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5901 | 2 | null | 5894 | 0 | null | Edit: I made a presumption before where after 27 tries it would then stop searching thus giving a bias on the results
```
# Number of repeats
iters = 10000
results = rep(0,iters)
# Ball to search for
searchNumber = 27
for(i in 1:iters) {
# A random permutation:
balls = sample(1:27,iters, replace=T)
# Numb... | null | CC BY-SA 2.5 | null | 2011-01-02T21:55:46.263 | 2011-01-03T00:10:55.017 | 2011-01-03T00:10:55.017 | null | null | null |
5902 | 2 | null | 5836 | 0 | null | Try the [directlabels](http://directlabels.r-forge.r-project.org/) package for some more control. As far as the caption goes, if you're automatically generating the graphs, you might simply use it as caption text (in LaTeX or HTML or what have you). Hope this helps.
| null | CC BY-SA 2.5 | null | 2011-01-02T22:56:50.777 | 2011-01-02T22:56:50.777 | null | null | 317 | null |
5903 | 1 | null | null | 16 | 6901 | Given two arrays x and y, both of length n, I fit a model y = a + b*x and want to calculate a 95% confidence interval for the slope. This is (b - delta, b + delta) where b is found in the usual way and
```
delta = qt(0.975,df=n-2)*se.slope
```
and se.slope is the standard error in the slope. One way to get the standar... | Confidence intervals for regression parameters: Bayesian vs. classical | CC BY-SA 2.5 | null | 2011-01-02T23:59:51.453 | 2012-06-22T14:09:32.070 | 2011-01-16T14:51:59.780 | 2116 | 2617 | [
"r",
"regression",
"bayesian",
"confidence-interval",
"frequentist"
] |
5904 | 2 | null | 5903 | 6 | null | If you sample from the posterior of b | y and calculate lims (as you define) it should be same as (b - delta, b + delta). Specifically, if you calculate the posterior distribution of b | y under a flat prior, it is same as the classical sampling distribution of b.
For more details refer to: Gelman et al. (2003). Bayesi... | null | CC BY-SA 2.5 | null | 2011-01-03T01:53:27.830 | 2011-01-03T13:56:55.263 | 2011-01-03T13:56:55.263 | 1307 | 1307 | null |
5905 | 2 | null | 5899 | 10 | null | To give some context, I don't view this as a "statistical" question as much of a "group preference" question. Economists and policy wonks do a lot of thinking about questions of how to convert individual preferences into a "will of the people." You will find lots of interesting reading if you search the web for "politi... | null | CC BY-SA 2.5 | null | 2011-01-03T01:59:54.670 | 2011-02-02T15:44:38.890 | 2011-02-02T15:44:38.890 | 660 | 660 | null |
5907 | 2 | null | 5893 | 1 | null | The amount of information that can be found varies wildly, from just race and gender, to all sorts of personal info. Your best bet at getting the information would be social network sites like facebook, as they generally provide more information than cencus databases.
| null | CC BY-SA 2.5 | null | 2011-01-02T19:30:45.810 | 2011-01-03T11:13:42.550 | null | null | null | null |
5908 | 2 | null | 5893 | 1 | null | There's quite a wide range of information you can get depending on the sources you use. Census data is an obvious one. You can also get information from Facebook, MySpace and other social networking sites. You could also probably search public news archives for mentions of their name. Maybe even those ubclained propert... | null | CC BY-SA 2.5 | null | 2011-01-02T19:41:43.203 | 2011-01-03T11:13:42.550 | null | null | null | null |
5909 | 2 | null | 5893 | 13 | null | This is not a serious answer, but I just remembered something from a book I read a year ago. There is a chapter in [Freakonomics](http://rads.stackoverflow.com/amzn/click/0060731338) devoted to what you can tell about a person from the name. The chapter is based on the author's research paper The causes and consequence... | null | CC BY-SA 2.5 | null | 2011-01-02T20:43:28.110 | 2011-01-03T11:13:42.550 | null | null | null | null |
5910 | 2 | null | 5893 | 2 | null | You probably could find out:
- Profession and possibly job history, if one participates in any professional discussions (current job usually can be found out from either domain name in email or signature, search would reveal past ones too)
- Relatives, if one maintains profile on social networks.
- Current location,... | null | CC BY-SA 2.5 | null | 2011-01-03T01:47:42.210 | 2011-01-03T11:13:42.550 | null | null | null | null |
5911 | 2 | null | 5893 | 4 | null | Just to add in to other suggestions here, one of the largest sources for family data is the raft of genealogy sites out there. I think most western people are probably listed by some family member, distant or otherwise on a few of them and any such inclusion comes with a usually comprehensive family tree attached, com... | null | CC BY-SA 2.5 | null | 2011-01-03T03:24:33.477 | 2011-01-03T11:13:42.550 | null | null | null | null |
5912 | 1 | 5920 | null | 10 | 1984 | A recent question about [alternatives to logistic regression in R](https://stats.stackexchange.com/questions/2234/alternatives-to-logistic-regression-in-r) yielded a variety of answers including randomForest, gbm, rpart, bayesglm, and generalized additive models. What are the practical and interpretation differences b... | What are the practical & interpretation differences between alternatives and logistic regression? | CC BY-SA 2.5 | null | 2011-01-03T13:13:27.213 | 2022-08-15T19:16:51.353 | 2017-04-13T12:44:48.343 | -1 | 196 | [
"r",
"hypothesis-testing",
"logistic",
"random-forest"
] |
5913 | 1 | 5921 | null | 7 | 2985 | I’m writing some code (JavaScript) to compare benchmark results. I’m using the [Welch T-test](http://frank.mtsu.edu/~dkfuller/notes302/welcht.pdf) because the variance and/or sample size between benchmarks is most likely different. The critical value is pulled from a T-distribution table at 95% confidence (two-sided).
... | Interpreting two-sided, two-sample, Welch T-Test | CC BY-SA 2.5 | null | 2011-01-03T13:17:49.960 | 2011-01-04T18:48:40.033 | 2017-04-13T12:44:52.277 | -1 | 2616 | [
"distributions",
"t-test",
"javascript"
] |
5915 | 2 | null | 5913 | 6 | null | Dividing by 2 is for p-values. If you compare critical values the division by 2 is not necessary. The function `getCriticalValue` should be the [quantile function](http://en.wikipedia.org/wiki/Quantile_function) of Student's t distribution. Thus it should take 2 values, the probability and the degrees of freedom. If yo... | null | CC BY-SA 2.5 | null | 2011-01-03T13:56:58.953 | 2011-01-03T13:56:58.953 | null | null | 2116 | null |
5916 | 2 | null | 5913 | 6 | null | It's not absolutely necessary to round the degrees of freedom to an integer. [Student's t-distribution](http://en.wikipedia.org/wiki/Student%27s_t-distribution) can be defined for all positive real values of this parameter. Restricting it to a positive integer may make the critical value easier to calculate though, dep... | null | CC BY-SA 2.5 | null | 2011-01-03T14:16:02.377 | 2011-01-03T14:16:02.377 | null | null | 449 | null |
5917 | 2 | null | 5899 | 2 | null | You're asking an intriguing question. I agree with the comments that are showing some apprehension at the "one-man-one-vote" system. I also agree that knowing the basic statistics (like standard deviation and mean) will not give you an insight into the will of the voters.
I would like to play off of David James's answe... | null | CC BY-SA 2.5 | null | 2011-01-03T15:06:52.347 | 2011-01-03T15:06:52.347 | null | null | 2591 | null |
5918 | 1 | 5988 | null | 24 | 8646 | Note: Case is n>>p
I am reading Elements of Statistical Learning and there are various mentions about the "right" way to do cross validation( e.g. page 60, page 245). Specifically, my question is how to evaluate the final model (without a separate test set) using k-fold CV or bootstrapping when there has been a model s... | Cross Validation (error generalization) after model selection | CC BY-SA 4.0 | null | 2011-01-03T15:08:29.897 | 2018-10-25T09:30:53.700 | 2018-10-25T09:30:53.700 | 128677 | 2040 | [
"machine-learning",
"model-selection",
"data-mining",
"cross-validation"
] |
5920 | 2 | null | 5912 | 9 | null | Disclaimer: It is certainly far from being a full answer to the question!
I think there are at least two levels to consider before establishing a distinction between all such methods:
- whether a single model is fitted or not: This helps opposing methods like logistic regression vs. RF or Gradient Boosting (or more ge... | null | CC BY-SA 4.0 | null | 2011-01-03T15:39:49.363 | 2022-08-15T19:16:51.353 | 2022-08-15T19:16:51.353 | 79696 | 930 | null |
5921 | 2 | null | 5913 | 9 | null | (1a) You don't need the Welch test to cope with different sample sizes. That's automatically handled by the Student t-test.
(1b) If you think there's a real chance the variances in the two populations are strongly different, then you are assuming a priori that the two populations differ. It might not be a difference ... | null | CC BY-SA 2.5 | null | 2011-01-03T15:45:13.360 | 2011-01-03T17:38:42.127 | 2011-01-03T17:38:42.127 | 919 | 919 | null |
5922 | 1 | 6045 | null | 11 | 891 | A colleague in applied statistics sent me this:
>
"I was wondering if you know any way
to find out the true dimension of the
domain of a function. For example, a
circle is a one dimensional function
in a two dimensional space. If I do
not know how to draw, is there a
statistic that I can compute that
... | Estimating the dimension of a data set | CC BY-SA 2.5 | null | 2011-01-03T15:47:12.577 | 2021-08-21T19:40:50.943 | 2021-08-21T19:40:50.943 | 11887 | null | [
"large-data",
"high-dimensional",
"dimensions"
] |
5923 | 2 | null | 5903 | 10 | null | The 'problem' is in the prior on sigma. Try a less informative setting
```
tau ~ dgamma(1.0E-3,1.0E-3)
sigma <- pow(tau, -1/2)
```
in your jags file. Then update a bunch
```
update(10000)
```
grab the parameters, and summarise your quantity of interest. It should line up reasonably well with the classic version.
... | null | CC BY-SA 3.0 | null | 2011-01-03T15:54:07.543 | 2012-06-22T14:09:32.070 | 2012-06-22T14:09:32.070 | 1739 | 1739 | null |
5924 | 2 | null | 5922 | 7 | null | [Principal Components Analysis](http://en.wikipedia.org/wiki/Principal_component_analysis) of local data is a good point of departure. We have to take some care, though, to distinguish local (intrinsic) from global (extrinsic) dimension. In the example of points on a circle, the local dimension is 1, but overall the ... | null | CC BY-SA 2.5 | null | 2011-01-03T16:03:25.970 | 2011-01-03T16:03:25.970 | null | null | 919 | null |
5925 | 2 | null | 5859 | 10 | null | The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of one month would be just fine over a 5-year period.
However, the large number of ties at 6 and 12 months makes one wonder ... | null | CC BY-SA 2.5 | null | 2011-01-03T16:07:14.337 | 2011-01-03T16:07:14.337 | null | null | 279 | null |
5926 | 1 | null | null | 11 | 24195 | The dependent variables in a MANOVA should not be "too strongly correlated". But how strong a correlation is too strong? It would be interesting to get people's opinions on this issue. For instance, would you proceed with MANOVA in the following situations?
- Y1 and Y2 are correlated with $r=0.3$ and $p<0.005$
- Y1 ... | MANOVA and correlations between dependent variables: how strong is too strong? | CC BY-SA 3.0 | null | 2011-01-03T17:12:09.590 | 2019-10-15T17:51:53.403 | 2014-10-27T11:57:01.623 | 28666 | 266 | [
"correlation",
"anova",
"multivariate-analysis",
"rule-of-thumb",
"manova"
] |
5927 | 1 | null | null | 2 | 555 | Are there meaningful ways to quantify how "flat" the log likelihood function is around the MLE when the parameter has more than one dimension? In particular is the determinant of the Hessian a reasonable measure?
| A measure of the "flatness" of log likelihood at the MLE | CC BY-SA 2.5 | null | 2011-01-03T18:01:37.370 | 2011-01-03T18:34:48.363 | null | null | 1004 | [
"maximum-likelihood"
] |
5929 | 2 | null | 5927 | 4 | null | You might find that the [Fisher Information](http://en.wikipedia.org/wiki/Fisher_information) has some properties you like.
Its the expectation that gives Fisher Information its interpretation as the 'informativeness' of a measurement. But if you're just looking for something geometrically descriptive then your sugges... | null | CC BY-SA 2.5 | null | 2011-01-03T18:25:09.920 | 2011-01-03T18:34:48.363 | 2011-01-03T18:34:48.363 | 1739 | 1739 | null |
5931 | 2 | null | 4519 | 4 | null | I may, after all this time, finally have understood the question. The data, if I'm correct, are a set of tuples $(i, j, y(i,j))$ where $i$ is one player, $j \ne i$ is another player, and $y(i,j)$ is the number of attacks of $i$ on $j$. In this notation the objective is to relate $y(i,j)$ to $y(j,i)$. There are some ... | null | CC BY-SA 2.5 | null | 2011-01-03T19:53:37.627 | 2011-01-03T19:53:37.627 | 2020-06-11T14:32:37.003 | -1 | 919 | null |
5932 | 2 | null | 4844 | 6 | null | First, it's almost impossible to drive a car "randomly." Did you periodically consult a random number generator to determine what direction to head in next? I don't think so. This calls into question the use of any statistical procedure that assumes randomness (even if it isn't simple and could lead to dependence). ... | null | CC BY-SA 4.0 | null | 2011-01-03T20:11:46.050 | 2022-08-18T20:59:10.793 | 2022-08-18T20:59:10.793 | 79696 | 919 | null |
5933 | 2 | null | 5893 | 4 | null | The last chapter of Freakonomics (2005, Steven D. Levitt and Stephen J. Dubner) has a fascinating discussion about names, particularly as they relate to socio-economic status and race.
They have a list of first names that might or might not correlate well with FB's analysis of last names. They also describe how name ch... | null | CC BY-SA 2.5 | null | 2011-01-03T20:34:28.793 | 2011-01-03T20:34:28.793 | null | null | 2591 | null |
5934 | 1 | 5942 | null | 4 | 25090 | Over the holidays I played a dice game where each player had 3 to 7 d6 to roll each turn. The game gave certain advantages to doubles and triples. I wanted to know the odds of rolling doubles or triples given N dice to understand the importance of "upgrading" to more dice.
My question is really about how to reason ab... | Odds of rolling doubles or triples given N 6-sided dice | CC BY-SA 2.5 | null | 2011-01-03T21:54:58.037 | 2011-01-04T01:04:18.533 | null | null | 2622 | [
"probability",
"games",
"dice"
] |
5935 | 1 | 5936 | null | 41 | 44088 | I am analyzing an experimental data set. The data consists of a paired vector of treatment type and a binomial outcome:
```
Treatment Outcome
A 1
B 0
C 0
D 1
A 0
...
```
In the outcome column, 1 denotes a success and 0 denotes a failure. I'd like to figure out ... | ANOVA on binomial data | CC BY-SA 2.5 | null | 2011-01-03T22:04:01.343 | 2020-02-09T15:23:30.847 | 2020-02-09T15:23:30.847 | 11887 | 2624 | [
"logistic",
"anova",
"data-transformation",
"binomial-distribution",
"experiment-design"
] |
5936 | 2 | null | 5935 | 22 | null | No to ANOVA, which assumes a normally distributed outcome variable (among other things). There are "old school" transformations to consider, but I would prefer logistic regression (equivalent to a chi square when there is only one independent variable, as in your case). The advantage of using logistic regression over a... | null | CC BY-SA 3.0 | null | 2011-01-03T22:21:28.357 | 2012-06-26T17:23:42.433 | 2012-06-26T17:23:42.433 | 7290 | 2040 | null |
5937 | 1 | 5940 | null | 44 | 38113 | When teaching an introductory level class, the teachers I know tend to invent some numbers and a story in order to exemplify the method they are teaching.
What I would prefer is to tell a real story with real numbers. However, these stories needs to relate to a very tiny dataset, which enables manual calculations.
An... | Tiny (real) datasets for giving examples in class? | CC BY-SA 3.0 | null | 2011-01-03T22:23:41.990 | 2015-11-17T21:10:43.313 | 2015-11-17T21:10:43.313 | 22468 | 253 | [
"dataset",
"references",
"teaching"
] |
5938 | 2 | null | 5937 | 13 | null | For two-way tables, I like the data on gender and survival of the titanic passengers:
```
| Alive Dead | Total
-------+-------------+------
Female | 308 154 | 462
Male | 142 709 | 851
-------+-------------+------
Total | 450 863 | 1313
```
With this data, one can discuss things like the chi-sq... | null | CC BY-SA 2.5 | null | 2011-01-03T22:58:26.610 | 2011-01-03T22:58:26.610 | null | null | 1934 | null |
5939 | 2 | null | 1164 | 9 | null | Wooldridge "Introductory Econometrics - A Modern Approach" 2E p.261.
If Heteroskedasticity-robust standard errors are valid more often than the usual OLS standard errors, why do we bother we the usual standard errors at all?...One reason they are still used in cross sectional work is that, if the homoskedasticity assum... | null | CC BY-SA 2.5 | null | 2011-01-03T23:00:01.527 | 2011-01-03T23:00:01.527 | null | null | null | null |
5940 | 2 | null | 5937 | 27 | null | The [data and story library](http://lib.stat.cmu.edu/DASL/) is an " online library of datafiles and stories that illustrate the use of basic statistics methods".
This site seems to have what you need, and you can search it for particular data sets.
| null | CC BY-SA 3.0 | null | 2011-01-03T23:03:39.257 | 2011-10-28T19:49:33.923 | 2011-10-28T19:49:33.923 | 1381 | 1381 | null |
5941 | 2 | null | 5937 | 9 | null | The [Journal of Statistical Education](http://www.amstat.org/publications/jse/jse_data_archive.htm) has an archive of educational data sets.
| null | CC BY-SA 2.5 | null | 2011-01-03T23:07:43.137 | 2011-01-03T23:07:43.137 | null | null | 1381 | null |
5942 | 2 | null | 5934 | 6 | null | You are looking at a much more manageable version of the famous [birthday problem](http://en.wikipedia.org/wiki/Birthday_problem), which has 365 choices for each "die". The link describes the solution as well. In short, the trick for counting duplicates is to rather counting the cases without a duplicate - that is much... | null | CC BY-SA 2.5 | null | 2011-01-03T23:12:30.220 | 2011-01-03T23:12:30.220 | null | null | 279 | null |
5943 | 2 | null | 5937 | 23 | null | There's a book called "A Handbook of Small Datasets" by D.J. Hand, F. Daly, A.D. Lunn, K.J. McConway and E. Ostrowski. The Statistics department at NCSU have electronically posted the datasets from this book [here](http://www.stat.ncsu.edu/sas/sicl/data/).
The website above gives only the data; you would need to read... | null | CC BY-SA 2.5 | null | 2011-01-03T23:15:30.717 | 2011-01-03T23:15:30.717 | null | null | null | null |
5944 | 2 | null | 5935 | 3 | null | I would like to differ from what you think about Chi-Sq test. It is applicable even if the data is not binomial. It's based on the asymptotic normality of mle (in most of the cases).
I would do a logistic regression like this:
$$\log \frac {\hat{\pi}} {1-\hat{\pi}} = \beta_0 + \beta_1 \times D_1 + \beta_2 \times D_2$$... | null | CC BY-SA 2.5 | null | 2011-01-03T23:27:03.747 | 2011-01-03T23:40:57.383 | 2011-01-03T23:40:57.383 | 1307 | 1307 | null |
5945 | 1 | 5950 | null | 13 | 6363 | SVMs for classification make intuitive sense to me: I understand how minimizing $||\theta||^2$ yields the maximum margin. However, I don't understand that objective in the context of regression. Various texts ([here](http://kernelsvm.tripod.com/) and [here](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.99.20... | Understanding SVM regression: objective function and "flatness" | CC BY-SA 2.5 | null | 2011-01-03T23:31:11.083 | 2019-08-09T08:52:20.067 | null | null | 1720 | [
"regression",
"svm"
] |
5946 | 2 | null | 5680 | 34 | null | Like other parametric tests, the analysis of variance assumes that the data fit the normal distribution. If your measurement variable is not normally distributed, you may be increasing your chance of a false positive result if you analyze the data with an anova or other test that assumes normality. Fortunately, an anov... | null | CC BY-SA 3.0 | null | 2011-01-04T00:00:42.387 | 2013-02-26T10:54:57.397 | 2013-02-26T10:54:57.397 | 2669 | null | null |
5947 | 2 | null | 5934 | 5 | null | You're really asking for instruction in [combinatorics](http://en.wikipedia.org/wiki/Combinatorics), which is a vast field. For this particular problem, a good method, if somewhat abstract, is to use [generating functions](http://en.wikipedia.org/wiki/Probability-generating_function). The probability generating funct... | null | CC BY-SA 2.5 | null | 2011-01-04T00:35:45.310 | 2011-01-04T00:35:45.310 | 2017-04-13T12:19:38.800 | -1 | 919 | null |
5948 | 2 | null | 5934 | 6 | null | You have posed the kind of combinatorics question that profs will ask you as an undergrad.
Let's reduce the problem to doubles of two dice. Instead of asking about doubles, let's ask how not to roll doubles. There are 6 ways to roll the first die and 5 ways (excluding the first die's roll) to roll the second die. Becau... | null | CC BY-SA 2.5 | null | 2011-01-04T01:04:18.533 | 2011-01-04T01:04:18.533 | null | null | 2591 | null |
5949 | 2 | null | 5937 | 4 | null | Probably such an obvious answer that it does not really need to be mentioned, but for correlation or linear regression [Anscombe's quartet](http://en.wikipedia.org/wiki/Anscombe%27s_quartet) is a logical choice. Although it is not a real story with real data I think it is such a simple example it would reasonably fit i... | null | CC BY-SA 2.5 | null | 2011-01-04T04:04:56.267 | 2011-01-04T04:04:56.267 | null | null | 1036 | null |
5950 | 2 | null | 5945 | 11 | null | One way that I think about the flatness is that it makes my predictions less sensitive to perturbations in the features. That is, if I am constructing a model of the form
$$y = x^\top \theta + \epsilon,$$
where my feature vector $x$ has already been normalized, then smaller values in $\theta$ mean my model is less sens... | null | CC BY-SA 2.5 | null | 2011-01-04T05:22:41.427 | 2011-01-14T17:48:48.753 | 2011-01-14T17:48:48.753 | 795 | 795 | null |
5951 | 2 | null | 5922 | 3 | null | I'm not sure about the 'domain of a function' part, but [Hausdorff Dimension](http://en.wikipedia.org/wiki/Hausdorff_dimension) seems to answer this question. It has the odd property of agreeing with simple examples (e.g. the circle has Hausdorff Dimension 1), but of giving non-integral results for some sets ('fractals... | null | CC BY-SA 2.5 | null | 2011-01-04T05:28:07.677 | 2011-01-04T05:28:07.677 | null | null | 795 | null |
5952 | 1 | 5956 | null | 10 | 6213 | Is there a way of plotting the regression line of a piecewise model like this, other than using `lines` to plot each segment separately, or using `geom_smooth(aes(group=Ind), method="lm", fill=FALSE)` ?
```
m.sqft <- mean(sqft)
model <- lm(price~sqft+I((sqft-m.sqft)*Ind))
# sqft, price: continuous variables, Ind: if sq... | Plotting a piecewise regression line | CC BY-SA 2.5 | null | 2011-01-04T08:14:55.153 | 2011-01-05T10:31:19.180 | 2011-01-05T10:31:19.180 | 339 | 339 | [
"r",
"data-visualization",
"regression"
] |
5954 | 1 | null | null | 47 | 1507 | How can a regression model be any use if you don't know the function you are trying to get the parameters for?
I saw a piece of research that said that mothers who breast fed their children were less likely to suffer diabetes in later life. The research was from a survey of some 1000 mothers and controlled for miscella... | Understanding regressions - the role of the model | CC BY-SA 4.0 | null | 2011-01-04T09:29:01.153 | 2019-08-08T14:41:28.020 | 2019-08-08T14:41:28.020 | 11887 | 2629 | [
"regression",
"modeling",
"epidemiology",
"log-linear"
] |
5955 | 2 | null | 5954 | 44 | null | It helps to view regression as a linear approximation of the true form. Suppose the true relationship is
$$y=f(x_1,...,x_k)$$
with $x_1,...,x_k$ factors explaining the $y$. Then first order Taylor approximation of $f$ around zero is:
$$f(x_1,...,x_k)=f(0,...,0)+\sum_{i=1}^{k}\frac{\partial f(0)}{\partial x_k}x_k+\vare... | null | CC BY-SA 2.5 | null | 2011-01-04T09:45:18.260 | 2011-01-04T09:45:18.260 | null | null | 2116 | null |
5956 | 2 | null | 5952 | 6 | null | The only way I know how to do this easily is to predict from the model across the range of `sqft` and plot the predictions. There isn't a general way with `abline` or similar. You might also take a look at the [segmented](http://cran.r-project.org/web/packages/segmented/index.html) package which will fit these models a... | null | CC BY-SA 2.5 | null | 2011-01-04T09:58:53.000 | 2011-01-04T12:26:42.980 | 2011-01-04T12:26:42.980 | 1390 | 1390 | null |
5957 | 2 | null | 5954 | 15 | null | An excellent first question! I agree with mpiktas's answer, i.e. the short answer is "they don't, but they hope to have an approximation to the right model that gives approximately the right answer".
In the jargon of epidemiology, this model uncertainty is one source of what's known as 'residual confounding'. See [Ste... | null | CC BY-SA 3.0 | null | 2011-01-04T10:38:37.040 | 2012-07-01T11:44:17.527 | 2012-07-01T11:44:17.527 | 449 | 449 | null |
5958 | 2 | null | 5954 | 18 | null | The other side of the answer, complementary to mpiktas's answer but not mentioned so far, is:
"They don't, but as soon as they assume some model structure, they can check it against the data".
The two basic things that could go wrong are: The form of the function, e.g. it's not even linear in logs. So you'd start b... | null | CC BY-SA 2.5 | null | 2011-01-04T12:06:35.013 | 2011-01-04T12:06:35.013 | null | null | 1739 | null |
5959 | 2 | null | 5854 | 12 | null | You might also want to have a look at [beanplots](http://www.jstatsoft.org/v28/c01/paper).
[[Source](http://jnlnet.wordpress.com/2008/11/30/beanplots/)]
Implemented in [R package](http://cran.r-project.org/web/packages/beanplot/index.html) by Peter Kampstra.
| null | CC BY-SA 2.5 | null | 2011-01-04T12:37:08.117 | 2011-01-04T12:37:08.117 | null | null | 22 | null |
5960 | 1 | 5966 | null | 58 | 46833 | I understand that once we plot the values as a chart, we can identify a bimodal distribution by observing the twin-peaks, but how does one find it programmatically? (I am looking for an algorithm.)
| How to identify a bimodal distribution? | CC BY-SA 3.0 | null | 2011-01-04T13:03:59.157 | 2022-08-03T13:26:32.640 | 2011-12-21T17:31:11.937 | 930 | 2535 | [
"distributions"
] |
5961 | 2 | null | 5913 | 2 | null | I'm working with the OP on the benchmarking project and wanted to thank you all for clearing some things up. Also I wanted to provide a bit more information in case that affects the advice.
The sample size ranges from 5 - 700+ (as many as can be completed in 8 seconds or until the margin of error is at or below 1%. The... | null | CC BY-SA 2.5 | null | 2011-01-04T13:04:57.233 | 2011-01-04T18:48:40.033 | 2020-06-11T14:32:37.003 | -1 | 2634 | null |
5962 | 1 | 5992 | null | 12 | 1696 | I have 12 positive training sets (cancer cells treated with drugs with each of 12 different mechanisms of action). For each of these positive training sets, I would like to train a support-vector machine to distinguish it from a negative set of equal size sampled from the experiment. Each set has between 1000 and 600... | Should an SVM grid search show a high-accuracy region with low accuracies around? | CC BY-SA 2.5 | null | 2011-01-04T14:06:34.460 | 2011-01-05T00:57:32.817 | null | null | 220 | [
"svm"
] |
5963 | 2 | null | 5960 | 0 | null | The definition in wiki is slightly confusing to me. The probability of a continous data set having just one mode is zero. A simple way to program a bimodal distrubiton is with two seperate normal distributions centered differently. This creates two peaks or what wiki calls modes. You can actually use almost any two... | null | CC BY-SA 2.5 | null | 2011-01-04T14:58:43.317 | 2011-01-04T14:58:43.317 | null | null | 2539 | null |
5964 | 2 | null | 5954 | 13 | null | There is the famous quote "Essentially, all models are wrong, but some are useful" of [George Box](http://en.wikiquote.org/wiki/George_Box). When fitting models like this, we try to (or should) think about the data generation process and the physical, real world, relationships between the response and covariates. We tr... | null | CC BY-SA 2.5 | null | 2011-01-04T15:34:50.540 | 2011-01-04T15:34:50.540 | null | null | 1390 | null |
5966 | 2 | null | 5960 | 34 | null | Identifying a mode for a continuous distribution requires smoothing or binning the data.
Binning is typically too procrustean: the results often depend on where you place the bin cutpoints.
[Kernel smoothing](http://en.wikipedia.org/wiki/Kernel_smoother) (specifically, in the form of [kernel density estimation](http://... | null | CC BY-SA 4.0 | null | 2011-01-04T16:44:31.697 | 2022-08-03T13:26:32.640 | 2022-08-03T13:26:32.640 | 919 | 919 | null |
5967 | 1 | 6002 | null | 5 | 2341 | We are working on a multivariate linear regression model. Our objective is to forecast the quarterly % growth in mortgage loans outstanding.
The independent variables are:
1) Dow Jones level.
2) % change in Dow Jones over past quarter.
3) Case Shiller housing price index.
4) % change in Case Shiller housing price in... | Can you use heteroskedastic time series variables within a regression model? | CC BY-SA 2.5 | null | 2011-01-04T16:55:36.283 | 2011-01-05T12:06:14.560 | null | null | 1329 | [
"regression",
"multicollinearity",
"stepwise-regression",
"heteroscedasticity"
] |
5968 | 2 | null | 3140 | 2 | null | ```
load fisheriris
indices = crossvalind('Kfold',species,10);
cp = classperf(species); % initializes the CP object
for i = 1:10
test = (indices == i); train = ~test;
class = knnclassify(meas(test,:),meas(train,:),species(train));
% updates the CP object with the current classification results
classperf... | null | CC BY-SA 2.5 | null | 2011-01-04T17:01:30.713 | 2011-01-04T19:11:00.380 | 2011-01-04T19:11:00.380 | 930 | null | null |
5969 | 1 | 6001 | null | 10 | 2360 | I do quasi-experimental individual differences psychology research. I examine how people who differ in a cognitive ability (that I measure) perform on another task that always at least involves within-subject manipulations (and sometimes between-subject)–DVs are usually response time and/or accuracy. For this question ... | How should I analyze repeated-measures individual differences experiments? | CC BY-SA 2.5 | null | 2011-01-04T17:37:50.460 | 2011-01-06T22:33:36.143 | 2011-01-05T15:21:18.433 | 2322 | 2322 | [
"repeated-measures"
] |
5970 | 2 | null | 5680 | 2 | null | Juan has offered a lot, although I'll echo others and repeat that for best accuracy the variables themselves can be nonnormal as long as their residuals aren't. Also, a simplified and slightly more structured answer (via an annotated flow chart) is available at [yellowbrickstats.com](http://yellowbrickstats.com).
| null | CC BY-SA 3.0 | null | 2011-01-04T17:44:03.453 | 2012-03-29T23:13:14.170 | 2012-03-29T23:13:14.170 | 2669 | 2669 | null |
5971 | 1 | null | null | 1 | 353 | I'm doing some software testing where we are measuring specific latencies. Generally we run the same test several times to just eyeball the results and make sure that they are consistent across runs. I usually do this by plotting the cumulative distributions for each run together on a graph, and look for an anomalies... | What is a good measure (or set of measures) for the difference between two sample sets? | CC BY-SA 2.5 | null | 2011-01-04T17:45:49.020 | 2011-01-04T20:03:10.713 | null | null | 2638 | [
"statistical-significance"
] |
5972 | 1 | 5986 | null | 7 | 2330 | Let's say we have two biased coins. The probability of tossing a head on the first coin is $\alpha$ and the probability of tossing a head on the second coin is $1-\alpha$. We toss both coins $n$ times and we say that there is a success when there is a head on both coins. If we denote this random variable by $X$ then
$$... | Estimation of probability of a success in binomial distribution | CC BY-SA 2.5 | null | 2011-01-04T17:50:44.507 | 2011-01-13T23:11:34.813 | 2011-01-13T23:11:34.813 | 919 | 1643 | [
"estimation",
"binomial-distribution",
"unbiased-estimator"
] |
5973 | 2 | null | 5918 | 5 | null | I have been doing an extensive cross-validation analysis on a data set that cost millions to acquire, and there is no external validation set available. In this case, I performed extensive nested cross validation to ensure validity. I selected features and optimized parameters only from the respective training sets. Th... | null | CC BY-SA 2.5 | null | 2011-01-04T18:25:18.910 | 2011-01-04T18:25:18.910 | null | null | 2643 | null |
5974 | 2 | null | 5971 | 1 | null | what kind of output are you getting? are you using regression? if you're trying to measure which set is best to use, you can use an F test using the sample errors, but I'm not sure what exactly you're taking about.
| null | CC BY-SA 2.5 | null | 2011-01-04T18:26:12.340 | 2011-01-04T18:26:12.340 | null | null | 2644 | null |
5975 | 1 | null | null | 3 | 1760 | This probably seems like a really strange question, but let me try to explain what I want to do; hopefully it will make sense.
I have a data set with a couple dozen variables, such as age, level of education, self-assessed (via a Likert scale) measures of technical ability, experience, willingness to share personal inf... | Regression with an unknown dependent variable - estimating "likelihood" to do something | CC BY-SA 4.0 | null | 2011-01-04T18:26:50.577 | 2019-07-25T10:13:07.933 | 2019-07-25T10:13:07.933 | 11887 | 2641 | [
"regression",
"multivariate-analysis",
"correspondence-analysis"
] |
5976 | 2 | null | 5975 | 2 | null | The simplest approach would be to do a literature search and see if someone else developed a model with your variables, and then generate predictions based on those prior models. Another possibility would be to make your own model from another data source that does have the required information, and then use that to ge... | null | CC BY-SA 2.5 | null | 2011-01-04T18:35:41.397 | 2011-01-04T18:35:41.397 | null | null | 1036 | null |
5977 | 2 | null | 5967 | 0 | null | well, to try and help, with heteroskedasticity present, the least squares estimators are still unbiased but no longer most efficient, they no longer have smallest variance violating part of the Gauss Markov theorum. I could be wrong, but I believe you're standard errors are more effected by multicollinearity than heter... | null | CC BY-SA 2.5 | null | 2011-01-04T18:43:15.373 | 2011-01-04T18:43:15.373 | null | null | 2644 | null |
5979 | 2 | null | 5680 | 8 | null | Specifically regarding error rates as a DV, [Dixon (2008)](http://linkinghub.elsevier.com/retrieve/pii/S0749596X07001283) very cogently demonstrates that null hypothesis testing via ANOVA can cause both increased false alarm rates (calling effects "significant" when they're not) and increased miss rates (missing real e... | null | CC BY-SA 2.5 | null | 2011-01-04T19:01:59.580 | 2011-01-04T19:01:59.580 | null | null | 364 | null |
5980 | 2 | null | 5967 | 1 | null | You are trying to mix level and flow data in one regression. Just notice, that the percent change could be considered as a certain transformation of the level variable (like returns and log-returns in financial econometrics). So since your dependent variable is % change, when you include levels, they try to "mimic" sta... | null | CC BY-SA 2.5 | null | 2011-01-04T19:07:38.950 | 2011-01-04T20:24:38.007 | 2011-01-04T20:24:38.007 | 2645 | 2645 | null |
5982 | 1 | 5996 | null | 2 | 1103 | We are trying to make simulation experiment involving a common stochastic trend, that is described by the random walk (or $I(1)$ process) $Y_t = Y_{t-1} + \varepsilon_t$, where innovations $\varepsilon_t$ ~ $N(0,1)$. However when could we be sure that the past innovations are more or less reasonably included into the s... | Burn-in period for random walk | CC BY-SA 2.5 | null | 2011-01-04T19:21:07.543 | 2011-01-21T08:46:29.903 | 2011-01-21T08:46:29.903 | 2645 | 2645 | [
"time-series",
"simulation"
] |
5983 | 2 | null | 5971 | 1 | null | The general approach is to model your latencies in each condition with some distribution, like a Gaussian, and test whether the means of those two distributions are different, e.g. using a [t-test](http://en.wikipedia.org/wiki/Student%27s_t-test). This lets you pick a significance threshold (p-value) and test for it au... | null | CC BY-SA 2.5 | null | 2011-01-04T19:30:05.887 | 2011-01-04T19:30:05.887 | null | null | 2489 | null |
5984 | 2 | null | 1164 | 5 | null | My knowledge of robust estimators is solely in regards to robust standard errors for regression parameters so my comment will only be in regards to those. I would suggest people read this article,
On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors"
by: Freedman, A. David
The American Statistician, ... | null | CC BY-SA 2.5 | null | 2011-01-04T19:39:25.350 | 2011-01-04T19:39:25.350 | null | null | 1036 | null |
5985 | 2 | null | 5971 | 1 | null | I would take your latency responses from running the operation and perform some tests for normality. If these hold true, then you can create some confidence intervals using a standard normal table. If your tests for normality don't hold true, then a t-table would be more appropriate. You said that you normally plot ... | null | CC BY-SA 2.5 | null | 2011-01-04T20:03:10.713 | 2011-01-04T20:03:10.713 | null | null | 2539 | null |
5986 | 2 | null | 5972 | 8 | null | The estimator is biased, regardless.
Note first that $\alpha$ is not identifiable because you cannot distinguish between $\alpha$ and $1-\alpha$. Let's accommodate this problem by allowing that we don't care which coin is which and stipulating (arbitrarily, but with no loss of generality), that $0 \le \alpha \le 1/2... | null | CC BY-SA 2.5 | null | 2011-01-04T20:23:32.117 | 2011-01-13T22:10:04.197 | 2011-01-13T22:10:04.197 | 919 | 919 | null |
5987 | 1 | 5989 | null | 3 | 779 | What is the worst classier that learns badly in practical problems?
Edit:
Especially bad on test data..
Thanks
| Worst classifier | CC BY-SA 2.5 | null | 2011-01-04T20:58:27.577 | 2011-01-05T13:27:22.290 | 2011-01-05T13:27:22.290 | 2599 | 2599 | [
"machine-learning",
"classification"
] |
5988 | 2 | null | 5918 | 21 | null | The key thing to remember is that for cross-validation to give an (almost) unbiased performance estimate every step involved in fitting the model must also be performed independently in each fold of the cross-validation procedure. The best thing to do is to view feature selection, meta/hyper-parameter setting and opti... | null | CC BY-SA 3.0 | null | 2011-01-04T21:04:30.177 | 2014-07-04T20:38:51.437 | 2014-07-04T20:38:51.437 | 17230 | 887 | null |
5989 | 2 | null | 5987 | 7 | null | It is usually the statistician using the classifier that is the problem (for picking the wrong tool for the particular problem at hand, or for using it incorrectly). The "no free lunch" theorems show there is no a-priori distinction between classifiers (which works best/worst depends on the data), so I'd say there isn... | null | CC BY-SA 2.5 | null | 2011-01-04T21:15:11.963 | 2011-01-04T21:15:11.963 | null | null | 887 | null |
5990 | 2 | null | 5987 | 9 | null | Consider the binary case. If you don't know the proportions of the two classes, then the worst you can do is to flip a fair coin in each case: the expected error rate is $1/2$. If you do know the proportions, and the smaller of the two is $p$, say, then you should always classify objects in that category: the expecte... | null | CC BY-SA 2.5 | null | 2011-01-04T21:17:03.833 | 2011-01-04T21:17:03.833 | null | null | 919 | null |
5991 | 2 | null | 5975 | 7 | null | >
"Data! Data! Data! I can't make bricks
without clay."
Sherlock Holmes
Sorry, you just can't do it. If you make up some measure that combines some of your variables, then guess what: those exact variables will be predictive of it.
| null | CC BY-SA 2.5 | null | 2011-01-04T22:49:00.363 | 2011-01-04T22:49:00.363 | null | null | 279 | null |
5992 | 2 | null | 5962 | 9 | null | The optimal values for the hyper-parameters will be different for different learning taks, you need to tune them separately for every problem.
The reason you don't get a single optimum is becuase both the kernel parameter and the regularisation parameter control the complexity of the model. If C is small you get a smo... | null | CC BY-SA 2.5 | null | 2011-01-05T00:57:32.817 | 2011-01-05T00:57:32.817 | null | null | 887 | null |
5993 | 2 | null | 5797 | 1 | null | This answer may be way, way off base as I don't understand the medical context of your question and the nature of the medical test results you allude to, but it might be possible to estimate data mining bias by some sort of Monte Carlo permutation of your results. The type of approach I'm thinking of is taken from a bo... | null | CC BY-SA 2.5 | null | 2011-01-05T01:32:02.787 | 2011-01-05T01:32:02.787 | null | null | 226 | null |
5995 | 1 | 6014 | null | 3 | 3420 | I apologize in advance for the vague title, but I couldn't think of anything better.
I have two datasets, where one is a very small subset of the other. The percentage of people who have a specific attribute in the large dataset is x%. The percentage of people who have the same attribute in the subset is y%. The subset... | Analyzing the difference between two datasets where one is a subset of the other | CC BY-SA 2.5 | null | 2011-01-05T05:27:36.953 | 2011-01-05T19:37:25.097 | 2011-01-05T07:40:00.260 | 930 | null | [
"statistical-significance",
"data-mining",
"unbiased-estimator"
] |
5996 | 2 | null | 5982 | 4 | null | Burn-in doesn’t make sense here. The random walk you describe does not have a stationary distribution.
| null | CC BY-SA 2.5 | null | 2011-01-05T05:51:44.557 | 2011-01-05T05:51:44.557 | null | null | 1670 | null |
5997 | 1 | 5998 | null | 11 | 1095 | I'm a beginner to statistics (taken only one college course), but I have a background in programming.
I just started playing with a Bayesian classifier library for Ruby and I am looking for ideas for things to analyze.
Right now I'm messing around with Tweet categorization, but do you have any ideas?
More importantly,... | What kinds of things can I predict with a naive Bayesian classifier? | CC BY-SA 2.5 | null | 2011-01-05T05:55:56.113 | 2011-03-26T14:30:20.593 | null | null | 2649 | [
"bayesian",
"naive-bayes"
] |
5998 | 2 | null | 5997 | 13 | null | [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/), by Hastie et al. has a lot of illustrations of Machine Learning applications, and all [data sets](http://www-stat.stanford.edu/~tibs/ElemStatLearn/data.html) are available on the companion website, including data on spam as on th... | null | CC BY-SA 2.5 | null | 2011-01-05T07:35:46.257 | 2011-01-05T07:35:46.257 | null | null | 930 | null |
5999 | 2 | null | 5975 | 3 | null | I agree with @Aniko that you won't be able to "predict" anything without an outcome. Now @Andy suggestion makes sense, provided you find a users database sharing similar characteristics. As an example of related studies, I guess you might find interesting google hits on users' characteristics in studies on Twitter, Fac... | null | CC BY-SA 2.5 | null | 2011-01-05T10:02:04.577 | 2011-01-05T10:02:04.577 | null | null | 930 | null |
6000 | 2 | null | 5995 | 1 | null | For question 2 (significance), could you simply use a randomised resampling procedure and calculate the percentage of subsets of n=500 where the attribute % is ≥60?
| null | CC BY-SA 2.5 | null | 2011-01-05T10:12:10.967 | 2011-01-05T10:12:10.967 | null | null | 266 | null |
6001 | 2 | null | 5969 | 7 | null | There were already some useful comments, that are probably waiting for some updates in the question, so I will just drop some general online references:
- Practical Data Analysis for the Language Sciences with R, Baayen (2008)
- Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mix... | null | CC BY-SA 2.5 | null | 2011-01-05T10:55:52.227 | 2011-01-05T10:55:52.227 | null | null | 930 | null |
6002 | 2 | null | 5967 | 4 | null | Your dependent variable is growth. For economic time-series data it is more likely that growth will be a stationary process. This means that it will have constant mean. The level data on the other hand is usually non-stationary. Since your model is linear regression you assume that the true data generating process is
$... | null | CC BY-SA 2.5 | null | 2011-01-05T12:06:14.560 | 2011-01-05T12:06:14.560 | null | null | 2116 | null |
6003 | 2 | null | 5995 | 2 | null | I interpret "has attribute" as "the value for this attribute is not missing". As you already pointed out, the question now is whether the value "missing" has a importance regarding the target "renewed" (to add another (fictional, but plausible) example: missing entries for attribute "income" in a customer database may ... | null | CC BY-SA 2.5 | null | 2011-01-05T12:07:25.033 | 2011-01-05T12:07:25.033 | null | null | 264 | null |
6004 | 2 | null | 5746 | 0 | null | Also not an answer, but a back-of-the envelope calculation for the simpler case of i.i.d. Xj from a known distribution, to help my (non-statistician's) intuition:
Example Monte Carlo, to show the wide variation in run lengths, payoffs and costs:
```
# sample exponential*100 until > the first 3
# c0 = break-even cost, p... | null | CC BY-SA 2.5 | null | 2011-01-05T12:29:37.580 | 2011-01-05T12:29:37.580 | null | null | 557 | null |
6005 | 1 | 6009 | null | 3 | 4231 | I am slightly new to writing functions in R.
Here I have a basic function that searches for a pattern and returns the indexes of where the occurence occurs given a list dataset
```
#this functions takes a pattern and prints the indexes for the matches
find_domain <- function(pattern,list) grep(pattern,list,ignore.case... | An R function for performing searches | CC BY-SA 2.5 | null | 2011-01-05T13:36:36.637 | 2011-01-05T16:59:03.550 | null | null | 18462 | [
"r",
"dataset"
] |
6007 | 2 | null | 6005 | 4 | null | You should first replace your for loop with something like `apply(d7_dataset, 1, foo)`, where `foo()` is either your function or something along those lines, e.g. `gregexpr()`. The result of `gregexpr()` is a list of numeric vectors with attributes similar to `regexpr()` but giving all matches in each element.
On a rel... | null | CC BY-SA 2.5 | null | 2011-01-05T13:53:05.480 | 2011-01-05T16:59:03.550 | 2011-01-05T16:59:03.550 | 930 | 930 | null |
6008 | 1 | 6011 | null | 4 | 6774 | I am trying to do Bayesian posterior predictive checking, whereby I calculate the DIC for my fitted model, and compare to DIC from data simulated from the fitted model. I can get the DIC out of winBUGS, however I am not sure how to calculate the likelihood (for the DIC) outside of winBUGS (i.e., without fitting new mod... | How to calculate likelihood for a bayesian model? | CC BY-SA 2.5 | null | 2011-01-05T13:56:46.507 | 2023-04-29T18:48:24.233 | 2011-01-05T15:59:50.490 | 930 | 2654 | [
"bayesian",
"likelihood"
] |
6009 | 2 | null | 6005 | 6 | null | If your dataset is a matrix, then `grep` will work directly:
```
> set.seed(1)
> bb=matrix(letters[sample(1:20, 100, rep=TRUE)], nrow=20)
> grep("b", bb)
[1] 10 55 56 69 92
> bb[grep("b", bb)]
[1] "b" "b" "b" "b" "b"
```
If you have multiple patterns, then use `ldply` from package [plyr](http://cran.r-project.org/web... | null | CC BY-SA 2.5 | null | 2011-01-05T14:06:47.837 | 2011-01-05T14:06:47.837 | null | null | 2116 | null |
6011 | 2 | null | 6008 | 4 | null | The [likelihood](http://en.wikipedia.org/wiki/Likelihood_function) is numerically equal to $\operatorname{P}(D\vert\theta)$, where D is the data vector and theta the parameter vector. Well, strictly speaking it's only equal up to a multiplicative factor, but most software packages including WinBUGS define it as being e... | null | CC BY-SA 2.5 | null | 2011-01-05T15:33:28.930 | 2011-01-05T16:01:11.860 | 2011-01-05T16:01:11.860 | 449 | 449 | null |
6013 | 1 | 6015 | null | 8 | 1481 | I have a python script that creates a list of lists of server uptime and performance data, where each sub-list (or 'row') contains a particular cluster's stats. For example, nicely formatted it looks something like this:
```
------- ------------- ------------ ---------- -------------------
Cluster %Availability ... | How to identify outliers in server uptime performance data? | CC BY-SA 4.0 | null | 2011-01-05T19:17:42.883 | 2023-01-04T16:08:29.513 | 2023-01-04T16:08:29.513 | 362671 | 2659 | [
"outliers",
"quantiles"
] |
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