Average customer rating:
- Worth the buy!
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- Application Oriented
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Design for Six Sigma : A Roadmap for Product Development
Kai Yang , and
Basem S. EI-Haik
Manufacturer: McGraw-Hill Professional
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Similar Items:
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Design for Six Sigma in Technology and Product Development
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The Design for Six Sigma Memory Jogger: Tools and Methods for Robust Processes and Products
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Design for Six Sigma for Service (Six SIGMA Operational Methods)
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Design for Six Sigma Statistics
ASIN: 0071412085 |
Book Description
Here's the book that clearly and logically answers the complex question quality managers and product developers face almost every day: WHICH PRODUCT DEVELOPMENT TOOLS SHOULD I USE AND WHEN?
This much-needed, well-written roadmap for robust, efficient product development features:
* All the coverage needed to implement six sigma in any manufacturing concern
* A complete review of both traditional and contemporary design methods
* Systems discussed include: DOE (Design Of Experiment), Taguchi Method, QFD (Quality Function Deployment), Axiomatic Design, and TRIZ (Theory for Inventive Problem-Solving)
* Practical examples to highlight important elements of each system
* A unique multi-systems approach to designing products, incorporating the traditional and contemporary methods discussed, detailing how and when to use them
* Valuable assistance when preparing for certification exams
Customer Reviews:
Worth the buy!.......2004-04-02
I have not found such a comprehensive book for design of six sigma. I started using this book for advanced experimental design and taguchi methods, but ended understanding the complete roadmap for design of six sigma. The systems approach allows an enthusiast reader to start anywhere, without having to spend time refering back to earlier chapters. The relatively newer trends as TRIZ and axiomatic design have also been nicely dealt with.
Overall, this is a very nice and easy read book, with excellent and well defined examples. A must for everyone who wants a quick refresher on the design principles of six sigma.
A book serves all your needs.......2004-04-02
This is an outstanding DFSS book for production development. It contains integrated information and some of which you could hardly find anywhere else, thus with one book in hand, you have all the tools to get to your destination. This is also a easy to read book providing the reader with a solid understanding- Concepts are clearly defined, real world examples/ case studies are fully described and the chapters are well organized. It can serve as a textbook for students/beginners and also can serve as a handbook for experienced engineers.
The title says it all- this is a roadmap for you to find the way correctly and easily. I am reading the book right now, and the book is really beneficial to me.
Full of information and errors.......2004-03-30
This is a book with a lot of information. Each chapter can be used as a starting point for a specific six sigma technique. However, this is the worst edited book I have ever read. You can hardly find one page without errors/typos.
A matchless guide.......2003-08-03
While the concept of six-sigma is a very popular one, it is not often that one can find such a comprehensive yet clearly-written volume devoted to the most important topics of six-sigma. A book that contains so much information and not just hot air is especially hard to find. Yang and El-Haik have successfully written one of the most impressive and useful reads I have ever encountered within this field. Especially intriguing and novel concept of TRIZ. A very worthwhile book, in any case.
Application Oriented.......2003-07-01
In recent times, there has been a lot of talk and hype about DFSS with very little substance thus far--until this one! I found the book an easy read, application oriented and a relatively prescritive approach to apply DFSS for products, processes and/or services. The sections on TRIZ and Axiomatic Design expose the opportunities largely untapped in the design world today. A must read book for organizations serious about Six Sigma--whether they are focused on delivering worldclass products and services to their customers or designing processes to run world class business operations--a thumbs up all the way!!
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
Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.
Customer Reviews:
Fantastic!.......2007-09-26
Just wanted to add to the praise of this book. If you're not following the backtesting practice of this book then you're playing slots with your trading (hey, maybe you'll get lucky!!). Some of the material is tough going and will require a second reading, but it'll be worth it. As another reviewer said about this being a kind of in-depth follow on to "Fooled by Randomness", I couldn't agree more. As matter of fact it's what I read just prior, so I couldn't help smiling as I went through this book, because he was putting the meat on the plate that Nassim set! Thank you, thank you..
The previous reviewer (Useless..) that gave it one star clearly did not get the concepts of the book. Did he even read it? That review does not compute. The *only* negative I would say is that if you're just looking for how to do robust backtesting, then the extensive material on the scientific method might be a bit much (but you can always read lightly those sections). But I understand why he put it in there, since it's the entire premise of taking a different and more rigorous approach to TA.
Now back to re-reading Chapter 6... Thank you Mr. Aronson!
Useless.......2007-08-29
I found this book useless..a total waste of time and money.Instead of analyzing the results obtained by using the various technical indicators,the author simply trashes their use,and does so in a preverse use of mathematical formulas,from which the reader gains nothing.I truly felt like my money was taken,for the purchase of the book,under false pretenses.
Make backtesting meaningful.......2007-08-24
Most trading books are pseudoscience or entertaining reminiscences of successful traders. Aronson has done an admirable job of applying the requisite rigor to the many difficulties associated with analyzing the results of historical backtesting.
Best for professional, intellectual and philosophical trading system developers.......2007-08-08
I had thought of using another review title "For fans and followers of Victor Niederhoffer" as inspired by his praise on the front cover. Pardon me to assume the following: if you had not heard of Niederhoffer, the chance is high that you have no prior experience/idea of testing the statistical significance of various TA tools, nor dwelling into the philosophical/scientific aspects of TA at all. Please accept the fact that this book is not for you. For trading professionals who deem themselves philosophical and intellectual (preferably with a college level of knowledge on statistics), this book is an inspiration. Highly recommended!
A must-have for TA practitioners.......2007-08-01
This book shakes some of your most deep beliefs in TA - and this is a healthy thing. Read it with an open mind.
Book Description
The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.
Customer Reviews:
Statistics for indoctrination, philosophy for real dummies.......2005-05-20
For academic philosophers of science sociology is not a paradigm of successful science. Earlier Bohrnstedt had enforced his ersatz philosophy of social science as editor of the journal Sociological Methods and Research. Now in this book, Statistics for Social Data Analysis, Bohrnstedt, Knoke and Mee attempt to indoctrinate students in this same ersatz philosophy of science.
The authors advocate their version of Haavelmo's "structural-equation" agenda, allege a distinction between unobserved conceptual variables and observable "indicators", and pontificate criteria for identifying causality prior to statistical modeling and empirical testing.
Contrast their views with some basics of contemporary pragmatism, which prevails in professional academic philosophy taught in universities today:
1. Pragmatist definition of "theory": A theory is any universally quantified statement proposed for testing. It is never defined in terms of any particular ontology - such as subjective motivations. Thus there is no philosophical problem of relating sociological theory to empirical model, because the theory is the model and the model is the theory.
2. Pragmatist criterion for criticism: Only empirical criteria may operate in the criticism of theories. Ontological ideas including preconceived claims about causality are never valid criteria. Thus theories/models may not be rejected merely because their equation specifications do not describe motivations, i.e. do not have a mentalistic ontology.
3. Pragmatist thesis of ontological relativity: The empirically tested and currently nonfalsified theory decides ontology including any claims about causality. Thus one does not firstly know causes and then make theories, but rather the empirically tested and nonfalsified theories/models describe the ontologies of their domains including causality.
4. Pragmatist thesis of pluralism: There may be and often are multiple empirically acceptable - i.e. tested and currently nonfalsified - theories/models. Thus they all make acceptably competing or complementary causal claims, so long as they are found to be empirically acceptable - i.e. not falsified.
In her book, History of Econometric Ideas, Mary S. Morgan writes that there are two ways in which econometrics has been used: (1) discovery or theory development and (2) empirical testing. The contemporary pragmatist philosophy of science assigns statistical analysis a fundamental role in theory development as well as in theory testing. Pragmatism thus invites use of data mining and artificial-intelligence computer systems, which can create and test literally billions of hypotheses.
I believe that this book, Statistics for Social Data Analysis, leaves the reader/student ignorant of the true capability of new technologies such as mechanized statistical analysis of social data for discovery, and that its provincial philosophy of science invites a Luddite attitude toward twenty-first century social science research.
Sociologists who are unaware of contemporary academic philosophy of science will likely not find this review helpful. More importantly such sociologists will also therefore be unable to exploit to their - or their students' - advantage the enabling freedom and contributing opportunities offered by the pragmatist philosophy.
For more: Google my book, History of Twentieth-Century Philosophy of Science at my web site philsci for free downloads, and to view my other book reviews at this Amazon site.
Thomas J. Hickey, Econometrician
For students of social sciences.......2003-03-31
This book is a statistics textbook for students of social sciences, not high-end users. I read earlier edition of this book in undergraduate statistics course. In that course, only basics of statistics were instructed. In social sciences, they don't need to know A to Z of statistics for all they have to know is what the function of SPSS or SAS means and what kind of data is needed and how the data would be analyzed in the statistics packages. There is no need to derive the functions in the textbook mathematically as they do in the courses of statistics department. We should understand what the function means, not how it is derived. This book is written in this regard. Unlike orthodox statistics textbook, this book tackles only the meaning of the statistical methods. In doing so, this book illustrates the methods with various field works and SPSS exercises. This is the stance most textbook written for social scientists takes. It seems that this book succeed in achieving the goal. Explanations are succinct and examples are apposite.
But this book is not that useful when you should do real research. Most social sciences articles use more advanced methods than what this book introduces. This book is good enough to beginners, but not so to who would be real researcher. At that point, you should have read more advanced ones already. If not, you couldn't read a piece of article in the common journals.
Book Description
STATISTICAL SLEUTH is an innovative treatment of general statistical methods, taking full advantage of the computer, both as a computational and an analytical tool. The material is independent of any specific software package. In "The American Statistician" (February 2000, Vol. 54, No. 1), George Cobb commented, "What is new and different about Ramsey and Schafer's book, what makes it a 'larger contribution,' is that it gives much more prominence to modeling and interpretation of the sort that goes beyond the routine patterns." His students did "substantially better" on term papers based on the analysis of data. In the book, the focus is on a serious analysis of real case studies; on strategies and tools of modern statistical data analysis; on the interplay of statistics and scientific learning; and on the communication of results. With interesting examples, real data, and a variety of exercise types (conceptual, computational, and data problems), the authors get readers excited about statistics.
Customer Reviews:
Good concept, more care needed with datasets.......2006-09-06
I have taught using this text 5 times now. I like the concept very much. My goals are to teach the students how to choose appropriate statistical tests and how to write up their results professionally. I very much like the concept of "project" exercises with real data sets.
Unfortunately, many of the datasets contain faulty data. I doubt that I have a full list of example, but Chapter 2, Problem 23 supposedly contains percent change in fatalities in states that retained and did not retain 55 mile per hour speed limits between 1995 and 1996. Unfortunately, the data reported have nothing in common with the real data. For instance, there were 87 fatalities in Alaska in 1995 and 81 in 1996 leading to a 7% decrease. The number used in the text is a 29% decrease. Other examples include Chapter 8, Problem 20 which is admittedly an approximation but the authors did not read the scale on the New York Times graphic correctly which places the supposed outlier rather differently than if the correct scale for the data is used. In Chapter 11, problem 24, there is an excessively heavy grasshopper mouse among other errant species weights.
The concept is great, a few data entry errors can be an effective learning tool, but too many blatantly incorrect datasets that lead to conclusions diametrically opposed to the real data seems sloppy to me.
Excellent survey and introduction to statistics.......2006-08-28
I have read more than 100 statistical texts on various subjects. This book is one of the best I've ever seen. It is extremely clear, well-organized, consistent in methodology and well-typeset. The use of well-documented case studies to illustrate every concept makes eclectic reading easy. The book also attempts to answer common trade-offs and philosophical points clearly. If I were teaching statistics, I'd use this text somewhere at the junior year level. I'd highly recommend it as a quick reference to anyone working with and familiar with statistics. It's very useful for a quick conceptual and practical overview.
Excellent resource for the non-specialist practitioner.......2004-11-03
During my professional life I was several times in the situation of having to do statistical analysis of some data. I also saw other colleagues struggle with the same situation and it seems that for someone without a formal training in statistics and a lot of practical experience this is a strange mixture of technical know-how, guess-work and pure superstition.
This is the book I've been looking for for years now, to learn how to do the job with a reasonable understanding of the choices involved and the assumptions that are made when using this or that tool. It is a wonderfully practical and clear exposition of the methods that are likely to be used by a practitioner to answer practical questions with the help of data - and definitely helps to avoid the uneasy feeling of having to apply some tool without really knowing what is going on.
Crappy book.......2004-07-16
Horrible, solution manual is a joke. I could have written a better book than this piece of garbage.
Great Intro Level Statistics Book.......2002-10-30
This is a great introductory statistics book, for students who are taking beginning level stats courses. The examples are cleary laid out and it's not too heavy on the theory.
Book Description
"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute
"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona
"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston
Popular in the
First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators
While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:
* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)
The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.
Customer Reviews:
pre-req: mid-level stats experience.......2006-07-12
I had taken a class in HLM before, and I bought this book to refresh myself on the details. It takes a good deal of attention to detail and concentration to really get the full measure from this book, although it's all in there. Despite the authors' best efforts, there is a good bit of stats jargon in the book, so a reader who is not familiar might have some difficulty. If you're at a point where learning HLM is a logical next step, you'll be fine and I recommend this book. However, if your over-eager faculty advisor told you to learn HLM, despite your minimal experience in stats, you're better off enrolling in a class or workshop.
Good but sometimes skipping ahead too fast.......2006-03-09
This book gives a detailed description of the use of an advanced method to deal with nested data sets.
At a general level the constructs and ideas are well written and can be followed reasonably easily.
However the mathematics is often written very dense, which makes reading and understanding complex.
My main problem with the book, is that in many of the examples they provide, the given formula's, and data skip rapidly to the solution. Thus it is often not insightfull at all, how the data led to the numerical outcome (and I and several of my colleagues could not reproduce all of the example outcomes). A more extensive discussion and a more step-by-step construction of the examples would have been helpful there.
So in short: Conceptually this book is fine, but for practical use mathematics are too dense, and examples are too hard to follow
Useful, but need solid background in stats.......2004-06-05
This book describes important advances in statistical analysis of social science data, circa 1992. Much of this data has a natural hierarchical grouping. But traditional statistical methods proved inadequate at coping. The biggest drawback was the failure of the assumption of independence. If at the lowest level, Items I1,...,In are grouped into sets J1,...,Jm, where m
To handle this, Hierarchical Linear Models were developed. The book gives a detailed treatment. A very comprehensive discussion. Including the handling of meta-analysis, where we wish to combine results across different studies. Which then involves using empirical Bayesian estimates. This method has also seen important usage in evaluating medical studies, conducted by different researchers on the same topic.
The book also illustrates the essential development of non-trivial computer programs to perform the gruntwork.
You will need a solid background in statistics to find this book useful. At a minimum, a year of statistics at the undergraduate level.
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
The SPSS 14.0 Guide to Data Analysis is a friendly introduction to both data analysis and SPSS. Easy-to-understand explanations and in-depth content make this guide both an excellent supplement to other statistics texts and a superb primary text for any introductory data analysis course. With the book, you get a jump-start on describing data, testing hypotheses, and examining relationships using SPSS. The goal of this book is to provide an unintimidating introduction to data analysis and to SPSS. This edition focuses on topics that interest today's students-in particular, the role of the Internet in society. It is designed for use with SPSS 14.0, including the Student Version. A data CD is included with this book.
For additional information, go to http://www.norusis.com This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.
Customer Reviews:
Excellent for learning to do SPSS software and/or to learn/understand statistics.......2007-09-21
I have used this book (previous editions) in teaching a graduate level research methods and statistical software class in the late 1990s. It is the best book available for anyone who needs to use SPSS or who needs to know how to organize data, interpret statistical output and understand the process of quantitative research. I now teach short courses and do statistical consulting for faculty, staff and students at a university. Whenever anyone asks what I recommend if they want to get SPSS and/or statistical thinking, its a no brainer. This book is hands-down the best for either or both of those goals.
Stats made easy.......2007-06-12
The book is written in an easy to understand language. The examples help to recreate the steps explained in the different chapters. I can only recommend this book.
Wrong Item.......2007-01-05
I had to return this since it did not indicate that it was "student version" which limits the number of variables.
teaches statistics and SPSS .......2006-07-11
In the humanities and social sciences, SPSS is probably the most heavily used statistical package. Norusis helps you understand why. Even if you do not have a strong background in statistics. The book teaches both statistics and the use of SPSS to analyse your statistical data.
The text starts off with the most basic material, like showing a simple frequency table. Or displaying it visually using a pie chart or a bar chart. Then, when there are too many values for a bar chart, you can use a histogram, which has bins, each representing a range of values of the independent variable. SPSS has the ability to quickly display in these formats.
Then the text progressively takes you into analysis. Starting with the computation of mean, median and variances. Later, when there are several independent variables, other graphing formats like scatterplots come into play. But the more challenging sections involve testing hypothesis. From these come the use of chi square tests, Student's T-distribution, nonparametric tests and so on.
If you make it through the book, you get an impressive self taught education in statistics and SPSS.
Book Description
Praise for the First Edition of Design and Analysis of Clinical Trials
"An excellent book, providing a discussion of the clinical trial process from designing the study through analyzing the data, and to regulatory requirement . . . could easily be used as a classroom text to understand the process in the new drug development area."
-Statistical Methods in Medicine
A complete and balanced presentation now revised, updated, and expanded
As the field of research possibilities expands, the need for a working understanding of how to carry out clinical trials only increases. New developments in the theory and practice of clinical research include a growing body of literature on the subject, new technologies and methodologies, and new guidelines from the International Conference on Harmonization (ICH).
Design and Analysis of Clinical Trials, Second Edition provides both a comprehensive, unified presentation of principles and methodologies for various clinical trials, and a well-balanced summary of current regulatory requirements. This unique resource bridges the gap between clinical and statistical disciplines, covering both fields in a lucid and accessible manner. Thoroughly updated from its first edition, the Second Edition of Design and Analysis of Clinical Trials features new topics such as:
* Clinical trials and regulations, especially those of the ICH
* Clinical significance, reproducibility, and generalizability
* Goals of clinical trials and target population
* New study designs and trial types
* Sample size determination on equivalence and noninferiority trials, as well as comparing variabilities
Also, three entirely new chapters cover:
* Designs for cancer clinical trials
* Preparation and implementation of a clinical protocol
* Data management of a clinical trial
Written with the practitioner in mind, the presentation assumes only a minimal mathematical and statistical background for its reader. Instead, the writing emphasizes real-life examples and illustrations from clinical case studies, as well as numerous references-280 of them new to the Second Edition-to the literature. Design and Analysis of Clinical Trials, Second Edition will benefit academic, pharmaceutical, medical, and regulatory scientists/researchers, statisticians, and graduate-level students in these areas by serving as a useful, thorough reference source for clinical research.
Customer Reviews:
Most complete reference on the topic.......2006-09-07
I own several books on clinical trials and this one is my favorite. It is biblical in its treatment of the topic and always seems to contain what my other books don't.
There are a few strengths that are particularly worth pointing out:
1) Makes many references to regulatory guidelines.
2) Excellent coverage of the various trial designs.
3) Good sample size chapter.
4) Several chapters on how to practically implement a trial.
Other options include:
-Piantodosi (Clinical Trials: methodologic perspective): my second favorite, not as comprehensive as Chow and Liu
-Freidman and DeMets (Fundamentals of Clinical Trials): a bit too superficial but very well written
-Pocock (Clinical trials: practical approach): a bit dated and superficial
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