A Modern Introduction To Probability And Statistics - Understanding Why And How
Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study.
A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap.
This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects.
Written for:
2nd and 3rd year undergraduate students in Computer Science, Physics, Mathematics, Engineering and Electrical Engineering, lecturers
Keywords:
Engineering Statistics
Probability
Simulation and Bootstrap
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Common Errors in Statistics and How to Avoid Them Wiley - 2003
A guide to choosing and using the right techniques
High-speed computers and prepackaged statistical routines would seem to take much of the guesswork out of statistical analysis and lend its applications readily accessible to all. Yet, as Phillip Good and James Hardin persuasively argue, statistical software no more makes one a statistician than a scalpel makes one a surgeon. Choosing the proper technique and understanding the analytical context is of paramount importance to the proper application of statistics. The highly readable Common Errors in Statistics (and How to Avoid Them) provides both newly minted academics and professionals who use statistics in their work with a handy field guide to statistical problems and solutions.
Good and Hardin begin their handbook by establishing a mathematically rigorous but readily accessible foundation for statistical procedures. They focus on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. A handy checklist is provided to summarize the necessary steps.
Topics covered include:
* Creating a research plan
* Formulating a hypothesis
* Specifying sample size
* Checking assumptions
* Interpreting p-values and confidence intervals
* Building a model
* Data mining
* Bayes' Theorem, the bootstrap, and many others
Common Errors in Statistics (and How to Avoid Them) also contains reprints of classic articles from statistical literature to re-examine such bedrock subjects as linear regression, the analysis of variance, maximum likelihood, meta-analysis, and the bootstrap. With a final emphasis on finding solutions and on the great value of statistics when applied in the proper context, this book will prove eminently useful to students and professionals in the fields of research, industry, medicine, and government.
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Heavy-Tail Phenomena - Probabilistic and Statistical Modeling - S. Resnick (Springer, 2007)
This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. Heavy tails are characteristic of many phenomena where the probability of a single huge value impacts heavily. Record-breaking insurance losses, financial-log returns, files sizes stored on a server, transmission rates of files are all examples of heavy-tailed phenomena.
Key features:
* Unique text devoted to heavy-tails
* Emphasizes both probability modeling and statistical methods for fitting models. Most treatments focus on one or the other but not both
* Presents broad applicability of heavy-tails to the fields of data networks, finance (e.g., value-at- risk), insurance, and hydrology
* Clear, efficient and coherent exposition, balancing theory and actual data to show the applicability and limitations of certain methods
* Examines in detail the mathematical properties of the methodologies as well as their implementation in Splus or R statistical languages
* Exposition driven by numerous examples and exercises
Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use (or at least to learn) a statistics package such as R or Splus. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.
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Probability is a vital measure in numerous disciplines, from bioinformatics and econometrics to finance/insurance and computer science. Developed from a successful course, Fundamental Probability provides an engaging and hands-on introduction to this important topic. Whilst the theory is explored in detail, this book also emphasises practical applications, with the presentation of a large variety of examples and exercises, along with generous use of computational tools.
Based on international teaching experience with students of statistics, mathematics, finance and econometrics, the book:
Presents new, innovative material alongside the classic theory.
Goes beyond standard presentations by carefully introducing and discussing more complex subject matter, including a richer use of combinatorics, runs and occupancy distributions, various multivariate sampling schemes, fat-tailed distributions, and several basic concepts used in finance.
Emphasises computational matters and programming methods via generous use of examples in MATLAB.
Includes a large, self-contained Calculus/Analysis appendix with derivations of all required tools, such as Leibniz’ rule, exchange of derivative and integral, Fubini’s theorem, and univariate and multivariate Taylor series.
Presents over 150 end-of-chapter exercises, graded in terms of their difficulty, and accompanied by a full set of solutions online.
This book is intended as an introduction to the theory of probability for students in biology, mathematics, statistics, economics, engineering, finance, and computer science who possess the prerequisite knowledge of basic calculus and linear algebra.
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i want to ask about EDB file. a consulting engineer give me a EDB file only to check the structure analysis. i try to open it using SAP2000 v14, but it cannot open. is it possible to open the file if we just supply by EDB file?
This book give us clearly knowledge about Liquefaction.
Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid loading. Liquefaction and related phenomena have been responsible for tremendous amounts of damage in historical earthquakes around the world.
Liquefaction occurs in saturated soils, that is, soils in which the space between individual particles is completely filled with water. This water exerts a pressure on the soil particles that influences how tightly the particles themselves are pressed together. Prior to an earthquake, the water pressure is relatively low. However, earthquake shaking can cause the water pressure to increase to the point where the soil particles can readily move with respect to each other
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