Book Description
As the SAS© programming language continues to evolve, this guide follows suit with timely coverage of the combination statistical package, database management system, and high-level programming language. Using current examples from business, medicine, education, and psychology, Applied Statistics and the SAS Programming Language is an invaluable resource for applied researchers, giving them the capacity to perform statistical analyses with SAS without wading through pages of technical documentation.
Includes the necessary SAS statements to run programs for most of the commonly used statistics, explanations of the computer output, interpretations of results, and examples of how to construct tables and write up results for reports and journal articles. Illustrated with SAS Graph™ output. Provides readers with ample models for developing programming skills.
For anyone interested in learning more about applied statistics and the SAS programming language.
Customer Reviews:
Poor book.......2007-03-20
I hoped more. It is a very basic book, with elemental examples and no clear directions on the best use of SAS.
Great for real life applications.......2007-01-01
Great book.. It is a great asset to my SAS library.
Too much unecessary detail about reading in data.......2006-11-10
Theres just too much unecessary material in this book about ways to read in data etc. Its all unecessary cos like myself most professional analysts and statisticians have access to SAS enterprise guide, which is incredibly powerful and so much less time consuming. The programming language is only useful for algorithms these days, as most professionals havent got the time to be debugging code all day. The text covers nothing about cluster analysis either which is central to a lot of multi variate analysis.
The mathematics and statistical detail is of far more interest and importance to us in the working environment than reading in of files.
Poor value.......2006-10-14
The book can be helpful to college students, but is of little benefit to more seasoned statisticians. The title notwithstanding, most of its chapters deal with general-purpose SAS programming; the rest (totaling 100-150 pages) offer a very basic review of REG, TTEST, LOGISTIC and FACTOR procedures, something that you can easily find online. (Check out the SAS tutorials offered by UCLA). I like and recommend Ron Cody's other books, but not this overpriced softcover.
Blergh.......2006-09-13
I did not like this book. The organization was abysmal, and I decided to sell it back to the college bookstore for use by future hapless students, because it is not good as a reference guide.
I did not like how the author seemed to gear the book to an audience that is terrified of their computers or anything dealing with *gasp* math.
Book Description
This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text.
The third edition features an increased emphasis on graphics and
the use of confidence intervals and effect size measures and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT.
Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on multiple regression and correlational methods.
Customer Reviews:
Second Grad Stats.......2007-05-06
I've adopted this text for my graduate seminar in Multiple Regression. I choose it over other texts for the topics AND because it's focus is on concepts rather than math. Now that we can carry SPSSX in our brief case, there is no need to focus on that computation.
Can't beat it.......2001-04-17
...This book is the source of all you need. It's hard going at times, but so's the subject. The book's 15 years old and remains the best guide to the analysis of correlated data. It's a reference book, one I value as much as a good dictionary. To use it as a text would be misguided unless the instruction was aimed at a sophisticated audience.
Best MRC Book Ever.......2000-03-24
I agree with the previous reviewer that there are times when the exposition in the book gets a bit intense; but c'mon! We're dealing with statistics. You gotta sweat a bit. That's when learning happens. In my opinion the book is extremely clearly written. And although you may have to re-read a few sentences a few times, the basic tools for understanding most every major aspect of MRC is embedded in the text. In sum, this was a great book that I read as a 2nd-year graduate student in psychology. Unlike the first reviewer, I turned to this text when I got confused during the course lectures!
MRC Analysis---good book overall.......1999-12-15
Cohen and Cohen's MRC analysis book is well versed and easy to understand for someone that is familiar with MRC terminology, however, for first year graduate students, the text is very equivocal. The book is lacking ample illustrations of complex problems, leaving students to rely on outside sources. Also, the book uses unfamiliar symbols that do not correspond with other MRC books, which intensifies the confusion level of the students even more.
Overall, the text is a great addition to a statistical library, and this reviewer recommends it, in spite of being a sub-par book for first year graduate students.
Book Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered."
Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book"
Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally."
Journal of the American Statistical Association, March 2006
Customer Reviews:
very good book, compact but comprehensive.......2007-05-12
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
Excellent book ..........2007-01-09
A very specific book, with a lot of details for a statistitian
Book Description
Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The text avoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
Customer Reviews:
Clear, rigorous, fascinating.......2007-01-20
I'm a Ph.D. student in biophysics. This is the best treatment of systems biology that I've encountered. It treats both the math and the biology with clarity, rigor, and respect. It simplifies without dumbing down. It's beautifully written. If you doubt that systems biology is a real scientific discipline, this book will change your mind.
Building Mathematical Models of Cells.......2006-09-25
The history of science over the past few centuries is to become ever more specialized. The physicists, becomming ever more concerned with the very large (stars, galaxies, the cosmos) or the very tiny (first atoms, then atomic components, now sub-components. The biologists on the other hand were studying much larger things, such as the cells that make up life. Both sciences developed techniques to facilitate their study.
In recent years, researchers have discovered that sometimes these specialized techniques can be used to develop greater insight into what is happening in other sciences.
In this book, Dr. Alon uses his training in physics to examine certain aspects of biology and to use the terminology and mathematics to describe the way these biological networks work.
The goal of the book is to begin the formulation of general laws that apply to biological networks. This is done by providing a mathematical framework in which some of the design principles of biological systems can help to understand biological networks. In looking at the results, an underlying simplicity not seen before appears in biological systems.
Great Job.......2006-09-09
A superb intro to the field. The math is moderate and helpful. Network concepts and their ties to examples and theory are clearly and succinctly presented. This is a textbook but reads easily like a book. Covers key elements while connecting them by at least mention to up-to-date further research. The basics and the grandeur of systems biology. I am trying to remember now anything on the negative side and cannot.
Average customer rating:
- Complicated language
- Principles of Biostatistics
- Hope this suggestion helps u decide
- Biostatistics simplified
- Great book - one of few!
|
Principles Of Biostatistics
Marcello Pagano
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ASIN: 0534229026 |
Book Description
Marcello Pagano and Kimberlee Gauvreau's PRINCIPLES OF BIOSTATISTICS, Second Edition is a concepts-based introduction to statistical procedures that prepares public health, medical, and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics, the emphasis is on concepts over formulas or rote memorization. Throughout the book, the authors use practical, interesting examples with real data to bring the material to life.
Customer Reviews:
Complicated language .......2007-09-29
The language in this book is very hard to understand. I took statistics when I was an undergrad and but this book is so hard to understand. The authors tried to show more steps in the math examples maybe try to make it easier for the reader but it just make it MUCH more complicated to follow. Sometimes I have to read my undergrad statistics book in order for me to understand the same thing it says in this book.
Principles of Biostatistics.......2007-09-19
This book does not give step-by-step instructions on how to solve problems. Therefore, I feel it is not a good book. I still feel very lost after reading chapters in this book.
Hope this suggestion helps u decide.......2007-09-09
This is my first time that I purchased online. I got the book within 3 days and it's in a good condition. Cheaper, got a book, and on time...isn't it enough?
Biostatistics simplified.......2007-06-27
Much easier to grasp than another text on the subject that I have experienced, (Biostatistics... A foundation for anlaysis in the Health Sciences; 8th Edition. Wayne W. Daniel). I am an MPH student with limited math background. Had a 4.0 going before my first experience with biostats. A "D" won't cut it in the program I am completing. Found an alternative Biostats course and the required text,(Principles of Biostats;Marcello Pagano), is much, much, easier for me to grasp. The real world examples and simplified methods make for an easier comprehension for those of us who lack a strong mathematic prerequisite.
Great book - one of few!.......2007-03-23
I've read the other reviews, and seems either you love it or hate it. I used this at Harvard School of Public Health on my 3rd time through bio-statistics (college, med school, MPH). Only this time I got it right, and think it was the balance between some non-calculus theory and practical examples in this book. Not understanding probability distributions at all leads to misapplication, and misuse. I have frequently turned to this as "tune-up book" if I've been away too long. The exercised sets are helpful and "realistic"; however, I do understand the problem with text-books and teacher's manuals that give only 1/2 the answers.
Book Description
This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data.
Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification.
For experimental scientists in a variety of disciplines.
Customer Reviews:
Not as Applied as I Hoped.......2007-06-22
While this text covers a variety of multivariate techniques, the term "applied" is used loosely, in my opinion.
This is more a math-stat textbook than an applied statistics text.
I wish I had read the reviews (if they existed when I purchased the 4th edition), for I would have purchased a different text.
NOT a good intro to MVA.......2007-02-28
My prof used this book for multivariate statisitical analysis. I absolutely despise this book. For one, the answers to exercises come in a separate book, so the homework questions are worthless to me. The solutions will cost you an extra $100 or so if you buy that book. The index is extremely light, so if you want a quick reference to a topic - forget it. You'll need to skim through hundereds of pages that aren't referenced in the index. Important topics are illustrated in 1 example usually, and the reader is often left to guess how such problems could be adapted to different situations than what is illustrated.
Book Contents.......2006-03-10
The "search inside this book" feature was not available when this review was posted. Hope it helps.
CONTENTS
I. GETTING STARTED.
1. Aspects of Multivariate Analysis.
2. Matrix Algebra and Random Vectors.
3. Sample Geometry and Random Sampling.
4. The Multivariate Normal Distribution.
II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
5. Inferences About a Mean Vector.
6. Comparisons of Several Multivariate Means.
7. Multivariate Linear Regression Models.
III. ANALYSIS OF A COVARIANCE STRUCTURE.
8. Principal Components.
9. Factor Analysis and Inference for Structured Covariance Matrices.
10. Canonical Correlation Analysis
IV. CLASSIFICATION AND GROUPING TECHNIQUES.
11. Discrimination and Classification.
12. Clustering, Distance Methods and Ordination.
Appendix.
Data Index.
Subject Index.
I'm also a dullard, like the other reviewers here..........2006-02-10
I'm also a dullard like the other people reviewing the book on this sight. I'm not Greek, so I've never seen a capital sigma or other squiggles - frankly I didn't know that these squiggles existed!! I was looking for a statistics book that described statistics in a wooly, general, hand waving sort of way, just the way that I b@##%&i! my boss in meetings when statistics arise. Unfortunatley, this was not the book for me to learn this technique from. I don't know - I might have to finish my study of arithmetic (including long division), and then algebra, and then squiggles before I try again with this book. If you're a dullard like me, then keep away from this book - on the other hand it's an excellent book on applied statistical methods.
Yipes!.......2006-02-01
I used this text in one class for a PhD program that is now complete. Oh Boy! This is probably a good text if you have a very solid mathematics background, but it is totally incomprehensible to a mathematics greenhorn. I spent hours and hours trying to decipher the mathematical codes and formulas. Some of the symbols were figures I didn't even know existed.
Applied? Nope - totally theoretical from stem to stern.
Book Description
This is the most widely used mathematical statistics book at the top 200 universities in the United States. 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 readers discover the nature of statistics and understand its essential role in scientific research.
Customer Reviews:
Non Fiction.......2007-09-03
Mathematical Statistics with Applications
by William Mendenhall and other authors is a higher level university text on probability and statistics, dealing with some of the underlying mathematics and calculus, moments, probability generation functions and all that sort of thing.
Definitely not your light right type of mathematics books by any stretch of the imaginaiton.
Many ,many errors to odd number problems.......2007-09-02
This book has good explanations of the topics covered in each section , but the solutions to the odd numbered problems are full of errors. I can't stand this book anymore , I need to check my solutions to reinforce my knowledge and the book doesn't provide correct solutions.
I would recommend to everyone to stay away from this book.
Anything but Wackerly.......2007-02-20
This is an adequate text up until chapter 6. The probability chapter the the discrete probability distributions are great. The concepts and derivations are logically laid out and make sense. Things begin to get frustrating in chapter 4 and really fall apart after chapter 6. After chapter four the author no longer tries to put anything in context. He derives very little and simply states long, cryptic formulas and expects the reader to magicaly understand the big picture and fill in the gaps on their own. He skips over the over 200 years of the development in statictical theory and simply states the results and the student is expected to fill in the gap. The most self evident properties are expounded on in the chapters and the most important and subtle properties are hidden in the exercises or neglected altogether. I am nearly finished with my undergraduate degree in applied mathematics and am mathematicaly mature. I am comfortable with both applied and pure/proof-type mathematics and this text makes my stomach turn. It makes me want to reconsider my major. I am giving this text 2 stars simply because the first 5 chapters are adequate. If you just need a course in probability, this might be the text for you. If you need to move on to statistical inference and you need the big picture/contextual background explained rather than pluging and chuging blindly with nonsensical formulas, then this is not the text for you. Way to drop the ball, Wackerly!
Very good textbook.......2007-01-08
I used Wackerly's book in an advanced stats class which covered all of the material up to chapter 5.8 (covariance). I am not a math major but an Economics major with mediocre math skills. I have to say that as math books go I really enjoyed using this book. The problems it presented challenged me but not so much so that I was completely frustrated 100% of the time. There were parts of the second and third chapters where I was a little lost, but the fourth and fifth chapters were excellently well written.
Unfortunately the class I was in did not go over the second half of the book over hypothesis testing and linear regression. Though in browsing through those sections, it seems they are given as thurough a treament as the first half of the book was. It was especially encourgaing for me to see the sections on linear regression handled in matrix notation (along with the well done matrix algebra appendix), which is what one would expect to see in a graduate level Econometrics or Applied Regression Analysis course.
So in short if you are a math major and want to do the derivation of the formulas, then I would suggest looking elsewhere. But if you are in the hard-sciences, engineering, business, or Economics (this is probably too mathy for most other social science persons), this is a terrific book with many, many applied examples.
My only qualm with this book is that it shied away from using a lot more examples from Economics and Finance when it would have been great to do so. So if you want to see how all of the analysis of probability and associated tools can be applied to Finance, then you will need to supplement Wackerly's textr with a graduate level Finance text in an area such as investments.
Average book.......2006-10-01
This book is not rigorous at all. If you are looking for rigor, look elsewhere. The proofs are very weak, and the example trite.
Still it is not a total loss. If you don't care about mathematical rigor, this can serve as a decent book.
I wouldn't dream of using it if you are working on a stats based degree.
Book Description
Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: BL Downloadable data sets BL Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more BL Additional material for data analysis
Customer Reviews:
A Wonderful Work.......2007-07-15
I find Professor Singer's Book to be a most informative and useful tool for anyone who wishes to better understand Multilevel Modeling.
Applied Longitudinal Data Analysis by Singer,et al.......2007-03-13
Clearly written text... and usefull for researchers.
I would recommend it to anyone starting to learn about the subject!
The Clearest and Most Useful Book on HLM for Longitudinal Studies.......2006-07-28
This is simply the best book for those analyzing longitudinal data (data measured at more than one time point). Singer's coverage of Hierarchical Linear MOdeling (HLM) is clear, well-written (sprinkled with humor, it's like a lecture by the most popular prof. at your school), and geared towards researchers who need their programs to run, not just learn the mathematical underpinnings. Singer and Willett (the coauthor, not listed above!) set the standard for presenting math/statistics book examples.
THe authors accomplish the latter by keying her examples to data located at a UCLA website; you can run the same programs on the same datasets used in the book (wow!), and compare your output, troubleshooting any problems you may have. Singer and Willett (her coauthor, not listed here!) provide outputs and programs correspoing to several of the most popular statistical programs, including SAS and SPSS.
SInger and Willet also explain the rationale for using HLM over more traditional techniques such as regression. Simply stated, regression aggregates at a level that cause one to lose information (and hence the power to detect differences.) HLM allows one to look at overall differences due to time, but also the trajectories of individual differences who are "nested" within those time points. It's the (relatively) new thing, and is increasing used by investigators, and desired by peer reviewers.
As supplements, I suggest using the UCLA website mentioned above, subscribing to an e-mail LISTSERV for interesting (though sometimes compicated discussions of "multilevel modeling" (MULTILEVEL@JISCMAIL.AC.UK), and searching for Judith Singer's website through Google or A9 (if you use A9--"Alexa"--enough you'll get a small discount at Amazon.com). Also, compare Amazon's and Judith Singer's (through her website) current prices on this book.
Breaking down complex analyses .......2006-03-18
This is an excellent book. Multilevel modeling and survival analysis are becoming increasingly important in psychological studies, but are pretty complicated procedures. Singer & Willet offer both a conceptual background and practical ways to do the analyses in a clear, understandable manner. The book is very readable and will be an important reference for future analyses!
very clear and thorough.......2006-03-16
This book does a particularly good job of explaining the substantive meaning of the equations involved in multilevel modeling analyses. It spends a lot of extra time explaining what the equations mean in real world terms using examples from actual data sets. I teach a graduate level course on HLM and I much prefer this book to the Raudenbush & Byrk book because it not only does a better job of explaining the math (for graduate students less comfortable with statistics) but the chapters are also sprinkled with incredibly useful advice on actually running the analyses (getting them to converge, interpreting them, etc.) The Raudenbush & Bryk book probably does a slightly better job of presenting the equations, but it falls short on explanation and practical advice. If you were only going to buy one HLM book, I would start with this one.
Book Description
In recent years, there has been growing interest in the development and application of quantitative statistical methods to study choices made by individuals. This primer provides an introduction to the main techniques of choice analysis and also includes details on data collection and preparation, model estimation and interpretation and the design of choice experiments. A companion website offers practice data sets and software to apply modeling and data skills presented in the book.
Customer Reviews:
Everything you need to learn to carry out a choice model.......2005-10-17
This book is massive, and hence the term "primer" may be a little misleading. But if you really want to understand how to model choice data for a range of models, the book is outstanding. Other books focus more on the econometrics of the models, which are pivotal to know. But this book builds upon that by walking you through a series of increasingly-complex models, allowing you to understand why you need to perform particular modeling tasks.
The book focuses on NLogit software, but once you understand how to actually estimate choice models utilizing software, the skills can be easily carried into other software programs. However, without such experience, the other books available may fall short in enabling you to estimate choice models. Indeed, this was the case for me -- I understood the econometrics of the models, but had difficulty estimating complex models using software, simply beacuse I was uncomfortable with the syntax.
Hensher et al. removes this obstacle by giving the reader thorough training in both understanding what a given set of choice data may represent (e.g., observations from a particular choice experiment), and how to physically estimate models. The increase in confidence I received from working through the exercises in the book is why I rate the book so highly. Not only are the econometric concepts explained, but the nuts and bolts of model estimation are revealed, and that made all the difference.
An ambiguous oriented book.......2005-10-05
The whole book serves as a software (NLogit) manual. If you already know about the discrete choice analysis, you might be able to find out the messages that the authors try to convey. And it contains barely new information, so it doesn't help you anyway. But if you are new to this area, this is not the good book for you to start.
The book is extremely verbose and the ideas are hidden behind lines and ill-presented. It turned out that it's very difficult to comprehend the essence or even sense of the choice methods from this book. The best one can get is becoming a software user of the authors' own program.
Besides, the software, Nlogit, is not user-friendly and can't serve as a mainsteam tool.
Book Description
Master data analysis, modeling, and spreadsheet use with DATA ANALYSIS AND DECISION MAKING WITH MICROSOFT EXCEL! With a teach-by-example approach, student-friendly writing style, and complete Excel integration, this business statistics text provides you with the tools you need to succeed. Margin notes, boxed-in definitions and formulas in the text, enhanced explanations in the text itself, and stated objectives for the examples found throughout the text make studying easy. Problem sets and cases provide realistic examples that enable you to see the relevance of the material to your future as a business leader.
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.
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- Classroom Assessment & Grading That Work
- Competing on Analytics: The New Science of Winning
- Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Books Index
Books Home
Recommended Books
- A Year In the Life of an ESL
- The Secret Power of Yoga: A Woman's Guide to the Heart and Spirit of the Yoga Sutras
- The Groves of Academe
- The Indoor Water Gardener's How-To Handbook
- The Mad Scientists' Club
- Spreadsheet Modeling and Decision Analysis
- The Simplest Path to Personal and Planetary Awakening, Step One: FREE YOUR MIND: 10 Keys for Unlocki
- Henry E. Huntington's Library of Libraries
- The Americanization of C.F.G. Meyer: From Rags to Riches
- From Skies of Blue: My Experiences With the Eighty-Second Airborne During World War II
The Little SAS Book: A Primer, Third Edition
SAS Programming by Example
Handbook Of Statistical Analysis Using Sas
Categorical Data Analysis Using the SAS System
Cody's Data Cleaning Techniques Using SAS Software
