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3496 | 1 | null | null | 27 | 14804 | (I'm a newbie at stats. I'm a mathematician and a programmer and I'm trying to build something like a naive Bayesian spam filter.)
I've noticed in many places that people tend to break down the denominator in the equation from Bayes' Theorem. So instead of this:
$\frac{P(A|B)\cdot P(B)}{P(A)}$
We are presented with thi... | Why break down the denominator in Bayes' Theorem? | CC BY-SA 2.5 | null | 2010-10-11T23:45:58.103 | 2010-10-12T08:30:55.770 | null | null | 5793 | [
"bayesian"
] |
3497 | 1 | null | null | 8 | 953 | I have a fairly larege file 100M rows and 30 columns or so on which I would like to run multiple regressions. I have specialized code to run the regressions on the entire file, but what I would like to do is draw random samples from the file and run them in R.
The strategy is:
randomly sample N rows from ... | Doing regressions on samples from a very large file: are the means and SEs of the sample coefficients consistent estimators? | CC BY-SA 2.5 | null | 2010-10-11T23:47:31.373 | 2010-12-19T21:49:54.970 | 2010-10-12T08:23:45.100 | 8 | null | [
"r",
"regression",
"large-data",
"bootstrap"
] |
3500 | 2 | null | 3496 | 9 | null | One reason for using the total probability rule is that we often deal with the component probabilities in that expression and it's straightforward to find the marginal probability by simply plugging in the values. For an illustration of this, see the following example on Wikipedia:
- Bayes' Theorem > Example 1: Drug ... | null | CC BY-SA 2.5 | null | 2010-10-12T01:53:18.930 | 2010-10-12T01:53:18.930 | null | null | 251 | null |
3501 | 2 | null | 3496 | 18 | null | The short answer to your question is, "most of the time we don't know what P(cheese) is, and it is often (relatively) difficult to calculate."
The longer answer why Bayes' Rule/Theorem is normally stated in the way that you wrote is because in Bayesian problems we have - sitting in our lap - a prior distribution (the ... | null | CC BY-SA 2.5 | null | 2010-10-12T02:16:56.347 | 2010-10-12T03:49:44.033 | 2010-10-12T03:49:44.033 | null | null | null |
3502 | 2 | null | 3489 | 5 | null | it sounds as tho the following simplified situation may capture the essence of your problem:
there are two populations of individuals: A = acceptable individuals and U = unacceptables. associated
with each individual is a 'score' $X$. suppose in each of the two populations, the scores have
gaussian distributions, wher... | null | CC BY-SA 2.5 | null | 2010-10-12T03:00:29.283 | 2010-10-12T03:00:29.283 | null | null | 1112 | null |
3503 | 2 | null | 3497 | 2 | null | The greater the sample N, the smaller the standard error (higher t stat, and smaller the respective p values) associated with all your regression coefficients. The greater M, the more datapoints you will have and the smaller will be your standard error of the mean of the coefficients over M runs. Such means should ha... | null | CC BY-SA 2.5 | null | 2010-10-12T04:24:05.447 | 2010-10-12T16:10:17.650 | 2010-10-12T16:10:17.650 | 1329 | 1329 | null |
3504 | 1 | 3506 | null | 5 | 10221 | I measured response variable $Y$ at three levels of factor $A$ and four levels of factor $B$, $n=6$ reps/treatment. Results include
- $A$ has strong effects on $Y$.
- $B$ has no effect on $Y$
- There is no $A*B$ interaction
I would like to report all three results (the second two are actually more interesting than... | If an ANOVA indicates no main effect and no interaction, should the lack of interaction be stated? | CC BY-SA 2.5 | null | 2010-10-12T04:26:28.497 | 2010-10-12T11:11:42.463 | 2010-10-12T08:34:15.140 | 1381 | 1381 | [
"anova",
"multiple-comparisons",
"interpretation"
] |
3505 | 2 | null | 3504 | 6 | null | Please let me know if you have replicates in your experiment. As you mention using Tukey HSD, I am guessing you don't have any replicates. If you experiment analyzes test of additivity in a two-way factorial Analysis of Variance (ANOVA) with one observation per cell, then please read ahead or otherwise ignore my soluti... | null | CC BY-SA 2.5 | null | 2010-10-12T07:34:16.077 | 2010-10-12T11:11:42.463 | 2010-10-12T11:11:42.463 | 1307 | 1307 | null |
3506 | 2 | null | 3504 | 8 | null | Well, it depends if the interaction was your main hypothesis or not. If this the case, then you are encouraged to report the negative result, otherwise you can simply refit your model (without the B and A:B terms) to get a better estimate of A.
Now, the part of your conclusion that you emphasized doesn't sound correct ... | null | CC BY-SA 2.5 | null | 2010-10-12T07:39:17.673 | 2010-10-12T07:39:17.673 | null | null | 930 | null |
3507 | 2 | null | 3497 | 5 | null | If you can assume that your rows of your data matrix are exchangeable then your modelling strategy should work well. Your method should be fine under the conditions stated by Gaetan Lion before.
The reason why your method will work (given the exchangeability assumption holds) is that it be taken as a special case of p... | null | CC BY-SA 2.5 | null | 2010-10-12T07:49:44.903 | 2010-10-12T07:57:05.120 | 2010-10-12T07:57:05.120 | 1307 | 1307 | null |
3508 | 2 | null | 3489 | 12 | null | Just to add to other responses, here is a brief recap' on terminology.
For any biometric or classification system, the main performance indicator is the [receiver operating characteristic](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) (ROC) curve, which is a plot of true acceptance rate (TAR=1-FRR, th... | null | CC BY-SA 2.5 | null | 2010-10-12T08:19:36.063 | 2010-10-16T09:07:13.943 | 2010-10-16T09:07:13.943 | 930 | 930 | null |
3509 | 2 | null | 3496 | 7 | null | Previous replies are detailed enough, but an intuitive way of looking why $P (A) $ (ie dinominator in the Bayes theorem) is broken into two cases.
It is hard to comment about what is the $P(A)$ without any knowledge whether the email is ham or spam. You are correct that "cheese" appears in spam as well as in ham, but i... | null | CC BY-SA 2.5 | null | 2010-10-12T08:30:55.770 | 2010-10-12T08:30:55.770 | null | null | 1307 | null |
3510 | 2 | null | 3458 | 15 | null | It's important to bear in mind that there's no one algorithm that's always better than others. As stated by Wolpert and Macready, "any two algorithms are equivalent when their performance is averaged across all possible problems." (See [Wikipedia](http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization) f... | null | CC BY-SA 2.5 | null | 2010-10-12T08:56:16.850 | 2010-10-12T08:56:16.850 | null | null | 1436 | null |
3511 | 1 | 3513 | null | 12 | 8881 | I have programmed a logistic regression using the [IRLS algorithm](http://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares). I would like to apply a [LASSO penalization](http://en.wikipedia.org/wiki/Least_squares#LASSO_method) in order to automatically select the right features. At each iteration, the followi... | How to Apply the Iteratively Reweighted Least Squares (IRLS) Method to the LASSO Model? | CC BY-SA 4.0 | null | 2010-10-12T09:01:26.290 | 2019-08-11T13:44:03.553 | 2019-08-11T13:44:03.553 | 6244 | 1351 | [
"logistic",
"generalized-linear-model",
"feature-selection",
"lasso",
"convex"
] |
3512 | 2 | null | 3511 | 5 | null | The LASSO loss function has a discontinuity at zero along each axis, so IRLS is going to have problems with it. I have found a sequential minimal optimisation (SMO) type approach very effective, see e.g.
[http://bioinformatics.oxfordjournals.org/content/19/17/2246](http://bioinformatics.oxfordjournals.org/content/19/1... | null | CC BY-SA 2.5 | null | 2010-10-12T09:53:32.290 | 2010-10-12T09:53:32.290 | null | null | 887 | null |
3513 | 2 | null | 3511 | 13 | null | This problem is typically solved by fit by coordinate descent ([see here](http://www.jstatsoft.org/v33/i01/paper)). This method is both safer more efficient numerically, algorithmically easier to implement and applicable to a more general array of models (also including Cox regression). An R implementation is availab... | null | CC BY-SA 2.5 | null | 2010-10-12T10:47:21.650 | 2010-10-12T10:47:21.650 | null | null | 603 | null |
3514 | 1 | null | null | 6 | 15725 | While most of the tests for normality help for continous variables, is there a way to test the normality assumptions for binary variables.
From what i've read on wiki, the K-S test can be applied for continous variables. How do we perform normality tests for binary (or even categorical variables)?
| What is the normality test for binary data? | CC BY-SA 3.0 | null | 2010-10-12T12:34:08.707 | 2011-07-22T14:35:13.750 | 2011-07-22T14:35:13.750 | null | null | [
"normality-assumption"
] |
3515 | 2 | null | 3514 | 13 | null | There is no such thing as normality of categorical variable. Normal distribution is a continuous distribution so in assumption don't cover categorical output.
| null | CC BY-SA 2.5 | null | 2010-10-12T12:38:31.813 | 2010-10-12T12:38:31.813 | null | null | null | null |
3516 | 1 | null | null | 7 | 4267 | I have a binary variable (which takes values 0,1). I have about 100k records of it. How do I determine if it follows the binomial distribution?
(I'm bascially trying to test for normality. And, if the data is not normal, I might have to apply a transformation to get the variable into a binomial distribution.)
---
He... | Binomial test for a binary variable | CC BY-SA 3.0 | null | 2010-10-12T13:17:55.790 | 2012-05-25T15:33:12.343 | 2012-05-25T15:33:12.343 | 919 | null | [
"binomial-distribution",
"assumptions"
] |
3517 | 2 | null | 3516 | 17 | null | You cannot determine this through a statistical test, for a trivial reason and a profound reason.
The trivial reason is that your data consist of $k$ ones and $n-k$ zeros with $n$ about 100k. These data conform extremely closely to a Bernoulli($k/n$) distribution. No testing is necessary.
The profound reason is that ... | null | CC BY-SA 2.5 | null | 2010-10-12T13:31:01.233 | 2010-10-12T13:31:01.233 | null | null | 919 | null |
3518 | 2 | null | 3516 | 5 | null | I completely agree with @whuber -- just wanted to add:
If you were to try to transform the data. How would you go about doing so? You would map 0 to some number say, -5 and 1 to some other number?, say 5?
So now instead of having:
```
0 0 0 1 0 1 1 0 1 0 1
```
You have:
```
-5 -5 -5 5 -5 5 5 -5 5 -5 5
```
This cann... | null | CC BY-SA 2.5 | null | 2010-10-12T13:40:22.543 | 2010-10-12T13:40:22.543 | null | null | 1499 | null |
3519 | 1 | 3521 | null | 14 | 9891 | I started to do Monte Carlo in R as a hobby, but eventually a financial analyst advised to migrate to Matlab.
I'm an experienced software developer.
but a Monte Carlo beginner.
I want to construct static models with sensitivity analysis, later dynamic models.
Need good libraries/ algorithms that guide me.
To me seems t... | Is Matlab/octave or R better suited for monte carlo simulation? | CC BY-SA 2.5 | null | 2010-10-12T13:58:35.760 | 2011-01-20T21:11:26.137 | null | null | 778 | [
"r",
"matlab",
"monte-carlo"
] |
3520 | 1 | 3523 | null | 63 | 23337 | I see the concept of 'exchangeability' being used in different contexts (e.g., bayesian models) but I have never understood the term very well.
- What does this concept mean?
- Under what circumstances is this concept invoked and why?
| Can someone explain the concept of 'exchangeability'? | CC BY-SA 2.5 | null | 2010-10-12T14:59:04.240 | 2017-11-03T23:18:45.340 | 2017-11-03T23:18:45.340 | 11887 | 1558 | [
"bayesian",
"intuition",
"exchangeability"
] |
3521 | 2 | null | 3519 | 18 | null | I use both. I often prototype functions & algorithms in Matlab because, as stated, it is easier to express an algorithm in something which is close to a pure mathematical language.
R does have excellent libraries. I'm still learning it, but I'm starting to leave Matlab in the dust because once you know R, it's also f... | null | CC BY-SA 2.5 | null | 2010-10-12T15:01:30.577 | 2010-10-12T17:30:48.863 | 2010-10-12T17:30:48.863 | 1499 | 1499 | null |
3523 | 2 | null | 3520 | 71 | null | Exchangeability is meant to capture symmetry in a problem, symmetry in a sense that does not require independence. Formally, a sequence is exchangeable if its joint probability distribution is a symmetric function of its $n$ arguments. Intuitively it means we can swap around, or reorder, variables in the sequence with... | null | CC BY-SA 2.5 | null | 2010-10-12T15:42:19.113 | 2010-10-12T15:42:19.113 | null | null | null | null |
3524 | 2 | null | 3519 | 2 | null | If your simulations will involve relatively sophisticated techniques, then R is the way to go, because it is likely that routines you'll need will be available in R, but not necessarily in matlab.
| null | CC BY-SA 2.5 | null | 2010-10-12T16:10:12.330 | 2010-10-12T16:10:12.330 | null | null | 247 | null |
3525 | 2 | null | 3519 | 9 | null | Although I almost exclusively use `R`, I really admire the profiler in `Matlab`.
When your program is kind of slow you normally want to know where the bottleneck is. Matlab's profiler is a great tool for achieving this as it tells you how much time is spend on each line of the code.
At least to me, using `Rprof` is inc... | null | CC BY-SA 2.5 | null | 2010-10-12T16:44:23.817 | 2010-10-12T16:44:23.817 | null | null | 442 | null |
3526 | 1 | null | null | 10 | 12500 | The Marascuilo procedure as described [here](http://www.itl.nist.gov/div898/handbook/prc/section4/prc474.htm) seems to be a test that addresses the issue of multiple comparisons for proportions when you want to test which specific proportions are different from each other after rejecting the null in an overall chi-squa... | Has anyone used the Marascuilo procedure for comparing multiple proportions? | CC BY-SA 3.0 | null | 2010-10-12T16:53:32.110 | 2013-05-24T18:02:22.337 | 2017-04-13T12:44:45.783 | -1 | null | [
"multiple-comparisons",
"chi-squared-test"
] |
3527 | 2 | null | 3519 | 15 | null | To be honest, I think any question you ask around here about R vs ... will be bias towards R. Remember that R is by far the most used [tag](https://stats.stackexchange.com/tags)!
What I do
My current working practice is to use R to prototype and use C when I need an extra boost of speed. It used to be that I would have... | null | CC BY-SA 2.5 | null | 2010-10-12T17:22:31.393 | 2010-10-12T17:22:31.393 | 2017-04-13T12:44:49.837 | -1 | 8 | null |
3528 | 2 | null | 1874 | 2 | null | I tend to use Gaussian process models for this and similar surface estimation (Possible relevant examples [here](http://www.ece.uvic.ca/~btill/papers/learning/Evans_etal_1993.pdf) and [here](http://www.springerlink.com/content/mv8888524v86043g/)). But perhaps your question would be best asked over on [Stack Overflow?]... | null | CC BY-SA 2.5 | null | 2010-10-12T18:05:37.703 | 2010-10-12T18:11:52.460 | 2017-05-23T12:39:27.620 | -1 | 1499 | null |
3529 | 2 | null | 3476 | 13 | null | ars has the right, and succinct answer. I'll add that when learning how to use matplotlib, I found the [thumbnail gallery](http://matplotlib.sourceforge.net/gallery.html#) to be really useful for finding relevant code and examples.
For your case, I submitted [this boxplot example](http://matplotlib.sourceforge.net/exam... | null | CC BY-SA 2.5 | null | 2010-10-12T18:59:33.793 | 2010-10-12T18:59:33.793 | null | null | 1080 | null |
3530 | 2 | null | 298 | 217 | null | I always hesitate to jump into a thread with as many excellent responses as this, but it strikes me that few of the answers provide any reason to prefer the logarithm to some other transformation that "squashes" the data, such as a root or reciprocal.
Before getting to that, let's recapitulate the wisdom in the existin... | null | CC BY-SA 3.0 | null | 2010-10-12T18:59:34.423 | 2011-10-16T16:27:44.830 | 2011-10-16T16:27:44.830 | 919 | 919 | null |
3531 | 1 | 7011 | null | 6 | 494 | I would like to perform reversible jump on a network model, but before arriving there, I'm wondering if there are any R packages which support reversible jump for a user specified generalized linear model or spatial-GLM?
Something as simple as an RJMCMC procedure (in R) for the selection of predictors in a logistic reg... | Are there any R functions which support Reversible Jump MCMC for a GLM or SGLM? | CC BY-SA 2.5 | null | 2010-10-12T19:54:05.787 | 2011-02-09T04:39:53.350 | 2010-10-12T20:07:43.090 | 8 | 1499 | [
"r",
"bayesian",
"markov-chain-montecarlo"
] |
3532 | 1 | 3535 | null | 15 | 1493 | When programming in R, I've used the [multicore](http://www.rforge.net/doc/packages/multicore/multicore.html) package a few times. However, I've never seen a statement about how it handles it's random numbers. When I use openMP with C, I'm careful to use a proper parallel RNG, but with R I've assume that something sens... | Random numbers and the multicore package | CC BY-SA 2.5 | null | 2010-10-12T20:14:49.203 | 2010-10-14T08:11:42.007 | 2010-10-14T08:11:42.007 | 8 | 8 | [
"r",
"random-generation",
"parallel-computing",
"multicore"
] |
3533 | 2 | null | 298 | 22 | null | For more on whuber's excellent point about reasons to prefer the logarithm to some other transformations such as a root or reciprocal, but focussing on the unique interpretability of the regression coefficients resulting from log-transformation compared to other transformations, see:
Oliver N. Keene. The log transforma... | null | CC BY-SA 4.0 | null | 2010-10-12T20:26:52.947 | 2021-12-09T20:01:45.203 | 2021-12-09T20:01:45.203 | 321901 | 449 | null |
3534 | 2 | null | 3532 | 7 | null | You might want to look at page 5 of this [document](http://cran.r-project.org/web/packages/multicore/multicore.pdf) and of this [document](http://cran.r-project.org/web/packages/doMC/vignettes/gettingstartedMC.pdf). By default, under R, each core sets is own seed (i seem to recall using high precision time).
NB: if yo... | null | CC BY-SA 2.5 | null | 2010-10-12T20:52:14.757 | 2010-10-13T06:40:54.883 | 2010-10-13T06:40:54.883 | 603 | 603 | null |
3535 | 2 | null | 3532 | 8 | null | I'm not sure how the `foreach` works (from the doMC package, I guess), but in multicore if you did something like `mclapply` the `mc.set.seed` parameter defaults to `TRUE` which gives each process a different seed (e.g. `mclapply(1:1000, rnorm)`). I assume your code is translated into something similar, i.e. it boils ... | null | CC BY-SA 2.5 | null | 2010-10-12T20:54:46.470 | 2010-10-12T20:54:46.470 | null | null | 251 | null |
3536 | 2 | null | 3526 | 8 | null | Just a partial answer because I've never heard of this method. From what I read in the link you provided, it seems to be a single-step procedure (much like Bonferroni, except we rework the test statistics instead of the p-value) which is likely to be too conservative.
In R, there is a function `pairwise.prop.test()` wh... | null | CC BY-SA 2.5 | null | 2010-10-12T20:56:39.273 | 2010-10-12T21:23:24.407 | 2010-10-12T21:23:24.407 | 930 | 930 | null |
3537 | 1 | null | null | 1 | 998 | I have 3 factors
factor1 (treatment) with 2 levels (control, stress)
factor2 (Variates) with 12 Levels (Var1, Var2,....Var12)
factor3 (Time) with 12 levels (Week1, Week2,..., Week12)
The treatment has 3 replicates for control and 6 replicates for stress.
Would it be unbalanced Design?
What design would you suggest in ... | Unbalanced repeated measure design for the given data? | CC BY-SA 2.5 | 0 | 2010-10-12T22:33:09.020 | 2011-01-14T19:30:39.253 | 2011-01-14T19:30:39.253 | 449 | null | [
"anova",
"experiment-design",
"split-plot"
] |
3538 | 2 | null | 3537 | 2 | null | Judging by your description- Yes, your design is unbalanced. But you haven't specified what happens with the factor2? or when you say variates - you mean to say that you include co-variates in the model as in ANCOVA. As you say, repeated measures, I am assuming each of the 3 replicates in control and 6 replicates in st... | null | CC BY-SA 2.5 | null | 2010-10-12T22:43:53.553 | 2010-10-13T17:37:12.843 | 2010-10-13T17:37:12.843 | 1307 | 1307 | null |
3539 | 1 | 3546 | null | 30 | 33502 | Which inter-rater reliability methods are most appropriate for ordinal or interval data?
I believe that "Joint probability of agreement" or "Kappa" are designed for nominal data. Whilst "Pearson" and "Spearman" can be used, they are mainly used for two raters (although they can be used for more than two raters).
What o... | Inter-rater reliability for ordinal or interval data | CC BY-SA 2.5 | null | 2010-10-12T22:48:56.690 | 2022-05-12T09:30:29.330 | 2011-07-29T01:01:31.163 | 183 | 1564 | [
"reliability",
"psychometrics",
"agreement-statistics",
"cohens-kappa"
] |
3540 | 2 | null | 3539 | 6 | null | The [Intraclass correlation](http://en.wikipedia.org/wiki/Intra-class_correlation_coefficient) may be used for ordinal data. But there are some caveats, primarily that the raters cannot be distinguished. For more on this and how to choose among different versions of the ICC, see:
- Intraclass correlations: uses in a... | null | CC BY-SA 2.5 | null | 2010-10-12T23:11:53.597 | 2010-10-12T23:11:53.597 | null | null | 251 | null |
3542 | 1 | 3543 | null | 51 | 2701 | I've seen various theoretical treatments of graphics, such as the [Grammar of Graphics](http://rads.stackoverflow.com/amzn/click/0387987746). But I have seen nothing equivalent with regards to tables. Over the while I have developed an informal model of good practice in table design.
However, I'd like to be able to pr... | What is a good resource on table design? | CC BY-SA 2.5 | null | 2010-10-13T01:57:30.580 | 2019-09-11T05:16:40.350 | 2010-11-11T00:44:27.600 | 183 | 183 | [
"tables"
] |
3543 | 2 | null | 3542 | 28 | null | Ed Tufte has a few pages on this in his classic ["The Visual Display of Quantitative Information"](http://rads.stackoverflow.com/amzn/click/0961392142).
For a much more detailed treatment, there is Jane Miller's [Chicago Guide to Writing about Numbers](http://rads.stackoverflow.com/amzn/click/0226526313). I've never se... | null | CC BY-SA 2.5 | null | 2010-10-13T02:26:22.177 | 2010-10-13T02:26:22.177 | null | null | 159 | null |
3544 | 2 | null | 3526 | 3 | null | I would like to see the Marascuilo procedure used more often. Quite frequently I see people calculating the chi-square on a subset of the main table ie two categories at the time but without actually doing the partitioning correctly. The reason why they do it this way as far as iI understood is that they can't bear gro... | null | CC BY-SA 3.0 | null | 2010-10-13T03:14:21.180 | 2013-05-24T18:02:22.337 | 2013-05-24T18:02:22.337 | 805 | 10229 | null |
3545 | 2 | null | 3542 | 15 | null | Stephen Few's book [Show Me the Numbers: Designing Tables and Graphs to Enlighten](http://www.powells.com/biblio/62-9780970601995-0) has a couple of chapters devoted to tabular display of information. It's good and recommended, but it's not quite Grammar of Graphics if that's what you're after.
Update This sounds inte... | null | CC BY-SA 2.5 | null | 2010-10-13T03:34:07.880 | 2010-10-13T03:39:35.697 | 2010-10-13T03:39:35.697 | 251 | 251 | null |
3546 | 2 | null | 3539 | 35 | null | The Kappa ($\kappa$) statistic is a quality index that compares observed agreement between 2 raters on a nominal or ordinal scale with agreement expected by chance alone (as if raters were tossing up). Extensions for the case of multiple raters exist (2, pp. 284–291). In the case of ordinal data, you can use the [weigh... | null | CC BY-SA 4.0 | null | 2010-10-13T06:12:24.867 | 2022-05-12T09:30:29.330 | 2022-05-12T09:30:29.330 | 79696 | 930 | null |
3547 | 2 | null | 3463 | 4 | null | How do you define correlation for non stationary time series? Do you plan to take the correlation of the diff or these time series?
If not, I suggest you look for cointegration rather than correlation (cf Granger etc...)
| null | CC BY-SA 2.5 | null | 2010-10-13T06:37:12.110 | 2010-10-13T06:37:12.110 | null | null | 1709 | null |
3548 | 2 | null | 555 | 10 | null | ANOVA can be used with categorical explanatory variables (factors) that take more than 2 values (levels), and gives a basic test that the mean response is the same for every value. This avoids the regression problem on carrying multiple pairwise t-tests between those levels:
- Multiple t-tests on a fixed 5% significan... | null | CC BY-SA 2.5 | null | 2010-10-13T08:53:32.477 | 2010-10-29T13:26:46.320 | 2010-10-29T13:26:46.320 | 1077 | 1077 | null |
3549 | 1 | 3555 | null | 88 | 82996 | In a multiple linear regression, why is it possible to have a highly significant F statistic (p<.001) but have very high p-values on all the regressor's t tests?
In my model, there are 10 regressors. One has a p-value of 0.1 and the rest are above 0.9
---
For dealing with this problem see the [follow-up question](ht... | Why is it possible to get significant F statistic (p<.001) but non-significant regressor t-tests? | CC BY-SA 3.0 | null | 2010-10-13T09:40:17.420 | 2020-10-17T02:24:14.720 | 2020-10-17T02:24:14.720 | 11887 | 1077 | [
"regression",
"hypothesis-testing",
"t-test",
"multicollinearity",
"faq"
] |
3550 | 2 | null | 1610 | 5 | null | (a bit joke answer I invented just a minute ago)
- A first class person thinks he is always right.
- A second class person thinks he is always wrong.
---
- The first class person can only make a type I error (because sometimes he will be wrong).
- The second class person can only make a type II error (because ... | null | CC BY-SA 2.5 | null | 2010-10-13T10:15:00.153 | 2010-10-13T10:15:00.153 | null | null | 1219 | null |
3551 | 2 | null | 3516 | 1 | null | ALL binary variables have the binomial distribution, provided that the probability of success (probability to observe 1) does not change and that all their instances are independent. A rule of thumb says that binomial distribution can be fairly approximated by normal distribution when n*p>30, with n=number of instances... | null | CC BY-SA 2.5 | null | 2010-10-13T11:07:31.753 | 2010-10-13T11:07:31.753 | null | null | 1219 | null |
3552 | 1 | null | null | 5 | 762 | I've got a model that I've developed in R, but also need to express in SAS. It's a double GLM, that is, I fit both the mean and (log-)variance as linear combinations of the predictors:
$E(Y) = X_1'b_1$
$\log V(Y) = X_2'b_2$
where Y has a normal distribution, $X_1$ and $X_2$ are the vectors of independent variables, and... | Replicating R model in SAS | CC BY-SA 2.5 | null | 2010-10-13T11:31:21.963 | 2010-10-14T03:36:05.547 | 2010-10-13T12:34:00.090 | null | 1569 | [
"r",
"sas"
] |
3553 | 2 | null | 3549 | 42 | null | This happens when the predictors are highly correlated. Imagine a situation where there are only two predictors with very high correlation. Individually, they both also correlate closely with the response variable. Consequently, the F-test has a low p-value (it is saying that the predictors together are highly signific... | null | CC BY-SA 2.5 | null | 2010-10-13T11:45:32.477 | 2010-10-13T11:45:32.477 | null | null | 159 | null |
3554 | 2 | null | 3352 | 1 | null | If $x_1=0$, then $Y$~$(\beta_0,\sigma^2)$ in model 1 and $Y$~$(0,\sigma^2)$ in model 2.
If $x_1=1$, then $Y$~$(\beta_0+\beta_1,\sigma^2)$ in model 1 and $Y$~$(\beta_1,\sigma^2)$ in model 2.
Look for example at the first line: is $\beta_0$ a random variable or is a zero constant?
| null | CC BY-SA 2.5 | null | 2010-10-13T11:46:32.707 | 2010-10-13T11:46:32.707 | null | null | 1219 | null |
3555 | 2 | null | 3549 | 61 | null | As Rob mentions, this occurs when you have highly correlated variables. The standard example I use is predicting weight from shoe size. You can predict weight equally well with the right or left shoe size. But together it doesn't work out.
Brief simulation example
```
RSS = 3:10 #Right shoe size
LSS = rnorm(RSS, RSS, 0... | null | CC BY-SA 2.5 | null | 2010-10-13T12:29:11.170 | 2010-10-13T12:29:11.170 | null | null | 8 | null |
3556 | 1 | null | null | 13 | 4636 | Can anyone report on their experience with an adaptive kernel density estimator?
(There are many synonyms:
adaptive | variable | variable-width, KDE | histogram | interpolator ...)
[Variable kernel density estimation](http://en.wikipedia.org/wiki/Variable_kernel_density_estimation)
says "we vary the width of the kernel... | Adaptive kernel density estimators? | CC BY-SA 2.5 | null | 2010-10-13T14:22:54.863 | 2011-11-26T20:26:33.493 | 2011-01-17T23:59:29.293 | 449 | 557 | [
"kde",
"k-nearest-neighbour"
] |
3557 | 2 | null | 3549 | 10 | null | A keyword to search for would be "collinearity" or "multicollinearity". This can be detected using diagnostics like [Variance Inflation Factors](http://en.wikipedia.org/wiki/Variance_inflation_factor) (VIFs) or methods as described inthe textbook ["Regression Diagnostics: Identifying Influential Data and Sources of Col... | null | CC BY-SA 2.5 | null | 2010-10-13T14:38:26.963 | 2010-10-13T14:38:26.963 | null | null | 1352 | null |
3558 | 2 | null | 1337 | 10 | null | This one's from the xkcd forums:
>
Three statisticians are out hunting.
Bird flies up out of the bush, and the
first statistician aims and fires.
Unfortunately for them, he missed, the
bullet going about a foot below the
bird. The second one fires, but the
bullet goes about a foot above the
bird.
The thi... | null | CC BY-SA 2.5 | null | 2010-10-13T15:07:55.360 | 2010-10-13T15:07:55.360 | null | null | 144 | null |
3559 | 1 | 3562 | null | 72 | 85563 | I have `SPSS` output for a logistic regression model. The output reports two measures for the model fit, `Cox & Snell` and `Nagelkerke`.
So as a rule of thumb, which of these $R^²$ measures would you report as the model fit?
Or, which of these fit indices is the one that is usually reported in journals?
---
Some Bac... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | CC BY-SA 3.0 | null | 2010-10-13T16:12:57.630 | 2022-04-08T13:17:46.170 | 2022-04-07T19:24:49.050 | 11887 | 442 | [
"logistic",
"goodness-of-fit",
"r-squared",
"pseudo-r-squared"
] |
3560 | 2 | null | 3559 | 3 | null | I would prefer the Nagelkerke as this model fit attains 1 when the model fits perfectly giving the reader a sense of how far your model is from perfect fit. The Cox & Shell does not attain 1 for perfect model fit and hence interpreting a value of 0.09 is a bit harder. See this url for further info on [Pseudo RSquared](... | null | CC BY-SA 2.5 | null | 2010-10-13T16:36:53.413 | 2010-10-13T16:36:53.413 | null | null | null | null |
3561 | 1 | 3569 | null | 27 | 16311 | In a multiple linear regression with highly correlated regressors, what is the best strategy to use? Is it a legitimate approach to add the product of all the correlated regressors?
| Dealing with correlated regressors | CC BY-SA 2.5 | null | 2010-10-13T17:34:00.057 | 2010-10-14T07:24:54.397 | 2010-10-13T19:36:47.703 | 1352 | 1077 | [
"regression",
"multicollinearity"
] |
3562 | 2 | null | 3559 | 94 | null | Normally I wouldn't report $R^2$ at all. Hosmer and Lemeshow, in their textbook [Applied Logistic Regression](http://rads.stackoverflow.com/amzn/click/0471356328) (2nd Ed.), explain why:
>
In general, [$R^2$ measures] are based on various comparisons of the predicted values from the fitted model to those from [the ba... | null | CC BY-SA 2.5 | null | 2010-10-13T17:46:39.857 | 2010-10-13T17:46:39.857 | null | null | 919 | null |
3563 | 2 | null | 3561 | 1 | null | I'm no expert on this, but my first thought would be to run a principal component analysis on the predictor variables, then use the resulting principal components to predict your dependent variable.
| null | CC BY-SA 2.5 | null | 2010-10-13T17:48:25.090 | 2010-10-13T18:00:55.567 | 2010-10-13T18:00:55.567 | 364 | 364 | null |
3564 | 1 | 3581 | null | 5 | 6863 | I would like to apply KDE to inventory replenishment, but I am not sure how to use the analysis to predict future sales based on past sales. Given a set of data and having applied KDE to it (probably using a Gaussian distribution), how do I make a prediction about the future?
Thanks for any help! Please let me know if... | How to use Kernel Density Estimation for Prediction? | CC BY-SA 2.5 | null | 2010-10-13T17:48:45.233 | 2015-04-23T05:53:04.807 | 2015-04-23T05:53:04.807 | 9964 | 1574 | [
"time-series",
"forecasting",
"smoothing",
"kernel-smoothing"
] |
3565 | 2 | null | 3561 | -1 | null | One of the ways to reduce the effects of correlation is to standardize the regressors. In standardizing, all the regressors are subtracted by their respective means and divided by their respective standard deviations. Specifically, if $X$ is the regression matrix:
$$x_{ij}^{standardized}=\frac {x_{ij}-\overline{x_{.j}}... | null | CC BY-SA 2.5 | null | 2010-10-13T17:48:53.860 | 2010-10-13T18:09:04.173 | 2010-10-13T18:09:04.173 | 1307 | 1307 | null |
3566 | 2 | null | 3559 | 32 | null | Both indices are measures of strength of association (i.e. whether any predictor is associated with the outcome, as for an LR test), and can be used to quantify predictive ability or model performance. A single predictor may have a significant effect on the outcome but it might not necessarily be so useful for predicti... | null | CC BY-SA 4.0 | null | 2010-10-13T18:02:06.783 | 2022-03-18T18:34:41.897 | 2022-03-18T18:34:41.897 | 4253 | 930 | null |
3567 | 2 | null | 3561 | 11 | null | You can use principal components or ridge regression to deal with this problem. On the other hand, if you have two variables that are correlated highly enough to cause problems with parameter estimation, then you could almost certainly drop either one of the two without losing much in terms of prediction--because the ... | null | CC BY-SA 2.5 | null | 2010-10-13T18:12:33.317 | 2010-10-13T19:44:22.127 | 2010-10-13T19:44:22.127 | 485 | 485 | null |
3568 | 2 | null | 1610 | 10 | null | I'll try not to be redundant with other responses (although it seems a little bit what J. M. already suggested), but I generally like showing the following two pictures:
| null | CC BY-SA 2.5 | null | 2010-10-13T18:43:21.297 | 2010-10-13T18:43:21.297 | null | null | 930 | null |
3569 | 2 | null | 3561 | 16 | null | Principal components make a lot of sense... mathematically. However, I'd be wary of simply using some mathematical trick in this case and hoping that I don't need to think about my problem.
I'd recommend thinking a little about what kind of predictors I have, what the independent variable is, why my predictors are corr... | null | CC BY-SA 2.5 | null | 2010-10-13T19:25:05.967 | 2010-10-13T19:25:05.967 | null | null | 1352 | null |
3570 | 2 | null | 3559 | 18 | null | I found Tue Tjur's short [paper "Coefficients of Determination in Logistic Regression Models - A New Proposal: The Coefficient of Discrimination" (2009,
The American Statistician)](http://pubs.amstat.org/doi/abs/10.1198/tast.2009.08210) on various proposals for a coefficient of determination in logistic models quite e... | null | CC BY-SA 4.0 | null | 2010-10-13T19:33:11.767 | 2018-07-12T21:29:56.463 | 2018-07-12T21:29:56.463 | 1352 | 1352 | null |
3571 | 2 | null | 3561 | 3 | null | Here is another thought that is inspired by Stephan's [answer](https://stats.stackexchange.com/questions/3561/dealing-with-correlated-regressors/3569#3569):
If some of your correlated regressors are meaningfully related (e.g., they are different measures of intelligence i.e., verbal, math etc) then you can create a sin... | null | CC BY-SA 2.5 | null | 2010-10-13T19:43:22.190 | 2010-10-13T19:43:22.190 | 2017-04-13T12:44:45.783 | -1 | null | null |
3572 | 2 | null | 3561 | 2 | null | I was about to say much the same thing as Stephan Kolassa above (so have upvoted his answer). I'd only add that sometimes multicollinearity can be due to using [extensive variables](http://en.wikipedia.org/wiki/Intensive_and_extensive_properties) which are all highly correlated with some measure of size, and things can... | null | CC BY-SA 2.5 | null | 2010-10-13T19:48:14.617 | 2010-10-14T07:24:54.397 | 2010-10-14T07:24:54.397 | 449 | 449 | null |
3573 | 2 | null | 3564 | 5 | null | I would have thought that KDE bear little if any relationship to predicting future sales based on past sales. Sounds more like [time series analysis](http://en.wikipedia.org/wiki/Time_series) to me, though that's really not my area.
| null | CC BY-SA 2.5 | null | 2010-10-13T19:54:25.117 | 2010-10-13T19:54:25.117 | null | null | 449 | null |
3574 | 2 | null | 3559 | 9 | null | I was also going to say 'neither of them', so i've upvoted whuber's answer.
As well as criticising R^2, Hosmer & Lemeshow did propose an alternative measure of goodness-of-fit for logistic regression that is sometimes useful. This is based on dividing the data into (say) 10 groups of equal size (or as near as possible)... | null | CC BY-SA 2.5 | null | 2010-10-13T20:08:11.973 | 2010-10-13T20:08:11.973 | null | null | 449 | null |
3575 | 1 | 3576 | null | 5 | 16484 | I've got a data table like:
```
ID Low Color Med Color High Color
1 234 123 324
2 4 432 3423
```
The rows are widgets, the columns are color levels. Would you call this table "widgets by color level" or "color levels by widget"?
| Is it "rows by columns" or "columns by rows"? | CC BY-SA 2.5 | null | 2010-10-13T20:45:25.290 | 2021-02-01T13:30:41.447 | 2021-02-01T13:30:41.447 | 101426 | 1531 | [
"terminology"
] |
3576 | 2 | null | 3575 | 9 | null | "That depends." Rows are usually considered observations, and columns are variables. So I would say widgets by color level in your context. But it really depends on which are your [dependent and independent variables](http://en.wikipedia.org/wiki/Dependent_and_independent_variables) (or how you're interpreting the d... | null | CC BY-SA 2.5 | null | 2010-10-13T20:50:40.067 | 2010-10-13T20:50:40.067 | null | null | 5 | null |
3577 | 2 | null | 3575 | 5 | null | Of course you can view this table either way by transposing it. Conventionally, in a database rows represent objects and columns contain their attributes, whence this presentation would typically be viewed as a list of widgets, not a list of color levels.
| null | CC BY-SA 2.5 | null | 2010-10-13T20:51:02.110 | 2010-10-13T20:51:02.110 | null | null | 919 | null |
3578 | 2 | null | 3575 | 3 | null | Typically, we talk about a r(ow) X (c)olumn matrix, from Linear Algebra. So, a matrix with 2 rows and 3 columns is a 2 X 3 matrix. By that logic, I'd call your data frame a "Widgets by Color" table.
| null | CC BY-SA 2.5 | null | 2010-10-13T20:54:42.053 | 2010-10-13T20:54:42.053 | null | null | 485 | null |
3579 | 2 | null | 3575 | 2 | null | for a table like that, I say it the same way I'd say "n by k" for a matrix with n rows and k columns (i.e. rows first).
| null | CC BY-SA 2.5 | null | 2010-10-13T21:46:38.637 | 2010-10-13T21:46:38.637 | null | null | 805 | null |
3581 | 2 | null | 3564 | 7 | null | You can use conditional kernel density estimation to obtain the density of sales at time $t+h$ conditional on the values of sales at times $t, t-1, t-2, \dots$ This gives you a density forecast rather than a point forecast. The problem is that the conditioning is difficult in a density setting when the number of condit... | null | CC BY-SA 2.5 | null | 2010-10-13T22:49:26.513 | 2010-10-13T22:49:26.513 | null | null | 159 | null |
3582 | 2 | null | 3556 | -1 | null | Loess/lowess is basically a variable KDE method, with the width of the kernel being set by the nearest-neighbour approach. I've found that it works pretty well, certainly much better than any fixed-width model when the density of data points varies markedly.
One thing to be aware of with KDE and multi-dimensional data ... | null | CC BY-SA 2.5 | null | 2010-10-14T01:16:51.910 | 2010-10-14T01:16:51.910 | null | null | 1569 | null |
3583 | 2 | null | 3552 | 4 | null | Rather than the code you present, I assume you're doing something like `class x21 x22` followed by the `repeated` clause `group=x21*x22` to end up with 12 parameters. This is the only option I'm aware of within SAS, i.e. I don't think you can get a straightforward stratification of the variation across the combined le... | null | CC BY-SA 2.5 | null | 2010-10-14T03:22:37.420 | 2010-10-14T03:36:05.547 | 2010-10-14T03:36:05.547 | 251 | 251 | null |
3584 | 1 | 3585 | null | 10 | 1350 | I have a dataset asking people whether they have been to a certain places (e.g. A, B, C, D), and they can make more than one choice, then a specimen is taken from their nose to see if they are infected with some disease.
I need to find out the relative risk of getting infected for one going to a certain place, I can on... | How to deal with survey question with multiple response? | CC BY-SA 2.5 | null | 2010-10-14T03:36:34.627 | 2010-10-14T20:37:16.073 | 2010-10-14T20:37:16.073 | 1352 | 588 | [
"logistic"
] |
3585 | 2 | null | 3584 | 2 | null | You can still use logistic regression because your outcome is dichotomous, infected vs not-infected. I would just simply take a dummy variable approach and use no travel as the reference category (i.e. for each of your places you have a variable coded as 1 if they visited that place and coded as 0 if they did not visit... | null | CC BY-SA 2.5 | null | 2010-10-14T04:40:09.423 | 2010-10-14T04:40:09.423 | null | null | 1036 | null |
3586 | 1 | 3593 | null | 2 | 1537 | I am trying to calculate the relative risk for being tested as positive for people aged >25 or <=25, and here is the result.
```
$data
Negative Positive Total
>25 115 11 126
<=25 117 3 120
Total 232 14 246
$measure
risk ratio with 95% C.I.
estimat... | Inconsistency between Chi-sq and CI Estimation using Wald test | CC BY-SA 2.5 | null | 2010-10-14T05:14:57.183 | 2010-10-15T06:16:16.390 | 2010-10-14T07:09:09.693 | 588 | 588 | [
"chi-squared-test",
"relative-risk"
] |
3587 | 2 | null | 3586 | 0 | null | I am not sure what is the $H_o$ in the case of the hypothesis test. Usually its $H_o: OR=1$. If this is true then, your situation can arise because of the following reasons:
The asymptotics that you comparing might be very different in the two cases. The test statistic's sampling distribution for relative risk and OR ... | null | CC BY-SA 2.5 | null | 2010-10-14T06:34:56.423 | 2010-10-14T06:53:23.313 | 2010-10-14T06:53:23.313 | 1307 | 1307 | null |
3588 | 2 | null | 3556 | 7 | null | The article * D. G. Terrell; D. W. Scott (1992). "Variable kernel density estimation". Annals of Statistics 20: 1236–1265.* cited at the end of the Wikipedia article you yourself cite clearly states that unless the observations space is very sparse the variable kernel method is not recommended on the basis of global ro... | null | CC BY-SA 2.5 | null | 2010-10-14T06:52:05.923 | 2010-10-14T06:52:05.923 | null | null | 603 | null |
3589 | 1 | 3605 | null | 10 | 32308 | In a problem I am working on, I have two random variables, X and Y. I need to figure out how closely correlated the two of them are, but they are of different dimensions. The rank of the row space of X is 4350, and the rank of the row space of Y is substantially larger, in the tens of thousands. Both X and Y have the s... | Correlation between two variables of unequal size | CC BY-SA 3.0 | null | 2010-10-14T06:56:42.463 | 2017-03-03T08:41:00.493 | 2017-03-03T08:41:00.493 | 11887 | 1118 | [
"time-series",
"correlation",
"missing-data",
"finance"
] |
3590 | 2 | null | 2948 | 8 | null | Gephi implements the Louvain Modularity method: [http://wiki.gephi.org/index.php/Modularity](http://wiki.gephi.org/index.php/Modularity)
cheers
| null | CC BY-SA 2.5 | null | 2010-10-14T07:15:50.350 | 2010-10-14T07:15:50.350 | null | null | null | null |
3591 | 2 | null | 3586 | 1 | null | I think suncoolsu is on the right track, though RR = 1 if and only if OR = 1, so those null hypotheses are in fact one and the same.
Pearson's chi-square test can be derived as a score test. In finite samples, score tests are, in general, better behaved than Wald tests, but less good than likelihood-ratio tests.
If you... | null | CC BY-SA 2.5 | null | 2010-10-14T07:44:10.447 | 2010-10-14T07:44:10.447 | null | null | 449 | null |
3592 | 1 | 3604 | null | 7 | 352 | I'm wondering about this question:
short version:
how to adequately compare the effect of a reclassification of the same subjects on survival
long version:
I have one cancer cohort that was sorted into TNM classes long time ago. The definition of the classes has now been updated and I like to know if, say, class 1 of t... | Survival analysis, one cohort, two classifications | CC BY-SA 2.5 | null | 2010-10-14T07:45:21.910 | 2010-11-11T23:24:58.520 | 2010-11-11T23:24:58.520 | 449 | 1573 | [
"hypothesis-testing",
"survival",
"non-independent"
] |
3593 | 2 | null | 3586 | 4 | null | The Wald test for contingency tables is known to be misleading or conservative and the general advice is to prefer the Likelihood Ratio Test:
```
> library(vcd)
> mat <- matrix(c(115, 117, 11, 3), 2, 2)
> assocstats(mat)$chisq_tests
X^2 df P(> X^2)
Likelihood Ratio 4.730771 1 0.02962760
Pearson... | null | CC BY-SA 2.5 | null | 2010-10-14T07:45:42.830 | 2010-10-15T06:16:16.390 | 2017-04-13T12:44:25.243 | -1 | 251 | null |
3594 | 2 | null | 3589 | 10 | null | So the problem is one of missing data (not all Y have a corresponding X, where correspondence is operationalized via time points). I don't think there is much to do here than just to throw away the Y you don't have an X for and calculate the correlation on the full pairs.
You may want to read up on financial time serie... | null | CC BY-SA 2.5 | null | 2010-10-14T08:59:31.170 | 2010-10-14T13:21:45.070 | 2010-10-14T13:21:45.070 | 1352 | 1352 | null |
3595 | 1 | 3597 | null | 28 | 15815 | Currently I have to analyze approximately 20M records and build prediction models. So far I have tried out Statistica, SPSS, RapidMiner and R. Among these Statistica seems to be most suitable to deal with data mining and RapidMiner user interface is also very handy but it seems that Statistica, RapidMiner and SPSS are ... | Statistics and data mining software tools for dealing with large datasets | CC BY-SA 2.5 | null | 2010-10-14T10:28:15.937 | 2014-12-22T15:47:25.553 | 2010-10-14T17:54:48.267 | null | 315 | [
"large-data",
"software",
"data-mining"
] |
3596 | 2 | null | 2061 | 3 | null | [http://datajournalism.stanford.edu/](http://datajournalism.stanford.edu/) : Video on visualisation
| null | CC BY-SA 2.5 | null | 2010-10-14T10:31:18.257 | 2010-10-14T10:31:18.257 | null | null | 183 | null |
3597 | 2 | null | 3595 | 19 | null | I'll second @suncoolsu comment: The dimensionality of your data set is not the only criterion that should orient you toward a specific software. For instance, if you're just planning to do unsupervised clustering or use PCA, there are several dedicated tools that cope with large data sets, as commonly encountered in ge... | null | CC BY-SA 2.5 | null | 2010-10-14T11:15:57.903 | 2010-10-15T11:39:14.457 | 2010-10-15T11:39:14.457 | 930 | 930 | null |
3598 | 2 | null | 3589 | 1 | null | Given the extra information in your comments I'd recommend looking at two correlations. The first would be the common time periods that the companies were both around. So, if one was around 2 years earlier you'd just drop that data and look at the rest. The second would be the relative time periods. In the second o... | null | CC BY-SA 2.5 | null | 2010-10-14T11:45:25.467 | 2010-10-14T19:09:49.620 | 2010-10-14T19:09:49.620 | 601 | 601 | null |
3599 | 1 | 3607 | null | 7 | 176 | Could anyone give me hints as to a model framework that can be used in the following setting:
The outcome A is dichotomous. I want to investigate the effect of a continuous variable B and a continous variable C on A in a longitudinal setting. So far so good.
The problem is that C depends on B, in that values of B abo... | Model suggestion | CC BY-SA 2.5 | null | 2010-10-14T12:36:05.257 | 2010-10-14T19:09:39.820 | 2010-10-14T19:09:39.820 | 1291 | 1291 | [
"modeling",
"causality"
] |
3600 | 2 | null | 3589 | 2 | null | @Jeromy Anglim specified this correctly. Having the extra information when only one of the time series existed would provide no value here. And in principle, the data should be sampled at the same time for it to be meaningful using conventional correlation measures.
As a more general problem, I would add that there a... | null | CC BY-SA 2.5 | null | 2010-10-14T13:31:19.233 | 2010-10-14T13:31:19.233 | null | null | 5 | null |
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