Book Description
The emphasis of the text is on data analysis, modeling, and spreadsheet use in statistics and management science. This text contains professional Excel software add-ins. The authors maintain the elements that have made this text a market leader in its first edition: clarity of writing, a teach-by-example approach, and complete Excel integration.
Customer Reviews:
Managerial Statistics Text book.......2006-11-03
It was the text book the professor wanted me to buy.
It was good.
Sanjay Chheda.......2006-10-06
The book is very good with really good explanations and examples on descriptive analysis and inferential analysis.
Better Title: Intro to Statistics using Excel Add-ins.......2001-06-04
On the positive side, this book has many excellent case studies and examples. It is well written and interesting. However, I was disappointed, as I was expecting use of Excel to rigorously solve decision making and data analysis problems. The focus of the book is mostly traditional statistics solved using a group of commercial add-ins for Excel. If this is what you want, then the book would get five stars. However, for data analysis and decision making, I think a more thorough treatment using Excel without relying so much on the add-ins would have been appropriate.
Serious Excel 2000 Problem.......2001-04-12
The text book is great. I have many of Winston's other books and they are all great. The Palisade stuff works just fine. However, the StatPro Addin that accompanies this text does not work with MS Excel 2000. I contacted the IT guy that the authors directed me to--he was stumped. He just gave up and suggested I return my book for a refund because he could not figure out it out. Again, the book is great but the StatPro Addin sucks!
No trouble with Excel.......2001-01-31
I find the text and software a useful set of tools. It assumes familiarity with basic statistics and Excel, and builds on them to develop a powerfull ability to analize data and make decisions from it. I experienced no trouble with the software install or operation.
Book Description
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book descibes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learing (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful
An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.
Customer Reviews:
Great statistics book........2007-09-24
I'm a machine learning person, and this book provides pretty thorough state-of-art and up-to-date (relatively well) summary of statistical methods being used in lots of pattern classification fields. One thing that does not exist in the book is generative models, although this book is the best of the kind that describes discriminitive models.
Most Useful Machine Learning Book.......2007-09-24
This book describes most of the important topics in machine learning. Most machine learning books just present a criterion and and an optimization algorithm. For instance, LDA is often presented as: here is the Fisher criterion, it seems like a good thing to maximize. "The Elements of Statistical Learning" also presents that this is the right criterion if the distributions of the data for each class are Gaussian with the same covariance. This book puts all the algorithms in the same statistical language, which makes them easy to compare and choose between.
I also appreciate the emphasis this book puts on algorithms that are more recently popular/effective. I very much appreciate the discussions of logistic regression vs. LDA, ridge and lasso regression, boosting/additive logistic regression and additive trees, decision and regression trees, ...
The only qualm I have with this book is that it is rather biased toward the authors' own research. It is difficult from reading this book alone to differentiate between classical techniques and the authors' recent proposed algorithms.
Best data mining book.......2007-09-21
If you are looking for a relatively rigorous but very readable data mining book, this is simply the best! It covers most of the modern techniques and is beautifully printed with high quality graphics.
A very introductory book and well-writen.......2007-02-05
The discussed book is very explanatory and could be students' material for academic lessons.
A must book for every statistician and data miner.......2007-01-18
This book has become a classic for any statistician and data miner by now.
It is a broad overview of regression, classification and clustering techniques (supervised and unsupervised machine learning).
Book Description
Now you can apply the techniques that business analysts at leading companies use to analyze and transform data into bottom line results. For more than 10 years, well-known consultant and business professor Wayne Winston has been teaching corporate clients and MBA candidates the most effective ways to use Microsoft Excel for data analysis, modeling, and decision making. This practical, business-focused guide delivers the best of Winston's classroom experience to you in 70+ concise chapters, organized by real-world scenarios. Quickly find and apply exactly the information you need to solve a specific business problem#151;from asset allocation modeling to estimating exponential growth, forecasting sales, optimizing portfolios, and other critical functions. You also get all the book's sample files on CD-ROM#151;ready for use in your own work.
Customer Reviews:
Great Book.......2007-07-21
Excellent...It really help me to better understand the data analysis with many differents case scenarios...exercises...its for everyone.
Real Good for a textbook........2007-05-12
I had to use for a college class, but great speed in shipping.
Not bad, but not as good as expected.......2007-04-13
I am an intermediate to advanced Excel user, so my review may reflect that level. As others have said, it looks like MS rushed this book to the market, evidenced by so many errata, which can be disappointing especially when the solution is wrong. On the other hand, there are some very interesting and genuine uses of various Excel functions to solve business problems.
I wouldn't recommend this book for beginners. If you're trying to learn Excel, this is not the book. It is not a book to teach excel, but a book to teach you what you can do with Excel to solve everyday problems, given you're familiar with the mechanics of excel.
I would recommend it with these caveats. And getting Walkenbach's book on Excel functions along with this would be very helpful in my opinion. Best of luck in your endeavors.
Excellent.......2007-04-11
I have read many books in excel, but this book is really the most beneficial and excellent book I've used in my life. It is full with practical not theoritical examples. and you can benefit form it in your work.
Very practical, but full of errors.......2007-04-02
Overall, I like this book, even though it is somewhat confusing, both in scope and in the target audience.
The techniques of "naming the range" or writing the "if" formula are certainly targeted for beginners, but most of statistical tools are normally used by more advanced users.
The worst thing, though, is that the book is full of errors, both typos and mistakes in problem solutions on the disk. I consider myself an intermediate user, so finding an error in "instructor solution" was more like an additional challenge for me, but for the beginner this could be very frustrating.
On the positive side - I really liked the idea of problems in the end of each chapter; so many books just give you the theory and then you do not know how to solve a real life problem. For most of chapters, I knew the tools, but still had to spend time figuring out the best way to implement it for problem solving.
Very practical book, good for an intermediate users. Just be aware of the typos !
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.
Average customer rating:
- An essential book for statistical analysts building predictive models for database marketing
- Data Mining for Database marketing
- "EDA III" for Database Marketing
|
Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
Bruce Ratner
Manufacturer: Chapman & Hall/CRC
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Similar Items:
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Optimal Database Marketing: Strategy, Development, and Data Mining
-
Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management
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Strategic Database Marketing
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
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Applied Data Mining: Statistical Methods for Business and Industry (Statistics in Practice)
ASIN: 1574443445 |
Book Description
Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models. By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.
Customer Reviews:
An essential book for statistical analysts building predictive models for database marketing.......2006-01-05
This is a must have introductory book for the practitioner using data mining to build predictive models in industry. While it does have a few snippets of SAS code, it is a conceptual book that explains the "why" and the "how" of practical model building. (If you want SAS code buy "The Data Mining Cookbook" by Olivia Parr Rud.) It dispenses of with the antiquated notion of the "true" model of classical statistics and econometrics, and shows how to arrive at an acceptable model that yeilds good predictions. As practitioner's, this is what we care about most. Among other things, it gives good explanations of: (1) the EDA paradigm versus classical statistics (2) Tukey's bulging rule for transforming variables (3) variable selection, though there is no mention of clustering to eliminate redundant variables. It discusses some of the weaknesses of automatic variable selection methods (4) smoothed scatterplots and logit plots (5) decile analysis and using bootstrapping to derive confidence intervals for cum lift.
The book shows you how to use logistic regression, OLS, and CHAID to build predictive models. For those interested in Genetic modeling, it has a clearly written chapter on the subject that explains how genetic modeling can be used to create new variables that can have more information than either of the original variables.
While this book does not cover everything, and is definitely not the last word on the subject, it is a solid first word. In particular, the book does not cover splines, shrinkage techniques such as model averaging, ridge regression, ..etc. For treatments of these and similar advanced topics see Frank Harrell's "Regression Modeling Strategies" and Hastie, Tibsharani and Friedman's "Elements of Statistical Learning".
Data Mining for Database marketing.......2003-06-10
I predict that Dr. Ratner's Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data will be on every database marketer's bookshelf. Dr Ratner has put together an assembly of chapters that provide an indispensable resource for the daily problems facing data analysts and model builders in the database/direct marketing community. In each of the seveenteen chatpers Dr. Ratner addresses a typical problem and discusses the common solution. He points out unknown working assumptions or weaknesses of the latter, and then offers better solutions, which require basic knowledge of EDA/data mining. Dr. Ratner's writing style is unique as he makes familar concepts new, and new concepts familar. Thus, the book is easy and enjoyable reading. I specially like chapter that blends statistics with the machine learning, such as the introduction of the GenIQ Model.
"EDA III" for Database Marketing.......2003-06-10
I consider myself fortunate to be the first to review this book. The title aptly indicates what the book is about: Statistical Modeling and Analysis for Database Marketers: Effective Techniques for Mining Big Data. The author provides in a Tukey-esque manner a collection of solutions to common problems facing database analysts, model builders, and marketers. The book can uniquely serve as a textbook, a how-to guide, and a reference source depending on the reader's statistical training and database marketing experience. Moreover, the author actually goes where other authors provide lip service: he creates the marriage of the "old" statistical methodologies with the new machine learning influence by introducing machine learning methods specifically tailored to database assessment of optimal model performance. The book's illustrations involve real problems, real data, and better solutions. This book is a keeper!
Customer Reviews:
Could have been better.......2006-05-26
I felt the author has a firm understanding of the concepts and truly what he wanted to convey, but this book lacked a great deal for beginners or newcomers to Java. Far too many of the examples were tough to understand and in many cases simply un-answered. This is not a book for beginners.
Not as good as I expected.......2006-02-24
It is a nice book but i expected something better. I don't know what it is but something is missing here. I like the C/C++ version of this book better for some serious learning. On the other hand this is a good opportunity to learn java programming at the same time if you haven't mastered it yet.
not a book for beginners.......2005-09-28
If you don't know Java, don't expect to be able to learn the things you need to for a class. If you learned how to do alorgithms in mathematics, it may not be enough for computer science. You probably want to get a supplemental or two if you have to get this book for class. It is quite advanced and a hard read.
Good for professors, bad for students.......2003-11-24
All of the practical algorithms are left as un-answered exercises! Great in depth discussion of introductory algorithms, but very few examples, mostly pseudo-code.
INFURIATING FOR INDEPENDENT STUDY, but good if you have a professor to explain everything as you go.
Knowledge is Power and Painful, too.......2002-11-01
Well, I have to admit I did not expect much from this book. But to my overly pleasant surprise, I found this volume quite useful both as a resource for algorhythms and data processing...Forget the Bible, this is the only book one truly needs. And it's fun to say "Java." Amen.
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
Sleeper provides six sigma practitioners with the tools which will allow them to stand out from your competitors by using advanced statistical and modeling tools for more in-depth analysis. Understanding and properly utilizing statistical data distributions is one of the most important and difficult skills for a six sigma practitioner to possess. Sleeper provides six sigma practitioners with a road map for selecting and using distributions for more precise outcomes. With the added value of Crystal Ball Modeling software, this book becomes a powerful tool for analyzing and modeling difficult data quickly and efficiently.
Customer Reviews:
Very useful reference.......2007-05-21
Excellent introduction regarding need for non-normal distributions for certain sets of data. Good examples make point easy to understand. Nice section on limitations of Excel statistics.
One of the best compilations of descriptive and mathematical details for distributions that could be useful in certain situations.
My only complaint is some missing details and calculation inconsistency in a Crystal Ball example based on a DOE analysis.
Average customer rating:
|
Statistical Procedures for Analysis of Environmental Monitoring Data and Risk Assessment (Ptr Environmental Management and Engineering Series , Vol 3)
Edward A. McBean , and
Frank Rovers
Manufacturer: Prentice Hall PTR
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ASIN: 0136750184 |
Book Description
Data mining can be defined as the process of selection, exploration and modelling of large databases, in order to discover models and patterns. The increasing availability of data in the current information society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract such knowledge from data. Applications occur in many different fields, including statistics, computer science, machine learning, economics, marketing and finance.
This book is the first to describe applied data mining methods in a consistent statistical framework, and then show how they can be applied in practice. All the methods described are either computational, or of a statistical modelling nature. Complex probabilistic models and mathematical tools are not used, so the book is accessible to a wide audience of students and industry professionals. The second half of the book consists of nine case studies, taken from the author's own work in industry, that demonstrate how the methods described can be applied to real problems.
- Provides a solid introduction to applied data mining methods in a consistent statistical framework
- Includes coverage of classical, multivariate and Bayesian statistical methodology
- Includes many recent developments such as web mining, sequential Bayesian analysis and memory based reasoning
- Each statistical method described is illustrated with real life applications
- Features a number of detailed case studies based on applied projects within industry
- Incorporates discussion on software used in data mining, with particular emphasis on SAS
- Supported by a website featuring data sets, software and additional material
- Includes an extensive bibliography and pointers to further reading within the text
- Author has many years experience teaching introductory and multivariate statistics and data mining, and working on applied projects within industry
A valuable resource for advanced undergraduate and graduate students of applied statistics, data mining, computer science and economics, as well as for professionals working in industry on projects involving large volumes of data - such as in marketing or financial risk management.
Data sets used in the case studies are available at ftp://ftp.wiley.co.uk/pub/books/giudici
Customer Reviews:
User-friendly textbook.......2006-07-05
I recently purchased the book by Dr. Giudici and I found it very informative for someone who is becoming acquainted with data mining. I would classify the book as intermediate/advanced. For a future edition, a further discussion on the technical issues involving the estimation methods would be desirable, in my view.
I would definitely recommend this textbook for a Master's level course.
Viviana Fernandez
Department of Industrial Engineering
University of Chile
not impressed.......2006-02-03
This book has little practical examples and too much mathematics. I'd recommend it for educational purposes only.
Excellent review and very useful case studies.......2003-10-23
The book clearly describes, in a rather systematic manner, which are the main tools employed in data mining activity.
The usage of such tools is clearly described in the case-study section
An excellent case-study book.......2003-10-23
I found the book very useful especially because it contains fully worked out case-studies which, supported by the theoretical chapters, give clear guidance on how to actually do data mining
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