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Resources on the topics covered in introductory statistics and data analysis classes (e.g., PUBP 511, COMM 650)

- Field, A. P. (2000). Discovering statistics using SPSS for windows: advanced techniques for the beginner. London: Sage.
- Structural Equation Modeling Handout

- Structural Equation Modeling (pdf)
- Slides and notes for a seminar on SEM by Jeromy Anglim

- SEM Essentials (pps)
- by Jim Grace at StructuralEquations.org

- Applied Structural Equation Modeling Using AMOS by Joel E. Collier This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. 　 Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. 　 With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.Call Number: Available ONLINE through MasonISBN: 9781003018414Publication Date: 2020-05-25

- Factor Analysis Articles (The Analysis Factor)
- Choosing the Right Type of Rotation in PCA and EFA pdf (James Dean Brown)

If you are going to do data analysis involving time, there are several issues you need to account for:

- Correlation with Time: Two variables are strongly correlated, but it is simply that both
*trend*over time.- You can control for the time variable (e.g., year)
- You can examine the
*changes*from year to year ("Differencing")

- Monetary Inflation: The value of money is not constant over time, and inflation is not constant either.
- Time-lagged relationships: One variable may affect the other only after a period of time.

- Decision 411: Forecasting
- Class materials from Robert F. Nau of Duke Fuqua School of Business

- Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos
- De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473.
- Understand the various kinds of forecasting analysis

Machine learning is a branch of artificial intelligence focusing on the training of statistical models and algorithms that can automatically improve their performance by discovering and retaining patterns these models observe in data (i.e., "learning"). In practice, this means being very accurate at **prediction**.

This focus on prediction differentiates machine learning from traditional statistical inference in important ways. Machine learning also uses different words than traditional statistics to describe the same thing (e.g., "features" instead of "variables")

- What is Machine Learning? (IBM)
- Classification vs Regression in Machine Learning (Geeks For Geeks)
- Video Series: StatQuest Machine Learning Playlist (Josh Starmer)
- MOOC: Machine Learning Crash Course (Google)
- Explore: A Visual Introduction to Machine Learning (r2d3.us)

- An Introduction to Statistical Learning by Gareth James; Daniela Witten; Trevor Hastie; Robert Tibshirani An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.Call Number: Free download (click link)! Note: although this book contains applications in R, it provides essential overviews of machine learning concepts necessary for performing machine learning in Python.ISBN: 9781071614174Publication Date: 2021-07-30
- Computer Age Statistical Inference by Bradley Efron; Trevor Hastie The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.Call Number: Available for FREE Online through Mason or from the authorISBN: 1107149894Publication Date: 2016-07-21
- Mathematics for Machine Learning by Marc Peter Deisenroth; A. Aldo Faisal; Cheng Soon Ong The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.Call Number: Free! Click link to download PDF.ISBN: 9781108455145Publication Date: 2020-04-23
- The Elements of Statistical Learning by Trevor Hastie; Robert Tibshirani; Jerome Friedman; J. H. Friedman This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.Call Number: Available ONLINE through Mason LibrariesISBN: 9780387848570Publication Date: 2017-04-21

- Last Updated: Feb 1, 2024 11:10 AM
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Subjects: Data & Statistics

Tags: applied_statistics, data, research methods, statistics

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