shows the field’s most elegant writer at the height of his powers. While most textbooks hurry past core assumptions in order to explicate technique, this book places the spotlight on the core assumptions, challenging readers to think critically about how they are invoked in practice.’

Donald Green - Yale University

‘Statistical Models, a modern introduction to the subject, discusses graphical models and simultaneous equations among other topics. There are plenty of instructive exercises and computer labs. Especially valuable is the critical assessment of the main ‘philosophers’s stones’ in applied statistics. This is an inspiring book and a very good read, for teachers as well as students.’

Gesine Reinert - Oxford University

'Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation.'

Source: Mathematical Reviews

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