Applied Statistical Inference with Minitab(r), Second Edition

Applied Statistical Inference with Minitab(r), Second Edition PDF

Author: SALLY A. LESIK

Publisher: CRC Press

Published: 2021-03-31

Total Pages: 0

ISBN-13: 9780367780579

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Covers material typically presented in an intermediate statistics course at the undergraduate level and a first course in applied statistics at the graduate level. Topics include basic statistical inference, regression, and ANOVA. Advanced topics include non-parametric statistics, logistic regression, and goodness-of-fit tests.

An Introduction to Probability and Statistical Inference

An Introduction to Probability and Statistical Inference PDF

Author: George G. Roussas

Publisher: Elsevier

Published: 2003-02-13

Total Pages: 523

ISBN-13: 0080495753

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Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. "The text is wonderfully written and has the most comprehensive range of exercise problems that I have ever seen." — Tapas K. Das, University of South Florida "The exposition is great; a mixture between conversational tones and formal mathematics; the appropriate combination for a math text at [this] level. In my examination I could find no instance where I could improve the book." — H. Pat Goeters, Auburn, University, Alabama * Contains more than 200 illustrative examples discussed in detail, plus scores of numerical examples and applications * Chapters 1-8 can be used independently for an introductory course in probability * Provides a substantial number of proofs

Statistical Inference

Statistical Inference PDF

Author: Helio S. Migon

Publisher: CRC Press

Published: 2014-09-03

Total Pages: 363

ISBN-13: 143987882X

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This text presents a balanced account of the Bayesian and frequentist approaches to statistical inference. Along with more examples and exercises, this second edition includes new material on empirical Bayes and penalized likelihoods and their impact on regression models and offers expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models. It also compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two.

Applied Statistical Inference

Applied Statistical Inference PDF

Author: Leonhard Held

Publisher: Springer Science & Business Media

Published: 2013-11-12

Total Pages: 381

ISBN-13: 3642378870

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This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.

Topics on Methodological and Applied Statistical Inference

Topics on Methodological and Applied Statistical Inference PDF

Author: Tonio Di Battista

Publisher: Springer

Published: 2016-10-11

Total Pages: 220

ISBN-13: 3319440934

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This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach, pseudo-likelihoods, regression, time series, and data analysis as well as applications in the life and social sciences. The software packages used in the papers are made available by the authors. This book is a result of the 47th Scientific Meeting of the Italian Statistical Society, held at the University of Cagliari, Italy, in 2014.

Statistical Inference

Statistical Inference PDF

Author: George Casella

Publisher: Brooks/Cole

Published: 2002

Total Pages: 692

ISBN-13:

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Casella and Berger's new edition builds the theoretical statistics from the first principals of probability theory. Thoroughly and completely, the authors start with the basics of probability and then move on to develop the theory of statistical inference using techniques, definitions, and statistical concepts.

Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP

Statistics and Probability with Applications for Engineers and Scientists Using MINITAB, R and JMP PDF

Author: Bhisham C. Gupta

Publisher: John Wiley & Sons

Published: 2020-02-05

Total Pages: 1040

ISBN-13: 1119516633

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Introduces basic concepts in probability and statistics to data science students, as well as engineers and scientists Aimed at undergraduate/graduate-level engineering and natural science students, this timely, fully updated edition of a popular book on statistics and probability shows how real-world problems can be solved using statistical concepts. It removes Excel exhibits and replaces them with R software throughout, and updates both MINITAB and JMP software instructions and content. A new chapter discussing data mining—including big data, classification, machine learning, and visualization—is featured. Another new chapter covers cluster analysis methodologies in hierarchical, nonhierarchical, and model based clustering. The book also offers a chapter on Response Surfaces that previously appeared on the book’s companion website. Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP, Second Edition is broken into two parts. Part I covers topics such as: describing data graphically and numerically, elements of probability, discrete and continuous random variables and their probability distributions, distribution functions of random variables, sampling distributions, estimation of population parameters and hypothesis testing. Part II covers: elements of reliability theory, data mining, cluster analysis, analysis of categorical data, nonparametric tests, simple and multiple linear regression analysis, analysis of variance, factorial designs, response surfaces, and statistical quality control (SQC) including phase I and phase II control charts. The appendices contain statistical tables and charts and answers to selected problems. Features two new chapters—one on Data Mining and another on Cluster Analysis Now contains R exhibits including code, graphical display, and some results MINITAB and JMP have been updated to their latest versions Emphasizes the p-value approach and includes related practical interpretations Offers a more applied statistical focus, and features modified examples to better exhibit statistical concepts Supplemented with an Instructor's-only solutions manual on a book’s companion website Statistics and Probability with Applications for Engineers and Scientists using MINITAB, R and JMP is an excellent text for graduate level data science students, and engineers and scientists. It is also an ideal introduction to applied statistics and probability for undergraduate students in engineering and the natural sciences.

Statistical Inference

Statistical Inference PDF

Author: S.D. Silvey

Publisher: CRC Press

Published: 1975-03-01

Total Pages: 196

ISBN-13: 9780412138201

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Minimum-variance unbiased estimation; The method of least squares; The method of maximum likelihood; Confidence sets; Hypothesis testing; The likelihood-ratio test and alternative 'large-sample' equivalents of it 108; Sequential tests; Non-parametric methods; The bayesian approach; An introduction to decision theory.