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643 | 1 | null | null | 34 | 3818 | My father is a math enthusiast, but not interested in statistics much. It would be neat to try to illustrate some of the wonderful bits of statistics, and the CLT is a prime candidate. How would you convey the mathematical beauty and impact of the central limit theorem to a non-statistician?
| How do you convey the beauty of the Central Limit Theorem to a non-statistician? | CC BY-SA 2.5 | null | 2010-07-26T19:26:37.037 | 2011-06-09T21:40:25.677 | 2010-10-19T06:42:19.517 | null | 7 | [
"mathematical-statistics",
"central-limit-theorem"
] |
644 | 2 | null | 638 | 7 | null | Well as you said there is no black and white answer. I generally don't divide the data in 2 parts but use methods like k-fold cross validation instead.
In k-fold cross validation you divide your data randomly into k parts and fit your model on k-1 parts and test the errors on the left out part. You repeat the process ... | null | CC BY-SA 2.5 | null | 2010-07-26T19:27:49.840 | 2010-07-26T19:27:49.840 | null | null | 288 | null |
645 | 1 | 680 | null | 13 | 6819 | Having just recently started teaching myself Machine Learning and Data Analysis I'm finding myself hitting a brick wall on the need for creating and querying large sets of data. I would like to take data I've been aggregating in my professional and personal life and analyze it but I'm uncertain of the best way to do th... | Best ways to aggregate and analyze data | CC BY-SA 3.0 | null | 2010-07-26T19:28:53.083 | 2015-12-17T22:39:25.767 | 2015-12-17T22:39:25.767 | 28666 | 9426 | [
"data-mining",
"dataset",
"exploratory-data-analysis"
] |
646 | 2 | null | 641 | 10 | null | The best place to find survey data related to the social sciences is the ICPSR data clearinghouse: [http://www.icpsr.umich.edu/icpsrweb/ICPSR/access/index.jsp](http://www.icpsr.umich.edu/icpsrweb/ICPSR/access/index.jsp)
Also, the 'survey' tag on Infochimps has many interesting and free data sets: [http://infochimps.org... | null | CC BY-SA 2.5 | null | 2010-07-26T19:31:36.057 | 2010-07-26T19:31:36.057 | null | null | 302 | null |
647 | 2 | null | 631 | -3 | null | @Macro provided a great mathematical explanation with equation to compute. Here is a more general explation for less mathematical people.
I think the terminology "SD of SD" is confusing to many. It is easier to think about the confidence interval of a SD. How precise is the standard deviation you compute from a sample?... | null | CC BY-SA 3.0 | null | 2010-07-26T19:34:07.533 | 2017-02-04T16:10:25.923 | 2017-02-04T16:10:25.923 | 25 | 25 | null |
648 | 2 | null | 643 | 15 | null | To fully appreciate the CLT, it should be seen.
Hence the notion of the [bean machine](http://en.wikipedia.org/wiki/Galton_board) and plenty of youtube [videos](http://www.youtube.com/results?search_query=galton+board&aq=0) for illustration.
| null | CC BY-SA 2.5 | null | 2010-07-26T19:34:09.857 | 2010-07-26T19:34:09.857 | null | null | 68 | null |
650 | 2 | null | 645 | 3 | null | Your question is so broad that the answer is: it depends. Still, to give some more useful answer I'll indicate what I think are common in Research.
Storing of data is very often done in text files. When doing statistical analyses you mostly work with a collection of one type of vectors. This can be seen as a table and ... | null | CC BY-SA 2.5 | null | 2010-07-26T19:34:52.007 | 2010-07-26T19:34:52.007 | null | null | 190 | null |
651 | 2 | null | 223 | 2 | null | My book, [Intuitive Biostatistics](http://www.intuitivebiostatistics.com), is written partly from a medical point of view. It focusses on the practical parts of interpreting statistical results, with almost no math.
| null | CC BY-SA 2.5 | null | 2010-07-26T19:39:05.273 | 2010-07-26T19:39:05.273 | null | null | 25 | null |
652 | 1 | 682 | null | 17 | 34034 | I bought this book:
[How to Measure Anything: Finding the Value of Intangibles in Business](http://rcm.amazon.com/e/cm?lt1=_blank&bc1=000000&IS2=1&bg1=FFFFFF&fc1=000000&lc1=0000FF&t=justibozon-20&o=1&p=8&l=as1&m=amazon&f=ifr&md=10FE9736YVPPT7A0FBG2&asins=0470539399)
and
[Head First Data Analysis: A Learner's Guide to... | Best books for an introduction to statistical data analysis? | CC BY-SA 2.5 | null | 2010-07-26T19:39:49.377 | 2015-07-28T15:33:51.747 | 2010-09-10T18:26:46.800 | null | 9426 | [
"machine-learning",
"bayesian",
"references"
] |
653 | 2 | null | 638 | 5 | null | It really depends on the amount of data you have, the specific cost of methods and how exactly you want your result to be.
Some examples:
If you have little data, you probably want to use cross-validation (k-fold, leave-one-out, etc.) Your model will probably not take much resources to train and test anyway. It are goo... | null | CC BY-SA 2.5 | null | 2010-07-26T19:42:50.803 | 2010-07-26T19:42:50.803 | null | null | 190 | null |
654 | 2 | null | 638 | 5 | null | 1:10 test:train ratio is popular because it looks round, 1:9 is popular because of 10-fold CV, 1:2 is popular because it is also round and reassembles bootstrap. Sometimes one gets a test from some data-specific criteria, for instance last year for testing, years before for training.
The general rule is such: the trai... | null | CC BY-SA 2.5 | null | 2010-07-26T19:44:44.183 | 2010-07-26T19:44:44.183 | null | null | null | null |
655 | 2 | null | 652 | 2 | null | You might find useful this one: [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](http://rads.stackoverflow.com/amzn/click/0387848576)
UPDATE #1:
This book might be useful as well: [O'Reilly: Statistics in a Nutshel](http://rads.stackoverflow.com/amzn/click/0596510497)l
| null | CC BY-SA 2.5 | null | 2010-07-26T19:49:53.953 | 2010-08-03T09:09:40.580 | 2010-08-03T09:09:40.580 | 315 | 315 | null |
656 | 2 | null | 423 | 132 | null | 
| null | CC BY-SA 2.5 | null | 2010-07-26T19:51:45.370 | 2010-08-11T08:50:54.893 | 2017-03-09T17:30:36.273 | -1 | 25 | null |
657 | 2 | null | 138 | 4 | null | I liked these lectures: [Statistical Aspects of Data Mining](http://www.youtube.com/results?search_query=statistical+aspects+of+data+mining&aq=0). The lecturer is solving example problems using R.
| null | CC BY-SA 2.5 | null | 2010-07-26T19:53:45.660 | 2010-07-26T19:53:45.660 | null | null | 315 | null |
658 | 2 | null | 643 | 16 | null | What I loved most with CLT is the cases when it is not applicable -- this gives me a hope that the life is a bit more interesting that Gauss curve suggests. So show him the Cauchy distribution.
| null | CC BY-SA 2.5 | null | 2010-07-26T19:56:23.970 | 2010-07-27T18:06:52.203 | 2010-07-27T18:06:52.203 | null | null | null |
659 | 2 | null | 222 | 4 | null | The principal components of a data matrix are the eigenvector-eigenvalue pairs of its variance-covariance matrix. In essence, they are the decorrelated pieces of the variance. Each one is a linear combination of the variables for an observation -- suppose you measure w, x, y,z on each of a bunch of subjects. Your fi... | null | CC BY-SA 2.5 | null | 2010-07-26T19:58:28.347 | 2010-07-26T19:58:28.347 | null | null | 317 | null |
660 | 1 | 677 | null | 9 | 1470 | I am collecting textual data surrounding press releases, blog posts, reviews, etc of certain companies' products and performance.
Specifically, I am looking to see if there are correlations between certain types and/or sources of such "textual" content with market valuations of the companies' stock symbols.
Such appare... | Automating statistical correlation between "texts" and "data" | CC BY-SA 2.5 | null | 2010-07-26T20:03:02.643 | 2013-10-04T06:19:18.250 | null | null | 292 | [
"finance",
"correlation",
"text-mining"
] |
661 | 2 | null | 155 | 9 | null | Sam Savage's book [Flaw of Averages](http://rads.stackoverflow.com/amzn/click/0471381977) is filled with good layman explanations of statistical concepts. In particular, he has a good explanation of Jensen's inequality. If the graph of your return on an investment is convex, i.e. it "smiles at you", then randomness i... | null | CC BY-SA 2.5 | null | 2010-07-26T20:06:09.983 | 2010-07-26T20:06:09.983 | null | null | 319 | null |
662 | 2 | null | 614 | 11 | null | Michael Lavine: [Introduction to Statistical Thought](http://www.math.umass.edu/~lavine/Book/book.html), licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
| null | CC BY-SA 3.0 | null | 2010-07-26T20:08:09.760 | 2016-10-06T19:37:44.953 | 2016-10-06T19:37:44.953 | 122650 | 319 | null |
663 | 2 | null | 170 | 27 | null | [Introduction to Statistical Thought](http://www.math.umass.edu/~lavine/Book/book.html)
| null | CC BY-SA 2.5 | null | 2010-07-26T20:09:20.320 | 2010-07-26T20:09:20.320 | null | null | 319 | null |
664 | 2 | null | 30 | 3 | null | It's seldom useful to conclude that something is "random" in the abstract. More often you want to test whether it has a certain kind of random structure. For example, you might want to test whether something has a uniform distribution, with all values in a certain range equally likely. Or you might want to test whet... | null | CC BY-SA 2.5 | null | 2010-07-26T20:12:15.570 | 2010-07-26T20:12:15.570 | null | null | 319 | null |
665 | 1 | null | null | 146 | 108768 | What's the difference between probability and statistics, and why are they studied together?
| What's the difference between probability and statistics? | CC BY-SA 2.5 | null | 2010-07-26T20:17:17.680 | 2021-02-10T10:04:52.513 | 2011-03-20T16:07:28.560 | 2645 | 327 | [
"probability",
"teaching",
"mathematical-statistics"
] |
667 | 2 | null | 665 | 12 | null | Probability is a pure science (math), statistics is about data. They are connected since probability forms some kind of fundament for statistics, providing basic ideas.
| null | CC BY-SA 2.5 | null | 2010-07-26T20:18:46.037 | 2010-07-26T20:18:46.037 | null | null | null | null |
668 | 2 | null | 138 | 5 | null | If you already know another programming language, [these notes](http://www.johndcook.com/R_language_for_programmers.html) may help point out some of the ways R might surprise you.
| null | CC BY-SA 2.5 | null | 2010-07-26T20:19:12.447 | 2010-07-26T20:19:12.447 | null | null | 319 | null |
669 | 2 | null | 421 | 4 | null | [The Flaw of Averages](http://rads.stackoverflow.com/amzn/click/0471381977) by Sam Savage.
| null | CC BY-SA 2.5 | null | 2010-07-26T20:22:30.303 | 2010-07-26T20:22:30.303 | null | null | 319 | null |
671 | 2 | null | 138 | 8 | null | If you have experience in other languages, these "R Rosetta Stone" videos may be useful:
- Python
- MATLAB
- SQL
These are all included in the [video list added by Jeromy](http://jeromyanglim.blogspot.com/2010/05/videos-on-data-analysis-with-r.html), so big +1 for his list.
| null | CC BY-SA 2.5 | null | 2010-07-26T20:25:16.363 | 2010-07-26T20:25:16.363 | null | null | 302 | null |
672 | 1 | 824 | null | 37 | 15545 | What are the main ideas, that is, concepts related to [Bayes' theorem](http://en.wikipedia.org/wiki/Bayes%27_theorem)?
I am not asking for any derivations of complex mathematical notation.
| What is Bayes' theorem all about? | CC BY-SA 2.5 | null | 2010-07-26T20:30:36.507 | 2014-10-03T15:35:13.163 | 2011-02-02T19:00:52.413 | 509 | 333 | [
"probability",
"bayesian",
"mathematical-statistics"
] |
673 | 2 | null | 665 | 16 | null | Table 3.1 of [Intuitive Biostatistics](http://www.intuitivebiostatistics.com) answers this question with the diagram shown below. Note that all the arrows point to the right for probability, and point to the left for statistics.
PROBABILITY
>
General ---> Specific
Population ---> Sample
Model ---> Data
STATISTICS
> ... | null | CC BY-SA 2.5 | null | 2010-07-26T20:34:45.657 | 2010-07-26T20:34:45.657 | null | null | 25 | null |
674 | 2 | null | 665 | 10 | null | Probability is about quantifying uncertainty whereas statistics is explaining the variation in some measure of interest (e.g., why do income levels vary?) that we observe in the real world.
We explain the variation by using some observable factors (e.g., gender, education level, age etc for the income example). Howeve... | null | CC BY-SA 3.0 | null | 2010-07-26T20:45:36.923 | 2018-03-06T22:33:34.797 | 2018-03-06T22:33:34.797 | 44269 | null | null |
675 | 2 | null | 665 | 157 | null | The short answer to this I've heard from [Persi Diaconis](https://en.wikipedia.org/wiki/Persi_Diaconis) is the following:
The problems considered by probability and statistics are inverse to each other. In probability theory we consider some underlying process which has some randomness or uncertainty modeled by random... | null | CC BY-SA 4.0 | null | 2010-07-26T20:47:19.230 | 2021-02-10T10:04:52.513 | 2021-02-10T10:04:52.513 | 89 | 89 | null |
676 | 2 | null | 36 | 2 | null | Well my Prof. used these in Introductory probability class:
1) Shoe size are correlated with reading ability
2) Shark attack is correlated with sale of ice cream.
| null | CC BY-SA 2.5 | null | 2010-07-26T20:49:33.217 | 2010-07-26T20:49:33.217 | null | null | 288 | null |
677 | 2 | null | 660 | 5 | null | My students do this as their class project. A few teams hit the 70%s for accuracy, with pretty small samples, which ain't bad.
Let's say you have some data like this:
```
Return Symbol News Text
-4% DELL Centegra and Dell Services recognized with Outsourcing Center's...
7% MSFT Rising Service Revenues Benefit V... | null | CC BY-SA 3.0 | null | 2010-07-26T20:56:22.360 | 2013-10-04T06:19:18.250 | 2013-10-04T06:19:18.250 | 74 | 74 | null |
678 | 2 | null | 665 | 6 | null | The probability of an event is its long-run relative frequency. So it's basically telling you the chance of, for example, getting a 'head' on the next flip of a coin, or getting a '3' on the next roll of a die.
A statistic is any numerical measure computed from a sample of the population. For example, the sample mean. ... | null | CC BY-SA 2.5 | null | 2010-07-26T21:00:00.617 | 2010-07-26T21:05:06.097 | 2010-07-26T21:05:06.097 | 81 | 81 | null |
680 | 2 | null | 645 | 21 | null | If you have large data sets - ones that make Excel or Notepad load slowly, then a database is a good way to go. Postgres is open-source and very well-made, and it's easy to connect with JMP, SPSS and other programs. You may want to sample in this case. You don't have to normalize the data in the database. Otherwise, CS... | null | CC BY-SA 3.0 | null | 2010-07-26T21:11:25.677 | 2012-07-22T11:15:41.390 | 2012-07-22T11:15:41.390 | 74 | 74 | null |
681 | 2 | null | 288 | 3 | null | You don't necessarily have to go Bayesian on your model, plain maximum likelihood estimation works just fine (though has no explicit solution). Multiple R packages (eg. aod or VGAM) will fit the distribution for you.
Alternatively, you can use the quasi-likelihood based overdispersed binomial model that does not assume... | null | CC BY-SA 2.5 | null | 2010-07-26T21:13:43.743 | 2010-07-26T21:13:43.743 | null | null | 279 | null |
682 | 2 | null | 652 | 6 | null | I didn't find How To Measure Anything, nor Head First, particularly good.
Statistics In Plain English (Urdan) is a good starter book.
Once you finish that, Multivariate Data Analysis (Joseph Hair et al.) is fantastic.
Good luck!
| null | CC BY-SA 3.0 | null | 2010-07-26T21:17:29.877 | 2013-05-27T02:35:13.913 | 2013-05-27T02:35:13.913 | 74 | 74 | null |
683 | 2 | null | 322 | 5 | null | If your interested in the mathematical statistic around entropy, you may consult this book
[http://www.renyi.hu/~csiszar/Publications/Information_Theory_and_Statistics:_A_Tutorial.pdf](http://www.renyi.hu/~csiszar/Publications/Information_Theory_and_Statistics:_A_Tutorial.pdf)
it is freely available !
| null | CC BY-SA 2.5 | null | 2010-07-26T21:34:14.130 | 2010-09-02T09:21:53.573 | 2010-09-02T09:21:53.573 | 223 | 223 | null |
684 | 2 | null | 672 | 5 | null | There are two main schools of thought is Statistics: [frequentist and Bayesian](https://stats.stackexchange.com/questions/22/bayesian-and-frequentist-reasoning-in-plain-english).
Bayes theorem is to do with the latter and can be seen as a way of understanding how the probability that a theory is true is affected by a ... | null | CC BY-SA 3.0 | null | 2010-07-26T21:35:29.510 | 2011-11-11T10:10:11.790 | 2017-04-13T12:44:53.513 | -1 | 81 | null |
685 | 1 | null | null | -4 | 433 | Is there something about statistics that lends itself to this sort of saying, or is it just that people will say anything to support their case, and this includes citing irrelevant or incomplete statistics?
| Lies, Damn Lies and Statistics | CC BY-SA 2.5 | null | 2010-07-26T21:40:20.870 | 2010-07-26T22:06:32.547 | null | null | 327 | [
"mathematical-statistics"
] |
686 | 2 | null | 485 | 10 | null | Check out the following links. I'm not sure what exactly are you looking for.
[Monte Carlo Simulation for Statistical Inference](http://videolectures.net/mlss08au_freitas_asm/)
[Kernel methods and Support Vector Machines](http://videolectures.net/mlss08au_smola_ksvm/)
[Introduction to Support Vector Machines](http://vi... | null | CC BY-SA 2.5 | null | 2010-07-26T21:58:37.497 | 2010-07-26T21:58:37.497 | null | null | 339 | null |
687 | 2 | null | 685 | 4 | null | Statistics is about inferring something about a population, and that requires some level of interpretation.
More intuitively, "is the glass half full or half empty?". They both mean the same thing, but may have a different effect on the person who hears it.
So I would say it's the interpretation aspect which is the p... | null | CC BY-SA 2.5 | null | 2010-07-26T22:00:01.037 | 2010-07-26T22:00:01.037 | null | null | 81 | null |
689 | 2 | null | 485 | 7 | null | See [Videos on data analysis using R](http://jeromyanglim.blogspot.com/2010/05/videos-on-data-analysis-with-r.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3a+jeromyanglim+%28Jeromy+Anglim%27s+Blog%3a+Psychology+and+Statistics%29) on Jeromy Anglim's blog. There are many links at that page and he updates... | null | CC BY-SA 2.5 | null | 2010-07-26T22:12:58.017 | 2010-07-26T22:12:58.017 | null | null | null | null |
691 | 2 | null | 118 | 19 | null | Just so people know, there is a Math Overflow question on the same topic.
[Why-is-it-so-cool-to-square-numbers-in-terms-of-finding-the-standard-deviation](https://mathoverflow.net/questions/1048/why-is-it-so-cool-to-square-numbers-in-terms-of-finding-the-standard-deviation)
The take away message is that using the squar... | null | CC BY-SA 2.5 | null | 2010-07-26T22:22:21.440 | 2010-07-26T22:22:21.440 | 2017-04-13T12:58:32.177 | -1 | 352 | null |
692 | 1 | 741 | null | 10 | 401 | Oversimplifying a bit, I have about a million records that record the entry time and exit time of people in a system spanning about ten years. Every record has an entry time, but not every record has an exit time. The mean time in the system is ~1 year.
The missing exit times happen for two reasons:
- The person ha... | How do I determine if a survival model with missing data is appropriate? | CC BY-SA 2.5 | null | 2010-07-26T22:29:48.407 | 2010-09-16T12:35:10.570 | 2010-09-16T12:35:10.570 | null | 72 | [
"survival",
"missing-data"
] |
693 | 2 | null | 665 | 93 | null | I like the example of a jar of red and green jelly beans.
A probabilist starts by knowing the proportion of each and asks the probability of drawing a red jelly bean. A statistician infers the proportion of red jelly beans by sampling from the jar.
| null | CC BY-SA 2.5 | null | 2010-07-26T22:48:51.740 | 2010-07-26T22:48:51.740 | null | null | 319 | null |
694 | 2 | null | 423 | 109 | null | This isn't technically a cartoon, but close enough:
| null | CC BY-SA 3.0 | null | 2010-07-26T23:04:39.127 | 2017-12-21T20:57:28.523 | 2017-12-21T20:57:28.523 | 74 | 74 | null |
695 | 2 | null | 652 | 3 | null | My favourite book on Statistics is is David William's [Weighing the Odds](http://amzn.to/aRoxQq). Davison's [Statistical Models](http://amzn.to/90gOnD) is good too.
| null | CC BY-SA 2.5 | null | 2010-07-26T23:10:16.777 | 2010-07-26T23:10:16.777 | null | null | 173 | null |
696 | 2 | null | 195 | 1 | null | I think that my question is subsumed by this more general discussion: [Motivation for Kolmogorov distance between distributions](https://stats.stackexchange.com/questions/411/motivation-for-kolmogorov-distance-between-distributions)
| null | CC BY-SA 2.5 | null | 2010-07-26T23:22:39.377 | 2010-07-26T23:22:39.377 | 2017-04-13T12:44:29.923 | -1 | 173 | null |
697 | 2 | null | 660 | 1 | null | As per above, you need a set of articles and responses, and then you train eg. a Neural Net to them. RapidMiner will let you do this but there are many other tools out there that will let you do regressions of this size. Ideally your response variable will be consistent (ie % change after 1 hour exactly, or % change af... | null | CC BY-SA 2.5 | null | 2010-07-26T23:48:24.280 | 2010-07-26T23:48:24.280 | null | null | 367 | null |
699 | 2 | null | 346 | 10 | null | The [Rivest-Tarjan-Selection algorithm](http://en.wikipedia.org/wiki/Selection_algorithm#Linear_general_selection_algorithm_-_Median_of_Medians_algorithm) (sometimes also called the median-of-medians algorithm) will let you compute the median element in linear-time without any sorting. For large data sets this is can b... | null | CC BY-SA 2.5 | null | 2010-07-27T00:01:58.560 | 2010-07-27T00:01:58.560 | null | null | 352 | null |
700 | 2 | null | 322 | 6 | null | These [lecture notes](http://www.statslab.cam.ac.uk/~yms/ICL0706.ps) on information theory by O. Johnson contain a good introduction to different kinds of entropy.
| null | CC BY-SA 2.5 | null | 2010-07-27T00:21:32.637 | 2010-07-27T00:21:32.637 | null | null | 368 | null |
701 | 2 | null | 118 | 4 | null | Because squares can allow use of many other mathematical operations or functions more easily than absolute values.
Example: squares can be integrated, differentiated, can be used in trigonometric, logarithmic and other functions, with ease.
| null | CC BY-SA 2.5 | null | 2010-07-27T00:24:09.637 | 2010-07-27T00:24:09.637 | null | null | 369 | null |
702 | 2 | null | 692 | 2 | null | You could use the estimated model to predict the exit times for all the people in your system. You could then compare the estimated exit times with the actual exit times (where you have this data) and compute a metric such as [RMSE](http://en.wikipedia.org/wiki/Root_mean_square_deviation) to assess how good your predic... | null | CC BY-SA 2.5 | null | 2010-07-27T00:37:33.963 | 2010-07-27T00:37:33.963 | null | null | null | null |
703 | 2 | null | 622 | 15 | null | All three are used when dealing with nuisance parameters in the completely specified likelihood function.
The marginal likelihood is the primary method to eliminate nuisance parameters in theory. It's a true likelihood function (i.e. it's proportional to the (marginal) probability of the observed data).
The partial... | null | CC BY-SA 2.5 | null | 2010-07-27T00:47:25.470 | 2010-07-27T00:47:25.470 | null | null | 251 | null |
704 | 2 | null | 346 | -1 | null | Here's an answer to the question asked on stackoverflow: [https://stackoverflow.com/questions/1058813/on-line-iterator-algorithms-for-estimating-statistical-median-mode-skewness/2144754#2144754](https://stackoverflow.com/questions/1058813/on-line-iterator-algorithms-for-estimating-statistical-median-mode-skewness/21447... | null | CC BY-SA 2.5 | null | 2010-07-27T00:54:58.187 | 2010-07-27T00:54:58.187 | 2017-05-23T12:39:26.143 | -1 | null | null |
705 | 2 | null | 570 | 4 | null | This is a very interesting question. Suppose that we have a 2 dimensional covariance matrix (very unrealistic example for SEM but please bear with me). Then you can plot the iso-contours for the observed covariance matrix vis-a-vis the estimated covariance matrix to get a sense of model fit.
However, in reality you wil... | null | CC BY-SA 2.5 | null | 2010-07-27T01:07:48.393 | 2010-07-27T02:14:37.583 | 2010-07-27T02:14:37.583 | null | null | null |
706 | 2 | null | 118 | 16 | null | Yet another reason (in addition to the excellent ones above) comes from Fisher himself, who showed that the standard deviation is more "efficient" than the absolute deviation. Here, efficient has to do with how much a statistic will fluctuate in value on different samplings from a population. If your population is norm... | null | CC BY-SA 2.5 | null | 2010-07-27T01:51:15.673 | 2010-07-27T01:51:15.673 | null | null | 378 | null |
707 | 2 | null | 638 | 4 | null | As an extension on the k-fold answer, the "usual" choice of k is either 5 or 10. The leave-one-out method has a tendency to produce models that are too conservative. FYI, here is a reference on that fact:
Shao, J. (1993), Linear Model Selection by Cross-Validation, Journal of the American
Statistical Association, Vol.... | null | CC BY-SA 2.5 | null | 2010-07-27T02:19:56.727 | 2010-07-27T02:19:56.727 | null | null | 188 | null |
708 | 2 | null | 672 | 4 | null | Let me give you a very very intuitional insight. Suppose you are tossing a coin 10 times and you get 8 heads and 2 tails. The question that would come to your mind is whether this coin is biased towards heads or not.
Now if you go by conventional definitions or the frequentist approach of probability you might say th... | null | CC BY-SA 2.5 | null | 2010-07-27T02:31:38.587 | 2011-02-02T19:11:03.597 | 2011-02-02T19:11:03.597 | 509 | 25692 | null |
709 | 2 | null | 563 | 12 | null | [Here are some slides that I prepared for an econometrics course at UC Berkeley.](http://gibbons.bio/courses/econ140/IVSlides.pdf) I hope that you find them useful---I believe that they answer your questions and provide some examples.
There are also more advanced treatments on the course pages for PS 236 and PS 239 (gr... | null | CC BY-SA 3.0 | null | 2010-07-27T03:46:45.583 | 2018-01-24T21:54:03.920 | 2018-01-24T21:54:03.920 | 401 | 401 | null |
710 | 2 | null | 612 | 7 | null | My understanding is that the distinction between PCA and Factor analysis primarily is in whether there is an error term. Thus PCA can, and will, faithfully represent the data whereas factor analysis is less faithful to the data it is trained on but attempts to represent underlying trends or communality in the data. Un... | null | CC BY-SA 2.5 | null | 2010-07-27T03:54:35.923 | 2010-07-27T03:54:35.923 | null | null | 196 | null |
711 | 2 | null | 452 | 16 | null | Using robust standard errors has become common practice in economics. Robust standard errors are typically larger than non-robust (standard?) standard errors, so the practice can be viewed as an effort to be conservative.
In large samples (e.g., if you are working with Census data with millions of observations or data... | null | CC BY-SA 3.0 | null | 2010-07-27T03:54:51.710 | 2017-02-13T05:19:26.987 | 2017-02-13T05:19:26.987 | 68473 | 401 | null |
712 | 1 | null | null | 13 | 4977 | Is anyone aware of good data anonymization software? Or perhaps a package for R that does data anonymization? Obviously not expecting uncrackable anonymization - just want to make it difficult.
| Data anonymization software | CC BY-SA 2.5 | null | 2010-07-27T03:58:22.863 | 2012-02-11T17:26:35.363 | 2011-05-01T17:23:11.270 | 930 | 402 | [
"software"
] |
713 | 2 | null | 118 | 3 | null | Naturally you can describe dispersion of a distribution in any way meaningful (absolute deviation, quantiles, etc.).
One nice fact is that the variance is the second central moment, and every distribution is uniquely described by its moments if they exist.
Another nice fact is that the variance is much more tractable ... | null | CC BY-SA 2.5 | null | 2010-07-27T04:04:15.027 | 2010-07-27T04:04:15.027 | null | null | null | null |
714 | 2 | null | 73 | 8 | null | data.table is my favorite now! Very look forward to the new version with the more wishlist implemented.
| null | CC BY-SA 2.5 | null | 2010-07-27T04:27:33.930 | 2010-07-27T04:27:33.930 | null | null | null | null |
715 | 1 | null | null | 3 | 1796 | I am developing a multi-class perceptron algorithm and was wondering if there are any datasets that could be used to test a multi-class perceptron? - A dataset where the classes are linearly separable and have at least 100 or more instances for training?
| Dataset for multi class perceptron | CC BY-SA 2.5 | null | 2010-07-27T04:42:25.210 | 2010-08-30T15:13:35.773 | 2010-08-30T15:13:35.773 | 442 | 130 | [
"classification",
"dataset",
"multivariable"
] |
717 | 2 | null | 225 | 2 | null | I asked about why there was a difference between the average of the maximum of 100 draws from a random normal distribution and the 98th percentile of the normal distribution. The answer I received from Rob Hyndman was mostly acceptable, but too technically dense to accept without revision. I was left wondering whethe... | null | CC BY-SA 2.5 | null | 2010-07-27T05:04:45.570 | 2010-07-27T05:04:45.570 | null | null | 196 | null |
720 | 2 | null | 486 | 55 | null | All that matters is the difference between two AIC (or, better, AICc) values, representing the fit to two models. The actual value of the AIC (or AICc), and whether it is positive or negative, means nothing. If you simply changed the units the data are expressed in, the AIC (and AICc) would change dramatically. But th... | null | CC BY-SA 2.5 | null | 2010-07-27T05:36:14.333 | 2010-07-27T05:36:14.333 | null | null | 25 | null |
721 | 2 | null | 39 | 4 | null | The [UCLA Statistical Computing](http://www.ats.ucla.edu/stat/) site has a number of examples in various languages (SAS, R, etc). In particular, see the following pages (look among the links titled logistic regression, categorical data analysis and generalized linear models):
- Data Analysis Examples
- Textbook Exam... | null | CC BY-SA 2.5 | null | 2010-07-27T05:40:44.950 | 2010-07-27T05:40:44.950 | null | null | 251 | null |
722 | 2 | null | 175 | 7 | null | I've published a method for identifying outliers in nonlinear regression, and it can be also used when fitting a linear model.
HJ Motulsky and RE Brown. [Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate](http://www.biomedcent... | null | CC BY-SA 2.5 | null | 2010-07-27T05:41:11.750 | 2010-07-27T05:41:11.750 | null | null | 25 | null |
723 | 1 | 758 | null | 13 | 8390 | I'm doing shopping cart analyses my dataset is set of transaction vectors, with the items the products being bought.
When applying k-means on the transactions, I will always get some result. A random matrix would probably also show some clusters.
Is there a way to test whether the clustering I find is a significant one... | How can I test whether my clustering of binary data is significant | CC BY-SA 2.5 | null | 2010-07-27T06:00:07.690 | 2021-08-12T13:32:05.167 | null | null | 190 | [
"clustering",
"statistical-significance",
"binary-data"
] |
724 | 2 | null | 712 | 9 | null | The [Cornell Anonymization Tookit](http://sourceforge.net/projects/anony-toolkit/) is open source. Their [research page](http://www.cs.cornell.edu/bigreddata/privacy/) has links to associated publications.
| null | CC BY-SA 3.0 | null | 2010-07-27T06:01:10.010 | 2011-04-13T10:35:33.393 | 2011-04-13T10:35:33.393 | 930 | 251 | null |
725 | 1 | 759 | null | 6 | 1381 | An hyperspectral image is a multidimensional image with more than 200 spectral bands i.e. an image for which each pixel is a vector of dimension 200 (most often it is a sampled spectral curve that is encoutered in satellite imagery or medical imagery).
What are the implemented package (I am especially interested in R ... | Suggested R packages for frontier estimation or segmentation of hyperspectral images | CC BY-SA 2.5 | null | 2010-07-27T06:10:43.347 | 2022-08-28T14:53:06.907 | 2010-12-17T10:22:25.583 | null | 223 | [
"machine-learning",
"multivariate-analysis",
"image-processing"
] |
726 | 1 | null | null | 281 | 146384 | What is your favorite statistical quote?
This is community wiki, so please one quote per answer.
| Famous statistical quotations | CC BY-SA 3.0 | null | 2010-07-27T06:20:38.880 | 2022-11-23T09:59:13.570 | 2015-11-10T22:07:37.400 | 22468 | 223 | [
"references",
"history"
] |
727 | 2 | null | 726 | 95 | null | >
The combination of some data and an
aching desire for an answer does not
ensure that a reasonable answer can be
extracted from a given body of data
Tukey
| null | CC BY-SA 2.5 | null | 2010-07-27T06:23:57.233 | 2010-07-27T06:23:57.233 | null | null | 223 | null |
728 | 2 | null | 726 | 49 | null | >
All we know about the world teaches us that the effects of A and B are always different---in some decimal place---for any A and B. Thus asking "are the effects different?" is foolish.
Tukey (again but this one is my favorite)
| null | CC BY-SA 2.5 | null | 2010-07-27T06:26:04.483 | 2010-07-27T06:42:07.557 | 2010-07-27T06:42:07.557 | 159 | 223 | null |
729 | 2 | null | 726 | 138 | null | >
In God we trust. All others must bring
data.
(W. Edwards Deming)
| null | CC BY-SA 2.5 | null | 2010-07-27T06:36:26.867 | 2010-07-31T00:19:53.500 | 2010-07-31T00:19:53.500 | 461 | 159 | null |
730 | 2 | null | 726 | 277 | null | >
All models are wrong, but some are useful. (George E. P. Box)
Reference: Box & Draper (1987), Empirical model-building and response surfaces, Wiley, p. 424.
Also: G.E.P. Box (1979), "Robustness in the Strategy of Scientific Model Building" in Robustness in Statistics (Launer & Wilkinson eds.), p. 202.
| null | CC BY-SA 3.0 | null | 2010-07-27T06:37:30.157 | 2016-07-09T13:19:32.007 | 2016-07-09T13:19:32.007 | null | 159 | null |
731 | 2 | null | 645 | 4 | null | If you're looking at system faults, you might be interested in the following paper employing machine learning techniques for fault diagnosis at eBay. It may give you a sense of what kind of data to collect or how one team approached a specific problem in a similar domain.
- Fault Diagnosis Using Decision Trees
If y... | null | CC BY-SA 2.5 | null | 2010-07-27T06:45:31.107 | 2010-07-27T06:45:31.107 | null | null | 251 | null |
732 | 2 | null | 726 | 80 | null | >
Strange events permit themselves the
luxury of occurring.
-- [Charlie Chan](http://gutenberg.net.au/ebooks02/0200691.txt)
| null | CC BY-SA 2.5 | null | 2010-07-27T06:51:24.730 | 2010-08-07T10:08:04.317 | 2010-08-07T10:08:04.317 | 380 | 251 | null |
733 | 2 | null | 723 | 5 | null | There is something like [silhouette](http://en.wikipedia.org/wiki/Silhouette_%28clustering%29), which to some extent defines statistic that determines the cluster quality (for instance it is used in optimizing k). Now a possible Monte Carlo would go as follows: you generate a lot of random dataset similar to your origi... | null | CC BY-SA 2.5 | null | 2010-07-27T06:52:09.080 | 2010-07-27T06:52:09.080 | null | null | null | null |
734 | 2 | null | 715 | 2 | null | Maybe the good old [iris](http://archive.ics.uci.edu/ml/datasets/Iris)? It suits your needs and is good for start.
| null | CC BY-SA 2.5 | null | 2010-07-27T06:55:06.470 | 2010-07-27T06:55:06.470 | null | null | null | null |
735 | 2 | null | 645 | 0 | null | The one thing [ROOT](http://root.cern.ch) is really good at is storing enourmous amounts of data. ROOT is a C++ library used in particle physics; it also comes with Ruby and Python bindings, so you could use packages in these languages (e.g. NumPy or Scipy) to analyze the data when you find that ROOT offers to few poss... | null | CC BY-SA 2.5 | null | 2010-07-27T07:19:13.377 | 2010-07-27T07:19:13.377 | null | null | 56 | null |
736 | 2 | null | 73 | 4 | null | zoo and xts are a must in my work!
| null | CC BY-SA 2.5 | null | 2010-07-27T07:24:19.963 | 2010-07-27T07:24:19.963 | null | null | 300 | null |
737 | 2 | null | 726 | 89 | null | >
There are no routine statistical
questions, only questionable
statistical routines.
D.R. Cox
| null | CC BY-SA 2.5 | null | 2010-07-27T07:26:34.187 | 2010-08-07T10:09:08.980 | 2010-08-07T10:09:08.980 | 380 | null | null |
738 | 2 | null | 726 | 58 | null | >
Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable.
-Bobby Bragan, 1963
| null | CC BY-SA 2.5 | null | 2010-07-27T07:38:19.743 | 2010-12-03T04:00:23.143 | 2010-12-03T04:00:23.143 | 795 | 188 | null |
739 | 2 | null | 726 | 153 | null | >
"To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of."
-- Ronald Fisher (1938)
The quotation can be read on page 17 of the article.
R. A. Fisher. Presidential Address by Professor R. A. Fishe... | null | CC BY-SA 3.0 | null | 2010-07-27T07:41:56.333 | 2015-07-26T21:54:46.423 | 2015-07-26T21:54:46.423 | 919 | null | null |
741 | 2 | null | 692 | 5 | null | The basic way to see if your data is Weibull is to [plot](http://www.itl.nist.gov/div898/handbook/eda/section3/weibplot.htm) the log of cumulative hazards versus log of times and see if a straight line might be a good fit. The cumulative hazard can be found using the non-parametric Nelson-Aalen estimator. There are s... | null | CC BY-SA 2.5 | null | 2010-07-27T07:56:42.460 | 2010-07-27T09:45:01.060 | 2010-07-27T09:45:01.060 | 251 | 251 | null |
743 | 1 | 839 | null | 5 | 874 | I was having a look round a few things yesturday and came across [Bayesian Search Theory](http://en.wikipedia.org/wiki/Bayesian_search_theory). Thinking about this theory led me to think about a problem I was working on a few years ago regarding geological interpretation.
We were looking at the geology at one specific... | Use of Bayesian Search Theory in geological interpretation | CC BY-SA 2.5 | null | 2010-07-27T08:13:26.197 | 2010-07-27T22:33:05.287 | null | null | 210 | [
"bayesian",
"search-theory"
] |
744 | 2 | null | 726 | 232 | null | >
"An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem." -- John Tukey
| null | CC BY-SA 2.5 | null | 2010-07-27T08:42:23.037 | 2010-07-28T06:55:37.570 | 2010-07-28T06:55:37.570 | 159 | 319 | null |
745 | 2 | null | 73 | 3 | null | I use
car, doBy, Epi, ggplot2, gregmisc (gdata, gmodels, gplots, gtools), Hmisc, plyr, RCurl, RDCOMClient, reshape, RODBC, TeachingDemos, XML.
a lot.
| null | CC BY-SA 2.5 | null | 2010-07-27T08:42:25.907 | 2010-07-27T08:42:25.907 | null | null | null | null |
746 | 2 | null | 726 | 10 | null | >
efficiency = statistical efficiency x usage.
-- John Tukey
| null | CC BY-SA 2.5 | null | 2010-07-27T08:43:15.033 | 2010-12-03T04:03:20.240 | 2010-12-03T04:03:20.240 | 795 | 319 | null |
747 | 2 | null | 73 | 6 | null | Packages I often use are [raster](http://cran.r-project.org/web/packages/raster/index.html), [sp](http://cran.r-project.org/web/packages/sp/index.html), [spatstat](http://cran.r-project.org/web/packages/spatstat/index.html), [vegan](http://cran.r-project.org/package=vegan) and [splancs](http://cran.r-project.org/web/pa... | null | CC BY-SA 2.5 | null | 2010-07-27T09:00:42.587 | 2010-07-27T09:00:42.587 | null | null | 144 | null |
748 | 2 | null | 726 | 103 | null | >
A big computer, a complex algorithm and a long time does not equal science.
-- Robert Gentleman
| null | CC BY-SA 2.5 | null | 2010-07-27T09:09:25.517 | 2010-10-02T17:09:45.723 | 2010-10-02T17:09:45.723 | 795 | 434 | null |
749 | 2 | null | 723 | 15 | null | Regarding shopping cart analysis, I think that the main objective is to individuate the most frequent combinations of products bought by the customers. The `association rules` represent the most natural methodology here (indeed they were actually developed for this purpose). Analysing the combinations of products bough... | null | CC BY-SA 2.5 | null | 2010-07-27T09:10:20.163 | 2010-07-27T14:44:54.903 | 2010-07-27T14:44:54.903 | 339 | 339 | null |
750 | 2 | null | 726 | 22 | null | >
There are three kinds of lies: lies,
damned lies, and statistics.
-- [probably: Charles Wentworth Dilke (1843–1911).](https://secure.wikimedia.org/wikipedia/en/wiki/Lies,_damned_lies,_and_statistics)
| null | CC BY-SA 2.5 | null | 2010-07-27T09:16:48.383 | 2010-07-27T09:16:48.383 | null | null | 127 | null |
751 | 2 | null | 726 | 5 | null | >
The Median Isn't the Message
--[Stephen Jay Gould](http://cancerguide.org/median_not_msg.html)
| null | CC BY-SA 2.5 | null | 2010-07-27T09:19:53.223 | 2010-07-27T09:19:53.223 | null | null | 127 | null |
752 | 2 | null | 726 | 35 | null | >
Figures don't lie, but liars do figure
--Mark Twain
| null | CC BY-SA 2.5 | null | 2010-07-27T09:20:27.967 | 2010-07-27T09:20:27.967 | null | null | 127 | null |
753 | 2 | null | 726 | 130 | null | >
Statistics are like bikinis. What
they reveal is suggestive, but what
they conceal is vital.
-Aaron Levenstein
| null | CC BY-SA 3.0 | null | 2010-07-27T09:22:04.753 | 2013-03-26T15:44:51.407 | 2013-03-26T15:44:51.407 | 603 | 127 | null |
754 | 2 | null | 726 | 5 | null | >
The mathematician, carried along on his flood of symbols, dealing apparently with purely formal thruths, may still reach results of endless importance for our description of physical universe
-- Karl Pearson
| null | CC BY-SA 2.5 | null | 2010-07-27T09:25:00.340 | 2010-10-02T17:11:34.173 | 2010-10-02T17:11:34.173 | 795 | 223 | null |
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