Book Description
This text employs the latest ideas in teaching business statistics and follows the philosophy espoused at the conference "Making Statistics More Effective in Schools of Business" (MSMESB). It emphasizes modern statistical methods and data analysis with a decreased emphasis on classical hypothesis testing and probability. It presents a problem-solving approach to the analysis of real data sets and procedures for data collection, design, and interpretation. It covers statistics in the context of the scientific method for problem recognition, problem formulation, and problem solving. Concrete examples of statistical techniques and computer use give students a practical framework of business statistics in practice.
Book Description
Management Decision Making is a spreadsheet-based introduction to the tools and techniques of modern managerial decision making. The author shows how to formulate models in Microsoft Excel that can be used to analyze complex problems taken from all the functional areas of management, including finance, marketing, operations, and human resources. Throughout, the goal is to understand how business decisions are reached, what tradeoffs are made, and how outcomes depend on the underlying data. A CD-ROM is included that contains the widely-used decision analysis software applications TreePlan and Crystal Ball. The book is aimed at students of business, economics and engineering, including those taking MBA courses.
Customer Reviews:
College text that is valuable to working professionals.......2002-05-05
This college-level text is also useful to business and IT professionals because it provides a refresher for decision techniques that are the foundation of a number of disciplines. The book is divided into two parts: (1) deterministic methods, mainly focused on linear programming and optimization, and (2) probability and queuing.
What makes this book valuable to the practicing professional is that it uses one of the most common business tools, Microsoft Excel, and shows how to apply this tool to real world problems. The accompanying CD ROM comes with TreePlan for developing decision trees and CrystalBall for Monte Carlo simulation, as well as workbooks that are used to support the plethora of realistic examples used throughout the book.
Although the book is business-oriented and better suited for operations analysis and MBA students and practitioners, I've used it as a reference for project planning, computer system capacity planning and performance analysis, and IT security risk management - these practical uses of the material show the value of the book in the real world. The supporting web site that the author maintains has materials for lesson plans, errata and additional resources that make this book particularly valuable for the academic and business environments.
Book Description
This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made.
Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models.
For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions.
Customer Reviews:
Covers points, but neither in a clear or concise manner........2005-03-18
If you're looking for a clear, easy to understand work, this is not for you. It seems the author fluctuates between writing for a population that is well versed in statistical understanding, to one that is just learning concepts and terms. The reader ends up being tossed around with too much information at times, and at other times too little information. There are many, many better works on these topics.
Book Description
This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book website, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. While R programs are provided on the book website and R hints are given in the computational sections of the book, The Bayesian Core requires no knowledge of the R language and it can be read and used with any other programming language.
The Bayesian Core can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It serves as a unique textbook for a service course for scientists aiming at analyzing data the Bayesian way as well as an introductory course on Bayesian statistics. The prerequisites for the book are a basic knowledge of probability theory and of statistics. Methodological and data-based exercises are included within the main text and students are expected to solve them as they read the book. Those exercises can obviously serve as assignments, as was done in the above courses. Datasets, R codes and course slides all are available on the book website.
Book Description
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.
Key features of the book include:
- Comprehensive coverage of an imporant area for both research and applications.
- Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.
- Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.
- Includes a number of applications from the social and health sciences.
- Edited and authored by highly respected researchers in the area.
Customer Reviews:
Infer causality!.......2005-04-14
I would recommend this book to any social scientist who is interested in learning statistical methods that can allow them to say that one thing causes another --- and who doesn't? The classical methods taught to social scientists do not allow you to infer causality, and these methods are actually conceptually a lot easier than the classical methods --- at least, I have a much easier time explaining them to non-statisticians than most regression methods. And now they are not even so hard to implement, especially with new software like Jas Sekhon's match library for R.
I also recommend this book to anyone who is working with these methods already. It is helpful to see others' completed projects on similar topics, and it is impressive to see the breadth of topics that the Rubin Causal Model has been applied to. As statistics books go, it is surprisingly human since they emphasize how many of the authors all belong to this one big Statistical Family, as they call it, and they even have a family tree; also, the introduction including the other titles that they considered for the book made me laugh out loud.
If you do decide to buy the book, I recommend that you froogle it first, since its price distribution is left-skewed with some surprisingly low outliers.
Customer Reviews:
Excellent Quality, quick delivery.......2005-09-18
The product was in excellent condition and arrived rapidly. The book was the paperback version, which is what the item listing by Amazon stated, although I had thought that the seller had listed it as a hardcover. No worries, though. The book (softcover) was in excellent condition and the service was fine. A smooth experience at a nice price.
Book Description
Providing an introduction to modern data analysis techniques, these casebooks can be used as the primary or secondary text for elementary business statistics courses. Statistics has the reputation of being a boring, complicated, and confusing mix of mathematical formulas accompanied by computers used to do something. This casebook nad its companion volume Business Analysis Usuing Regression change that impression by showing how statistics gives insights and ansswers interesting business questions. The material is organized into classes of related case studies that develop a single key idea of statistics. The authors begin by discussing an application that motivates the key idea. Students are then shown how to analyze a data set. The emphasis of the analysis is to answer important business questions with statistics rather than talk about statistics. Basic Business Statistics introduces ideas often not emphasized in elementary texts such as issues of robustness, the use of transformations to simplify problems, sampling bias, confounding, kernel density, quality control, and scatterplot matrices. Business Analysis with Regression includes a discussion of scatterplot smoothing, prediction intervals for new observations, collinearity, logistic regression, nonlinear models, and multiple comparisons in regression. The text includes directions for data analyses with JMP and an appendix with Minitab commands. Professors Dean P. Foster, Robert A. Stine, and Richard P. Waterman are members of the Department of Statistics at the Wharton School, University of Pennsylvania.
Customer Reviews:
GOOD FOCUS, POORLY PRESENTED.......2003-03-20
This book has an interesting premise of focusing statistics for business at the mid-level using the JMP software. The concepts addressed are the right ones, even the order is good.
However, on the downside, it is very difficult to read. The cases are not well explained. This book is used at Wharton's 1st year core course in statistics and the general agreement is that it is very hard to comprehend it. I recommend an editor to the authors.
Average customer rating:
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Exploratory and Multivariate Data Analysis (Statistical Modeling and Decision Science)
Michel Jambu
Manufacturer: Academic Press
ProductGroup: Book
Binding: Hardcover
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ASIN: 0123800900 |
Book Description
With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques.
Key Features
* Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones
* Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data analysis techniques, illustrated with concrete and understandable examples
* Includes a modern and up-to-date description of clustering algorithms with many properties which gives a new role of clustering in data analysis techniques
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An Introduction to Queueing Theory: Modeling and Analysis in Applications (Statistics for Industry and Technology)
U. Narayan Bhat
Manufacturer: Birkhäuser Boston
ProductGroup: Book
Binding: Hardcover
Operations Research
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ASIN: 0817647244 |
Book Description
This introductory textbook is designed for a one-semester course on queueing theory that does not require a course in stochastic processes as a prerequisite. Oftentimes students in applied disciplines such as computer science, operations research, and engineering do not take a course on queueing theory because it requires a prerequisite course on stochastic processes; or they take a course without the necessary background and learn as one would use a cookbook. By integrating the necessary background on stochastic processes with the analysis of models, this book provides a sound foundational introduction to the modeling and analysis of queueing systems for a broad interdisciplinary audience.
Key features:
* An introductory chapter including a historical account of the growth of queueing theory in the last 100 years.
* A modeling-based approach with emphasis on identification of models using topics such as collection of data and tests for stationarity and independence of observations.
* Rigorous treatment of the foundations of basic models commonly used in applications with appropriate references for advanced topics.
* A chapter on modeling and analysis using computational tools.
* A comprehensive treatment of statistical inference for queueing systems.
* A discussion of operational and decision problems.
* Modeling exercises as a motivational tool, and review exercises covering background material on statistical distributions.
* Minimal prerequisites: calculus with some differential equations and an undergraduate course in probability and statistics.
* Appendices containing essential background material.
Introduction to Queueing Theory may be used as a textbook by first-year graduate students in fields such as computer science, operations research, industrial and systems engineering, as well as related fields such as manufacturing and communications engineering. Upper-level undergraduate students in mathematics, statistics, and engineering may also use the book in an elective introductory course on queueing theory. With its rigorous coverage of basic material and extensive bibliography of the queueing literature, the work may also be useful to applied scientists and practitioners as a self-study reference for applications and further research.
Average customer rating:
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Multivariate Statistical Modeling and Data Analysis (Theory and Decision Library B:)
Manufacturer: Springer
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Binding: Hardcover
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ASIN: 9027725926 |
Books:
- Statistics for Management and Economics (with CD-ROM and InfoTrac )
- Ten Best Teaching Practices: How Brain Research, Learning Styles, and Standards Define Teaching Competencies
- The Anger Workbook: A 13-Step Interactive Plan to Help You... (Minirth-Meier Clinic Series)
- The Art of Innovation: Lessons in Creativity from IDEO, America's Leading Design Firm
- The Elements of Journalism: What Newspeople Should Know and The Public Should Expect
- The Essays of Warren Buffett : Lessons for Corporate America
- The Fires of Heaven (The Wheel of Time, Book 5)
- The First 90 Days: Critical Success Strategies for New Leaders at All Levels
- The First 90 Days: Critical Success Strategies for New Leaders at All Levels
- The First 90 Days: Critical Success Strategies for New Leaders at All Levels
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