Probability and statistics Books
Taylor & Francis Ltd Semisupervised Learning for Computational Linguistics Chapman HallCRC Computer Science Data Analysis
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£58.89
Taylor & Francis Ltd Statistical Test Theory for the Behavioral Sciences Chapman HallCRC Statistics in the Social and Behavioral Sciences
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£58.89
Taylor & Francis Ltd Niche Modeling Predictions from Statistical Distributions
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Taylor & Francis Ltd Statistical Methods for SpatioTemporal Systems Monographs on Statistic and Applied Probability
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Taylor & Francis Ltd A Course on Queueing Models
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Taylor & Francis Ltd Introductory Statistical Inference
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Taylor & Francis Ltd Integer Programming
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Taylor & Francis Ltd Nonparametric Statistical Methods For Complete and Censored Data
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Taylor & Francis Ltd Components of Variance Chapman HallCRC Monographs on Statistics and Applied Probability
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Taylor & Francis Ltd Topics in Modelling of Clustered Data
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Taylor & Francis Ltd Statistics in Drug Research Methodologies and Recent Developments
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Taylor & Francis Ltd Accelerated Life Models Modeling and Statistical Analysis Chapman HallCRC Monographs on Statistics and Applied Probability
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Taylor & Francis Ltd An Invariant Approach to Statistical Analysis of Shapes
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Taylor & Francis Ltd Statistical Modelling with Quantile Functions
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Taylor & Francis Ltd MetaAnalysis in Medicine and Health Policy Chapman HallCRC Biostatistics Series
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Taylor & Francis Ltd Introduction to Statistical Methods in Modern Genetics
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Taylor & Francis Ltd Classification
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Taylor & Francis Ltd Misused Statistics
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Taylor & Francis Ltd Statistical Genomics Linkage Mapping and Qtl Analysis
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Taylor & Francis Ltd Bayesian Biostatistics
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Taylor & Francis Ltd Sample Size Choice Charts for Experiments with Linear Models Second Edition 122 Statistics Textbooks and Monographs
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Taylor & Francis Ltd Statistical Inference in Stochastic Processes
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£58.89
Taylor & Francis Ltd Elements of Bayesian Statistics
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Taylor & Francis Ltd Analysis of Messy Data Volume II
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Taylor & Francis Ltd Design of Experiments A Realistic Approach
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Taylor & Francis Ltd Integrals Related to the Error Function
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£147.25
Taylor & Francis Ltd Data Science in Education Using R
Book SynopsisData Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like?If you're just getting started with R in an education job, this is the book you'll want with you. This book gets you started with R by teaching the building blocks of programming that you'll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job.This book will be an essential resource for education professionals and researchers looking to iTrade Review"The authors have provided the definitive guide to the topic. The combination of theory and hands-on practical tutorials make this an invaluable resource for the growing fields of learning analytics and educational data science." - Mark Warschauer, Professor of Education and Informatics at the University of California, Irvine"This book is a clear, compelling guide for real-world practitioners who are ready to use modern tools of data science in the education domain. The effective data analysis content would benefit almost anyone getting started with data today, but these authors' thoughtful, focused handling of the specific issues involved in working with education data sets it apart from most introductory data science books." - Julia Silge, Software Engineer at RStudio"There are many resources for learning how to analyze education data. But what has long been missing is an inclusive and pedagogically refined resource on how to leverage modern data science principles, workflows, and tools. Data Science in Education using R fills this massive gap and more. It will be the go to resource for the next generation of data driven education professionals. And is a beautiful exposition of how to responsibly work with data from the real, messy, world." - Dustin Tingley, Deputy Vice Provost for Advances in Learning at Harvard University"Data Science in Education Using R provides a wide menu of resources for individuals in education who wish to both learn R and think more deeply about their relationship to data. The book provides both practical guidance through worked examples and discussions of relevant theory and past research. That the book is written by authors who have varied research and practice positions lends itself to its broad-ranging appeal." - Teomara (Teya) Rutherford, Assistant Professor of Learning Sciences, University of Delaware"Doing data science in education is an interdisciplinary endeavor. If you are a teacher, an administrator, or an educational researcher who's looking for a theory-informed, practice-oriented, and equity-minded introduction to educational data science, no need to look further than this book. It will help you unleash your data science 'superpower,' turbocharge your practice, and make real-world changes in your organization." - Bodong Chen, Associate Professor and Co-Director of Learning Informatics Lab at the University of MinnesotaTable of Contents1. Introduction: Data Science in Education - You’re Invited to the Party! 2. How to Use this Book 3. What Does Data Science in Education Look Like? 4. Special Considerations 5. Getting Started with R and RStudio 6. Foundational Skills 7. Walkthrough 1: The Education Data Science Pipeline with Online Science Class Data 8. Walkthrough 2: Approaching Gradebook Data from a Data Science Perspective 9. Walkthrough 3: Using School-Level Aggregate Data to Illuminate Educational Inequities 10. Walkthrough 4: Longitudinal Analysis with Federal Students with Disabilities Data 11. Walkthrough 5: Text Analysis with Social Media Data 12. Walkthrough 6: Exploring Relationships Using Social Network Analysis with Social Media Data 13. Walkthrough 7: The Role (and Usefulness) of Multilevel Models 14. Walkthrough 8: Predicting Students’ Final Grades Using Machine Learning Methods with Online Course Data 15. Introducing Data Science Tools to Your Education Job 16. Teaching Data Science 17. Learning More 18. Additional Resources 19. Conclusion: Where to Next?
£128.25
Taylor & Francis Ltd Data Science in Education Using R
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£43.99
Taylor & Francis Ltd An Introduction to Optimization with Applications
Book SynopsisThe primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitTable of Contents1. 1. Preamble. 2. The Language of Optimization. 3. Computational Complexity. 4. Algebra Review. 5. Matrix Factorization. 6. Linear Programming. 7. Sensitivity Analysis. 8. Integer Linear Programing. 9. Calculus Review. 10. A Calculus Approach to Nonlinear Programming. 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions. 12. Optimization involving Quadratic Forms. 13. Iterative Methods. 14. Derivative-Free Methods. 15. Search Algorithms. 16. Important Sets for Optimization. 17. The Fundamental Theorem of Linear Programming. 18. Convex Functions. 19. Convex Optimization. 20. An Introduction to Combinatorics. 21. An Introduction to Graph Theory. 22. Network Flows. 23. Minimum-Weight Spanning Trees and Shortest Paths. 24. Network Modeling and the Transshipment Problem. 25. The Traveling Salesperson Problem. Probability. 27. Regression Analysis via Least Squares. 28. Forecasting. 29. Introduction to Machine Learning.
£84.99
Taylor & Francis Ltd Design and Analysis of Ecological Experiments
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£56.04
Taylor & Francis Ltd CRC Handbook of Tables for Order Statistics from Inverse Gaussian Distributions with Applications
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£56.04
Taylor & Francis Ltd IG Petrowsky Selected Works Selected Wrks P
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Taylor & Francis Ltd Computer Intensive Statistical Methods
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Taylor & Francis Ltd Predictive Inference
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Taylor & Francis Ltd Longitudinal Data with Serial Correlation A StateSpace Approach
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Taylor & Francis Ltd Analysis of Quantal Response Data
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Taylor & Francis Ltd Bivariate Discrete Distributions
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Taylor & Francis Ltd Engineering Mathematics and Statistics
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Taylor & Francis Ltd Analysis of Infectious Disease Data
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Taylor & Francis Ltd The Fascination of Statistics
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Taylor & Francis Ltd Statistical Methods for Pharmaceutical Research Planning 67 Statistics Textbooks and Monographs
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Taylor & Francis Ltd Bayesian Analysis of Linear Models
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Taylor & Francis Ltd UtilityBased Learning from Data
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£47.99
Taylor & Francis Ltd Reliability and Warranties
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Taylor & Francis Ltd Multivariate Bayesian Statistics Models for Source Separation and Signal Unmixing
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Taylor & Francis Ltd Multifractals Theory and Applications
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Taylor & Francis Ltd Large Deviations and Idempotent Probability CRC Monographs and Surveys in Pure and Applied Math Hardcover
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£56.04
Taylor & Francis Ltd Design and Analysis of Experiments and Observational Studies using R
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£84.99