Data And Probability Connections
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Author | : Debra A. Perkowski |
Publisher | : Prentice Hall |
Total Pages | : 422 |
Release | : 2007 |
Genre | : Mathematics |
ISBN | : |
Part of a project funded by the National Science Foundation to improve the quality of mathematics and science teaching in grades K-12, this new guide models the student-centered approach recommended by the National Council of Teachers of Mathematics to teach introductory statistics. Provides future middle grade mathematics teachers with a strong foundation, connecting the mathematics they are learning with the mathematics they will be teaching. Gives enhanced meaning to formulas via a visual or geometric approach. Uses numerous illustrations to assist readers in making explicit connections between a typical college elementary statistics course and the statistical concepts taught by middle school teachers. For anyone interested in introductory statistics.
Author | : Perkowski |
Publisher | : |
Total Pages | : |
Release | : 2006-06-15 |
Genre | : |
ISBN | : 9780131449213 |
Author | : Michael J. Evans |
Publisher | : Macmillan |
Total Pages | : 704 |
Release | : 2004 |
Genre | : Mathematics |
ISBN | : 9780716747420 |
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
Author | : David Diez |
Publisher | : |
Total Pages | : |
Release | : 2015-07-02 |
Genre | : |
ISBN | : 9781943450046 |
The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources.
Author | : Bowen Kerins |
Publisher | : American Mathematical Soc. |
Total Pages | : 152 |
Release | : 2018-01-24 |
Genre | : Education |
ISBN | : 1470440636 |
Designed for precollege teachers by a collaborative of teachers, educators, and mathematicians, Probability and Games is based on a course offered in the Summer School Teacher Program at the Park City Mathematics Institute. This course leads participants through an introduction to probability and statistics, with particular focus on conditional probability, hypothesis testing, and the mathematics of election analysis. These ideas are tied together through low-threshold entry points including work with real and fake coin-flipping data, short games that lead to key concepts, and inroads to connecting the topics to number theory and algebra. But this book isn't a “course” in the traditional sense. It consists of a carefully sequenced collection of problem sets designed to develop several interconnected mathematical themes. These materials provide participants with the opportunity for authentic mathematical discovery—participants build mathematical structures by investigating patterns, use reasoning to test and formalize their ideas, offer and negotiate mathematical definitions, and apply their theories and mathematical machinery to solve problems. Probability and Games is a volume of the book series “IAS/PCMI—The Teacher Program Series” published by the American Mathematical Society. Each volume in this series covers the content of one Summer School Teacher Program year and is independent of the rest.
Author | : Brendan Kelly |
Publisher | : Brendan Kelly Publishing Inc. |
Total Pages | : 100 |
Release | : 2000 |
Genre | : Mathematics |
ISBN | : 9781895997194 |
Author | : Larry Wasserman |
Publisher | : Springer Science & Business Media |
Total Pages | : 446 |
Release | : 2013-12-11 |
Genre | : Mathematics |
ISBN | : 0387217363 |
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Author | : David F. Anderson |
Publisher | : Cambridge University Press |
Total Pages | : 447 |
Release | : 2017-11-02 |
Genre | : Mathematics |
ISBN | : 110824498X |
This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.
Author | : Avrim Blum |
Publisher | : Cambridge University Press |
Total Pages | : 433 |
Release | : 2020-01-23 |
Genre | : Computers |
ISBN | : 1108617360 |
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author | : Andrew Gelman |
Publisher | : CRC Press |
Total Pages | : 677 |
Release | : 2013-11-01 |
Genre | : Mathematics |
ISBN | : 1439840954 |
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.