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5357 | 2 | null | 5340 | 11 | null | I mistrust all but the lowest-level functions in Excel, and for good reason: many procedures that go beyond simple arithmetic operations have flaws or errors and most of them are poorly documented. This includes all the probability distribution functions.
Numerical flaws are inevitable due to limitations in floating p... | null | CC BY-SA 2.5 | null | 2010-12-10T17:11:32.343 | 2010-12-10T17:11:32.343 | null | null | 919 | null |
5358 | 2 | null | 5347 | 26 | null | If it often resembles a [Poisson](http://en.wikipedia.org/wiki/Poisson_distribution), have you tried approximating it by a Poisson with parameter $\lambda = \sum p_i$ ?
EDIT: I've found a theoretical result to justify this, as well as a name for the distribution of $Y$: it's called the [Poisson binomial distribution](h... | null | CC BY-SA 2.5 | null | 2010-12-10T18:23:10.457 | 2010-12-12T17:20:27.390 | 2010-12-12T17:20:27.390 | 449 | 449 | null |
5359 | 1 | 5365 | null | 5 | 1537 | >
Diagonal elements of Psi (...)
represent independent noise variances
for each of the variables
C.M. Bishop, Pattern Recognition and Machine Learning
...but I'm not clear on what does the Psi signify.
I've applied FA to a series of images, received Psi matrix and now struggling with interpreting the results.... | What does the Psi term in factor analysis signify? | CC BY-SA 2.5 | null | 2010-12-10T18:26:17.983 | 2010-12-11T12:52:08.337 | 2010-12-11T12:52:08.337 | null | 1371 | [
"factor-analysis"
] |
5360 | 1 | 5361 | null | 13 | 5126 | I am fitting a stepwise logistic regression on a set of data in SPSS. In the procedure, I am fitting my model to a random subset that is approx. 60% of the total sample, which is about 330 cases.
What I find interesting is that every time I re-sample my data, I am getting different variables popping in and out in the ... | Stepwise logistic regression and sampling | CC BY-SA 2.5 | null | 2010-12-10T18:42:31.760 | 2015-12-02T14:51:50.233 | 2010-12-11T12:56:21.487 | null | 569 | [
"logistic",
"spss",
"stepwise-regression"
] |
5361 | 2 | null | 5360 | 16 | null | If you're going to use a stepwise procedure, don't resample. Create one random subsample once and for all. Perform your analysis on it. Validate the results against the held-out data. It's likely most of the "significant" variables will turn out not to be significant.
(Edit 12/2015: You can indeed go beyond such a ... | null | CC BY-SA 3.0 | null | 2010-12-10T19:05:02.207 | 2015-12-02T14:51:50.233 | 2015-12-02T14:51:50.233 | 919 | 919 | null |
5362 | 2 | null | 5278 | 14 | null | Thank you for your great answer so far. While they where very enlightening, I felt that using them for the course I am currently teaching (well, TA'ing) will be too much for my students. (I help teach the course BioStatistics for students from advanced degrees in medicine sciences)
Therefore, I ended up creating two i... | null | CC BY-SA 2.5 | null | 2010-12-10T19:15:07.527 | 2010-12-10T20:26:42.770 | 2010-12-10T20:26:42.770 | 253 | 253 | null |
5363 | 1 | null | null | 24 | 8461 | I got a slight confusion on the [backpropagation](http://en.wikipedia.org/wiki/Backpropagation) algorithm used in [multilayer perceptron](http://en.wikipedia.org/wiki/Multilayer_perceptron) (MLP).
The error is adjusted by the cost function. In backpropagation, we are trying to adjust the weight of the hidden layers. Th... | Backpropagation algorithm and error in hidden layer | CC BY-SA 4.0 | null | 2010-12-10T19:21:44.130 | 2021-07-26T16:04:02.020 | 2021-05-05T17:56:24.207 | 155836 | 2329 | [
"machine-learning",
"neural-networks",
"backpropagation"
] |
5364 | 1 | 5385 | null | 10 | 4734 | The underlying model of [PLS](http://en.wikipedia.org/wiki/Partial_least_squares_regression) is that a given $n \times m$ matrix $X$ and $n$ vector $y$ are related by
$$X = T P' + E,$$
$$y = T q' + f,$$
where $T$ is a latent $n \times k$ matrix, and $E, f$ are noise terms (sssuming $X, y$ are centered).
PLS produce... | How to compute the confidence intervals on regression coefficients in PLS? | CC BY-SA 2.5 | null | 2010-12-10T20:19:19.707 | 2020-11-08T19:14:42.627 | null | null | 795 | [
"regression",
"confidence-interval",
"latent-variable",
"partial-least-squares"
] |
5365 | 2 | null | 5359 | 6 | null | Following Bishop's notation, the FA model is written as (Eq. 12.64, p. 584):
$$
p(x|z)=\mathcal{N}(x|\mathbf{W}z+\boldsymbol{\mu},\boldsymbol{\psi})
$$
where $\boldsymbol{\psi}$ is a $D\times D$ diagonal matrix of so-called variable uniquenesses, that is the variance not accounted for by the latent factors, whereas $\m... | null | CC BY-SA 2.5 | null | 2010-12-10T20:34:45.023 | 2010-12-10T20:42:01.910 | 2017-04-13T12:44:20.943 | -1 | 930 | null |
5366 | 1 | null | null | 23 | 18356 | I need to cluster units into $k$ clusters to minimize within-group sum of squares (WSS), but I need to ensure that the clusters each contain at least $m$ units. Any idea if any of R's clustering functions allow for clustering into $k$ clusters subject to a minimum cluster size constraint? kmeans() does not seem to of... | Clustering (k-means, or otherwise) with a minimum cluster size constraint | CC BY-SA 2.5 | null | 2010-12-10T20:53:42.207 | 2019-12-09T16:16:06.647 | null | null | 96 | [
"r",
"clustering"
] |
5367 | 2 | null | 5366 | 4 | null | I think it would just be a matter of running the k means as part of an if loop with a test for cluster sizes, I.e. Count n in cluster k - also remember that k means will give different results for each run on the same data so you should probably be running it as part of a loop anyway to extract the "best" result
| null | CC BY-SA 2.5 | null | 2010-12-10T21:31:02.173 | 2010-12-10T21:31:02.173 | null | null | null | null |
5368 | 1 | null | null | 2 | 451 | I think that after apply PCA the covariance between components is reduced since the aim of PCA is to maximize variance. Covariance is needed by Factor analysis and thus it does not make sense to operate on the data in PCA space.
Does this make any sense? Does someone have any other explanation?
| Why would it not make sense to apply factor analysis after PCA? | CC BY-SA 2.5 | null | 2010-12-11T03:34:15.997 | 2010-12-13T04:35:59.257 | 2010-12-11T12:50:40.273 | null | null | [
"pca",
"factor-analysis"
] |
5369 | 2 | null | 5368 | 4 | null | PCA actually ends up with orthogonal variables, therefore the covariance between the components should be 0.
It does not make sense to do factor analysis which selects the factors from a dataset after a procedure that makes factors from the dataset.
| null | CC BY-SA 2.5 | null | 2010-12-11T06:27:19.523 | 2010-12-11T06:27:19.523 | null | null | 1808 | null |
5370 | 2 | null | 5366 | 6 | null | Use EM Clustering
In EM clustering, the algorithm iteratively refines an initial cluster model to fit the data and determines the probability that a data point exists in a cluster. The algorithm ends the process when the probabilistic model fits the data. The function used to determine the fit is the log-likelihood of ... | null | CC BY-SA 2.5 | null | 2010-12-11T06:33:24.350 | 2010-12-11T06:33:24.350 | null | null | 1808 | null |
5371 | 2 | null | 5366 | 2 | null | How large is your data set? Maybe you could try to run a hierarchical clustering and then decide which clusters retain based on your dendrogram.
If your data set is huge, you could also combine both clustering methods: an initial non-hierarchical clustering and then a hierarchical clustering using the groups from the n... | null | CC BY-SA 2.5 | null | 2010-12-11T08:04:01.883 | 2010-12-11T15:52:15.773 | 2010-12-11T15:52:15.773 | 221 | 221 | null |
5372 | 1 | 5383 | null | 3 | 463 | I need to detect Out Of Shelf situations in reatil stores. What I had in mind is to assume that purchase frequency of some article (SKU) has certain ditribution (I tried with Poisson) and then calculate probability (according to that distribution) of that specific article was not bought since last purchase, and if that... | What distribution should be used for the detection of out of shelf situations in a retail stores? | CC BY-SA 2.5 | null | 2010-12-11T11:31:49.120 | 2011-10-16T11:19:48.770 | 2010-12-11T18:57:33.810 | null | 2341 | [
"distributions",
"probability",
"poisson-distribution"
] |
5373 | 2 | null | 5344 | 23 | null | Others have summarized the differences very well. My impression is that `lme4` is more suited for clustered data sets especially when you need to use crossed random effects. For repeated measures designs (including many longitudinal designs) however, `nlme` is the tool since only `nlme` supports specifying a correlatio... | null | CC BY-SA 3.0 | null | 2010-12-11T11:45:13.167 | 2016-12-08T15:40:34.870 | 2016-12-08T15:40:34.870 | 60613 | 2020 | null |
5374 | 2 | null | 5333 | 7 | null | You're misinterpreting these results, which is easy to do as with mixed models there's more than one type of 'fitted value' and the documentation of `lmer` isn't as clear as it might be. Try using `fixed.effects()` in place of `fitted()` and you should get correlations which makes more intuitive sense if you're interes... | null | CC BY-SA 2.5 | null | 2010-12-11T11:52:26.187 | 2010-12-11T11:52:26.187 | null | null | 449 | null |
5375 | 2 | null | 5278 | 25 | null | How about something like this?
Following Crawley (2005). Statistics. An introduction using R: Wiley.
| null | CC BY-SA 2.5 | null | 2010-12-11T13:19:29.733 | 2010-12-11T13:19:29.733 | null | null | 1050 | null |
5376 | 2 | null | 5278 | 2 | null | It seems the ship has already sailed in terms of an answer, but I think that if this is an introductory course that most of the displays offered here are going to be too difficult to grasp for introductory students... or at the very least too difficult to grasp without an introductory display which provides a very simp... | null | CC BY-SA 2.5 | null | 2010-12-11T14:43:17.020 | 2010-12-12T04:24:44.690 | 2010-12-12T04:24:44.690 | 196 | 196 | null |
5377 | 2 | null | 5347 | 4 | null | Well, based on your description and the discussion in the comments it is clear that $Y$ has mean $\sum_i p_i$ and variance $\sum_i p_{i}(1-p_{i})$. The shape of $Y$'s distribution will ultimately depend on the behavior of $p_i$. For suitably "nice" $p_i$ (in the sense that not too many of them are really close to zer... | null | CC BY-SA 2.5 | null | 2010-12-11T17:05:23.770 | 2010-12-11T17:05:23.770 | null | null | null | null |
5378 | 2 | null | 5278 | 12 | null | Since we gather certain types of nice graphs in this post, here is another one that I recently found and may help you understand how ANOVA works and how the F statistic is generated. The graphic was created using the [granova](http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=granova:granova.1w) package in R.
![alt text](... | null | CC BY-SA 2.5 | null | 2010-12-11T17:33:56.597 | 2010-12-11T17:33:56.597 | null | null | 339 | null |
5379 | 2 | null | 5360 | 10 | null | In general, there are two problems of feature selection:
- minimal optimal, where you seek for smallest set of variables that give you the smallest error
- all relevant, where you seek for all variables relevant in a problem
The convergence of predictor selection is in a domain of the all relevant problem, which is... | null | CC BY-SA 2.5 | null | 2010-12-11T18:04:50.730 | 2010-12-11T23:40:23.283 | 2010-12-11T23:40:23.283 | null | null | null |
5380 | 2 | null | 5360 | 12 | null | An important question is "why do why do you want a model with as few variables a possible?". If you want to have as few variables as possible to minimize the cost of data collection for the operational use of your model, then the answers given by whuber and mbq are an excellent start.
If predictive performance is what... | null | CC BY-SA 2.5 | null | 2010-12-11T18:34:21.803 | 2010-12-11T18:34:21.803 | null | null | 887 | null |
5381 | 2 | null | 5360 | 6 | null | You might glance at the paper [Stability Selection](http://www.stats.ox.ac.uk/~meinshau/stability.pdf) by Meinshausen and Buhlmann in J R Statist. Soc B (2010) 72 Part 4, and the discussion after it. They consider what happens when you repeatedly divide your set of data points at random into two halves and look for fea... | null | CC BY-SA 2.5 | null | 2010-12-11T19:10:31.897 | 2010-12-12T11:40:31.173 | 2010-12-12T11:40:31.173 | 930 | 1789 | null |
5382 | 1 | 5644 | null | 2 | 2325 | I'm a new guy here. Hopefully, I'm asking this question to right forum.
## Problem:
We have data of a group of people (P1, P2, P3). They rank their expertise (1-10, where higher number is better) in a list of components (G1, G2, G3).
```
P1 P2 P3
--------------
G1 | 8 4 7
G2 | 7 3 7
G3 | 9 6 5
```
... | Normalizing rating in a group of people [finding effectiveness] | CC BY-SA 2.5 | null | 2010-12-11T19:54:14.823 | 2010-12-20T07:25:21.537 | 2010-12-11T20:26:26.090 | 2344 | 2344 | [
"normalization",
"rating"
] |
5383 | 2 | null | 5372 | 4 | null | Poisson makes a certain amount of sense if "purchase frequency" is e.g. number of items bought per day. Negative binomial would also make sense (probably more sensible, as it will allow for more variation). If you have a huge amount of data, then you could just use the empirical distribution (i.e. across days, what is... | null | CC BY-SA 2.5 | null | 2010-12-11T20:40:51.063 | 2010-12-11T20:40:51.063 | null | null | 2126 | null |
5384 | 2 | null | 5114 | 2 | null | Since the log-normal distribution has two parameters, you can't satisfactorily fit it to three constraints that don't naturally fit it. With extreme quantiles of 2.5 and 7.5, the mode is ~4, and there is not much you can do about it. Since the scale of the errors for `a` and `b` is much smaller than for `c`, one of the... | null | CC BY-SA 2.5 | null | 2010-12-11T20:49:36.103 | 2010-12-11T20:49:36.103 | null | null | 279 | null |
5385 | 2 | null | 5364 | 9 | null | Do you know this article: [PLS-regression: a basic tool of chemometrics](https://www.sciencedirect.com/science/article/abs/pii/S0169743901001551) ([PDF](http://libpls.net/publication/PLS_basic_2001.pdf))? Deriving SE and CI for the PLS parameters is described in §3.11.
I generally rely on Bootstrap for computing CIs, a... | null | CC BY-SA 4.0 | null | 2010-12-11T21:07:43.147 | 2020-11-08T19:14:42.627 | 2020-11-08T19:14:42.627 | 930 | 930 | null |
5387 | 1 | 5398 | null | 19 | 12398 | In a logit model, is there a smarter way to determine the effect of an independent ordinal variable than to use dummy variables for each level?
| Logit with ordinal independent variables | CC BY-SA 2.5 | null | 2010-12-11T22:50:26.880 | 2010-12-12T11:26:47.150 | 2010-12-11T23:15:04.053 | 82 | 82 | [
"logistic",
"logit",
"ordinal-data"
] |
5388 | 2 | null | 5387 | 6 | null | it's perfectly fine to use a categorical predictor in a logit (or OLS) regression model if the levels are ordinal. But if you have a reason to treat each level as discrete (or if in fact your categorical variable is nominal rather than ordinal), then, as alternative to dummy coding, you can also use orthogonal contrast... | null | CC BY-SA 2.5 | null | 2010-12-11T23:29:31.963 | 2010-12-11T23:29:31.963 | null | null | 11954 | null |
5389 | 2 | null | 5347 | 9 | null | @onestop provides good references. The Wikipedia article on the Poisson binomial distribution gives a recursive formula for computing the exact probability distribution; it requires $O(n^2)$ effort. Unfortunately, it's an alternating sum, so it will be numerically unstable: it's hopeless to do this computation with f... | null | CC BY-SA 2.5 | null | 2010-12-11T23:50:58.420 | 2010-12-11T23:50:58.420 | null | null | 919 | null |
5390 | 2 | null | 1870 | 1 | null | In youtube there are a lot of videos related with SPSS, I usually learn a gui with them. I recommend you record videos to teach how to apply methods and encourage them to use the "paste" button to learn a little of syntax
| null | CC BY-SA 2.5 | null | 2010-12-12T01:27:00.017 | 2010-12-12T01:39:25.333 | 2010-12-12T01:39:25.333 | null | null | null |
5391 | 2 | null | 1870 | 1 | null | Your method will certainly work for most introductory techniques you'd need to do. I am quite familiar with R, but I was required to learn Minitab to teach to non-statistics people. In one afternoon, I had a basic enough understanding of Minitab to explain how to use it in the limited framework of an introductory stats... | null | CC BY-SA 2.5 | null | 2010-12-12T02:13:30.870 | 2010-12-12T02:13:30.870 | null | null | 1118 | null |
5392 | 1 | 5407 | null | 14 | 15148 | Let:
Standard deviation of random variable $A =\sigma_{1}=5$
Standard deviation of random variable $B=\sigma_{2}=4$
Then the variance of A+B is:
$Var(w_{1}A+w_{2}B)= w_{1}^{2}\sigma_{1}^{2}+w_{2}^{2}\sigma_{2}^{2} +2w_{1}w_{2}p_{1,2}\sigma_{1}\sigma_{2}$
Where:
$p_{1,2}$ is the correlation between the two random variab... | Variance of two weighted random variables | CC BY-SA 2.5 | null | 2010-12-12T03:21:44.353 | 2010-12-12T21:40:58.743 | 2010-12-12T10:40:35.683 | 1636 | 1636 | [
"random-variable"
] |
5393 | 2 | null | 5347 | 3 | null | I think other answers are great, but I didn't see any Bayesian ways of estimating your probability. The answer doesn't have an explicit form, but the probability can be simulated using R.
Here is the attempt:
$$ X_i | p_i \sim Ber(p_i)$$
$$ p_i \sim Beta(\alpha, \beta) $$
Using [wikipedia](http://en.wikipedia.org/wik... | null | CC BY-SA 2.5 | null | 2010-12-12T03:38:17.960 | 2010-12-13T00:00:30.870 | 2010-12-13T00:00:30.870 | 1307 | 1307 | null |
5395 | 2 | null | 5382 | 3 | null | It sounds like you have multiple sources of information about measurements of interest.
The aim is to combine the multiple sources so as to maximise measurement accuracy.
In general you could adopt a formula that weights the different sources of information based on the confidence you ascribe to the source.
It also see... | null | CC BY-SA 2.5 | null | 2010-12-12T08:57:36.583 | 2010-12-12T08:57:36.583 | 2017-04-13T12:44:33.310 | -1 | 183 | null |
5396 | 1 | null | null | 1 | 1749 |
- Does anyone know how to create a nice looking summary table in JMP for several variables? I use Tables>Summary and the table I get has one row that includes for example mean(var1) mean(var2) mean(var3) median(var1) median(var2) median(var3) and so on. I would like each variable to have row of its own, with columns r... | Summary and frequency tables in JMP | CC BY-SA 2.5 | null | 2010-12-12T10:18:17.687 | 2011-04-20T23:57:53.813 | 2010-12-12T13:02:56.367 | null | null | [
"data-visualization",
"jmp"
] |
5397 | 2 | null | 5363 | 6 | null | I haven't dealt with Neural Networks for some years now, but I think you will find everything you need here:
[Neural Networks - A Systematic Introduction, Chapter 7: The backpropagation algorithm](http://page.mi.fu-berlin.de/rojas/neural/chapter/K7.pdf)
I apologize for not writing the direct answer here, but since I ha... | null | CC BY-SA 2.5 | null | 2010-12-12T11:13:06.010 | 2010-12-12T11:13:06.010 | null | null | 264 | null |
5398 | 2 | null | 5387 | 17 | null | To add to @dmk38's response, "any set of scores gives a valid test, provided they are constructed without consulting the results of the experiment. If the set of scores is poor, in that it badly distorts a numerical scale that really does underlie the ordered classification, the test will not be sensitive. The scores s... | null | CC BY-SA 2.5 | null | 2010-12-12T11:21:34.503 | 2010-12-12T11:26:47.150 | 2010-12-12T11:26:47.150 | 930 | 930 | null |
5399 | 1 | null | null | 11 | 16840 | Say we have two Gaussian random vectors $p(x_1) = N(0,\Sigma_1), p(x_2) = N(0,\Sigma_2)$, is there a well known result for the expectation of their product $E[x_1x_2^T]$ without assuming independence?
| Expectation of product of Gaussian random variables | CC BY-SA 2.5 | null | 2010-12-12T12:11:28.810 | 2010-12-21T05:30:02.967 | 2010-12-12T15:04:13.580 | null | null | [
"random-variable",
"normal-distribution",
"expected-value"
] |
5400 | 2 | null | 5321 | 4 | null | (This was supposed to be a comment, but it got too long)
I'd say
```
L = data'*data;
[Ev, Vals] = eig(L);
[Ev,Vals] = sort(Ev,Vals);
```
is pretty dodgy; it's a common notion in numerical linear algebra that forming matrices like $\mathbf A^T\mathbf A$ worsens the so-called "conditioning" of your problem, in that... | null | CC BY-SA 2.5 | null | 2010-12-12T12:52:20.237 | 2010-12-12T12:52:20.237 | 2017-04-13T12:19:38.800 | -1 | 830 | null |
5401 | 2 | null | 5382 | 1 | null | I'm going to give a stab at an answer, if I misunderstand your question, please comment here or revise your question and I'll do my best to revise my answer.
If I understand you correctly you are trying to find a users 'true level of expertise'. An elusive quantity to be sure. Given what you have written, I think you... | null | CC BY-SA 2.5 | null | 2010-12-12T13:51:48.310 | 2010-12-12T14:00:57.243 | 2010-12-12T14:00:57.243 | 196 | 196 | null |
5402 | 1 | 5410 | null | 5 | 579 | I have heard that many problems in Mathematical Statistics can be stated and solved in terms of Symplectic Geometry. Unfortunately this was a pretty vague statement and I am interested in something more concrete. Also there seem to be some books written form this perspective, but I couldn't find any.
| References for use of symplectic geometry in statistics? | CC BY-SA 2.5 | null | 2010-12-12T14:10:55.290 | 2020-08-12T11:56:11.283 | 2019-04-29T07:40:24.520 | 11887 | 2354 | [
"mathematical-statistics",
"references",
"information-geometry"
] |
5403 | 2 | null | 5392 | 11 | null | It isn't linear. The formula says it isn't linear. Trust your mathematical instinct!
It only appears linear in the graph because of the scale, with $\sigma_{1}=5$ and $\sigma_{2}=4$. Try it yourself: calculate the slopes at a few places and you will see that they differ. You can exaggerate the difference by pickin... | null | CC BY-SA 2.5 | null | 2010-12-12T15:24:15.363 | 2010-12-12T15:29:50.743 | 2010-12-12T15:29:50.743 | null | null | null |
5404 | 2 | null | 5343 | 2 | null | I can tell you what I would do as a machine learner ;):
- Creating two models $M_1,M_2$ for x using $y_1,y_2$ respectively
- A prediction for x is calculated as the average of the predictions of $M_1$ and $M_2$
A "model" can be either a gaussian distribution (as described, if you know that x a) has a gaussian dist... | null | CC BY-SA 2.5 | null | 2010-12-12T15:52:47.647 | 2010-12-12T19:04:16.523 | 2010-12-12T19:04:16.523 | 264 | 264 | null |
5405 | 2 | null | 5327 | 5 | null | To echo Aniko's comment: The primary assumption is the existence of truncation. This is not the same assumption as the two other possibilities that your post suggests to me: boundedness and sample selection.
If you have a fundamentally bounded dependent variable rather than a truncated one you might want to move to a ... | null | CC BY-SA 2.5 | null | 2010-12-12T16:10:31.023 | 2010-12-12T16:10:31.023 | null | null | 1739 | null |
5407 | 2 | null | 5392 | 14 | null | Using $w_1 + w_2 = 1$, compute
$$\eqalign{
\text{Var}(w_1 A + w_2 B) &= \left( w_1 \sigma_1 + w_2 \sigma_2 \right)^2 \cr
&= \left( w_1(\sigma_1 - \sigma_2) + \sigma_2 \right)^2 \text{.}
} $$
This shows that when $\sigma_1 \ne \sigma_2$, the graph of the variance versus $w_1$ (shown sideways in the illustration) is a pa... | null | CC BY-SA 2.5 | null | 2010-12-12T17:13:53.450 | 2010-12-12T21:40:58.743 | 2010-12-12T21:40:58.743 | 919 | 919 | null |
5408 | 2 | null | 4360 | 11 | null | A subjectivist Bayesian argument is practically the only way (from a statistical standpoint) you could go about understanding your intuition, which is--properly speaking--the subject of a psychological investigation, not a statistical one. However, it is patently unfair--and therefore invalid--to use a Bayesian approa... | null | CC BY-SA 2.5 | null | 2010-12-12T17:50:29.487 | 2010-12-12T17:50:29.487 | null | null | 919 | null |
5409 | 2 | null | 5402 | 6 | null | A direct connection would be unexpected: the two fields appear to have little in common. For example, [a modern introduction to symplectic geometry published by the American Mathematical Society](http://loveguests.com/books/science/mathematics-statistics/3103-an-introduction-to-symplectic-geometry.html) appears to mak... | null | CC BY-SA 2.5 | null | 2010-12-12T18:01:19.650 | 2010-12-12T18:01:19.650 | null | null | 919 | null |
5410 | 2 | null | 5402 | 5 | null | I know nothing whatsoever about symplectic geometry, but a bit of googling brought up a [1997 article in the Journal of Statistical Planning & Inference by Barndorff-Nielsen & Jupp](http://dx.doi.org/10.1016/S0378-3758%2897%2900006-2), which contains this quote:
>
Some other links between statistics and symplectic geo... | null | CC BY-SA 2.5 | null | 2010-12-12T18:33:17.970 | 2010-12-12T18:33:17.970 | null | null | 449 | null |
5411 | 2 | null | 5114 | 5 | null | If, given an answer to my comment above, you wish to bound the range of the distribution, why not simply fit a Beta distribution where you rescale to the unit interval? In other words, if you know that the parameter of interest should fall between $[2 , 8]$, then why not define $Y = \frac{X - 5}{6} + \frac{1}{2} = \fr... | null | CC BY-SA 2.5 | null | 2010-12-12T18:38:47.193 | 2010-12-12T18:44:14.977 | 2010-12-12T18:44:14.977 | 1499 | 1499 | null |
5412 | 2 | null | 5304 | 35 | null | Following up: profile confidence intervals are more reliable (choosing the appropriate cutoff for the likelihood does involve an asymptotic (large sample) assumption, but this is a much weaker assumption than the quadratic-likelihood-surface assumption underlying the Wald confidence intervals). As far as I know, there ... | null | CC BY-SA 2.5 | null | 2010-12-12T19:47:32.683 | 2010-12-12T19:47:32.683 | null | null | 2126 | null |
5414 | 1 | 5417 | null | 19 | 1808 | What are some of the main pitfalls of using linear mixed-effects models? What are the most important things to test/watch out for in assessing the appropriateness of your model? When comparing models of the same dataset, what are the most important things to look for?
| Pitfalls of linear mixed models | CC BY-SA 2.5 | null | 2010-12-12T21:53:35.743 | 2010-12-13T08:21:53.467 | null | null | 52 | [
"mixed-model",
"model-comparison"
] |
5415 | 2 | null | 5399 | 9 | null | Yes, there is a well-known result. Based on your edit, we can focus first on individual entries of the array $E[x_1 x_2^T]$. Such an entry is the product of two variables of zero mean and finite variances, say $\sigma_1^2$ and $\sigma_2^2$. The [Cauchy-Schwarz Inequality](http://en.wikipedia.org/wiki/Cauchy%E2%80%93... | null | CC BY-SA 2.5 | null | 2010-12-12T22:41:14.540 | 2010-12-12T22:41:14.540 | null | null | 919 | null |
5417 | 2 | null | 5414 | 17 | null | This is a good question.
Here are some common pitfalls:
- Using standard likelihood theory, we may derive a test to compare two nested
hypotheses, $H_0$ and $H_1$, by computing the likelihood ratio test statistic. The null distribution of this test statistic is approximately chi-squared with degrees of freedom equal t... | null | CC BY-SA 2.5 | null | 2010-12-13T00:15:05.537 | 2010-12-13T00:15:05.537 | null | null | 1307 | null |
5418 | 1 | null | null | 48 | 2384 | I'm planning to start writing R packages.
I thought it would be good to study the source code of existing packages
in order to learn the conventions of package construction.
My criteria for good packages to study:
- Simple statistical/technical ideas: The point is to learn about the mechanics of package construction. ... | First R packages source code to study in preparation for writing own package | CC BY-SA 2.5 | null | 2010-12-13T00:56:07.200 | 2010-12-20T19:51:03.293 | 2010-12-13T03:21:06.557 | 183 | 183 | [
"r"
] |
5419 | 2 | null | 5418 | 1 | null | i would recommend hadley's reshape package. you can find the source at [https://github.com/hadley/reshape](https://github.com/hadley/reshape)
| null | CC BY-SA 2.5 | null | 2010-12-13T01:42:30.347 | 2010-12-13T01:42:30.347 | null | null | 277 | null |
5420 | 2 | null | 5414 | 6 | null | Modeling the variance structure is arguably the most powerful and important single feature of mixed models. This extends beyond variance structure to include correlation among observations. Care must be taken to build an appropriate covariance structure otherwise tests of hypotheses, confidence intervals, and estimat... | null | CC BY-SA 2.5 | null | 2010-12-13T02:13:33.037 | 2010-12-13T02:13:33.037 | null | null | 2310 | null |
5421 | 2 | null | 5418 | 7 | null | Why not take an empirically-driven random sampling approach? Just pick a few and see which work for you.
Kidding aside, just look at a few packages you yourself use and are familiar with. Downloading them is easy, or if you prefer you can also view them via a web interface at R-Forge, RForge, or Github.
You will most... | null | CC BY-SA 2.5 | null | 2010-12-13T02:47:06.790 | 2010-12-13T02:47:06.790 | null | null | 334 | null |
5422 | 2 | null | 3511 | 4 | null | You may check the paper: Efficient L1-regularized logistic regression, which is an IRLS-based algorithm for LASSO.
Regarding the implementation, the link may be useful for you
(http://ai.stanford.edu/~silee/softwares/irlslars.htm).
| null | CC BY-SA 2.5 | null | 2010-12-13T03:03:02.913 | 2010-12-13T03:03:02.913 | null | null | null | null |
5423 | 2 | null | 5368 | 0 | null | Factor Analysis differs from PCA by an extra "rotation" step. The purpose is to make the resulting factors more interpretable. check out my blog post for more details and a good resource: [http://blog.bzst.com/2009/03/principal-components-analysis-vs-factor.html](http://blog.bzst.com/2009/03/principal-components-analys... | null | CC BY-SA 2.5 | null | 2010-12-13T04:35:59.257 | 2010-12-13T04:35:59.257 | null | null | 1945 | null |
5425 | 2 | null | 1995 | 9 | null | Here's another perspective on using multilevel vs. regression models: In an interesting paper by Afshartous and de Leeuw, they show that if the purpose of the modeling is predictive (that is, to predict new observations), the choice of model is different from when the goal is inference (where you try to match the model... | null | CC BY-SA 2.5 | null | 2010-12-13T05:08:35.643 | 2010-12-13T05:08:35.643 | null | null | 1945 | null |
5426 | 2 | null | 1995 | 4 | null | I learned from Snijders and Bosker, Multilevel Analysis: An introduction to basic and advanced multilevel modeling. It is very well pitched at the beginner I think, it must be because I am a thicko where these things are concerned and it made sense to me.
I second the Gelman and Hill as well, a truly brilliant book.
| null | CC BY-SA 2.5 | null | 2010-12-13T07:47:49.127 | 2010-12-13T07:47:49.127 | null | null | 199 | null |
5427 | 2 | null | 5414 | 9 | null | The common pitfall which I see is the ignoring the variance of random effects. If it is large compared to residual variance or variance of dependent variable, the fit usually looks nice, but only because random effects account for all the variance. But since the graph of actual vs predicted looks nice you are inclined ... | null | CC BY-SA 2.5 | null | 2010-12-13T08:21:53.467 | 2010-12-13T08:21:53.467 | null | null | 2116 | null |
5428 | 2 | null | 5351 | 15 | null | Suppose we have a regression
\begin{align*}
y=X\beta+u
\end{align*}
Then OLS estimate $\hat{\beta}$ is
\begin{align*}
\widehat{\beta}-\beta=(X'X)^{-1}X'u
\end{align*}
and assuming that $\hat{\beta}$ is unbiased estimate we have
\begin{align*}
Var(\widehat{\beta})=E\left[(X'X)^{-1}X'uu'X(X'X)^{-1}\right]
\end{align*}
Th... | null | CC BY-SA 3.0 | null | 2010-12-13T09:08:56.433 | 2014-01-02T14:13:55.960 | 2014-01-02T14:13:55.960 | 2116 | 2116 | null |
5429 | 2 | null | 3849 | 3 | null | As suggested by [whuber](https://stats.stackexchange.com/users/919/whuber) & [Srikant Vadali](https://stats.stackexchange.com/users/28/srikant-vadali) I posted [this question](http://www.stata.com/statalist/archive/2010-12/msg00443.html) on Statalist.
[Maarten L. Buis](http://www.maartenbuis.nl/) [pointed](http://www.s... | null | CC BY-SA 2.5 | null | 2010-12-13T12:09:19.943 | 2010-12-13T12:09:19.943 | 2017-04-13T12:44:24.677 | -1 | 22 | null |
5430 | 1 | null | null | 4 | 277 | This image show a histogram (200 bins) of accumulated distances from a radar distance meter (very noisy).
The peak around 7 meters is an object. At thought this looked kind of like a normal distribution, at least if you ignore values <4m (which for this application is reasonable).
I have also achieved a reasonable fit ... | How would you filter this? | CC BY-SA 2.5 | null | 2010-12-13T12:43:59.630 | 2011-07-17T06:19:51.090 | 2017-04-13T12:19:38.853 | -1 | 1411 | [
"distributions",
"signal-processing"
] |
5431 | 1 | null | null | 4 | 1818 | Should small sample sizes of 5 and less be included in reports?
This is an area of medical training that is currently being reported only to the course level trainers and statistically not included in reports to upper management. All of the data is mined from course-level surveys.
| Statistical significance with sample size of 5 or less | CC BY-SA 2.5 | null | 2010-12-13T15:32:39.450 | 2010-12-17T07:43:57.623 | 2010-12-17T07:43:57.623 | 223 | 2362 | [
"hypothesis-testing",
"statistical-significance"
] |
5432 | 2 | null | 5431 | 5 | null | If you are doing things properly the worst thing you can do is not to reject $H_0$ (does not mean $H_0$ is true, only mean you do not have enough evidence to reject)
Anyway, there can be something significant with 5 observations:
If you need to test, for example, wether your random variable has a negative mean under... | null | CC BY-SA 2.5 | null | 2010-12-13T15:48:08.347 | 2010-12-13T15:48:08.347 | null | null | 223 | null |
5433 | 2 | null | 5418 | 15 | null | I would suggest looking at the [zoo](http://cran.r-project.org/web/packages/zoo/index.html) package for the following reasons:
- It has several well-written vignettes;
- It uses a namespace using useDynLib, import, export, and S3method;
- It has several unit tests using RUnit;
- It provides good examples of how to ... | null | CC BY-SA 2.5 | null | 2010-12-13T16:50:50.383 | 2010-12-20T19:51:03.293 | 2010-12-20T19:51:03.293 | 1657 | 1657 | null |
5434 | 1 | 5437 | null | 6 | 9726 | I am doing multiple regression with some data (5 predictors, 1 response). Since the response is discrete and non-negative, I thought I would try Poisson regression. However, the data are significantly overdispersed (variance > mean), so I am now trying negative binomial regression.
I was able to fit the model with this... | No valid coefficients for NegBin regression | CC BY-SA 2.5 | null | 2010-12-13T17:06:52.920 | 2010-12-14T22:23:25.407 | 2010-12-13T17:36:44.883 | 1390 | 1973 | [
"r",
"regression",
"negative-binomial-distribution"
] |
5435 | 2 | null | 5431 | 2 | null | It depends what you're testing.
If you are testing for variance and your $H_0$ is that $V = 1$, but your values are $0, +1000, -1000, +2000, -2000$, then it's clear that you should reject $H_0$. If you are testing the hypothesis that the mean is not 2, then you do not have enough data to reject it.
For most things, tho... | null | CC BY-SA 2.5 | null | 2010-12-13T17:17:46.440 | 2010-12-13T17:17:46.440 | null | null | 2067 | null |
5436 | 2 | null | 5434 | 9 | null | Your model is too complex for the computer to work out some reasonable starting values that do not lead to infinite deviance when doing the `glm.fit` iterations.
Have you got enough data to estimate all these interactions? Do you think it is plausible for all predictors to interact with each other? If not, think about ... | null | CC BY-SA 2.5 | null | 2010-12-13T17:33:49.410 | 2010-12-13T17:46:32.053 | 2010-12-13T17:46:32.053 | 1390 | 1390 | null |
5437 | 2 | null | 5434 | 10 | null | Before jumping to a model that includes all interactions, you can try adding only the 2-way interactions:
```
model.nb.intr <- glm.nb(Response ~ (Pred1 + Pred2 + Pred3 + Pred4 + Pred5)^2 - 1, data=d)
```
| null | CC BY-SA 2.5 | null | 2010-12-13T17:57:44.083 | 2010-12-13T19:32:30.540 | 2010-12-13T19:32:30.540 | 930 | 279 | null |
5438 | 2 | null | 5115 | 7 | null | [David Donoho](http://stat.stanford.edu/~donoho/) development of multiscale ideas in statistics, and a lot of theoretically justified while practically very efficient ideas in very high dimensional statistics, CHA: computational harmonic analysis,...
| null | CC BY-SA 2.5 | null | 2010-12-13T18:20:38.607 | 2010-12-15T17:36:43.840 | 2010-12-15T17:36:43.840 | 919 | 223 | null |
5439 | 2 | null | 5431 | 2 | null | Five is pretty small, but not so small that it doesn't contain some information. If you get 5 course evaluations that agree an instructor is ineffective, that's pretty strong evidence. That said, the only time the evidence will be conclusive if it is rather extreme, e.g. all zeroes or all tens. Still, it seems fooli... | null | CC BY-SA 2.5 | null | 2010-12-13T18:33:03.063 | 2010-12-13T18:33:03.063 | null | null | 5792 | null |
5440 | 2 | null | 5115 | 6 | null | [Emanuel Parzen](http://en.wikipedia.org/wiki/Emanuel_Parzen) for kernel density estimation and reproducing kernel Hilbert space theory for stochastic processes.
| null | CC BY-SA 3.0 | null | 2010-12-13T18:39:58.043 | 2013-10-17T21:59:51.590 | 2013-10-17T21:59:51.590 | 8960 | 223 | null |
5441 | 2 | null | 5115 | 7 | null | [Lucien Le Cam](http://en.wikipedia.org/wiki/Lucien_Le_Cam) for his contribution to mathematical statistics. (maybe Local asymptotic normality and contiguity made him famous)
| null | CC BY-SA 2.5 | null | 2010-12-13T18:42:29.023 | 2010-12-13T18:42:29.023 | null | null | 223 | null |
5442 | 2 | null | 1995 | 6 | null | Here's an example where a multilevel model might be "essential." Suppose you want to rate the "quality" of the education provided by a set of schools using students' test scores. One way to define school quality is in terms of average test performance after taking student characteristics into account. You could conce... | null | CC BY-SA 2.5 | null | 2010-12-13T19:08:53.720 | 2010-12-13T19:08:53.720 | null | null | 96 | null |
5443 | 1 | 5547 | null | 5 | 2672 | When building a CART model (specifically classification tree) using rpart (in R), it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other y's only.
How can it be estimated, which explanatory variable is "... | Estimate the "meaningful" predictors for a value in a CART model (rpart) | CC BY-SA 2.5 | null | 2010-12-13T19:24:57.693 | 2016-12-14T12:53:00.710 | null | null | 253 | [
"r",
"cart",
"rpart"
] |
5444 | 1 | 5445 | null | 4 | 939 | Motivation
I have a prior on a random variable $X\sim \beta^\prime(\alpha,\beta)$ that I would like to use in JAGS. JAGS does not support the $\beta^\prime$ distribution, but it does support the $F$ distributions, and the F distribution is related to the $\beta^\prime(\alpha,\beta)$ like this:
$$\text{if } X\sim \beta^... | How do I transform the parameters of the F distribution? | CC BY-SA 2.5 | 0 | 2010-12-13T19:41:36.827 | 2010-12-13T23:31:16.857 | 2010-12-13T23:31:16.857 | 1381 | 1381 | [
"data-transformation",
"random-variable"
] |
5445 | 2 | null | 5444 | 6 | null | I don't quite get it: why can't you just generate an $F_{2\alpha, 2\beta}$ variate and rescale it by $\beta/\alpha$? I also don't see the connection between the code in your current approach and your question, because $Y$ appears to be an $F_{2\alpha, 2\beta}$ variate rescaled by $\alpha/\beta$ rather than $\beta/\al... | null | CC BY-SA 2.5 | null | 2010-12-13T20:30:30.397 | 2010-12-13T20:30:30.397 | null | null | 919 | null |
5446 | 2 | null | 5115 | 12 | null | [George Dantzig](http://en.wikipedia.org/wiki/George_Dantzig) for the Simplex Method, and for being the student who mistook two open statistics problems that Neyman had written on the board for homework problems, and in his "ignorance" solving them. I'd vote for him just for the story.
| null | CC BY-SA 2.5 | null | 2010-12-13T22:10:48.643 | 2010-12-13T22:10:48.643 | null | null | 1764 | null |
5447 | 2 | null | 5115 | 22 | null | [Blaise Pascal and Pierre de Fermat](http://en.wikipedia.org/wiki/Problem_of_points) for creating the theory of probability and inventing the idea of expected value (1654) in order to solve a problem grounded in statistical observations (from gambling).
| null | CC BY-SA 2.5 | null | 2010-12-13T23:21:55.837 | 2010-12-13T23:21:55.837 | null | null | 919 | null |
5448 | 1 | null | null | 3 | 1256 | I have conducted a confirmatory factor analysis (CFA) to test the fit of a model with 5 factors and 5 items per factor. I used the modification indices to alter the model until I obtained statistics indicating an acceptable fit of the model to my data.
As others have done in my area I then conducted a principal compon... | Why would the results of PCA differ from a confirmatory factor analysis? | CC BY-SA 3.0 | null | 2010-12-13T23:25:11.007 | 2012-05-15T05:17:25.970 | 2012-05-15T05:17:25.970 | 183 | null | [
"pca",
"factor-analysis"
] |
5450 | 1 | 5455 | null | 28 | 30144 | In a regression, the interaction term wipes out both related direct effects. Do I drop the interaction or report the outcome? The interaction was not part of the original hypothesis.
| What if interaction wipes out my direct effects in regression? | CC BY-SA 2.5 | null | 2010-12-13T23:43:17.117 | 2013-10-10T09:11:52.483 | 2010-12-14T08:35:11.153 | 930 | 2367 | [
"regression",
"interaction"
] |
5451 | 2 | null | 4551 | 9 | null | Requesting, and perhaps obtaining The Flow Chart: That graphical thing where you say what the level of your variables are and what sort of relationship you're looking for, and you follow the arrows down to get a Brand Name Test or a Brand Name Statistic. Sometimes offered with mysterious 'parametric' and 'non-parametr... | null | CC BY-SA 2.5 | null | 2010-12-13T23:50:20.977 | 2010-12-13T23:50:20.977 | null | null | 1739 | null |
5452 | 1 | 5471 | null | 7 | 4469 | All,
I am trying to study the GBM package in R.
I. I wanted to try and figure out where the deviance, initial value, gradient and terminal node estimates came from. Please see this snippet:
To start out, I was not sure where the Bernoulli deviance came from. I expected... | R Package GBM - Bernoulli Deviance | CC BY-SA 2.5 | null | 2010-12-14T00:09:21.473 | 2016-04-25T10:02:21.510 | null | null | 2040 | [
"machine-learning"
] |
5453 | 1 | 5458 | null | 4 | 424 | Let's say you have a process that generates data according to r = sin(t) + epsilon, where epsilon ~ N(0,V) is Gaussian noise. The unconditional variance of r is 0.5 + V.
Let's say we're forecasting r with a model m, and that our forecast is "perfect" in that m = sin(t). Construct v = r - m, which is the forecast error... | Why does continuous Bayesian analysis seem to give this contradictory result? | CC BY-SA 3.0 | null | 2010-12-14T01:45:45.857 | 2014-12-01T12:09:50.643 | 2014-12-01T12:09:50.643 | 930 | 2379 | [
"bayesian",
"inference"
] |
5454 | 2 | null | 5418 | 9 | null | I do not consider myself an established R package developer but have recently undergone the process of writing and maintaining a package for my work environment.
I had previously been writing / maintaining / updating a set of scripts that I would pass from project to project via the `source()` function. The end result ... | null | CC BY-SA 2.5 | null | 2010-12-14T02:02:46.990 | 2010-12-14T02:02:46.990 | null | null | 696 | null |
5455 | 2 | null | 5450 | 27 | null | I think this one is tricky; as you hint, there's 'moral hazard' here: if you hadn't looked at the interaction at all, you'd be free and clear, but now that you have there is a suspicion of data-dredging if you drop it.
The key is probably a change in the meaning of your effects when you go from the main-effects-only to... | null | CC BY-SA 2.5 | null | 2010-12-14T02:25:20.237 | 2010-12-14T02:25:20.237 | null | null | 2126 | null |
5456 | 2 | null | 5115 | 29 | null | [Jerzy Neyman](http://en.wikipedia.org/wiki/Jerzy_Neyman) and [Egon Pearson](http://en.wikipedia.org/wiki/E.S._Pearson) for work on experimental design, hypothesis testing, confidence intervals, and the Neyman-Pearson lemma.
| null | CC BY-SA 2.5 | null | 2010-12-14T03:02:22.327 | 2010-12-14T03:02:22.327 | null | null | 2077 | null |
5457 | 1 | 5459 | null | 12 | 4404 | When I tell my non-statistical friends I am a grad student pursuing a PhD in statistics they naturally say "oh so you want to be a professor?". I tell them no I actually plan to work in industry. Then they respond with, "and do what?". I have not found a good response to this question. I want to give them a range o... | What is it that a statistician does? | CC BY-SA 2.5 | null | 2010-12-14T03:09:29.683 | 2014-06-04T15:11:30.807 | 2010-12-14T04:19:24.930 | null | 2310 | [
"careers"
] |
5458 | 2 | null | 5453 | 3 | null | Actually, the variances are zero: $V(v|r) = V(r|v) = 0$.$p(v|r)$ is a Dyrac function which has a peak at the right spot (where $r = v -m$).
If you know $v$ or $r$, the other one is a deterministic function of the one you know.
| null | CC BY-SA 2.5 | null | 2010-12-14T03:28:01.720 | 2010-12-14T16:28:03.290 | 2010-12-14T16:28:03.290 | 2067 | 2067 | null |
5459 | 2 | null | 5457 | 21 | null | The area I am most interested in is the realm of biostatistics. Statistics can be used in this regard to do anything from summarize the results of a drug trial, determining whether Prozac really is more effective than the placebo sugar pill, to tumor detection in cancer patients. Please check out this presentation I fo... | null | CC BY-SA 3.0 | null | 2010-12-14T03:54:53.847 | 2011-10-17T15:45:36.370 | 2011-10-17T15:45:36.370 | 1118 | 1118 | null |
5460 | 2 | null | 5457 | 4 | null | A statistician performs the following generic steps:
- Has a goal- test a theory, generate a prediction etc
- Obtains data (via surveys, experiments, thrid-parties etc) that is consistent with the above goal.
- Develops a statistical model that says roughly: Variable of interest = function of covariates + random err... | null | CC BY-SA 2.5 | null | 2010-12-14T04:18:06.930 | 2010-12-14T04:18:06.930 | null | null | null | null |
5461 | 1 | null | null | 1 | 201 | The title might be a bit misleading. Unfortunately statistics is not my area of forte, so gentle guidance along the right path is much appreciated. That said, here's my problem:
I'm working on analyzing precise time measurements from an experimental process. I have 16 measurement groups. Of these, one should (on averag... | Removing macro-level time variance | CC BY-SA 2.5 | null | 2010-12-14T04:19:26.873 | 2010-12-15T03:04:59.567 | 2010-12-14T18:38:37.697 | 1750 | 1750 | [
"time-series",
"hypothesis-testing",
"statistical-significance",
"multiple-comparisons"
] |
5462 | 1 | 5464 | null | 11 | 26301 | This is only for my high school senior project, so it doesn't have to be perfect. I'm doing a project on global warming, and I want to survey people for their opinions. I know that if I use a convenience sample of my classmates, I'll have a lot of bias. I was wondering if there was a site on the Internet where I could ... | Is there a site to post my survey to so I can get a sample representative of the population? | CC BY-SA 2.5 | null | 2010-12-14T05:23:40.037 | 2014-04-28T11:30:47.080 | 2012-02-01T18:52:08.653 | 919 | 2371 | [
"survey",
"internet"
] |
5463 | 2 | null | 5462 | 4 | null | I admire your ambition! To answer your question directly, I'm not familiar with any site. Perhaps a message board you're familiar with? Has your instructor addressed the issue of bias? It may be enough that you recognize the issue of bias in your sample and speak to how this will affect your survey. Most properly rando... | null | CC BY-SA 3.0 | null | 2010-12-14T05:45:34.817 | 2014-04-28T11:30:47.080 | 2014-04-28T11:30:47.080 | 22047 | 2262 | null |
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