I want to say, SAS is fast. Taking an example. Of course, all final results were listed in a nice formatted word file without any manual work. The logic of SAS is elegant as well. I believe, most of ambiguity in SAS syntax was resulted from our unfamiliarity and ignorance. As professional software, it is supposed to be working in a very reliable way. Being an applied statistician, I appreciate some very delicate and well-written procedures in SAS, including proc logistic, genmod, phreg, nlimixed, optmodel, and mcmc.
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I like their coding inside and well-documented. Pingback: Software tools for data analysis — an overview R User Groups. Nice discussion. Quite many of commentators have the background on academic environment as do I where the requirements are different than on business side. On research most of the things done are new and are done once whereas on commerce the matter is usually the automation of certain repeated tasks. But anyway, the thing I wanted to bring into the discussion is that for light weight analytics some database systems like PostgreSql seem to provide built in tools well comparable to Excel.
And at least certain commercial extensions of PG have a bit more advanced stuff such as linear regression and stuff built in. Most likely Oracle and some others have all this and even more but I am not familiar with them. How are you doing with it? R has more than enough power, particularly in the single user setting you are in.
At the last ISBE behavioural ecology conference in Perth, the statistical symposium that followed the conference focused entirely on R, including to my memory some nice new routines for MCMC. If you asked me, I would say that R is alive and kicking in science. Well, at least in my corner of it. I am teaching a high school course in introductory statistics.
I also want the students to use a relevant tool — meaning something that can be used into college and maybe beyond. The three choices I am currently thinking about are: 1 Excel 2 R and 3 Mathematica Any comments would be great. I am speechless that you are even putting R and Mathematica there for high school students. Of course Excel.
Gee Correlation Structure Sas
It is the tool your students will be most likely to use in their future career. In this world, not everyone will become statistician….. Mathematica is a lot more than statistics, and can be very affordable for educational applications. You have to teach it as though everybody is going to become a statistician.
The goal of a good education is to show people the heights and inspire them to continue on their own, not to teach them what average bureaucrats do with their boring jobs. Mathematica has huge numbers of teaching modules, as well as effective ways notebooks for the students to communicate their results. Students can even create workbooks to teach a concept to their classmates by writing neat mathematica widgetry that does not require programming knowledge. Second R — being a real programming language, and it can help them develop logical thinking skills in addition to their stats skills.
SAS offers completly free access to its flagship products: Enterprise Guide, Enterprise Miner, Forecast Server for acadamics, both professors and students. It can be used not only for teaching but also for reserach purposes free of charge. I have been using SAS for more time than I care to admit, but still only one digit when you write it in Hexadecimal. SAS, in many ways, is not a real programming language. This frustrated me to no end when I started with it, and that was before the era of ODS Statistical Graphics that make producing graphics as easy as producing any other kind of tabular data.
After all, if you are a in a data analysis and reporting position, and you have a real programming language, you also need incredible discipline to keep everybody rowing in the same direction. SAS is designed to keep you from programming, by using pre-written procedures with lots-and-lots-o-options, as was pointed out earlier. Yet for a data analysis system, it has several extremely coherent way of carrying results from one step of the analysis to the next, but it is restrictive enough that people can generally pick up SAS quickly.
Hint: it does a lot more than just transpose a single matrix. Something is not outdated just because you say it is. What features, or lack thereof, make it outdated. Remember again, that SAS was never intended to be a full programming language in the sense that most professional programmers think of the term.
Therefore, it should not be compared with things like Python, or even to your greatest programming language ever invented. But it is misleading just to throw out a figure without stating what it is based upon. At this time, R and Python used together gives the most power and possibilities. We need both at this time. Excel with VBA macros is necessity at lower stratum. I think there is a lot of misinformation about SAS. Also, SAS has really focused alot of their efforts on industry solutions. It is more of a point and click environment for the different type of analytics needed. Most of my projects last a few months at a time and subsequent projects are usually too different to make much code reuse practical.
As for the cost, a week or two shaved off of analysis SW development due to the good documentation and strong user community pretty much pays for the license. Here are they key features that I use: -Ability to handle, search, collapse, and reshape multidimensional matrices -Linear and non-linear filtering methods for images -Ability to make simple GUIs quickly -Nice handling of complex numbers, necessary for Fourier analysis.
One day, I hope to become a real programmer with a pony tail and a deep-seated disdain for a handful of shameful programming practices, but until then, MATLAB will keep helping me get work done quickly. One of the steps involve estimating a VAR p model. Results are similar, but statistically different! One place where I as a non-statistician working in biology ecology got results fairly quickly and in a way I could actually understand was using the Resampling Stats add-in for Excel written in VBA, I think.
Is there anything similar for Gnumeric or Libre Office Calc? Just wanted to say that at least for these kind of non-parametric tests and teaching Excel might have a role to play. Disclaimer: this was many years ago , so things might have changed since then. While SAS has a lot of weaknesses like a patchwork of language syntaxes it can easily handle large data sets. This was all messy data ie.. SAS has the flexibility to force things together that I think is hard to find elsewhere.
Finally clicked run and went home for the weekend. If there is a problem with the code, SAS will email me. Do that in R. So SAS is powerful, however it is prohibitively expensive; even my company government contractor cannot afford it, we use the clients copies instead. The programming language sucks, I hate the development environment, and the list can go on.
I think the main advantage SAS has is its ability to handle big datasets, its hundreds of built in functions and its SQL pass through language for manipulating said large data and analyzing it in one place. Other more open source solutions are eroding these advantages. Cons: Confused-on-Multi-thread processing, expensive, developer environment sucks.
Are we talking about doing statistics or are we talking about data analysis? I think some people need to let go of their elitist attitude and be a little more open-minded. But times have changed recently. I am in a more dynamic environment. What do I do then? Call my IT department? Haha yeah right! Unfortunately, scenarios like these call for a more lower level programming language. But who wants to go that route?! It is a relatively simple language to learn. Python is truly a hidden gem. With Python coupled with Pandas and Matplotlib, I have the best of all worlds. I can choose the right tool for the right job, and yes, that includes using Excel too if I have to.
If you have an inclination to learning programming, I would definitely give Python a try and check out Pandas and Matplotlib. Pingback: Quora. I am wondering if the Statistics Toolbox is going to be sufficient for the things I need to do with it, or whether I am going to have to take the plunge with R. SAS is outdated; R is updated. SAS grows slowly, with annual updates that also not free ; R grows exponentially with several newly tested and validated packages all FREE.
If a Firm is smart, then they use the right tool for the job. They use multiple criteria in decision making. SAS is not outdated. Usually people who make these statements have NO clue what SAS is, have never used it, or are biased. I do this because I use the right code for the job. If you have the SAS server to deal with, yes it will be much more complex to install.
You can run SAS as a standalone desktop version. R has only recently been able to handle the amounts of large data that SAS has been able to handle for years. By the way, the support you get from SAS the company is hands down the best support you will ever get from any company. With R, you rely on yourself or the community. Although you could get support from some companies like Revolution who have commercial R support.
Design of Experiments. I am by no means an expert user, and I am not sure if the following list is true for most people. Anyways, here are the pros and cons in my personal opinion:. This program is used extensively in an experiment I used to work for. Nuff said. Use SAS is you are absolutely forced to.
No other reasons to. Excel is fine for extremely simple things. Want to add two columns of data that are already in Excel? Want to sum a column? Sure do it in Excel. Excel is only useful in that it allows you to look at the data.
If you add a new column or define a column based on a formula then it is fairly useful because these things persist. Anyone can look at your spreadsheet and tell what you are up to. Excel is therefore useful for calculations that often occur in business especially when the amount of data is small. Beyond that … move on. First ask yourself: Do you need a real general purpose programming language with an enormous set of libraries to analyze your data.
That is, is it important that your analysis is integrated into a larger system that needs to do a lot more than data analysis. If so, you want someting like Python. Then ask yourself, if you are willing to pay a lot of money to develop on a platform that is closed source and expensive and therefore has a small community. If you are not, then avoid matlab or IDL or similar big ticket packages. You will want to avoid these anyway unless you are working closely with people who are tied to them. Python is a powerful and fairly easy to learn general purpose programming language.
It is used for data analysis and statistics and it is very good for that. In that sense it feels more like matplab, IDL, mathematica etc. In short, programmers are going to prefer Python and scientsts, engineers and business folk will probably prefer R. R is just simpler to use. Not simple like Excel, but simpler than python. Installing packages is faster.
The platform is not changing rapidly. There are more good books and free tutorials on getting started. There are more data analysis, machine learning and statistics packages for R. In short, R is just made to be usable by people who may not be real programmers. Yeah, you can program and may have done so for years but your focus is not really on software development. If you are a real developer, you are likely to find R and certainly matlab pretty awkward languges.
However R has many flaws. It handles memory poorly. It is rather awkward and old fashioned in many respects. Integrating into other programming frameworks is not easy. People do not often deploy R software to clients. R feels a bit old. So I think the choice is simple. Do you want to create and deploy real software? If the first, go with python. If you are just analyzing data and not deploying anthing and you are greatly attracted to the number of libraries already written for R, you might preffer to go that way.
In the longer run, I think R will be replaced with something else, perhaps Python, perhaps something else. But R still feels easier to work with and I think most people who are not excellent programmers will prefer it.
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I will be glad to learn R if it is in demand. I see no means to learn another needless stat software. There is new kid on the block — Julia julialang. Pingback: Take care with those units… Lonely Joe Parker. Pingback: Comparison of data analysis packages - Homologus. We use SPSS on large data-sets in production, terabytes of data and billions of records.
Mixed Models for Logistic Regression in SPSS - The Analysis Factor
It works well and has proven very stable and cost effective running on large servers over the years. The user base using the program in this way is small in comparison to SAS. SAS, historically in my mind, is less a statistical program than it is a career. SAS is just a shit. Is there an alternative as Mallab is expensive? Please do give a comparative analysis if you have one. I think PDL pdl. R is also better and no doubt soaring in popularity. I have found R to be the most robust and amenable tool, and learned it well before the books were written on the subject using the free manuals off Google.
Debugging is easy in R if you have a good design philosophy and spend more time planning and less time coding. Not surprisingly he never got back to me. R frees the hamster from its wheel and leaves the SAS guys in the dust. With packages like swirl, etc, there really is no excuse as to not being able to jump into R within 2 hours or less if you have a basic knowledge of object oriented programming. Pingback: Stock market live today - binary option contractor comparison.
My website is brenocon. Name Advantages Disadvantages Open source? Among other things: Two big divisions on the table: The more programming-oriented solutions are R, Matlab, and Python. Why is there duplication between numpy and scipy e. In terms of functionality and approach, SciPy is closest to Matlab, but it feels much less mature. Python is clearly better on most counts. Everyone says SAS is very bad. Matlab is the best for developing new mathematical algorithms. Very popular in machine learning. SPSS and Stata in the same category: they seem to have a similar role so we threw them together.
Stata is a lot cheaper than SPSS, people usually seem to like it, and it seems popular for introductory courses. My impression is they get used by people who want the easiest way possible to do the sort of standard statistical analyses that are very orthodox in many academic disciplines. ANOVA, multiple regressions, t- and chi-squared significance tests, etc. I know dozens of people under 30 doing statistical stuff and only one knows SAS.
At that R meetup last week, Jim Porzak asked the audience if there were any recent grad students who had learned R in school. Many hands went up. Then he asked if SAS was even offered as an option. All hands went down. That is, ones that mostly have to stay on disk? There are a few multi-machine data processing frameworks that are somewhat standard e. Or quite possibly something else. This was an interesting point at the R meetup.
SAS people complain about poor graphing capabilities. Matlab visualization support is controversial. Matplotlib follows the Matlab model, which is fine, but is uglier than either IMO. Excel has a far, far larger user base than any of these other options. Most of the packages listed above run Fortran numeric libraries for the heavy lifting.
Another option: Mathematica. Can anyone prove me wrong? Another option: the pre-baked data mining packages. The open-source ones I know of are Weka and Orange. I hear there are zillions of commercial ones too. Jerome Friedman, a big statistical learning guy, has an interesting complaint that they should focus more on traditional things like significance tests and experimental design. Here ; the article that inspired this rant. What do people think? Aug update: Serbo-Croatian translation. Apr update: Slovenian translation. May update: Portugese translation.
This entry was posted in Best Posts. Bookmark the permalink. February 23, at pm. April 5, at am. Eric Sun says:. Justin says:. That said, these are flaws, but they seem pretty minor to me. Edward Ratzer says:. TS Waterman says:. Michael E. Driscoll says:. Mike says:. Chris Vighagen says:. April 19, at pm. Pete Skomoroch says:. February 24, at am. John says:. Jean-Luc Pikachu says:. February 24, at pm. Gaurav says:. Disclosure: I work for the parallel computing team at The MathWorks. February 25, at am.
Peter Skomoroch says:. Stefan says:. February 25, at pm. Lou Pecora says:. For anyone in this situation I unequivocally recommend: Python. David Warde-Farley says:. Bob Carpenter says:. February 26, at am. Stewart says:. February 26, at pm. February 27, at am. I will note that no one defended SAS. John Dudley says:. March 4, at pm. Andy Malner says:. StatSoft is the only major package with R integration…The best of both worlds. Wesley Deelman says:. February 21, at pm. Abhijit says:. March 5, at am.
John Johnson says:. March 5, at pm. I used SPSS a long time ago, and have no interest in trying it again. Jon Peck says:.
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For some data files from the 2nd edition, click on data files for Intro CDA. Here are some corrections for the 1st edition of this book, a pdf file of corrections for the 2nd edition , and a pdf file of corrections for the 3rd edition. The text Foundations of Linear and Generalized Linear Models , published by Wiley in February , presents an overview of the most commonly used statistical models by discussing the theory underlying the models and showing examples using R software.
The book begins with the fundamentals of linear models, such as showing how least squares projects the data onto a model vector subspace and orthogonal decompositions of the data yield comparisons of models. The book then covers the theory of generalized linear models, with chapters on binomial and multinomial logistic regression for categorical data and Poisson and negative binomial loglinear models for count data.
The book also introduces quasi-likelihood methods such as generalized estimating equations , linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian linear and generalized linear modeling, and regularization methods for high-dimensional data.
The book has more than exercises. The book's website contains supplementary information, including data sets and corrections. Here is an interview about the book in the Wiley publication "Statistics Views. This book has a chapter for each of about 40 Statistics and Biostatistics departments founded in the U. Included are about historical photos.
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See the Springer site for other details. I've constructed a Website for Categorical Data Analysis that provides datasets used for examples, solutions to some exercises, information about using R, SAS, Stata, and SPSS software for conducting the analyses in the text, and a list of some typos and errors. Here is an interview that the Wiley publication "Statistics Views" conducted with me to mark the publication of the new edition. A website for second edition has some material for the 2nd edition.
Laura Thompson has prepared a detailed manual on the use of R or S-Plus to conduct all the analyses in the 2nd edition. The text Analysis of Ordinal Categorical Data Wiley, has been revised, and the second edition was published in My ordinal categorical website contains 1 data sets for some examples in the form of SAS programs for conducting the analyses, 2 examples of the use or R for fitting various ordinal models, 3 examples of the use of Joe Lang's mph. The latest 4th edition was co-authored by Bernhard Klingenberg of Williams College, who has developed a wonderful set of applets and other resources for teaching from the book see Art of Stat.
This text is designed for a one-term or two-term undergraduate course or a high school AP course on an introduction to statistics, presented with a conceptual approach. Many supplemental materials are available from Pearson, including an annotated instructor's edition, a lab workbook, videotaped lectures, and software supplements.
Contact Ms. Agresti and B. Finlay, published is designed for a two-semester sequence. The book begins with the basics of statistical description and inference, and the second half concentrates on regression methods, including multiple regression, ANOVA and repeated measures ANOVA, analysis of covariance, logistic regression, and generalized linear models. The new edition adds R and Stata for software examples as well as introductions to new methodology such as multiple imputation for missing data, random effects modeling including multilevel models, robust regression, and the Bayesian approach to statistical inference.
For applets used in some examples and exercises of the new edition, go to applets. See R data files. He has also put the data files at a GitHub site, data files at GitHub. For examples of the use of the software Stata for various analyses for examples in the 4th edition of this text, see the useful site set up by the UCLA Statistical Computing Center.
Thanks to Margaret Ross Tolbert for the cover art for the 5th edition.
Margaret is an incredibly talented artist who has helped draw attention to the beauty but environmental degradation of the springs in north-central Florida see www. I am also pleased to report due to my partial Italian heritage that there is also an Italian version of the first ten chapters of the 4th edition of this book Statistica per le Scienze Sociali and of the entire book Metodi Statistici di Base e Avanzati per le scienze sociali published by Pearson, and there is also a Portuguese version -- see "Metodos Estatisticos para as Ciencas Socias" at Portuguese SMSS -- and a Chinese version, and it is being translated into Spanish.
I have developed Powerpoint files for lectures from Chapters of this text that are available to instructors using this text. Please contact me for details. Finally, here is a link to a workshop held by the Department of Sociology, Oxford University, in that discussed issues in the teaching of quantitative methods to social science students. Analysis of Ordinal Categorical Data , 2nd ed.
An Introduction to Categorical Data Analysis , 3rd ed. Categorical Data Analysis , 3rd edition, Wiley Some Articles Bounds on the extinction time distribution of a branching process. Advances in Applied Probability , 6 , Journal of Applied Probability , 12 , Journal of the American Statistical Association , 71 , Some exact conditional tests of independence for r x c cross-classification tables. Wackerly Psychometrika , 42 , Journal of the American Statistical Association , 72 , A coefficient of multiple association based on ranks. Communications in Statistics , A6 , Statistical analysis of qualitative variation.
Agresti , Chapter 10, in Sociological Methodology ed. Schuessler, Jossey-Bass Publ. Descriptive measures for rank comparisons of groups. Exact conditional tests for cross-classifications: Approximation of attained significance level. Wackerly and J. Boyett , Psychometrika , 44 , Schollenberger, A. Agresti, and D. Generalized odds ratios for ordinal data. Biometrics , 36 , Journal of the Royal Statistical Society B , 43 , Measures of nominal-ordinal association, Journal of the American Statistical Association , 76 , Encyclopedia of the Statistical Sciences , Vol.
Testing marginal homogeneity for ordinal categorical variables, Biometrics , 39, , Association models for multidimensional cross-classifications of ordinal variables with A.
How To Obtain a Copy of SAS
Kezouh , invited paper for issue on categorical data, Communications in Statistics , A12 , A simple diagonals-parameter symmetry and quasisymmetry model, Statistics and Probability Letters , 1 , Morey , Educational and Psychological Measurement , 44 , Ordinal data. Comparing mean ranks for repeated measures data with J. Pendergast , Communications in Statistics , A15 , Chuang , Statistics in Medicine , 5 , Applying R-squared type measures to ordered categorical data, Technometrics , 28 , Fixed Effects Logistic Regression Model.
Heteroscedastic Logistic Regression Model. Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research.
Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. For readers interested in learning more about the languages, though, there are short tutorials in the appendix.