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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
755 | 2 | null | 726 | 56 | null | I don't know about famous, but the following is one of my favourites:
>
Conducting data analysis is like
drinking a fine wine. It is important
to swirl and sniff the wine, to unpack
the complex bouquet and to appreciate
the experience. Gulping the wine
doesn’t work.
-Daniel B. Wright (2003), see [PDF of Ar... | null | CC BY-SA 3.0 | null | 2010-07-27T09:44:12.930 | 2013-02-25T00:40:34.507 | 2013-02-25T00:40:34.507 | 183 | 183 | null |
757 | 2 | null | 73 | 4 | null | I find [lattice](http://cran.r-project.org/web/packages/lattice/index.html) along with the companion book "Lattice: Multivariate Data Visualization with R" by Deepayan Sarkar invaluable.
| null | CC BY-SA 2.5 | null | 2010-07-27T09:51:19.900 | 2010-07-27T09:51:19.900 | null | null | 439 | null |
758 | 2 | null | 723 | 9 | null | This is a method using Monte Carlo to show whether a result is correct.
Our Null Hypothesis H_0 is that our dataset does not have an interesting clustering. Our alternative hypothesis H_1 is that our dataset contains an interesting clustering.
Hereby we think of interesting as, more interesting than the clustering stru... | null | CC BY-SA 2.5 | null | 2010-07-27T10:00:23.720 | 2010-07-27T10:00:23.720 | null | null | 190 | null |
759 | 2 | null | 725 | 3 | null | I am afraid there is no; during my little adventure with such data we have just converted it to a data frame form, added some extra attributes made from neighborhoods of pixels and used standard methods. Still, packages [ripa](http://cran.r-project.org/web/packages/ripa/index.html) and [hyperSpec](http://cran.r-project... | null | CC BY-SA 2.5 | null | 2010-07-27T10:07:42.560 | 2010-07-27T10:07:42.560 | null | null | null | null |
760 | 2 | null | 575 | 22 | null | I am currently writing a paper in which I have the pleasure to conduct both between and within subjects comparisons. After discussion with my supervisor we decided to run t-tests and use the pretty simple `Holm-Bonferroni method` ([wikipedia](http://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method)) for correcting ... | null | CC BY-SA 2.5 | null | 2010-07-27T10:24:44.870 | 2010-07-27T13:08:54.103 | 2010-07-27T13:08:54.103 | 442 | 442 | null |
761 | 2 | null | 665 | 21 | null | I like this from Steve Skienna's [Calculated Bets](http://books.google.com/books?id=UvWGgaE4ZA8C&lpg=PA86&ots=RXQyDddYcu&dq=In%20summary%2C%20probability%20theory%20enables%20us%20to%20find%20the%20consequences%20of%20a%20given%20ideal%20world%2C%20while%20statistical%20theory%20enables%20us%20to%20to%20measure%20the%2... | null | CC BY-SA 2.5 | null | 2010-07-27T10:26:36.600 | 2010-07-27T10:26:36.600 | null | null | 251 | null |
762 | 2 | null | 575 | 7 | null | Have a look at the [multcomp](http://cran.r-project.org/web/packages/multcomp/index.html)-package and its vignette [Simultaneous Inference in General Parametric Models](http://cran.r-project.org/web/packages/multcomp/vignettes/generalsiminf.pdf). I think it should do what wan't and the vignette has very good examples a... | null | CC BY-SA 2.5 | null | 2010-07-27T11:04:03.853 | 2010-07-27T11:04:03.853 | null | null | 214 | null |
763 | 2 | null | 562 | 11 | null | The final year of the NetFlix Prize competition (2009) seemed to me to have sharply changed the general community-wide presumption against combining multiple learning algorithms.
For instance, my formal training (university courses) and later on-the-job oversight/mentoring taught us to avoid algorithm combination unle... | null | CC BY-SA 2.5 | null | 2010-07-27T11:04:08.207 | 2010-08-04T07:39:52.463 | 2010-08-04T07:39:52.463 | 438 | 438 | null |
764 | 1 | 772 | null | 16 | 6585 | I have commonly heard that LME models are more sound in the analysis of accuracy data (i.e., in psychology experiments), in that they can work with binomial and other non-normal distributions that traditional approaches (e.g., ANOVA) can't.
What is the mathematical basis of LME models that allow them to incorporate the... | Linear Mixed Effects Models | CC BY-SA 2.5 | null | 2010-07-27T11:29:20.193 | 2010-08-07T17:52:12.747 | 2010-08-07T17:52:12.747 | null | 445 | [
"mixed-model"
] |
765 | 2 | null | 283 | 17 | null | A model is saturated if and only if it has as many parameters as it has data points (observations). Or put otherwise, in non-saturated models the degrees of freedom are bigger than zero.
This basically means that this model is useless, because it does not describe the data more parsimoniously than the raw data does (an... | null | CC BY-SA 2.5 | null | 2010-07-27T11:56:42.250 | 2010-07-27T12:32:15.423 | 2010-07-27T12:32:15.423 | 442 | 442 | null |
766 | 2 | null | 270 | 3 | null | Most statistical packages have a function to calculate the natural logarithm of the factorial directly (e.g. the lfactorial() function in R, the lnfactorial() function in Stata). This allows you to include the constant term in the log-likelihood if you want.
| null | CC BY-SA 2.5 | null | 2010-07-27T12:10:34.937 | 2010-07-27T12:10:34.937 | null | null | 449 | null |
767 | 2 | null | 577 | 235 | null | Your question implies that AIC and BIC try to answer the same question, which is not true. The AIC tries to select the model that most adequately describes an unknown, high dimensional reality. This means that reality is never in the set of candidate models that are being considered. On the contrary, BIC tries to find... | null | CC BY-SA 4.0 | null | 2010-07-27T12:31:57.280 | 2019-08-26T17:08:49.033 | 2019-08-26T17:08:49.033 | 7290 | 447 | null |
768 | 2 | null | 665 | 5 | null | In probability theory, we are given random variables X1, X2, ... in some way, and then we study their properties, i.e. calculate probability P{ X1 \in B1 }, study the convergence of X1, X2, ... etc.
In mathematical statistics, we are given n realizations of some random variable X, and set of distributions D; the proble... | null | CC BY-SA 2.5 | null | 2010-07-27T12:36:47.457 | 2010-07-27T12:36:47.457 | null | null | null | null |
769 | 1 | null | null | 2 | 585 | When does data analysis cease to be statistics ?
Are the following examples all applications of statistics ?: computer vision, face recognition, compressed sensing, lossy data compression, signal processing.
| What types of data analysis do not count as statistics? | CC BY-SA 2.5 | null | 2010-07-27T12:46:48.420 | 2010-07-27T16:00:22.087 | null | null | 327 | [
"mathematical-statistics"
] |
770 | 1 | null | null | 6 | 345 | During every machine learning tutorial you'll find, there is the common "You will need to know x amount of stats before starting this tutorial". As such, using your knowledge of stats, you will learn about machine learning.
My question is whether this can be reversed. Can a computer science student learn statistics t... | Is it possible to use machine learning as a method for learning stats, rather than vice-versa? | CC BY-SA 2.5 | null | 2010-07-27T12:53:30.250 | 2011-02-14T23:04:02.267 | 2010-09-17T20:31:31.940 | null | 453 | [
"machine-learning",
"teaching"
] |
771 | 2 | null | 770 | -1 | null | I think that learning machine learning requires only an elementary subset of statistics; too much may be dangerous, since some intuitions are in conflict. Still, the answer to the question can it be reversed is no.
| null | CC BY-SA 2.5 | null | 2010-07-27T13:02:58.047 | 2010-07-27T13:02:58.047 | null | null | null | null |
772 | 2 | null | 764 | 16 | null | One major benefit of mixed-effects models is that they don't assume independence amongst observations, and there can be a correlated observations within a unit or cluster.
This is covered concisely in "Modern Applied Statistics with S" (MASS) in the first section of chapter 10 on "Random and Mixed Effects". V&R walk t... | null | CC BY-SA 2.5 | null | 2010-07-27T13:07:38.643 | 2010-07-27T13:07:38.643 | null | null | 5 | null |
773 | 2 | null | 283 | 20 | null | As everybody else said before, it means that you have as much parameters have you have data points. So, no goodness of fit testing. But this does not mean that "by definition", the model can perfectly fit any data point. I can tell you by personal experience of working with some saturated models that could not predict ... | null | CC BY-SA 2.5 | null | 2010-07-27T13:09:54.817 | 2010-07-27T13:09:54.817 | null | null | 447 | null |
774 | 2 | null | 770 | 8 | null | I really wouldn't suggest using machine learning in order to learn statistics. The mathematics employed in machine learning is often different because there's a real emphasis on the computational algorithm. Even treatment of the same concept will be different.
A simple example of this would be to compare the treatm... | null | CC BY-SA 2.5 | null | 2010-07-27T13:14:19.160 | 2010-07-27T13:14:19.160 | null | null | 5 | null |
775 | 1 | 777 | null | 10 | 8324 | What is the difference between operations research and statistical analysis?
| Operations research versus statistical analysis? | CC BY-SA 3.0 | null | 2010-07-27T13:14:54.837 | 2014-02-04T05:06:46.563 | 2012-02-23T10:41:34.117 | null | 460 | [
"operations-research"
] |
776 | 2 | null | 769 | 3 | null | This may be slightly controversial, but certainly most of the examples that you give are not statistics (some of these would more properly fall under machine learning):
Statistics usually covers the scenario where you are making inferences something when you only have a subset of the data (hence the use of things like ... | null | CC BY-SA 2.5 | null | 2010-07-27T13:25:13.050 | 2010-07-27T13:25:13.050 | 2017-04-13T12:44:24.667 | -1 | 5 | null |
777 | 2 | null | 775 | 13 | null | Those are entire academic discplines so I do not think you can expect much more here than pointers to further, and more extensive, documentation as e.g. Wikipedia on [Operations Research](http://en.wikipedia.org/wiki/Operations_research) and [Statistics](http://en.wikipedia.org/wiki/Statistics).
Let me try a personal d... | null | CC BY-SA 2.5 | null | 2010-07-27T13:35:21.463 | 2010-07-27T13:35:21.463 | null | null | 334 | null |
778 | 2 | null | 672 | 15 | null | I'm sorry, but there seems to be some confusion here:
Bayes' theorem is not up for discussion of the neverending Bayesian-[Frequentist](http://en.wikipedia.org/wiki/Probability_interpretations#Frequentism) debate. It is a theorem that is consistent with both schools of thought (given that it is consistent with Kolmogo... | null | CC BY-SA 2.5 | null | 2010-07-27T13:39:35.543 | 2011-02-02T19:09:56.450 | 2011-02-02T19:09:56.450 | 509 | 447 | null |
779 | 1 | null | null | 9 | 3152 | I've heard that a lot of quantities that occur in nature are normally distributed. This is typically justified using the central limit theorem, which says that when you average a large number of iid random variables, you get a normal distribution. So, for instance, a trait that is determined by the additive effect of a... | Normal distribution and monotonic transformations | CC BY-SA 2.5 | null | 2010-07-27T13:49:41.073 | 2013-03-16T00:54:30.960 | 2013-03-16T00:54:30.960 | 805 | 463 | [
"data-transformation",
"normality-assumption"
] |
780 | 2 | null | 775 | 9 | null | Operations Research (OR), sometimes called "Management Science", consists of three main topics, Optimization, Stochastic Processes, Process and Production Methodologies.
OR uses statistical analysis in many contexts (for example discrete event simulations) but they should not be considered the same, additionally one o... | null | CC BY-SA 2.5 | null | 2010-07-27T13:55:18.623 | 2010-07-27T19:10:25.243 | 2010-07-27T19:10:25.243 | 172 | 172 | null |
781 | 2 | null | 712 | 3 | null | One approach would be to use Bloom filters. Check [SAFELINK](http://www.uni-due.de/soziologie/schnell_forschung_safelink_software.php) project website for [programs](http://www.uni-due.de/methods/bloom/index-en-bloom.html) in Java and Python. Paper explaining method is [here](http://dx.doi.org/10.1186/1472-6947-9-41). ... | null | CC BY-SA 3.0 | null | 2010-07-27T13:59:02.623 | 2011-04-30T18:31:33.180 | 2011-04-30T18:31:33.180 | 22 | 22 | null |
782 | 2 | null | 779 | 4 | null | I think you missunderstood (half of) the use statistician make of the normal distribution but I really like your question.
I don't think it is a good idea to assume systematically normality and I admit it is done sometime (maybe because the normal distribution is tractable, unimodal ...) without verification. Hence y... | null | CC BY-SA 2.5 | null | 2010-07-27T14:02:37.327 | 2010-07-27T15:44:37.567 | 2010-07-27T15:44:37.567 | 223 | 223 | null |
783 | 2 | null | 726 | 53 | null | >
... surely, God loves the .06 nearly as much as the .05. Can there be any
doubt that God views the strength of evidence for or against the null as a
fairly continuous function of the magnitude of p? (p.1277)
Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychol... | null | CC BY-SA 4.0 | null | 2010-07-27T14:03:52.940 | 2022-11-23T09:59:13.570 | 2022-11-23T09:59:13.570 | 362671 | 442 | null |
784 | 2 | null | 614 | 5 | null | [Street-Fighting Mathematics](https://mitpress.mit.edu/books/street-fighting-mathematics). The Art of Educated Guessing and Opportunistic Problem Solving by Sanjoy Mahajan from MIT. Available under a Creative Commons Noncommercial Share Alike license.
Available as a free download on the MIT Press website (but not from ... | null | CC BY-SA 3.0 | null | 2010-07-27T14:04:13.360 | 2016-10-06T19:41:41.493 | 2016-10-06T19:41:41.493 | 122650 | 22 | null |
785 | 2 | null | 726 | 25 | null | >
The statistician cannot evade the responsibility for understanding the
process he applies or recommends.
-– Sir Ronald A. Fisher
| null | CC BY-SA 2.5 | null | 2010-07-27T14:10:31.507 | 2010-12-03T04:02:46.950 | 2010-12-03T04:02:46.950 | 795 | 447 | null |
786 | 2 | null | 726 | 31 | null | >
The death of one man is a tragedy.
The death of millions is a statistic.
-- Kurt Tucholsky, in: Französischer Witz, 1925
| null | CC BY-SA 3.0 | null | 2010-07-27T14:14:10.623 | 2015-12-05T15:54:52.310 | 2015-12-05T15:54:52.310 | 36678 | 460 | null |
787 | 2 | null | 726 | 6 | null | >
Statistics are the triumph of the
quantitative method, and the
quantitative method is the victory of
sterility and death.
~ Hillaire Belloc in The Silence of the Sea
| null | CC BY-SA 2.5 | null | 2010-07-27T14:20:42.650 | 2010-07-27T14:20:42.650 | null | null | 460 | null |
788 | 2 | null | 726 | 2 | null | >
A witty statesman said, you might
prove anything by figures.
~ Thomas Carlyle, Chartism (1839) ch. 2
| null | CC BY-SA 2.5 | null | 2010-07-27T14:25:39.673 | 2010-07-27T14:25:39.673 | null | null | 460 | null |
789 | 2 | null | 614 | 5 | null | [Statistical Analysis with the General Linear Model](http://psy.otago.ac.nz/miller/index.htm#GLMBook)
It covers basic linear models (ANOVA, ANCOVA, multiple regression). I can tell by personal experience that it is really really good book to get into the general framework of linear models, which are very useful in many... | null | CC BY-SA 3.0 | null | 2010-07-27T14:38:34.183 | 2014-06-20T06:34:29.530 | 2014-06-20T06:34:29.530 | 930 | 447 | null |
790 | 1 | null | null | 4 | 6855 | I am looking at a scientific paper in which a single measurement is calculated using a logarithmic mean
>
'triplicate spots were combined to
produce one signal by taking the
logarithmic mean of reliable spots'
Why choose the log-mean?
Are the authors making an assumption about the underlying distribution?
That ... | Why (or when) to use the log-mean? | CC BY-SA 2.5 | null | 2010-07-27T14:46:49.640 | 2022-02-11T14:00:06.143 | 2022-02-11T14:00:06.143 | 11887 | 228 | [
"distributions",
"mean",
"logarithm",
"geometric-mean"
] |
791 | 2 | null | 770 | 3 | null | I really dont think so, as there are fundamental aspects in statistics that are simply overlooked in machine learning. For instance, in statistics, when fitting a model to data, the discrpeancy function that is used (e.g., G^2, RMSEA) is essential because they have different statistical properties. In machine learning,... | null | CC BY-SA 2.5 | null | 2010-07-27T14:52:19.113 | 2010-07-27T14:52:19.113 | null | null | 447 | null |
792 | 2 | null | 779 | 4 | null | Simply CLT (nor any other theorem) does not state that every quantity in the universe is normally distributed. Indeed, statisticians often use monotonic transformations to improve normality, so they could use their favorite tools.
| null | CC BY-SA 2.5 | null | 2010-07-27T15:03:28.010 | 2010-07-27T15:03:28.010 | null | null | null | null |
793 | 2 | null | 790 | 3 | null | >
Are the authors making an assumption
about the underlying distribution?
You are making an assumption whether you choose to use it or whether you choose against using it.
For Power Law distributions it usually makes sense to look at the logarithms.
| null | CC BY-SA 2.5 | null | 2010-07-27T15:04:58.067 | 2010-07-27T15:04:58.067 | null | null | 3807 | null |
794 | 2 | null | 726 | 45 | null | >
The subjectivist (i.e. Bayesian)
states his judgements, whereas the
objectivist sweeps them under the
carpet by calling assumptions
knowledge, and he basks in the
glorious objectivity of science.
I.J. Good
| null | CC BY-SA 2.5 | null | 2010-07-27T15:05:38.800 | 2010-07-27T15:05:38.800 | null | 7620 | 7620 | null |
795 | 1 | null | null | -3 | 2133 | I have data compiled by someone else where score averages have been computed over time- averages range from 0-100. The original scores have negative values in many cases and the average would have been negative also, raw average ranges from -30 to 90. How is this 'normalization' accomplished?
Thanks
| Normalization of series | CC BY-SA 2.5 | null | 2010-07-27T15:08:11.493 | 2010-07-27T15:36:41.480 | null | null | 474 | [
"mean"
] |
796 | 2 | null | 305 | 38 | null | I would like to oppose the other two answers based on a paper (in German) by [Kubinger, Rasch and Moder (2009)](http://www.psycontent.com/content/t5726m72644gq457/).
They argue, based on "extensive" simulations from distributions either meeting or not meeting the assumptions imposed by a t-test, (normality and homogeni... | null | CC BY-SA 2.5 | null | 2010-07-27T15:14:22.937 | 2010-10-19T15:28:42.237 | 2010-10-19T15:28:42.237 | 442 | 442 | null |
797 | 2 | null | 779 | 6 | null | Very good question. I feel that the answer depends on the whether you can identify the underlying process that gives rise to the measurement in question. If for example, you have evidence that height is a linear combination of several factors (e.g., height of parents, height of grandparents etc) then it would be natura... | null | CC BY-SA 2.5 | null | 2010-07-27T15:19:43.983 | 2010-07-27T15:25:00.930 | 2017-04-13T12:44:37.420 | -1 | null | null |
798 | 1 | 862 | null | 118 | 265258 | I'm interested in finding as optimal of a method as I can for determining how many bins I should use in a histogram. My data should range from 30 to 350 objects at most, and in particular I'm trying to apply thresholding (like Otsu's method) where "good" objects, which I should have fewer of and should be more spread o... | Calculating optimal number of bins in a histogram | CC BY-SA 3.0 | null | 2010-07-27T15:21:48.417 | 2022-11-17T19:53:13.150 | 2016-06-03T00:57:01.100 | 28666 | 476 | [
"rule-of-thumb",
"histogram"
] |
799 | 2 | null | 764 | 9 | null | The main advantage of LME for analysing accuracy data is the ability to account for a series of random effects. In psychology experiments, researchers usually aggregate items and/or participants. Not only are people different from each other, but items also differ (some words might be more distinctive or memorable, for... | null | CC BY-SA 2.5 | null | 2010-07-27T15:26:34.537 | 2010-07-27T15:26:34.537 | null | null | 447 | null |
800 | 2 | null | 795 | 1 | null | The question is a bit unclear. But, perhaps the normalization you are looking for is this:
Normalized Score = 100 * (Raw Score - min(Raw_score)) / (max(Raw Score) - min(Raw Score))
| null | CC BY-SA 2.5 | null | 2010-07-27T15:27:18.890 | 2010-07-27T15:27:18.890 | null | null | null | null |
801 | 2 | null | 798 | 6 | null | I'm not sure this counts as strictly good practice, but I tend to produce more than one histogram with different bin widths and pick the histogram which histogram to use based on which histogram fits the interpretation I'm trying to communicate best. Whilst this introduces some subjectivity into the choice of histogram... | null | CC BY-SA 4.0 | null | 2010-07-27T15:30:31.073 | 2020-01-24T17:34:43.113 | 2020-01-24T17:34:43.113 | -1 | 210 | null |
802 | 2 | null | 779 | 5 | null | The rescaling of a particular variable should, when possible, relate to some comprehensible scale for the reason that it helps make the resulting model interpretable. However, the resulting transformation need not absolutely carry a physical significance. Essentially you have to engage in a trade off between the viol... | null | CC BY-SA 2.5 | null | 2010-07-27T15:35:14.460 | 2010-07-27T15:35:14.460 | null | null | 196 | null |
803 | 2 | null | 795 | 4 | null | Or more generically,
`Index = ( ((RawValue - Min(Raw))/(Max(Raw)-Min(Raw)) * (Max(Out)-Min(Out) ) + Min(Out)`
Where `Raw` is the input vector, `Out` is the output vector, and `RawValue` is the value in question. Srikant's answer is the same for an output range of 0 to 100.
To convert back, rearrange to get:
`RawValue ... | null | CC BY-SA 2.5 | null | 2010-07-27T15:36:41.480 | 2010-07-27T15:36:41.480 | null | null | 257 | null |
804 | 2 | null | 305 | 13 | null | Of course, one could ditch both tests, and start using a Bayesian t-test (Savage-Dickey ratio test), which can account for equal and unequal variances, and best of all, it allows for a quantification of evidence in favor of the null hypothesis (which means, no more of old "failure to reject" talk)
This test is very sim... | null | CC BY-SA 4.0 | null | 2010-07-27T15:43:51.857 | 2021-11-06T11:36:47.957 | 2021-11-06T11:36:47.957 | 11887 | 447 | null |
805 | 1 | null | null | 2 | 1738 | I am attempting to calculate the standard error (SE) for the positive predictive value (PPV), negative predictive value (NPV), and diagnostic odds ratio (DOR) that I have obtained using the rates of true positives, false positives, true negatives, and false negatives in a sample. I am able to get 95% CIs but not SE.
T... | How do I calculate the SE for PPV, NPV, and DOR? | CC BY-SA 2.5 | null | 2010-07-27T15:43:55.057 | 2010-08-13T13:35:40.603 | 2010-08-13T13:35:40.603 | 8 | null | [
"error",
"standard-error"
] |
806 | 1 | 829 | null | 6 | 10198 | Lately, there have been numerous questions about [normalization](http://en.wikipedia.org/wiki/Normalization_%28statistics%29)
What are some of the situations where you never ever ever should normalize your data, and what are the alternatives?
| When should normalization never be used? | CC BY-SA 2.5 | null | 2010-07-27T15:56:38.297 | 2010-09-27T23:40:16.273 | null | null | 59 | [
"normality-assumption"
] |
807 | 2 | null | 769 | 1 | null | A statistic is any sample estimation of a population characteristic is it not? Thus, I would suggest so long as inference from a characterized sample to a population is taking place what is occurring is, at least in part, statistics. Under my definition machine learning would be a discipline that makes use of statist... | null | CC BY-SA 2.5 | null | 2010-07-27T16:00:22.087 | 2010-07-27T16:00:22.087 | null | null | 196 | null |
808 | 2 | null | 726 | 10 | null | >
A statistical analysis, properly conducted, is a delicate dissection of uncertainties, a surgery of suppositions.
-- M.J. Moroney
| null | CC BY-SA 2.5 | null | 2010-07-27T16:04:42.707 | 2010-10-02T17:12:07.793 | 2010-10-02T17:12:07.793 | 795 | 482 | null |
809 | 2 | null | 726 | 3 | null | >
An argument over the meaning of words
is a matter of law, an argument
grounded in empirical data and
quantitative estimates is an argument
about science.
~ Razib Khan (though he is not a statistician or famous)
| null | CC BY-SA 2.5 | null | 2010-07-27T16:14:26.303 | 2010-07-27T16:14:26.303 | null | null | null | null |
810 | 2 | null | 798 | 26 | null | If you use too few bins, the histogram doesn't really portray the data very well. If you have too many bins, you get a broken comb look, which also doesn't give a sense of the distribution.
One solution is to create a graph that shows every value. Either a dot plot, or a cumulative frequency distribution, which doesn't... | null | CC BY-SA 3.0 | null | 2010-07-27T16:17:43.867 | 2012-12-23T06:12:04.710 | 2012-12-23T06:12:04.710 | 25 | 25 | null |
811 | 2 | null | 726 | 15 | null | >
It is the mark of a truly intelligent
person to be moved by statistics.
George Bernard Shaw
| null | CC BY-SA 2.5 | null | 2010-07-27T16:18:28.013 | 2010-07-27T16:18:28.013 | null | null | null | null |
812 | 1 | 904 | null | 11 | 3856 | There are a lot of references in the statistic literature to "functional data" (i.e. data that are curves), and in parallel, to "high dimensional data" (i.e. when data are high dimensional vectors). My question is about the difference between the two type of data.
When talking about applied statistic methodologies tha... | What is the difference between functional data analysis and high dimensional data analysis | CC BY-SA 2.5 | null | 2010-07-27T16:19:42.880 | 2019-12-03T03:18:34.650 | 2019-12-03T03:18:34.650 | 11887 | 223 | [
"data-mining",
"curve-fitting",
"signal-processing",
"functional-data-analysis",
"wavelet"
] |
813 | 2 | null | 726 | 97 | null | >
Statistical thinking will one day be
as necessary a qualification for
efficient citizenship as the ability
to read and write.
--H.G. Wells
| null | CC BY-SA 2.5 | null | 2010-07-27T16:23:12.707 | 2010-07-27T16:23:12.707 | null | null | null | null |
814 | 2 | null | 726 | 9 | null | >
These days the statistician is often
asked such questions as "Are you a
Bayesian?" "Are you a frequentist?"
"Are you a data analyst?" "Are you a
designer of experiments?". I will
argue that the appropriate answer to
ALL of these questions can be (and
preferably should be) "yes", and that
we can see w... | null | CC BY-SA 2.5 | null | 2010-07-27T16:26:53.157 | 2010-07-27T16:26:53.157 | null | null | null | null |
815 | 2 | null | 779 | 5 | null | I must admit that I do not really understand your question:
- your raindrops example is not very satisfying since this is not illustrating the fact that the Gaussian behaviour comes from the "average of a large number of iid random variables".
- if the quantity $X$ that you are interested in is an average $\frac{Y_1+... | null | CC BY-SA 2.5 | null | 2010-07-27T16:34:22.917 | 2010-07-27T16:34:22.917 | null | null | 368 | null |
816 | 2 | null | 806 | 4 | null | Of course one should never try to blindly normalize data if the data does not follow a (single) normal distribution.
For example one might want to rescale observables $X$ to all be normal with $(X-\mu)/\sigma$, but this can only work if the data is normal and if both $\mu$ and $\sigma$ are the same for all data points ... | null | CC BY-SA 2.5 | null | 2010-07-27T16:34:55.863 | 2010-09-27T23:40:16.273 | 2010-09-27T23:40:16.273 | 159 | 56 | null |
817 | 2 | null | 779 | 5 | null | Vipul, you're not being totally precise in your question.
>
This is typically justified using the
central limit theorem, which says that
when you average a large number of iid
random variables, you get a normal
distribution.
I'm not entirely sure this is what you're saying, but keep in mind that the raindro... | null | CC BY-SA 2.5 | null | 2010-07-27T16:37:40.463 | 2010-07-27T16:37:40.463 | null | null | 62 | null |
818 | 2 | null | 73 | 2 | null | We mostly use:
- ggplot - for charts
- stats
- e1071 - for SVMs
| null | CC BY-SA 2.5 | null | 2010-07-27T16:37:42.413 | 2010-08-09T08:15:05.380 | 2010-08-09T08:15:05.380 | 509 | 480 | null |
819 | 2 | null | 805 | 3 | null | The PPV and NPV are proportions. You know the numerator and denominator, which both are positive integers, so you can calculate the proportion. Your goal, I presume, is to quantify how well you have determined those values. If your sample size is huge, then those values are likely to be very close to their true populat... | null | CC BY-SA 2.5 | null | 2010-07-27T16:40:37.527 | 2010-07-27T16:40:37.527 | 2017-03-09T17:30:36.287 | -1 | 25 | null |
820 | 1 | 822 | null | 0 | 140 | I am building a web application for used book trading and I am adding a feature to propose other book that would be interesting when they view an offer.
Currently the data that I store are the following (they are updated each time someone visit an offer) :
ISBN (book of the offer)
SessId (a unique id that everyone has ... | How can I link item by relevance? | CC BY-SA 2.5 | null | 2010-07-27T16:42:39.070 | 2010-08-07T17:52:01.997 | 2010-08-07T17:52:01.997 | null | 488 | [
"algorithms"
] |
821 | 2 | null | 820 | 1 | null | You probably want to look at [recommender systems](http://en.wikipedia.org/wiki/Recommender_system).
| null | CC BY-SA 2.5 | null | 2010-07-27T16:46:45.040 | 2010-07-27T16:46:45.040 | null | null | null | null |
822 | 2 | null | 820 | 2 | null | There are many,many ways to do this....I'd suggest googling for some search terms to get started, such as "market basket analysis", or having a look at Toby Segaran's "Programming Collective Intelligence" if you know python (even if you don't - it is pretty easy to understand).
| null | CC BY-SA 2.5 | null | 2010-07-27T16:50:45.797 | 2010-07-27T16:50:45.797 | null | null | 247 | null |
823 | 2 | null | 726 | -3 | null | >
Let’s be clear: the work of science
has nothing whatever to do with
consensus. Consensus is the business
of politics. Science, on the contrary,
requires only one investigator who
happens to be right, which means that
he or she has results that are
verifiable by reference to the real
world. In science... | null | CC BY-SA 2.5 | null | 2010-07-27T16:50:51.430 | 2010-07-27T16:50:51.430 | null | null | 25 | null |
824 | 2 | null | 672 | 25 | null | Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those probabilities that reflect the influence of one event on the probability of another.
Simply put, in its most famous form, i... | null | CC BY-SA 2.5 | null | 2010-07-27T16:56:35.273 | 2010-07-27T17:15:57.083 | 2017-03-09T17:30:36.300 | -1 | 39 | null |
825 | 1 | 827 | null | 28 | 9273 | I have R-scripts for reading large amounts of csv data from different files and then perform machine learning tasks such as svm for classification.
Are there any libraries for making use of multiple cores on the server for R.
or
What is most suitable way to achieve that?
| Any suggestions for making R code use multiple processors? | CC BY-SA 2.5 | null | 2010-07-27T17:04:37.710 | 2010-10-14T18:01:00.103 | 2010-10-14T18:01:00.103 | 930 | 480 | [
"r",
"parallel-computing",
"multicore"
] |
826 | 2 | null | 643 | 5 | null | I like to demonstrate sampling variation and essentially the Central Limit Theorem through an "in-class" exercise. Everybody in the class of say 100 students writes their age on a piece of paper. All pieces of paper are the same size and folded in the same fashion after I've calculated the average. This is the populati... | null | CC BY-SA 3.0 | null | 2010-07-27T17:06:17.613 | 2011-06-02T08:09:07.907 | 2011-06-02T08:09:07.907 | 2116 | 215 | null |
827 | 2 | null | 825 | 16 | null | If it's on Linux, then the most straight-forward is [multicore](http://cran.r-project.org/web/packages/multicore/index.html). Beyond that, I suggest having a look at [MPI](http://www.stats.uwo.ca/faculty/yu/Rmpi/) (especially with the [snow](http://cran.r-project.org/web/packages/snow/index.html) package).
More genera... | null | CC BY-SA 2.5 | null | 2010-07-27T17:06:30.563 | 2010-07-27T17:06:30.563 | null | null | 5 | null |
828 | 2 | null | 825 | 5 | null | Shane is correct. Both [multicore](http://cran.r-project.org/package=multicore) and [Rmpi](http://cran.r-project.org/package=Rmpi) are winners.
Slightly broader coverage of the topic is in the [CRAN Task View on High-Performance Computing](http://cran.r-project.org/web/views/HighPerformanceComputing.html). This also l... | null | CC BY-SA 2.5 | null | 2010-07-27T17:12:15.030 | 2010-07-27T17:12:15.030 | null | null | 334 | null |
829 | 2 | null | 806 | 3 | null | Whether one can normalize a non-normal data set depends on the application. For example, data normalization is required for many statistical tests (i.e. calculating a z-score, t-score, etc.) Some tests are more prone to failure when normalizing non-normal data, while some are more resistant ("robust" tests).
One le... | null | CC BY-SA 2.5 | null | 2010-07-27T17:15:37.367 | 2010-07-27T17:15:37.367 | null | null | 491 | null |
830 | 2 | null | 825 | 3 | null | Both Shane and Dirk's responses are spot on.
Nevertheless, you might wanna take a look at a commercial version of R, called [Revolution R](http://www.revolutionanalytics.com/) which is built to deal with big datasets and run on multiple cores. This software is free for academics (which might be your case, I dont know... | null | CC BY-SA 2.5 | null | 2010-07-27T17:18:36.487 | 2010-07-27T17:18:36.487 | null | null | 447 | null |
831 | 2 | null | 790 | 1 | null | In the case of log-normally distributed data .... the geometric mean is a better measure of central tendancy than the arithmetic mean. I mean I would guess they look at the paper and have seen a log-normal distribution.
Spots ... makes me think its referring to probes from a microarray .. in which case they do tend to... | null | CC BY-SA 2.5 | null | 2010-07-27T17:19:27.563 | 2010-07-27T17:19:27.563 | null | null | 494 | null |
832 | 2 | null | 643 | 2 | null | If you use Stata, you can use the -clt- command that creates graphs of sampling distributions, see
[http://www.ats.ucla.edu/stat/stata/ado/teach/clt.htm](http://www.ats.ucla.edu/stat/stata/ado/teach/clt.htm)
| null | CC BY-SA 2.5 | null | 2010-07-27T17:37:35.300 | 2010-07-27T17:37:35.300 | null | null | null | null |
833 | 2 | null | 812 | 18 | null | Yes and no. At the theoretical level, both cases can use similar techniques and frameworks (an excellent example being Gaussian process regression).
The critical difference is the assumptions used to prevent overfitting (regularization):
- In the functional case, there is usually some assumption of smoothness, in othe... | null | CC BY-SA 2.5 | null | 2010-07-27T17:44:35.993 | 2010-07-28T11:37:58.753 | 2010-07-28T11:37:58.753 | 495 | 495 | null |
834 | 1 | null | null | 10 | 13137 | I've heard that AIC can be used to choose among several models (which regressor to use).
But i would like to understand formally what it is in a kind of "advanced undergraduated" level, which I think would be something formal but with intuition arising from the formula.
And is it possible to implement AIC in stata wit... | What is AIC? Looking for a formal but intuitive answer | CC BY-SA 4.0 | null | 2010-07-27T17:46:25.223 | 2019-09-12T13:05:52.073 | 2019-09-12T13:05:52.073 | 11887 | 498 | [
"stata",
"aic",
"intuition"
] |
835 | 2 | null | 726 | 12 | null | [Data is the sword of the 21st century, those who wield it well, the Samurai.](http://googleblog.blogspot.com/2009/02/from-height-of-this-place.html)
| null | CC BY-SA 2.5 | null | 2010-07-27T17:57:48.807 | 2010-07-27T17:57:48.807 | null | null | 62 | null |
836 | 2 | null | 834 | 4 | null | It is a heuristic, and as such, has been subjected to extensive testing. So when to trust it or not is not simple clear-cut and always-true decision.
At a rough approximation, it trades off goodness of fit and number of variables ("degrees of freedom"). Much more, as usual, [at the Wikipedia article about AIC](http://... | null | CC BY-SA 2.5 | null | 2010-07-27T18:14:44.467 | 2010-07-27T18:14:44.467 | null | null | 334 | null |
837 | 1 | 858 | null | 2 | 7702 | I am creating multiple logistic regression models using lrm from Harrell's Design package in R. One model I would like to make is the model with no predictors. For example, I want to predict a constant c such that:
```
logit(Y) ~ c
```
I know I how to compute c (divide the number of "1"s by the total), what I would... | R lrm model with no predictors | CC BY-SA 3.0 | null | 2010-07-27T18:25:40.277 | 2013-08-09T00:48:47.660 | 2013-08-09T00:47:53.890 | 7290 | 501 | [
"r",
"logistic"
] |
838 | 2 | null | 834 | 5 | null | Basically one needs a loss function in order to optimize anything. AIC provides the loss function which when minimized gives a "optimal"* model which fits the given data. The AIC loss function (2k-2*log(L)) tries to formulate the bias variance trade off that every statistical modeler faces when fitting a model to finit... | null | CC BY-SA 2.5 | null | 2010-07-27T18:28:36.730 | 2010-07-27T18:28:36.730 | null | null | 288 | null |
839 | 2 | null | 743 | 5 | null | Though it is not generally labeled as Bayesian search theory, such methods are pretty widely used in oil exploration. There are, however, important differences in the standard examples that drive different features of their respective modeling problems.
In the case of lost vessel exploration (in Bayesian search theo... | null | CC BY-SA 2.5 | null | 2010-07-27T18:33:51.930 | 2010-07-27T18:39:12.480 | 2010-07-27T18:39:12.480 | 39 | 39 | null |
840 | 2 | null | 73 | 2 | null | I work with both R and [MATLAB](http://en.wikipedia.org/wiki/MATLAB) and I use [R.matlab](http://cran.r-project.org/web/packages/R.matlab/index.html) a lot to transfer data between the two.
| null | CC BY-SA 2.5 | null | 2010-07-27T18:33:56.767 | 2010-08-09T08:13:30.967 | 2010-08-09T08:13:30.967 | 509 | 288 | null |
841 | 1 | 844 | null | 17 | 6126 | I have $N$ paired observations ($X_i$, $Y_i$) drawn from a common unknown distribution, which has finite first and second moments, and is symmetric around the mean.
Let $\sigma_X$ the standard deviation of $X$ (unconditional on $Y$), and $\sigma_Y$ the same for Y. I would like to test the hypothesis
$H_0$: $\sigma_X ... | Comparing the variance of paired observations | CC BY-SA 2.5 | null | 2010-07-27T18:38:02.297 | 2011-03-13T20:31:13.040 | 2011-02-10T07:50:44.253 | 223 | 30 | [
"distributions",
"hypothesis-testing",
"standard-deviation",
"normal-distribution"
] |
842 | 2 | null | 825 | 5 | null | I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter optimization or cross-validation, still you must write your own functions for that. Or of course you may run the job for dif... | null | CC BY-SA 2.5 | null | 2010-07-27T18:39:32.153 | 2010-07-27T18:39:32.153 | null | null | null | null |
843 | 2 | null | 798 | 3 | null | If I need to determine the number of bins programmatically I usually start out with a histogram that has way more bins than needed. Once the histogram is filled I then combine bins until I have enough entries per bin for the method I am using, e.g. if I want to model Poisson-uncertainties in a counting experiment with ... | null | CC BY-SA 2.5 | null | 2010-07-27T18:47:25.880 | 2010-07-27T18:47:25.880 | null | null | 56 | null |
844 | 2 | null | 841 | 5 | null | You could use the fact that the [distribution of the sample variance](http://en.wikipedia.org/wiki/Variance#Distribution_of_the_sample_variance) is a chi square distribution centered at the true variance. Under your null hypothesis, your test statistic would be the difference of two chi squared random variates centered... | null | CC BY-SA 2.5 | null | 2010-07-27T18:48:54.927 | 2010-07-27T18:48:54.927 | null | null | null | null |
845 | 2 | null | 841 | 7 | null | If you want to go down the non-parametric route you could always try the squared ranks test.
For the unpaired case, the assumptions for this test (taken from [here](http://www.stat.wvu.edu/~xiawang/courses/stat551/hwk/hw14.pdf)) are:
- Both samples are random samples from their respective populations.
- In addition ... | null | CC BY-SA 2.5 | null | 2010-07-27T19:02:09.860 | 2010-07-28T00:08:20.203 | 2010-07-28T00:08:20.203 | 8 | 8 | null |
846 | 1 | 851 | null | 2 | 553 | I'm working on regression models in STATISTICA application and I need to know what is Fisher-Snedecor distribution for and how to analyze my regression model in this distribution.
What the significance level means? What is v1 and v2? I need an explanation and little tutorial on real data.
| Regression output and Fisher-Snedecor distribution | CC BY-SA 3.0 | null | 2010-07-27T19:10:53.667 | 2014-12-01T17:25:36.057 | 2014-12-01T17:25:36.057 | 503 | 503 | [
"regression"
] |
847 | 1 | null | null | 0 | 148 | Google Website Optimizer (GWO) is a tool provided by Google to do [A/B](http://en.wikipedia.org/wiki/A/B_testing) and [MVT](http://en.wikipedia.org/wiki/Multivariate_testing) experiments on websites.
This has been an unanswered question for a long time so I thought I'd ask it here and see if I can get any help. Here is... | What statistical model is used to calculate the test results for GWO? | CC BY-SA 3.0 | null | 2010-07-27T19:12:53.037 | 2013-09-16T22:37:44.463 | 2013-09-16T22:37:44.463 | 22468 | 500 | [
"computational-statistics",
"model"
] |
848 | 2 | null | 805 | 0 | null | As an addition to Harvey's response if you have a small sample of data then there are alternatives to the standard error approximation formula shown above. I would recommend using software, one example would be to use the `binom.test` function in R, to calculate these confidence intervals rather than doing it "by hand"... | null | CC BY-SA 2.5 | null | 2010-07-27T19:19:46.097 | 2010-07-27T19:19:46.097 | null | null | null | null |
849 | 2 | null | 612 | 29 | null | Principal Components Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar results and PCA is used as the default extraction method in the SPSS Factor Analysis routines. This undoubtedly results in a lot of confusion about the distinction between the two.
The bottom line is... | null | CC BY-SA 2.5 | null | 2010-07-27T19:22:33.407 | 2010-07-27T19:27:45.063 | 2010-07-27T19:27:45.063 | 485 | 485 | null |
850 | 2 | null | 837 | 0 | null | Try creating a dummy variable with so many ones as the number of rows in mtcars and put it formula instead of one.
| null | CC BY-SA 2.5 | null | 2010-07-27T19:28:38.133 | 2010-07-27T19:28:38.133 | null | null | null | null |
851 | 2 | null | 846 | 5 | null | The Fisher Snedecor distribution is another name for the [F-distribution](http://en.wikipedia.org/wiki/F-distribution). The F-distribution comes from the ratio of two chi-squared variables.
In regression, you use the F-distribution in the [ANOVA](http://en.wikipedia.org/wiki/Analysis_of_variance) table. This table give... | null | CC BY-SA 2.5 | null | 2010-07-27T19:34:06.107 | 2010-07-27T19:34:06.107 | null | null | 8 | null |
852 | 1 | null | null | 5 | 1272 | The coefficient on a logged explanatory variable when the dependent variable also is in log form is an elasticity (or the percentage change in the dependent variable if the explanatory variable changes by one percent). Suppose I estimate a regression without logging the dependent variable but I use a log link in a Gen... | Elasticities Using GLM | CC BY-SA 2.5 | null | 2010-07-27T19:52:26.970 | 2010-11-02T04:32:16.943 | 2010-11-02T04:32:16.943 | 159 | null | [
"econometrics",
"generalized-linear-model"
] |
853 | 2 | null | 847 | 1 | null | It sounds like they simply use Choice Modeling. You would use conditional logistic regression to analyze it.
The people from JMP (SAS) have a nice (free) book about Design of Experiments and Choice Modeling.
| null | CC BY-SA 2.5 | null | 2010-07-27T19:55:30.113 | 2010-07-27T19:55:30.113 | null | null | 74 | null |
854 | 2 | null | 192 | 2 | null | I would use Fisher's Exact Test, even for large N. I wouldn't know why not. Any performance argument predates today's fast computers.
| null | CC BY-SA 2.5 | null | 2010-07-27T19:58:44.973 | 2010-07-27T19:58:44.973 | null | null | 506 | null |
855 | 2 | null | 806 | 2 | null | I thought this was too obvious, until I saw this [question](https://stats.stackexchange.com/questions/795/normalization-of-series)!
When you normalise data, make sure you always have access to the raw data after normalisation. Of course, you could break this rule if you have a good reason, e.g. storage.
| null | CC BY-SA 2.5 | null | 2010-07-27T20:05:20.027 | 2010-07-27T20:05:20.027 | 2017-04-13T12:44:52.277 | -1 | 8 | null |
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