Alan Agresti
- Auteur
Alan (University of Florida, Gainesville) Agresti
Alan Agresti
Boeken van Alan Agresti
Alan Agresti
Christine Franklin
Bernhard Klingenberg
Statistics: The Art and Science of Learning from Data, Global Edition
Introduce your students to the art and science of learning from data. Statistics: The Art and Science of Learning from Data, Global Edition, 5th edition is the ideal introduction to statistics, encouraging students to analyse data the right way by enquiring and searching for the right questions and information rather than just memorising procedures.
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Alan Agresti
Maria Kateri
Foundations of Statistics for Data Scientists
Shows the elements of statistical science that are highly relevant for students who plan to become data scientists less emphasis on probability theory and methods of probability such as combinatorics, derivations of probability distributions of transformations of random variables (except for explanations of t, chi-squared, and F constructions)Formal statements and proofs of theorems, and decision theoryIntroduces some modern topics that do not normally appear in "math stat" texts but are especially relevant for data scientists, such as generalized linear models for non-normal responses (e.
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Alan (University of Florida, Gainesville) Agresti
Alan Agresti
Foundations of Linear and Generalized Linear Models
Alan Agresti
Xiao-Li Meng
Strength in Numbers: The Rising of Academic Statistics Departments in the U. S.
Statistical science as organized in formal academic departments is relatively new; largely the creation of the last sixty years. These memoirs by key players in academic development covers every statistics department founded in the US up to the mid-1960s.
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Alan Agresti
Maria Kateri
Ranjini Grove
Antonietta (Universita della Svizzera italiana Campus Est) Mira
Foundations of Bayesian Statistics for Data Scientists
This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and master’s students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.
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