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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7009 | 1 | 7010 | null | 2 | 919 | I have the pdf
$$f(y ; \theta) = \frac{1}{\theta} \exp( \frac{-y}{\theta}), \ y > 0$$
and I'm supposed to determine if the following two estimators are unbiased or not: $ \hat \theta = nY_{min} $ and $ \hat \theta = \frac{1}{n}\sum_{i=1}^n Y_i $. I'm running into some problems because when I try to find the expected ... | Seeing if estimators are unbiased | CC BY-SA 2.5 | null | 2011-02-09T04:10:04.727 | 2011-02-09T23:10:24.540 | 2011-02-09T09:32:50.203 | 8 | 2387 | [
"self-study",
"unbiased-estimator"
] |
7010 | 2 | null | 7009 | 2 | null | Please see the following Wikipedia [article](http://en.wikipedia.org/wiki/Exponential_distribution) and it shows that both the integral exist. Also, if you look lower in the article, you will also see that both the estimators are unbiased.
$Y_{min}$ is also exponentially distributed with mean parameter $\frac {\theta}... | null | CC BY-SA 2.5 | null | 2011-02-09T04:26:13.793 | 2011-02-09T09:33:03.997 | 2011-02-09T09:33:03.997 | 8 | 1307 | null |
7011 | 2 | null | 3531 | 4 | null | WinBUGS has support for RJMCMC with an addon. I've used it for GLMs, including those with an intrinsic CAR component. Not R, obviously, but through R2WinBUGS you can patch them together.
| null | CC BY-SA 2.5 | null | 2011-02-09T04:39:53.350 | 2011-02-09T04:39:53.350 | null | null | 26 | null |
7012 | 2 | null | 6990 | 1 | null | This post explains it quite well: [http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/](http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/)
| null | CC BY-SA 2.5 | null | 2011-02-09T06:40:13.363 | 2011-02-09T06:40:13.363 | null | null | 582 | null |
7013 | 2 | null | 841 | 4 | null | The difficulty clearly comes because $X$ and $Y$ are corellated (I assume $(X,Y)$ is jointly gaussian, as Aniko) and you can't make a difference (as in @svadali's answer) or a ratio (as in Standard Fisher-Snedecor "F-test") because those would be of dependent $\chi^2$ distribution, and because you don't know what this ... | null | CC BY-SA 2.5 | null | 2011-02-09T08:45:33.313 | 2011-03-13T20:31:13.040 | 2011-03-13T20:31:13.040 | 223 | 223 | null |
7015 | 1 | 7017 | null | 2 | 5430 |
- Is linear regression only suitable for variables with a normal
distribution?
- If so, is there an alternative nonparametric test to test mediation or moderation?
| Normal distribution necessary to assess moderating and mediating effects? | CC BY-SA 2.5 | null | 2011-02-09T11:01:45.140 | 2011-02-09T20:01:38.423 | 2011-02-09T11:10:17.940 | 183 | null | [
"distributions",
"mediation",
"interaction",
"multiple-regression"
] |
7017 | 2 | null | 7015 | 6 | null | For those not familiar with the language, moderation and mediation were both discussed in Barron and Kenny's influential article ([free pdf](http://www.public.asu.edu/~davidpm/classes/psy536/Baron.pdf)).
### Mediation
With regards to mediation, bootstrapping is often used where normality does not seem like a reasona... | null | CC BY-SA 2.5 | null | 2011-02-09T12:17:35.667 | 2011-02-09T12:17:35.667 | null | null | 183 | null |
7019 | 1 | null | null | 1 | 435 | I have a data set with a range of 0 to 65,000. The vast majority of data points (it is a huge sample) are concentrated between 0 and 1000. There is only one point that has 65,000. I want to plot this using a semi-logarithmic plot. However, I would like the graph to have around 50 points. If I use scales like 2,4,6... | Log-scale with concentrated data using integers | CC BY-SA 2.5 | null | 2011-02-09T13:16:41.170 | 2011-04-11T01:13:15.487 | null | null | 2405 | [
"scales",
"logarithm"
] |
7020 | 1 | 7021 | null | 9 | 3625 | Suppose I will be getting some samples from a binomial distribution. One way to model my prior knowledge is with a Beta distribution with parameters $\alpha$ and $\beta$. As I understand it, this is equivalent to having seen "heads" $\alpha$ times in $\alpha + \beta$ trials. As such, a nice shortcut to doing the ful... | Bayesian inference for multinomial distribution with asymmetric prior knowledge? | CC BY-SA 2.5 | null | 2011-02-09T14:22:32.753 | 2011-02-09T23:27:35.783 | 2011-02-09T23:27:35.783 | 2485 | 2485 | [
"probability",
"bayesian",
"prior",
"multinomial-distribution",
"dirichlet-distribution"
] |
7021 | 2 | null | 7020 | 3 | null | You have framed your question very well.
I think what you are looking for here is a case of hierarchical modeling. And you may want to model multiple layers of hierarchy (at the moment you only talk about priors). Having another layer of hyper-priors for the hyper--parameters lets you model the additional variabilities... | null | CC BY-SA 2.5 | null | 2011-02-09T14:51:50.390 | 2011-02-09T17:09:59.697 | 2011-02-09T17:09:59.697 | 1307 | 1307 | null |
7022 | 1 | null | null | 5 | 3882 | Given a set of data (~5000 values) I'd like to draw random samples from the same distribution as the original data. The problem is there is no way to know for sure what distribution the original data comes from.
It makes sense to use normal distribution in my case, although I'd like to be able to motivate that decisio... | Parameter estimation for normal distribution in Java | CC BY-SA 2.5 | 0 | 2011-02-09T16:50:00.400 | 2012-01-19T13:04:51.117 | 2011-02-09T17:13:52.090 | null | 3014 | [
"estimation",
"normal-distribution",
"java"
] |
7024 | 2 | null | 7019 | 2 | null | Can you just use a scale comprised of powers of (1+r) for some small r, and round to the nearest integer? For example, in R, with r = 0.25:
```
> x <- unique(round(1.25^(0:50)))
> x
[1] 1 2 3 4 5 6 7 9 12 15 18 23 28 36 44 56 69 87 108 136 169 212 ... | null | CC BY-SA 2.5 | null | 2011-02-09T17:35:34.287 | 2011-02-09T17:35:34.287 | null | null | 2425 | null |
7026 | 2 | null | 7007 | 1 | null | There are all kinds of analyses you can do. It really depends on what you want to know.
Let me show you, with the example of gender: If you want to know wheter there are gender differences, you can apply a variance test (univariate or multivariate) on the likert scales with the demographic variable as independent facto... | null | CC BY-SA 2.5 | null | 2011-02-09T19:12:23.193 | 2011-02-09T19:12:23.193 | null | null | 1435 | null |
7029 | 1 | 7039 | null | 26 | 1295 | I ran across this density the other day. Has someone given this a name?
$f(x) = \log(1 + x^{-2}) / 2\pi$
The density is infinite at the origin and it also has fat tails. I saw it used as a prior distribution in a context where many observations were expected to be small, though large values were expected as well.
| Does the distribution $\log(1 + x^{-2}) / 2\pi$ have a name? | CC BY-SA 2.5 | null | 2011-02-09T19:34:28.307 | 2011-02-12T05:07:24.860 | 2011-02-12T05:07:24.860 | 183 | 319 | [
"distributions",
"probability"
] |
7030 | 2 | null | 7022 | 4 | null | How big are the samples that you need? If substantially smaller than the 5000 points you have, say maximum 100 points or so, you could just take a random subset of your sample. Then you don't even need to assume normality - it's guaranteed to come from the distribution you want!
Otherwise, it seems that the `org.apache... | null | CC BY-SA 2.5 | null | 2011-02-09T19:37:08.393 | 2011-02-09T19:37:08.393 | null | null | 2898 | null |
7031 | 2 | null | 7015 | 2 | null | Many of us use linear regression in rough-and-ready fashion to learn about the relative importance of predictors, to assess the shape of relationships, and so on. But if one wants to make strict probabilistic inferences one needs to satisfy the set of standard assumptions entailed in such regression. The most importa... | null | CC BY-SA 2.5 | null | 2011-02-09T20:01:38.423 | 2011-02-09T20:01:38.423 | null | null | 2669 | null |
7032 | 1 | 7043 | null | 6 | 2040 | Why is it that "missing data" and "outliers" can affect the performance of least square estimation?
| Effect of missing data and outliers on least square estimation | CC BY-SA 2.5 | null | 2011-02-09T20:20:17.480 | 2012-07-24T10:41:12.550 | 2011-02-12T05:03:00.997 | 183 | 3125 | [
"regression",
"estimation",
"least-squares"
] |
7033 | 2 | null | 7029 | 5 | null | Perhaps not.
I could not find it in this fairly extensive list of distributions:
[Leemis and McQuestion 2008 Univariate Distribution Relationships. American Statistician 62(1) 45:53](http://www.math.wm.edu/~leemis/2008amstat.pdf)
| null | CC BY-SA 2.5 | null | 2011-02-09T23:08:02.450 | 2011-02-10T20:59:20.080 | 2011-02-10T20:59:20.080 | 449 | 2750 | null |
7034 | 2 | null | 7009 | 0 | null | The fact that the sample mean is an unbiased estimator is obtained combining these two facts:
1. The sample mean is an unbiased estimator of the population mean
2. The population mean is equal to theta
| null | CC BY-SA 2.5 | null | 2011-02-09T23:10:24.540 | 2011-02-09T23:10:24.540 | null | null | null | null |
7036 | 1 | 7041 | null | 6 | 4618 | This question may have been asked before, but I couldn't find it. So, here goes.
From about 3000 data points that can be characterized as "wins" or "losses" (binomial), it turns out that there are 52.8% dumb luck wins. This is my dependent variable.
I also have some additional data that may help in predicting the a... | Number of trials required from a binomial distribution to get the desired odds | CC BY-SA 2.5 | null | 2011-02-10T01:24:08.580 | 2011-02-13T20:36:13.740 | 2011-02-13T20:36:13.740 | 2775 | 2775 | [
"r",
"binomial-distribution",
"p-value"
] |
7037 | 2 | null | 7032 | 4 | null | If you're using R, try the following example.
```
library(tcltk)
demo(tkcanvas)
```
Move the dots around to create all of the outliers you want. The regression will keep up with you.
| null | CC BY-SA 2.5 | null | 2011-02-10T02:16:59.203 | 2011-02-10T02:16:59.203 | null | null | 2775 | null |
7038 | 2 | null | 7036 | 4 | null | ```
numtri[c(min(which(perwin <= 0.55)),max(which(perwin >= 0.55)))]
```
| null | CC BY-SA 2.5 | null | 2011-02-10T02:20:51.027 | 2011-02-10T02:20:51.027 | null | null | 159 | null |
7039 | 2 | null | 7029 | 15 | null | Indeed, even the first moment does not exist. The CDF of this distribution is given by
$$F(x) = 1/2 + \left(\arctan(x) - x \log(\sin(\arctan(x)))\right)/\pi$$
for $x \ge 0$ and, by symmetry, $F(x) = 1 - F(|x|)$ for $x \lt 0$. Neither this nor any of the obvious transforms look familiar to me. (The fact that we can o... | null | CC BY-SA 2.5 | null | 2011-02-10T02:41:06.410 | 2011-02-10T02:41:06.410 | null | null | 919 | null |
7040 | 1 | 7042 | null | 4 | 14543 | In Meta analysis, how to interpret the Egger’s linear regression method intercept (B0) 10.34631, 95% confidence interval (1.05905, 19.63357), with t=3.54535, df=3. The 1-tailed p-value (recommended) is 0.01911, and the 2-tailed p-value is 0.03822. I am a medical doctor.
*Updated*
The data are comparison of Regions 1 a... | Egger’s linear regression method intercept in meta analysis | CC BY-SA 2.5 | null | 2011-02-10T05:59:08.420 | 2011-02-10T15:35:02.927 | 2011-02-10T15:35:02.927 | 2956 | 2956 | [
"meta-analysis",
"funnel-plot",
"publication-bias"
] |
7041 | 2 | null | 7036 | 6 | null | I wonder what it means to be "99% sure."
The code seems to equate "dumb luck" with $p$ = 52.8% probability of wins. Let's imagine conducting $N$ trials, during which we observe $k$ wins. Suppose, for instance, $N$ = 1000 and you observe $k$ = 530 wins. That's greater than the expected number $p N$ = 528, but it's so... | null | CC BY-SA 2.5 | null | 2011-02-10T07:24:36.527 | 2011-02-10T14:25:46.007 | 2011-02-10T14:25:46.007 | 919 | 919 | null |
7042 | 2 | null | 7040 | 3 | null | I suppose you are not interested in "hardcore" statistical explanation. So, more the intercept deviates from zero, the more pronounced the asymmetry. If the p-value of the intercept is 0.1 or smaller, the asymmetry is considered to be statistically significant. More [here](http://goo.gl/PlNhR).
| null | CC BY-SA 2.5 | null | 2011-02-10T08:09:11.320 | 2011-02-10T08:09:11.320 | null | null | 609 | null |
7043 | 2 | null | 7032 | 6 | null | I'm not sure about the "missing data", but I can give an answer on "outliers"
This is basically due to the "unbounded" influence that a single observation can have in least squares (or at least in conventional least squares). A very, very simple example of least squares should show this. Suppose you only estimate an ... | null | CC BY-SA 2.5 | null | 2011-02-10T08:13:06.480 | 2011-02-10T08:13:06.480 | null | null | 2392 | null |
7045 | 1 | 10475 | null | 4 | 4318 | Is there a package or library that can help me suggest a formula given the independent variables which will work well in glm, for example this formula can be something like x^2+log(y)+Z, it does not necessarily need to be the standard linear model x+y+z in order to explain a variable.
| How to obtain in R a good formula for glm (general linear models) to predict a binomial variable? | CC BY-SA 2.5 | null | 2011-02-10T11:40:35.520 | 2012-07-31T19:27:54.183 | 2011-02-10T13:59:01.247 | 919 | 1808 | [
"r",
"logistic",
"generalized-linear-model"
] |
7046 | 2 | null | 7040 | 4 | null | @Andrej has already given an answer, i.e there is evidence of funnel plot asymmetry.
@DrWho, I would be interested in the reference that suggests using a one-tailed test.
The following can give you an idea of the underlying logic of applying this regression model to test for publication bias:
Most of these regressio... | null | CC BY-SA 2.5 | null | 2011-02-10T12:11:08.863 | 2011-02-10T12:11:08.863 | null | null | 307 | null |
7047 | 1 | 7067 | null | 2 | 1157 | Hey guys. I got two images from video frames. They have a certain portion of overlap. After warping one of them, I'm currently trying to blend them together. In other words, I would like to stitch them together. But I don't know how to accomplish that. Can anybody please give me some help? Thank you!
Let's say the ima... | Matlab image blending | CC BY-SA 2.5 | null | 2011-02-10T12:18:06.050 | 2011-02-10T21:15:49.847 | null | null | 3133 | [
"matlab",
"image-processing"
] |
7048 | 1 | 7416 | null | 17 | 1385 | While preparing for a talk I will give soon, I recently started digging into two major (Free) tools for interactive data visualization: [GGobi](http://www.ggobi.org/) and [mondrian](http://rosuda.org/mondrian/) - both offer a great range of capabilities (even if they're a bit buggy).
I wish to ask for your help in art... | When is interactive data visualization useful to use? | CC BY-SA 2.5 | null | 2011-02-10T14:49:20.220 | 2013-02-22T00:04:37.230 | 2011-02-20T14:30:18.040 | null | 253 | [
"data-visualization",
"data-mining",
"interactive-visualization"
] |
7049 | 1 | 7076 | null | 13 | 15513 | Can the poisson distribution be used to analyze continuous data as well as discrete data?
I have a few data sets where response variables are continuous, but resemble a poisson distribution rather than a normal distribution. However, the poisson distribution is a discrete distribution and is usually concerned with numb... | Using poisson regression for continuous data? | CC BY-SA 3.0 | null | 2011-02-10T14:59:26.717 | 2015-07-14T19:46:48.197 | 2015-07-14T19:46:48.197 | 34826 | 3136 | [
"distributions",
"regression",
"poisson-distribution",
"continuous-data"
] |
7050 | 2 | null | 7048 | 8 | null | Dynamic linking of graphics is natural and effective for exploratory spatial data analysis, or [ESDA](http://www.ncgia.ucsb.edu/giscc/units/u128/u128_f.html). ESDA systems typically link one or more quantitative maps (such as [choropleth maps](http://en.wikipedia.org/wiki/Choropleth_map)) with tabular views and statis... | null | CC BY-SA 2.5 | null | 2011-02-10T15:13:17.287 | 2011-02-10T15:13:17.287 | null | null | 919 | null |
7051 | 2 | null | 7049 | 9 | null | If you're talking about using a Poisson response in a generalized linear model, then yes, if you are willing to make the assumption that the variance of each observation is equal to its mean.
If you don't want to do that, another alternative may be to transform the response (e.g. take logs).
| null | CC BY-SA 2.5 | null | 2011-02-10T15:15:28.673 | 2011-02-10T15:15:28.673 | null | null | 495 | null |
7052 | 2 | null | 6225 | 2 | null | Technically, no, a null hypothesis cannot be proven. For any fixed, finite sample size, there will always be some small but nonzero effect size for which your statistical test has virtually no power. More practically, though, you can prove that you're within some small epsilon of the null hypothesis, such that deviat... | null | CC BY-SA 2.5 | null | 2011-02-10T15:29:44.943 | 2011-02-10T15:29:44.943 | null | null | 1347 | null |
7053 | 1 | 7064 | null | 6 | 540 | I’m reviewing an article, and can’t give details but here is the situation, and it’s got me puzzled
Patients were divided into 4 categories (call them A B C and D), which were exhaustive and exclusive. Adjusted hazard ratios were computed for these four groups for all patients and for two subgroups of patients (call t... | Question about combining hazard ratios - Maybe Simpson's paradox? | CC BY-SA 3.0 | null | 2011-02-10T15:35:57.853 | 2017-03-06T18:43:56.290 | 2017-03-06T18:43:56.290 | -1 | 686 | [
"survival",
"simpsons-paradox"
] |
7054 | 1 | 7131 | null | 5 | 146 | Can we do a meta-analysis of data of 3 regions. A particular disease was treated with the same treatment but implemented thoroughly in 2 regions and not so thoroughly in 1 region. How to proceed with analysis? What is the best way to analyse these data. The data are of a disease (cases and deaths) as follows:
```
... | Meta-analysis of 3 regions' data for 5 years | CC BY-SA 2.5 | null | 2011-02-10T15:38:40.207 | 2011-02-12T16:39:30.410 | 2011-02-12T05:52:05.690 | 183 | 2956 | [
"meta-analysis",
"panel-data"
] |
7056 | 1 | 7075 | null | 3 | 524 | readHTMLTable seems pretty robust, but when I try to use it on this page, I get an error.
Any ideas what it means or how I could get around it? The page's biggest table is nested inside another table... is that a problem?
```
> tables<-readHTMLTable(myURL, header=NA,a.data.frame=TRUE)
Error in htmlParse(doc) :
e... | Error creating parser in readHTMLTable | CC BY-SA 2.5 | null | 2011-02-10T16:11:00.267 | 2011-02-10T23:28:32.947 | 2011-02-10T17:53:56.070 | null | 1463 | [
"r",
"dataset"
] |
7057 | 1 | null | null | 14 | 9313 | I would like to get the coefficients for the LASSO problem
$$||Y-X\beta||+\lambda ||\beta||_1.$$
The problem is that glmnet and lars functions give different answers. For the glmnet function I ask for the coefficients of $\lambda/||Y||$ instead of just $\lambda$, but I still get different answers.
Is this expected? Wh... | GLMNET or LARS for computing LASSO solutions? | CC BY-SA 3.0 | null | 2011-02-10T16:23:48.187 | 2021-09-24T05:02:58.787 | 2016-08-08T02:43:39.467 | 805 | null | [
"r",
"machine-learning",
"regression",
"lasso",
"regularization"
] |
7058 | 1 | 7169 | null | 11 | 428 | I have three features that I use to solve a classification problem. Originally, these features produced boolean values, so I could evaluate their redundancy by looking at how much the sets of positive and negative classifications overlap. Now I have extended the features to produce real values (scores) instead, and I w... | How to quantify redundancy of features? | CC BY-SA 2.5 | null | 2011-02-10T16:35:26.570 | 2011-02-13T22:04:12.390 | 2011-02-10T17:33:23.853 | 977 | 977 | [
"correlation",
"feature-selection"
] |
7059 | 1 | null | null | 5 | 1320 | I am managing many people entering data into a database. I have a log of user, date, time, table, and action that each person makes:
```
records <- data.frame(user = c('bob', 'bob', 'jane', 'jane', 'bob', 'bob', 'bob', 'jane', 'jane', 'bob'),
date = c("2010-06-24", "2010-06-28", "2010-06-29", "201... | How can I effectively summarize and visualize time series of employee activities? | CC BY-SA 2.5 | null | 2011-02-10T18:02:16.093 | 2011-02-12T21:34:48.187 | 2011-02-11T08:08:51.310 | 2116 | 1381 | [
"r",
"time-series",
"data-visualization"
] |
7061 | 1 | 7078 | null | 3 | 500 | Given two basketball players.
John made 38/50 free throws.
Mike made 80/100 free throws.
What is probability that Mike is better at free throws than John?
| Binomial Probability Question | CC BY-SA 2.5 | null | 2011-02-10T19:10:16.860 | 2011-02-11T04:02:45.067 | null | null | 3143 | [
"probability",
"binomial-distribution"
] |
7062 | 2 | null | 7057 | 1 | null | LASSO is non-unique in the case where multiple features have perfect collinearity. Here's a simple thought experiment to prove it.
Let's say you have three random vectors $y$, $x_1$, $x_2$. You're trying to predict $y$ from $x_1$, $x_2$. Now assume $y$ = $x1$ = $x2$. An optimal LASSO solution would be $\beta_1 = ... | null | CC BY-SA 2.5 | null | 2011-02-10T19:19:24.763 | 2011-02-10T20:30:18.040 | 2011-02-10T20:30:18.040 | 1347 | 1347 | null |
7063 | 2 | null | 7053 | 2 | null | Yes. It is certainly possible that this is due to something like Simpson's paradox. If the data looked like
$$\begin{array}{rrrrrr}
\textit{Organ}&\textit{Outcome}&A&B&C&D\\
\textrm{Lung}&\textrm{Bad}&371&2727&2374&418\\
\textrm{Lung}&\textrm{Good}&556&3199&2740&558\\
\textrm{Heart}&\textrm{Bad}&214&245&195&273\... | null | CC BY-SA 2.5 | null | 2011-02-10T20:13:28.700 | 2011-02-10T20:13:28.700 | null | null | 2958 | null |
7064 | 2 | null | 7053 | 5 | null | Strictly, [Simpson's paradox](http://en.wikipedia.org/wiki/Simpson%27s_paradox) refers to a reversal in the direction of effect, which hasn't happened here as all the hazard ratios are above 1, so I'd refer to this by the more general term [confounding](http://en.wikipedia.org/wiki/Confounding). You can certainly have ... | null | CC BY-SA 2.5 | null | 2011-02-10T20:17:34.793 | 2011-02-10T20:17:34.793 | null | null | 449 | null |
7066 | 2 | null | 7061 | 1 | null | I think what you want to do is compare the predictive distributions of both players. The predictive distribution describes the probability that Mike/John will make his next shot given the data (integrating out the parameters).
Here is some Matlab code you can play with:
```
clear;
clc;
rng = linspace(0, 1, 100);
% da... | null | CC BY-SA 2.5 | null | 2011-02-10T21:11:36.403 | 2011-02-10T21:11:36.403 | null | null | 1913 | null |
7067 | 2 | null | 7047 | 3 | null | I don't know algorithms off the top of my head, but I would start by having a look at [Survey of image registration techniques](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.86.8364&rep=rep1&type=pdf)
| null | CC BY-SA 2.5 | null | 2011-02-10T21:15:49.847 | 2011-02-10T21:15:49.847 | null | null | 1913 | null |
7068 | 2 | null | 7059 | 2 | null | Below the code to plot the numbers of actions per week/per user:
```
load("records.Rdata")
library(ggplot2)
records$posdate <- as.POSIXlt(records$date,format="%Y-%m-%d")
records$week <- as.numeric(format(records$posdate,"%W")) #changed from previous hack!
numberActions <- by(records$action,records[,c("user","week")],fu... | null | CC BY-SA 2.5 | null | 2011-02-10T21:27:30.477 | 2011-02-11T09:08:07.167 | 2011-02-11T09:08:07.167 | 1443 | 1443 | null |
7069 | 1 | null | null | 3 | 327 | Is it possible to correct for violating the assumption of independence for nonparametric tests?
I have a categorical independent variable and a categorical and binary dependent variable, and each subject was exposed to multiple levels of treatment. I have done some preliminary analysis with binary logistic regression ... | Is it possible to correct for violating the assumption of independence for nonparametric tests? | CC BY-SA 2.5 | null | 2011-02-10T21:33:27.913 | 2011-02-10T22:48:44.833 | null | null | null | [
"nonparametric",
"non-independent"
] |
7070 | 1 | 7071 | null | 27 | 115599 | What is the best way of defining white noise process so it is intuitive and easy to understand?
| What is a white noise process? | CC BY-SA 3.0 | null | 2011-02-10T22:13:25.977 | 2016-06-03T08:02:12.637 | 2012-08-20T06:18:15.407 | 2116 | 333 | [
"time-series"
] |
7071 | 2 | null | 7070 | 18 | null | A white noise process is one with a mean zero and no correlation between its values at different times. See the ['white random process' section of Wikipedia's article on white noise](http://en.wikipedia.org/wiki/White_noise#White_random_process_.28white_noise.29).
| null | CC BY-SA 2.5 | null | 2011-02-10T22:23:25.730 | 2011-02-10T22:23:25.730 | null | null | 449 | null |
7072 | 2 | null | 7069 | 2 | null | You may be having trouble finding info on this because you're looking for it under 'nonparametric tests'. This wouldn't usually be included under the (rather confusing) 'nonparametric' heading.
Logistic regression is sensible, but you'll need one of its extensions to deal with correlated outcomes, such as conditional ... | null | CC BY-SA 2.5 | null | 2011-02-10T22:30:01.883 | 2011-02-10T22:48:44.833 | 2011-02-10T22:48:44.833 | 449 | 449 | null |
7074 | 1 | 7083 | null | 10 | 14623 | Is there a formal statistical test to test if process is a white noise?
| Formal statistical test for whether a process is a white noise | CC BY-SA 2.5 | null | 2011-02-10T23:25:06.553 | 2018-12-02T08:21:33.307 | 2011-02-12T15:39:03.473 | 1036 | 333 | [
"time-series",
"white-noise"
] |
7075 | 2 | null | 7056 | 2 | null | The table in question seems to need use of a Javascript button to download. If you just want this particular data then save it as the offered untitle.txt or something else (it is basically tab-delimited) and then
```
tables <- read.delim("untitle.txt", skip=1)
colnames(tables) <- c(colnames(tables)[-1],"jun... | null | CC BY-SA 2.5 | null | 2011-02-10T23:28:32.947 | 2011-02-10T23:28:32.947 | null | null | 2958 | null |
7076 | 2 | null | 7049 | 13 | null | The key assumption of a generalized linear model that's relevant here is the relationship between the variance and mean of the response, given the values of the predictors. When you specify a Poisson distribution, what this implies is that you are assuming the conditional variance is equal to the conditional mean.* The... | null | CC BY-SA 2.5 | null | 2011-02-11T01:29:36.383 | 2011-02-11T01:29:36.383 | null | null | 1569 | null |
7077 | 2 | null | 7061 | 2 | null | This is very much a problem in Bayesian inference. You appear to have taken the first step in realizing that the sample is not the same as the underlying probability distribution. Even though Mike had a higher mean in his sample, John might have a higher mean for his true talent distribution. When applying Bayesian ... | null | CC BY-SA 2.5 | null | 2011-02-11T03:47:17.390 | 2011-02-11T03:47:17.390 | null | null | 2485 | null |
7078 | 2 | null | 7061 | 2 | null | Okay. I think I figured out the general answer:
Given a sample of n/N made, the probability that the population success rate is greater than x is defined by the posterior probability distribution:
>
$\frac{\int_{x}^1 r^n(1-r)^{N-n}\ dr}{\int_{0}^1 r^n(1-r)^{N-n}\ dr}$
So for my example the chance that Mike's free th... | null | CC BY-SA 2.5 | null | 2011-02-11T03:55:13.450 | 2011-02-11T04:02:45.067 | 2011-02-11T04:02:45.067 | 3143 | 3143 | null |
7079 | 1 | null | null | 7 | 23409 |
- How do you carry out analysis of covariance using R?
- How do you interpret the results?
- A practical example will be highly appreciated.
| Analysis of covariance in R | CC BY-SA 2.5 | null | 2011-02-11T06:27:08.743 | 2011-11-05T02:58:25.970 | 2011-02-12T05:49:02.013 | 183 | 3107 | [
"r",
"ancova"
] |
7081 | 2 | null | 7070 | 12 | null | I myself usually think of white noise as an iid sequence with zero mean. At different times values of the process are then independent of each other, which is much stronger requirement than correlation zero. What is the best with this definition that it works in any context.
Side note. I only explained my intuition, t... | null | CC BY-SA 2.5 | null | 2011-02-11T07:35:13.443 | 2011-02-11T09:13:59.350 | 2011-02-11T09:13:59.350 | 2116 | 2116 | null |
7082 | 2 | null | 7079 | 7 | null | Here is a [detailed presentation](http://www3.imperial.ac.uk/pls/portallive/docs/1/1171922.PDF)
| null | CC BY-SA 2.5 | null | 2011-02-11T07:44:39.227 | 2011-02-11T07:44:39.227 | null | null | 339 | null |
7083 | 2 | null | 7074 | 14 | null | In time-series analysis usually [Ljung-Box test](http://en.wikipedia.org/wiki/Ljung-Box_test) is used. Note though that it tests the correlations. If the correlations are zero, but variance varies, then the process is not white noise, but Ljung-Box test will fail to reject the null-hypothesis. Here is an example in R:
... | null | CC BY-SA 2.5 | null | 2011-02-11T07:47:54.650 | 2011-02-11T07:47:54.650 | 2017-04-13T12:44:56.303 | -1 | 2116 | null |
7084 | 1 | 7543 | null | 3 | 2933 |
### Background:
In connection with the question [here](https://stats.stackexchange.com/questions/7022/parameter-estimation-for-normal-distribution-in-java) I came upon a more interesting question. I believe the question is large and distinct enough to have it's own thread. Of course I might be mistaken, in that case... | Justifying normal approximation of experimental data | CC BY-SA 2.5 | null | 2011-02-11T09:09:36.410 | 2011-02-23T18:11:56.453 | 2017-04-13T12:44:20.840 | -1 | 3014 | [
"normality-assumption",
"hypothesis-testing",
"goodness-of-fit",
"approximation"
] |
7085 | 2 | null | 6225 | 2 | null | There is a case where a proof is possible. Suppose you have a school and your null hypothesis is that the numbers of boys and of girls is equal. As the sample size increases, the uncertainty in the ratio of boys to girls tends to reduce, eventually reaching certainty (which is what I assume you mean by proof) when th... | null | CC BY-SA 2.5 | null | 2011-02-11T09:27:37.847 | 2011-02-11T09:27:37.847 | null | null | 2958 | null |
7086 | 2 | null | 7084 | 5 | null | If your ultimate aim is, as you say, "to see if the results from experimental values show significant deviation from randomized data" then you'd be better to directly use the observed distribution by performing a [permutation (re-randomization) test](http://en.wikipedia.org/wiki/Resampling_%28statistics%29#Permutation_... | null | CC BY-SA 2.5 | null | 2011-02-11T09:31:33.927 | 2011-02-11T09:31:33.927 | null | null | 449 | null |
7088 | 1 | 7119 | null | 3 | 1261 | I am doing some class work and I was wondering if there is a universally accepted format for analyzing and presenting the data in a Word document for data gathered from questionnaires.
| Is there a standard format for presenting a data analysis report based on a questionnaire? | CC BY-SA 3.0 | null | 2011-02-11T11:41:17.083 | 2011-10-04T21:54:54.830 | 2011-10-04T21:54:54.830 | 183 | 3150 | [
"survey",
"reporting"
] |
7089 | 1 | null | null | 22 | 18742 | I need a formula for the probability of an event in a n-variate Bernoulli distribution $X\in\{0,1\}^n$ with given $P(X_i=1)=p_i$ probabilities for a single element and for pairs of elements $P(X_i=1 \wedge X_j=1)=p_{ij}$. Equivalently I could give mean and covariance of $X$.
I already learned that there exist many $\{... | Probability formula for a multivariate-bernoulli distribution | CC BY-SA 2.5 | null | 2011-02-11T12:30:24.763 | 2022-01-18T05:14:29.933 | 2011-02-11T12:51:48.473 | 2116 | null | [
"multivariate-analysis",
"discrete-data"
] |
7090 | 1 | 14308 | null | 4 | 400 | When generating survival times to simulate Cox proportional hazards models, does it matter to generate them in days or in years?
In theory, I guess it does not matter. But in practice? Is there a preference regarding computational issues?
Thank you!
Marco
| Generating survival times in days or in years | CC BY-SA 2.5 | null | 2011-02-11T13:11:47.690 | 2011-08-17T16:51:46.080 | 2011-02-13T14:35:26.377 | null | 3019 | [
"survival",
"hazard",
"units"
] |
7091 | 2 | null | 7089 | 14 | null | The random variable taking values in $\{0,1\}^n$ is a discrete random variable. Its distribution is fully described by probabilities
$p_{\mathbf{i}}=P(X=\mathbf{i})$ with $\mathbf{i}\in\{0,1\}^n$. The probabilities $p_{i}$ and $p_{ij}$ you give are sums of $p_{\mathbf{i}}$ for certain indexes $\mathbf{i}$.
Now it se... | null | CC BY-SA 3.0 | null | 2011-02-11T13:19:26.237 | 2017-12-02T19:35:33.767 | 2017-12-02T19:35:33.767 | 69834 | 2116 | null |
7093 | 2 | null | 5465 | 4 | null | How will you count the number of sandal wood trees in Bangalore ?
| null | CC BY-SA 2.5 | null | 2011-02-11T13:31:52.880 | 2011-02-11T13:31:52.880 | null | null | null | null |
7094 | 2 | null | 7089 | 1 | null | I don't know what the resulting distribution is called, or if it even has a name, but it strikes me the obvious way to set this up is to think of the model you'd use to model a 2×2×2×…×2 table using a log-linear (Poisson regression) model. As you know the 1st-order interactions only, it's then natural to assume that al... | null | CC BY-SA 2.5 | null | 2011-02-11T13:39:43.833 | 2011-02-14T12:48:07.520 | 2011-02-14T12:48:07.520 | 449 | 449 | null |
7095 | 2 | null | 5465 | 3 | null | We are running a customer service centre. We are getting 1 million calls per month. How do we reduce it to ten thousand ?
| null | CC BY-SA 2.5 | null | 2011-02-11T13:40:00.073 | 2011-02-11T13:40:00.073 | null | null | null | null |
7096 | 1 | null | null | 1 | 529 | Does anybody know whether R has a package/routine for estimating spline function with unknown number of knots?
| Spline function with unknown knots | CC BY-SA 4.0 | null | 2011-02-11T13:48:51.977 | 2019-10-31T13:56:58.320 | 2019-10-31T13:56:58.320 | 92235 | null | [
"r",
"splines"
] |
7097 | 2 | null | 7096 | 3 | null | Have a look at the facilities for fitting smoothing splines in the `gam` package.
| null | CC BY-SA 2.5 | null | 2011-02-11T13:56:45.177 | 2011-02-11T13:56:45.177 | null | null | 449 | null |
7098 | 2 | null | 7048 | 7 | null | To me interactive visualization is useful only for my own exploration, or when working with a very hands-on client. When dealing with a final presentation, I prefer to choose the static graph that best makes my point. Otherwise clients can get totally distracted by the gee-whiz factor.
The biggest benefit I get from i... | null | CC BY-SA 3.0 | null | 2011-02-11T14:21:54.300 | 2013-02-22T00:04:37.230 | 2013-02-22T00:04:37.230 | -1 | 3155 | null |
7099 | 2 | null | 5465 | 16 | null | Two questions I've been asked:
1) You fit a multiple regression to examine the effect of a particular variable a worker in another department is interested in. The variable comes back insignificant, but your co-worker says that this is impossible as it is known to have an effect. What would you say/do?
2) You have ... | null | CC BY-SA 2.5 | null | 2011-02-11T15:01:37.667 | 2011-02-11T15:01:37.667 | null | null | 2310 | null |
7100 | 1 | null | null | 7 | 3245 | I am doing research and trying to use a questionnaire that is available in English in another language.
- Is there a systematic approach for validating a questionnaire in another language?
- What statistical test should I be undertake and why?
| Validating an existing questionnaire into another language | CC BY-SA 2.5 | null | 2011-02-11T15:52:57.197 | 2015-11-09T12:13:56.343 | 2012-06-20T17:28:51.590 | 930 | null | [
"correlation",
"factor-analysis",
"reliability",
"psychometrics"
] |
7101 | 1 | null | null | 4 | 2838 | I am interested in applying Bayesian additive regression trees (BART) for classification analysis of gene expression data. I am relatively new to R (and Bioconductor packages) and I am unable to find some code or vignette that I can use to learn from. I will be thankful if someone can point me in a good direction.
| Bayesian additive regression trees (BART) for classification analysis of gene expression data | CC BY-SA 2.5 | null | 2011-02-11T08:01:40.737 | 2022-12-17T03:34:55.783 | 2022-12-17T03:34:55.783 | 11852 | 4045 | [
"r",
"regression",
"bayesian",
"classification",
"bart"
] |
7102 | 1 | 7104 | null | 4 | 1748 | I'm fitting a 4 parameter nonlinear regression model to multiple datasets, some of which fail to converge, however, the parameters output after a failure provide a fit that looks good, if not exceptional to my (and other's) eyes.
I've explored convergence criteria and they do converge eventually but the visual fit is t... | Non-linear regression fails to converge, but fit appears good | CC BY-SA 3.0 | null | 2011-02-11T16:05:49.630 | 2011-12-27T15:21:40.740 | 2011-12-27T15:21:40.740 | 919 | 3158 | [
"r",
"curve-fitting",
"nonlinear-regression"
] |
7103 | 1 | null | null | 7 | 273 | Example of problem: Part of our research team is working on providing operationally wind power forecast. Usually, since there are different time scalse that interest forecast user, a forecast is issued every 15 min (it has even happened that 5 seconds was requirer) for every 15 minutes ahead up to serveral days. Obviou... | Compression theory, practice, for time series with values in a space of distributions (say of a real random variable) | CC BY-SA 2.5 | null | 2011-02-11T17:29:15.263 | 2011-03-14T12:55:11.880 | 2011-02-12T07:23:26.243 | 223 | 223 | [
"distributions",
"time-series",
"signal-processing",
"quantiles",
"compression"
] |
7104 | 2 | null | 7102 | 7 | null | I will assume the values of all the variables and constants are such that there won't be problems with obtaining square roots of negative numbers. Then
$$\frac{\sqrt{c_1 x + c_2 \exp(x)^y}}{\sqrt{\exp(x)^y}} + c_3 =\sqrt{c_2 + c_1 x \exp(-y x)} + c_3.$$
When $y \gt 0$ then eventually, for sufficiently large $x$, $\fra... | null | CC BY-SA 2.5 | null | 2011-02-11T17:44:46.580 | 2011-02-11T21:06:12.897 | 2011-02-11T21:06:12.897 | 919 | 919 | null |
7105 | 2 | null | 3 | 4 | null | This falls on the outer limits of 'statistical analysis', but [Eureqa](https://web.archive.org/web/20110201181704/http://ccsl.mae.cornell.edu/eureqa) is a very user friendly program for data-mining nonlinear relationships in data via genetic programming. Eureqa is not as general purpose, but it does what it does fairly... | null | CC BY-SA 4.0 | null | 2011-02-11T17:52:56.833 | 2022-11-27T23:12:34.187 | 2022-11-27T23:12:34.187 | 362671 | 795 | null |
7106 | 2 | null | 3 | 8 | null | Colin Gillespie mentioned BUGS, but a better option for Gibbs Sampling, etc, is [JAGS](http://mcmc-jags.sourceforge.net/).
If all you want to do is ARIMA, you can't beat [X12-ARIMA](https://web.archive.org/web/20120120205049/http://www.census.gov/srd/www/x12a/), which is a gold-standard in the field and open source. It... | null | CC BY-SA 4.0 | null | 2011-02-11T18:44:38.463 | 2022-11-27T23:22:26.533 | 2022-11-27T23:22:26.533 | 362671 | 1764 | null |
7107 | 2 | null | 7100 | 2 | null | I found some good ideas in the short Sage book, Translating Questionnaires and Other Research Instruments, at
[http://www.uk.sagepub.com/books/Book5861](http://www.uk.sagepub.com/books/Book5861) . I wouldn't call it the most systematic or the most entertaining read, but it was helpful and it's fairly up-to-date and in... | null | CC BY-SA 2.5 | null | 2011-02-11T18:46:41.513 | 2011-02-11T18:46:41.513 | null | null | 2669 | null |
7109 | 2 | null | 7101 | 2 | null | I would suggest looking at the [BayesTree](http://cran.r-project.org/web/packages/BayesTree/index.html) package, from CRAN. I have no experience with it, so I cannot say if there are better option from there. Try looking at the [Machine Learning](http://cran.r-project.org/web/views/MachineLearning.html) Task View, or d... | null | CC BY-SA 2.5 | null | 2011-02-11T20:04:18.110 | 2011-02-11T20:04:18.110 | null | null | 930 | null |
7110 | 1 | 7147 | null | 39 | 30084 | What is/are the difference(s) between a longitudinal design and a time series?
| Difference between longitudinal design and time series | CC BY-SA 2.5 | null | 2011-02-11T22:51:58.117 | 2013-08-18T22:42:37.990 | null | null | 2956 | [
"time-series",
"panel-data"
] |
7111 | 1 | 7114 | null | 18 | 14667 | To perform principal component analysis (PCA), you have to subtract the means of each column from the data, compute the correlation coefficient matrix and then find the eigenvectors and eigenvalues. Well, rather, this is what I did to implement it in Python, except it only works with small matrices because the method t... | How to perform PCA for data of very high dimensionality? | CC BY-SA 3.0 | null | 2011-02-11T22:56:39.283 | 2021-08-09T13:53:56.283 | 2015-02-07T22:09:00.433 | 28666 | null | [
"pca",
"python"
] |
7112 | 1 | 7118 | null | 45 | 47014 | I have 2 simple questions about linear regression:
- When is it advised to standardize the explanatory variables?
- Once estimation is carried out with standardized values, how can one predict with new values (how one should standardize the new values)?
Some references would be helpful.
| When and how to use standardized explanatory variables in linear regression | CC BY-SA 4.0 | null | 2011-02-11T23:09:54.510 | 2018-06-26T16:38:23.270 | 2018-06-26T16:38:23.270 | 11887 | 1443 | [
"regression",
"predictive-models",
"references",
"standardization",
"predictor"
] |
7113 | 2 | null | 7110 | 13 | null | A [time series](http://en.wikipedia.org/wiki/Time_series) is simple a sequence of data points spaced out over time, usually with regular time intervals. A [longitudinal design](http://en.wikipedia.org/wiki/Longitudinal_study) is rather more specific, keeping the same sample for each observation over time.
An example o... | null | CC BY-SA 2.5 | null | 2011-02-12T00:20:17.573 | 2011-02-12T00:20:17.573 | null | null | 2958 | null |
7114 | 2 | null | 7111 | 11 | null | The easiest way to do standard PCA is to center the columns of your data matrix (assuming the columns correspond to different variables) by subtracting the column means, and then perform an SVD. The left singular vectors, multiplied by the corresponding singular value, correspond to the (estimated) principal component... | null | CC BY-SA 3.0 | null | 2011-02-12T01:19:41.250 | 2015-02-07T22:09:44.477 | 2015-02-07T22:09:44.477 | 28666 | 1670 | null |
7115 | 1 | 7188 | null | 6 | 567 | I'm wondering how far along the natural language processing is in determining the semantic distance between two excerpts of text.
For instance, consider the following phrases
- Early today, I got up and washed my car.
- I cleaned my truck up this morning.
- Bananas are an excellent source of potassium.
Clearly (to... | Semantic distance between excerpts of text | CC BY-SA 2.5 | null | 2011-02-12T03:28:54.630 | 2022-06-20T14:19:06.153 | 2011-02-12T12:46:47.723 | null | 1026 | [
"clustering",
"classification",
"text-mining"
] |
7116 | 2 | null | 7111 | 11 | null | It sounds like what you want is the NIPALS algorithm for performing PCA. It's a very popular algorithm among statisticians. It has many advantages:
- Computationally less expensive than SVD or eigenvalue decomposition methods if only the first few components are required.
- Has more modest storage requirements in gen... | null | CC BY-SA 2.5 | null | 2011-02-12T03:43:19.633 | 2011-02-12T03:43:19.633 | null | null | 2833 | null |
7117 | 2 | null | 7115 | 2 | null | Check out the work by [Jones & Mewhort (2007)](https://doi.org/10.1037/0033-295X.114.1.1). This [more recent work](http://www.springerlink.com/content/qr50708142958x51/) may also be of interest, particularly their [online tool](http://www.casstools.org).
| null | CC BY-SA 4.0 | null | 2011-02-12T03:50:03.520 | 2022-06-20T14:19:06.153 | 2022-06-20T14:19:06.153 | 361019 | 364 | null |
7118 | 2 | null | 7112 | 31 | null | Although terminology is a contentious topic, I prefer to call "explanatory" variables, "predictor" variables.
### When to standardise the predictors:
- A lot of software for performing multiple linear regression will provide standardised coefficients which are equivalent to unstandardised coefficients where you man... | null | CC BY-SA 2.5 | null | 2011-02-12T04:27:47.997 | 2011-02-12T11:13:03.750 | 2011-02-12T11:13:03.750 | 183 | 183 | null |
7119 | 2 | null | 7088 | 4 | null | I'll do my best to interpret your question.
### Style Rules
- Many journals and disciplines adopt a style guide (e.g., APA, Chicago, MLA, etc.).
If one applies to you, then you are likely to have many relevant rules to guide you in the presentation of tables, figures, and much more.
### Table and Figure Design
... | null | CC BY-SA 2.5 | null | 2011-02-12T05:00:37.070 | 2011-02-12T05:00:37.070 | null | null | 183 | null |
7120 | 2 | null | 7088 | 2 | null | Jeremy has offered many constructive suggestions. I'll add the point that
>
"If there were one approach that was
clearly superior, then the Law of the
Statistical Jungle would dictate that
it would survive, and all of the other
techniques would exist only as
historical footnotes. The continued
survival o... | null | CC BY-SA 2.5 | null | 2011-02-12T05:00:38.817 | 2011-02-12T05:18:18.587 | 2011-02-12T05:18:18.587 | 183 | 2669 | null |
7121 | 2 | null | 7032 | 1 | null | An graphical example on outliers that requires no software and can be read in 2 minutes is [Wikipedia on Anscombe's quartet](http://en.wikipedia.org/wiki/Anscombe%27s_quartet)
| null | CC BY-SA 3.0 | null | 2011-02-12T05:12:06.707 | 2012-07-24T10:41:12.550 | 2012-07-24T10:41:12.550 | 2795 | 2669 | null |
7122 | 2 | null | 6913 | 4 | null | Just a quick thought:
If you assume that each rating is drawn from a latent continuous variable, then you could define the median of this underlying continuous variable of interest as your value of interest, rather than the mean of this underlying distribution. Where the distribution is symmetric, then the mean and the... | null | CC BY-SA 2.5 | null | 2011-02-12T05:31:00.017 | 2011-02-12T05:31:00.017 | null | null | 183 | null |
7123 | 2 | null | 7110 | 25 | null | If we think of designs made up of $n$ cases measured on $k$ occasions, then the following loose definition seems to me to be descriptive of the distinction:
- longitudinal designs: high $n$, low $k$
- time series: low $n$, high $k$
Of course, this raises the question of what is high and what is low.
Summarising my ... | null | CC BY-SA 2.5 | null | 2011-02-12T05:41:19.063 | 2011-02-13T11:51:25.673 | 2011-02-13T11:51:25.673 | 183 | 183 | null |
7124 | 2 | null | 7100 | 0 | null | The procedure i have normally seen followed is to translate the questionnaire from english, and then have it back translated by someone else. If the two english translations match up, then you are good to go, otherwise repeat until they do.
| null | CC BY-SA 2.5 | null | 2011-02-12T08:45:04.460 | 2011-02-12T08:45:04.460 | null | null | 656 | null |
7125 | 2 | null | 7100 | 7 | null | I don't know what your questionnaire aims to assess. In Health-related Quality-of-Life studies, for example, there are a certain number of recommendations for translation issues that were discussed in the following papers (among others):
- Marquis et al., Translating and evaluating questionnaires: Cultural issues for ... | null | CC BY-SA 2.5 | null | 2011-02-12T09:00:13.563 | 2011-02-12T09:00:13.563 | 2017-04-13T12:44:37.583 | -1 | 930 | null |
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