Introduction to the Theory of Statistics PDF Download
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Author: Alexander M. Mood
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 608
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Book Description
Probability; Random variables, distribution functions, and expectation; Special parametric families of univariate distributions; Joint and conditional distributions, stochastic independence, more expectation; Distributions of functions of random variables; Sampling and sampling distributions; Parametric interval estimation; Tests of hypotheses; Linear models; Nonparametric method.
Author: Alexander M. Mood
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 608
Get Book
Book Description
Probability; Random variables, distribution functions, and expectation; Special parametric families of univariate distributions; Joint and conditional distributions, stochastic independence, more expectation; Distributions of functions of random variables; Sampling and sampling distributions; Parametric interval estimation; Tests of hypotheses; Linear models; Nonparametric method.
Author: George Udny Yule
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 738
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Book Description
Author: George Udny Yule
Publisher: Legare Street Press
ISBN: 9781015597501
Category :
Languages : en
Pages : 0
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Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Alexander M. Mood
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 476
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Book Description
Author: Hannelore Liero
Publisher: CRC Press
ISBN: 1466503203
Category : Mathematics
Languages : en
Pages : 280
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Book Description
Based on the authors' lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.
Author: Harold J. Larson
Publisher: John Wiley & Sons
ISBN:
Category : Mathematics
Languages : en
Pages : 262
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Book Description
Probability; Populations and samples; Estimation; Confidence sets; Tests of hypotheses; Decision theory and bayesian methods; Linear models; Nonparametric methods.
Author: Alexander MacFarlane Mood
Publisher: McGraw-Hill Publishing Company
ISBN: 9780070854659
Category : Mathematical statistics
Languages : en
Pages : 564
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Book Description
This text offers a sound and self-contained introduction to classical statistical theory. The material is suitable for students who have successfully completed a single year's course in calculus, and no prior knowledge of statistics or probability is assumed. Practical examples and problems are included.
Author: Alexander M. Mood
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 474
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Book Description
Author: Solomon Kullback
Publisher: Courier Corporation
ISBN: 0486142043
Category : Mathematics
Languages : en
Pages : 460
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Book Description
Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.
Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
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Book Description
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, marketing, and 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. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
