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Author : Yuri Suhov,Mark Kelbert
Publisher : Cambridge University Press
Page : 477 pages
File Size : 43,6 Mb
Release : 2014-09-22
Category : Mathematics
ISBN : 9781107603585
A valuable resource for students and teachers alike, this second edition contains more than 200 worked examples and exam questions.
Author : F.M. Dekking,C. Kraaikamp,H.P. Lopuhaä,L.E. Meester
Publisher : Springer Science & Business Media
Page : 488 pages
File Size : 55,6 Mb
Release : 2006-03-30
Category : Mathematics
ISBN : 9781846281686
Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Author : Michael J. Evans,Jeffrey S. Rosenthal
Publisher : WH Freeman
Page : 200 pages
File Size : 51,8 Mb
Release : 2010-03-01
Category : Mathematics
ISBN : 1429224630
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor to the course, incorporating the computer and offering an integrated approach to inference that includes the frequency approach and the Bayesian 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. The new edition includes a number of features designed to make the material more accessible and level-appropriate to the students taking this course today.
Author : Larry Wasserman
Publisher : Springer Science & Business Media
Page : 446 pages
File Size : 46,6 Mb
Release : 2013-12-11
Category : Mathematics
ISBN : 9780387217369
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 : Yu. M. Suhov
Publisher : Unknown
Page : 500 pages
File Size : 53,7 Mb
Release : 2005
Category : Mathematical statistics
ISBN : OCLC:667074292
Author : G. Jay Kerns
Publisher : Lulu.com
Page : 388 pages
File Size : 48,7 Mb
Release : 2010-01-10
Category : Education
ISBN : 9780557249794
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
Author : William Mendenhall
Publisher : Duxbury Resource Center
Page : 486 pages
File Size : 49,7 Mb
Release : 1975
Category : Mathematics
ISBN : UCAL:B3764031
What is statistics? Useful mathematical notation; Describing distributions of measurements; Probability; Random variables and probability distributions; The binomial probability distribution; The normal probability distribution; Statistical inference; Inference from small samples; Linear regression and correlation; Analysis of enumerative data; Considerations in designing experiments; The analysis of variance; Nonparametric statistics.
Author : Sheldon M. Ross
Publisher : Unknown
Page : 532 pages
File Size : 55,5 Mb
Release : 1987
Category : Mathematics
ISBN : UOM:39015013026342
Elements of probability; Random variables and expectation; Special; random variables; Sampling; Parameter estimation; Hypothesis testing; Regression; Analysis of variance; Goodness of fit and nonparametric testing; Life testing; Quality control; Simulation.
Author : Norman Matloff
Publisher : CRC Press
Page : 295 pages
File Size : 46,5 Mb
Release : 2019-06-21
Category : Business & Economics
ISBN : 9780429687112
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Author : Yuri Suhov,Mark Kelbert
Publisher : Cambridge University Press
Page : 477 pages
File Size : 52,8 Mb
Release : 2014-09-22
Category : Mathematics
ISBN : 9781316062203
Probability and statistics are as much about intuition and problem solving as they are about theorem proving. Consequently, students can find it very difficult to make a successful transition from lectures to examinations to practice because the problems involved can vary so much in nature. Since the subject is critical in so many applications from insurance to telecommunications to bioinformatics, the authors have collected more than 200 worked examples and examination questions with complete solutions to help students develop a deep understanding of the subject rather than a superficial knowledge of sophisticated theories. With amusing stories and historical asides sprinkled throughout, this enjoyable book will leave students better equipped to solve problems in practice and under exam conditions.
Author : Yu. M. Suhov,Mark Kelbert
Publisher : Cambridge University Press
Page : 499 pages
File Size : 52,7 Mb
Release : 2008-04-24
Category : Mathematics
ISBN : 9780521847674
The subject is critical in many modern applications such as mathematical finance, quantitative management, insurance and actuarial studies.
Author : Daniel Navarro
Publisher : Lulu.com
Page : 617 pages
File Size : 46,8 Mb
Release : 2013-01-13
Category : Psychology
ISBN : 9781326189723
"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Author : Jim Albert,Maria Rizzo
Publisher : Springer Science & Business Media
Page : 359 pages
File Size : 45,6 Mb
Release : 2012-01-28
Category : Mathematics
ISBN : 9781461413653
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
Author : Peter Olofsson,Mikael Andersson
Publisher : John Wiley & Sons
Page : 573 pages
File Size : 46,8 Mb
Release : 2012-05-04
Category : Mathematics
ISBN : 9781118231326
Praise for the First Edition ". . . an excellent textbook . . . well organized and neatly written." —Mathematical Reviews ". . . amazingly interesting . . ." —Technometrics Thoroughly updated to showcase the interrelationships between probability, statistics, and stochastic processes, Probability, Statistics, and Stochastic Processes, Second Edition prepares readers to collect, analyze, and characterize data in their chosen fields. Beginning with three chapters that develop probability theory and introduce the axioms of probability, random variables, and joint distributions, the book goes on to present limit theorems and simulation. The authors combine a rigorous, calculus-based development of theory with an intuitive approach that appeals to readers' sense of reason and logic. Including more than 400 examples that help illustrate concepts and theory, the Second Edition features new material on statistical inference and a wealth of newly added topics, including: Consistency of point estimators Large sample theory Bootstrap simulation Multiple hypothesis testing Fisher's exact test and Kolmogorov-Smirnov test Martingales, renewal processes, and Brownian motion One-way analysis of variance and the general linear model Extensively class-tested to ensure an accessible presentation, Probability, Statistics, and Stochastic Processes, Second Edition is an excellent book for courses on probability and statistics at the upper-undergraduate level. The book is also an ideal resource for scientists and engineers in the fields of statistics, mathematics, industrial management, and engineering.
Author : Michael Baron
Publisher : CRC Press
Page : 475 pages
File Size : 42,6 Mb
Release : 2013-08-05
Category : Mathematics
ISBN : 9781498760607
Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling ToolsIncorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make o