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Mathematical Statistics with Applications in R
Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to...
Mathematical Statistics with Applications
In their bestselling MATHEMATICAL STATISTICS WITH APPLICATIONS, premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Student Solutions Manual for Wackerly/Mendenhall/Scheaffer's Mathematical Statistics with Applications, 7th
Prepare for exams and succeed in your mathematics course with this comprehensive solutions manual! Featuring worked out-solutions to the problems in MATHEMATICAL STATISTICS WITH APPLICATIONS, 7th Edition, this manual shows you how to approach and solve problems using the same step-by-step explanations found in your textbook examples.
Essential Statistical Inference
This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods.
Financial Economics, Risk and Information
Financial Economics, Risk and Information presents the fundamentals of finance in static and dynamic frameworks with focus on risk and information. The objective of this book is to introduce undergraduate and first-year graduate students to the methods and solutions of the main problems in finance theory relating to the economics of uncertainty and information. The main goal of the second edition is to make the materials more accessible to a wider audience of students and finance professionals. The focus is on developing a core body of theory that will provide the student with a solid intellectual foundation for more advanced topics and methods. The new edition has streamlined chapters and t...
A Course in Mathematical Modeling
The emphasis of this book lies in the teaching of mathematical modeling rather than simply presenting models. To this end the book starts with the simple discrete exponential growth model as a building block, and successively refines it. This involves adding variable growth rates, multiple variables, fitting growth rates to data, including random elements, testing exactness of fit, using computer simulations and moving to a continuous setting. No advanced knowledge is assumed of the reader, making this book suitable for elementary modeling courses. The book can also be used to supplement courses in linear algebra, differential equations, probability theory and statistics.
Modern Mathematical Statistics with Applications
This 3rd edition of Modern Mathematical Statistics with Applications tries to strike a balance between mathematical foundations and statistical practice. The book provides a clear and current exposition of statistical concepts and methodology, including many examples and exercises based on real data gleaned from publicly available sources. Here is a small but representative selection of scenarios for our examples and exercises based on information in recent articles: Use of the “Big Mac index” by the publication The Economist as a humorous way to compare product costs across nations Visualizing how the concentration of lead levels in cartridges varies for each of five brands of e-cigaret...
Statistics and Probability for Engineering Applications
Statistics and Probability for Engineering Applications provides a complete discussion of all the major topics typically covered in a college engineering statistics course. This textbook minimizes the derivations and mathematical theory, focusing instead on the information and techniques most needed and used in engineering applications. It is filled with practical techniques directly applicable on the job. Written by an experienced industry engineer and statistics professor, this book makes learning statistical methods easier for today's student. This book can be read sequentially like a normal textbook, but it is designed to be used as a handbook, pointing the reader to the topics and secti...
Nuclear Energy
Energy -- Atoms and nuclei -- Radioactivity -- Nuclear processes -- Radiation and materials -- Fission -- Fusion -- Particle accelerators -- Isotope separators -- Radiation detectors -- Neutron chain reactions -- Nuclear heat energy -- Breeder reactors -- Fusion reactors -- The history of nuclear energy -- Biological effects of radiation -- Information from isotopes -- Useful radiation effects -- Reactor safety -- Nuclear propulsion -- Radiation protection -- Radioactive waste disposal -- Laws, regulations, and organizations -- Energy economics -- International nuclear power -- Nuclear explosions -- The future.
Statistics and Probability for Engineering Applications
Makes statistical methods easier and accessible to engineers. This book points the reader to the topics and sections pertinent to a particular type of statistical problem. It includes a CD-ROM that contains the Excel data sets for the examples and case studies given in the book, along with other statistical tools and software.
