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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6570 | 1 | 6576 | null | 7 | 2015 | I want to conduct a poll on the quality of a product containing several questions with five possible answers:
- Very poor
- Poor
- OK / No opinion
- Good
- Very good
A colleague has advised me to ditch option 3 (OK / No opinion) to force people to choose.
Which will produce the most reliable / useful data? Is th... | Should a multiple choice poll contain a neutral response? | CC BY-SA 2.5 | null | 2011-01-26T13:24:23.333 | 2014-12-22T18:14:19.050 | 2011-01-26T15:12:29.600 | 919 | 1259 | [
"polling"
] |
6571 | 1 | null | null | 3 | 478 | Let us say we have some demographic time series data which tells us how many hours people spend in front of a computer screen each day, grouped by age and gender:
```
set.seed(42)
dates = seq(as.Date("2011/1/1"), by="day", length.out = 100)
male.age1 = round(runif(100, min = 1, max = 10))
female.age1 = round(runif(100,... | What types of analysis are appropriate for demographic time series data? | CC BY-SA 2.5 | null | 2011-01-26T14:02:10.713 | 2011-01-26T19:19:10.297 | 2011-01-26T14:07:52.420 | 539 | 539 | [
"r",
"time-series"
] |
6572 | 2 | null | 6570 | 0 | null | The fact that you are forcing the respondent for a positive answer or negative answer, in this situation, is not correct; the respondent may be undecided and it is a possibility; it is more when it is a new product. If you are developing an instrument to measure quality it is better that you use 5 options as given by y... | null | CC BY-SA 2.5 | null | 2011-01-26T15:32:22.127 | 2011-01-26T15:32:22.127 | null | null | 2926 | null |
6573 | 2 | null | 6554 | 3 | null | (Disclaimer: I am by no means very experienced in Bayesian stats or using BUGS!)
What parameters would you like to estimate? What does "mu = 10, tau=1/10" mean? Are these numbers considered to be priors?
Let us assume your interested in estimating $\mu$ and $\sigma^2$. (Keep in mind that Win/OpenBUGS use the precision... | null | CC BY-SA 2.5 | null | 2011-01-26T15:40:51.817 | 2011-01-26T15:45:58.250 | 2011-01-26T15:45:58.250 | 307 | 307 | null |
6575 | 1 | 6589 | null | 7 | 1125 | Consider the following picture representing the experimental data sequence obtained by two 1D-sensors (each point of the sequence is plotted on XY plane according to the respective sensor reading):
It's visually obvious that two modes have been registered. L... | How to do primary component analysis on multi-mode data with non-orthogonal primary components? | CC BY-SA 2.5 | null | 2011-01-26T15:51:26.317 | 2021-03-22T02:09:36.077 | null | null | 2820 | [
"pca",
"multivariate-analysis",
"mode"
] |
6576 | 2 | null | 6570 | 10 | null | Neutral points can mean many different things to many people. The way you labeled the middle choice yourself reflects this uncertainty. Some reasons for choosing the neutral point from the perspective of a participant:
- I don't care to really think about my answer to this question (I just want to get paid and leave)
... | null | CC BY-SA 2.5 | null | 2011-01-26T16:03:55.117 | 2011-01-26T16:03:55.117 | null | null | 1909 | null |
6577 | 1 | null | null | 3 | 672 | I've got some data that has this basic shape (using R):
```
df <- data.frame(group=sample(LETTERS, 500, T, log(2:27)),
type=sample(c("x","y"), 500, T, c(.4,.6)),
value=sample(0:20, 500, T))
```
I want to investigate the ratios between `x` and `y` within each group.
One way would be t... | Investigate ratios between two groups | CC BY-SA 2.5 | null | 2011-01-26T16:07:42.770 | 2011-01-27T19:53:37.743 | 2011-01-27T19:53:37.743 | 1434 | 1434 | [
"r",
"statistical-significance"
] |
6578 | 2 | null | 6080 | 1 | null | By zero-truncated, do you mean that any data that would have had a 0 as a response is just missing? In that case, can't you just put the 0s in?
Or do you mean that some proportion of the time, instead of getting a sensical answer, you get a 0 instead? That sounds like zero-inflation to me. In that case, there are zero-... | null | CC BY-SA 2.5 | null | 2011-01-26T16:20:22.180 | 2011-01-26T16:20:22.180 | null | null | 6 | null |
6579 | 1 | null | null | 3 | 1831 | I am doing an experiment whereby I have 4 different conditions. Within each condition, I do 4-7 technical replicates (cell counts in 4-7 high powered fields). I have also repeated the experiment 3 times (3 biological replicate (rats)). What test will compare the 4 different conditions, but will also take into accoun... | Statistical test for multiple biological replicates | CC BY-SA 2.5 | null | 2011-01-26T16:24:01.237 | 2011-01-26T18:46:56.767 | 2011-01-26T18:46:56.767 | 449 | null | [
"anova"
] |
6580 | 1 | 6602 | null | 5 | 2818 | Question
Is there such concept in econometrics/statistics as a derivative of parameter $\hat{b_{p}}$ in a linear model with respect to some observation $X_{ij}$?
By derivative I mean $\frac{\partial \hat{b_{p}}}{\partial X_{ij}}$ - how would change parameter $\hat{b_{p}}$ if we changed $X_{ij}$?
Motivation
I was thin... | Derivative of a linear model | CC BY-SA 2.5 | null | 2011-01-26T16:25:10.783 | 2011-01-27T04:21:12.800 | 2011-01-26T19:06:17.867 | 1643 | 1643 | [
"regression",
"uncertainty"
] |
6581 | 1 | 6610 | null | 52 | 70366 | What is "Deviance," how is it calculated, and what are its uses in different fields in statistics?
In particular, I'm personally interested in its uses in CART (and its implementation in rpart in R).
I'm asking this since the [wiki-article](http://en.wikipedia.org/wiki/Deviance_%28statistics%29) seems somewhat lacking... | What is Deviance? (specifically in CART/rpart) | CC BY-SA 2.5 | null | 2011-01-26T16:27:34.040 | 2019-09-15T17:04:48.410 | null | null | 253 | [
"r",
"cart",
"rpart",
"deviance"
] |
6582 | 1 | null | null | 7 | 1799 | When deconstructing my mixed effects model, I found a three-way significant interaction. I calculated my p-value by using maximum likelihood ratio tests allowing for a comparison of the fit of the two models (the model with all predictors minus the model with all predictors but the predictor of interest - in this case,... | Conducting planned comparisons in mixed model using lmer | CC BY-SA 2.5 | null | 2011-01-26T17:23:46.763 | 2011-09-24T03:28:02.923 | 2011-01-26T20:57:29.597 | null | 2934 | [
"mixed-model",
"interaction",
"model-comparison"
] |
6583 | 2 | null | 6579 | 1 | null | Sounds like you'll need to do a two-way analysis of variance to me. I'm assuming the 'technical replicates' are 3 repeats of the same measurement procedure in the same rat with the same condition, and all the rats are subjected to all the conditions. The rats are then a 'blocking' factor, and the condition is your 'tre... | null | CC BY-SA 2.5 | null | 2011-01-26T18:45:42.947 | 2011-01-26T18:45:42.947 | null | null | 449 | null |
6584 | 2 | null | 6570 | 1 | null | If you wish to detect overt opinions then put in a neutral option. If you wish to detect any potential positive or negative bias then leave it out. As caracal said, label things as unambiguously as possible with respect to what you wish the options to reflect.
I've seen studies where only the form of response was cha... | null | CC BY-SA 2.5 | null | 2011-01-26T18:49:01.063 | 2011-01-26T18:56:44.410 | 2011-01-26T18:56:44.410 | 601 | 601 | null |
6585 | 2 | null | 6580 | 6 | null | I guess this would come under the heading of regression diagnostics. I haven't seen this precise statistic before, but something that comes fairly close is DFBETAij, which is the the change in regression coefficient i when the jth observation is omitted divided by the estimated standard error of coefficient i.
The book... | null | CC BY-SA 2.5 | null | 2011-01-26T19:14:09.810 | 2011-01-26T19:21:21.437 | 2011-01-26T19:21:21.437 | 449 | 449 | null |
6586 | 2 | null | 6570 | 2 | null | I try to avoid questions with more than two answers, as it is impossible to compare them between users. (good vs. very good can be very subjective).
I rephrase most questions into binary type (giving though the possibility to be indiffernt):
"Would you use the product everyday?"
Yes No Indifferent
"Would you recommend ... | null | CC BY-SA 2.5 | null | 2011-01-26T19:16:51.430 | 2011-01-26T19:16:51.430 | null | null | 2904 | null |
6587 | 2 | null | 6571 | 2 | null | If you're trying to learn time series in R, I would suggest you to use real data and not simulated data.
This is so because in time series there many effects due to time, such as seasonality and trend.
I would suggest you to take a look at
```
?ts
?ts.plot
?decompose
?arima
```
If you want to study this simulated data... | null | CC BY-SA 2.5 | null | 2011-01-26T19:19:10.297 | 2011-01-26T19:19:10.297 | null | null | 2902 | null |
6588 | 1 | 6596 | null | 13 | 2812 | I have a data set with lots of zeros that looks like this:
```
set.seed(1)
x <- c(rlnorm(100),rep(0,50))
hist(x,probability=TRUE,breaks = 25)
```
I would like to draw a line for its density, but the `density()` function uses a moving window that calculates negative values of x.
```
lines(density(x), col = 'grey')
```
... | How can I estimate the density of a zero-inflated parameter in R? | CC BY-SA 2.5 | null | 2011-01-26T20:01:23.850 | 2013-04-19T15:21:32.397 | 2013-04-18T19:19:02.937 | 1036 | 2750 | [
"r",
"probability",
"kde"
] |
6589 | 2 | null | 6575 | 1 | null | There are factor analysis techniques that allow oblique rotation, not just the orthogonal rotation that PCA uses. Take a look at direct oblimin rotation or promax rotation.
Not sure what statistical application you are using. In R, the psych and HDMD packages have commands that allow oblique rotations.
| null | CC BY-SA 2.5 | null | 2011-01-26T20:30:43.377 | 2011-01-26T20:30:43.377 | null | null | 2933 | null |
6592 | 1 | 6597 | null | 4 | 260 | I'm not sure that I've titled this question correctly, but here is my query.
Suppose you are given a set of measurements and the uncertainty (variance) associated with each. The task is to statistically figure out how many different objects were likely measured and finally, to combine measurements into a single estima... | Multiple hypothesis ANOVA | CC BY-SA 2.5 | null | 2011-01-26T21:02:50.433 | 2011-06-26T00:03:58.643 | 2011-01-26T23:07:52.737 | 449 | 2932 | [
"anova",
"clustering",
"meta-analysis"
] |
6593 | 2 | null | 6581 | 11 | null | Deviance is the likelihood-ratio statistic for testing the null hypothesis that the model holds agains the general alternative (i.e., the saturated model). For some Poisson and binomial GLMs, the number of observations $N$ stays fixed as the individual counts increase in size. Then the deviance has a chi-squared asymp... | null | CC BY-SA 2.5 | null | 2011-01-26T22:13:54.307 | 2011-01-26T22:20:50.633 | 2011-01-26T22:20:50.633 | null | 1307 | null |
6595 | 2 | null | 6588 | 0 | null | You may try lowering bandwidth (blue line is for `adjust=0.5`),
but probably KDE is just not the best method to deal with such data.
| null | CC BY-SA 2.5 | null | 2011-01-26T22:19:10.947 | 2011-01-26T22:19:10.947 | null | null | null | null |
6596 | 2 | null | 6588 | 17 | null | The density is infinite at zero because it includes a discrete spike. You need to estimate the spike using the proportion of zeros, and then estimate the positive part of the density assuming it is smooth. KDE will cause problems at the left hand end because it will put some weight on negative values. One useful approa... | null | CC BY-SA 2.5 | null | 2011-01-26T22:39:44.857 | 2011-01-26T23:02:53.137 | 2011-01-26T23:02:53.137 | 159 | 159 | null |
6597 | 2 | null | 6592 | 2 | null | One approach would be a finite mixture model with an unknown number of components. A set of measurements and their variances sounds like meta-analysis. I suggest you have at look at [Peter Schlattmann's webpage for his book 'Medical Applications of Finite Mixture Models'](http://www.charite.de/biometrie/schlattmann/boo... | null | CC BY-SA 2.5 | null | 2011-01-26T22:41:50.113 | 2011-01-26T22:41:50.113 | null | null | 449 | null |
6598 | 2 | null | 6588 | 4 | null | I'd agree with Rob Hyndman that you need to deal with the zeroes separately. There are a few methods of dealing with a kernel density estimation of a variable with bounded support, including 'reflection', 'rernormalisation' and 'linear combination'. These don't appear to have been implemented in R's `density` function,... | null | CC BY-SA 2.5 | null | 2011-01-26T23:05:31.587 | 2011-01-26T23:05:31.587 | null | null | 449 | null |
6599 | 1 | 6665 | null | 23 | 6161 | I have read Alexandru Niculescu-Mizil and Rich Caruana's paper "[Obtaining Calibrated Probabilities from Boosting](http://aaaipress.org/Papers/Workshops/2007/WS-07-05/WS07-05-006.pdf)" and the discussion in [this](https://stats.stackexchange.com/questions/5196/why-use-platts-scaling) thread. However, I am still having ... | Calibrating a multi-class boosted classifier | CC BY-SA 2.5 | null | 2011-01-26T23:48:06.297 | 2018-07-06T15:42:46.263 | 2017-04-13T12:44:32.747 | -1 | 2798 | [
"machine-learning",
"boosting"
] |
6600 | 2 | null | 6582 | 1 | null | It sounds like you basically have a problem of model choice. I think this is best treated as a decision problem. You want to act as if the final model you select is the true model, so that you can make conclusions about your data.
So in decision theory, you need to specify a loss function, which says how you are goin... | null | CC BY-SA 2.5 | null | 2011-01-27T01:07:12.153 | 2011-01-27T01:07:12.153 | null | null | 2392 | null |
6601 | 1 | 6605 | null | 32 | 14624 | This is a similar question to the one [here](https://stats.stackexchange.com/questions/155/what-is-your-favorite-laymans-explanation-for-a-difficult-statistical-concept), but different enough I think to be worthwhile asking.
I thought I'd put as a starter, what I think one of the hardest to grasp is.
Mine is the diff... | What is the hardest statistical concept to grasp? | CC BY-SA 2.5 | null | 2011-01-27T03:57:01.977 | 2015-05-23T23:01:52.830 | 2017-04-13T12:44:33.550 | -1 | 2392 | [
"teaching"
] |
6602 | 2 | null | 6580 | 5 | null | @onestop points in the right direction. Belsley, Kuh, and Welsch describe this approach on pp. 24-26 of their book. To differentiate with respect to an observation (and not just one of its attributes), they introduce a weight, perform weighted least squares, and differentiate with respect to the weight.
Specifically,... | null | CC BY-SA 2.5 | null | 2011-01-27T04:21:12.800 | 2011-01-27T04:21:12.800 | null | null | 919 | null |
6603 | 2 | null | 6176 | 3 | null | Jumping straight into non-parametric Bayesian analysis is quite a big first leap! Maybe get a bit of parametric Bayes under your belt first?
Three books which you may find useful from the Bayesian part of things are:
1) Probability Theory: The Logic of Science by E. T. Jaynes, Edited by G. L. Bretthorst (2003)
2) Baye... | null | CC BY-SA 2.5 | null | 2011-01-27T04:35:11.757 | 2011-01-27T04:35:11.757 | null | null | 2392 | null |
6604 | 1 | null | null | 4 | 11098 | I am running a bivariate correlation analysis in SPSS, and I am performing multiple comparisons (there are 8 variables in total). I want to correct for multiple comparisons because I am aware that any 'significant' results could simply be flukes.
However, the Bonferroni correction is not appropriate in this case (it is... | Correcting for multiple comparisons when running a bivariate correlation in SPSS | CC BY-SA 2.5 | null | 2011-01-27T05:51:07.740 | 2011-01-27T10:46:55.900 | null | null | 2938 | [
"correlation",
"multiple-comparisons",
"spss"
] |
6605 | 2 | null | 6601 | 31 | null | for some reason, people have difficulty grasping what a p-value really is.
| null | CC BY-SA 2.5 | null | 2011-01-27T06:06:18.073 | 2011-01-27T06:06:18.073 | null | null | 795 | null |
6606 | 2 | null | 6601 | 23 | null | Similar to shabbychef's answer, it is difficult to understand the meaning of a confidence interval in frequentist statistics. I think the biggest obstacle is that a confidence interval doesn't answer the question that we would like to answer. We'd like to know, "what's the chance that the true value is inside this part... | null | CC BY-SA 2.5 | null | 2011-01-27T06:46:32.313 | 2011-01-27T06:46:32.313 | null | null | 401 | null |
6607 | 2 | null | 6601 | 6 | null | What do the different distributions really represent, besides than how they are used.
| null | CC BY-SA 2.5 | null | 2011-01-27T07:51:33.693 | 2011-01-27T07:51:33.693 | null | null | 1808 | null |
6608 | 2 | null | 6581 | 31 | null | It might be a bit clearer if we think about a perfect model with as many parameters as observations such that it explains all variance in the response. This is the saturated model. Deviance simply measures the difference in "fit" of a candidate model and that of the saturated model.
In a regression tree, the saturated ... | null | CC BY-SA 2.5 | null | 2011-01-27T08:47:16.440 | 2011-01-27T08:47:16.440 | null | null | 1390 | null |
6609 | 1 | null | null | 6 | 2203 |
### Question:
- Can you do a repeated measures multinomial logistic regression using SPSS?
### Context:
I need to do a regression on data at two points of time and I think this maybe the only way to go(?).
To elaborate: I work for a national health service supporting individuals with psychosis. I want to inves... | How to do a repeated measures multinomial logistic regression using SPSS? | CC BY-SA 2.5 | null | 2011-01-27T08:53:01.223 | 2011-02-25T03:26:34.467 | 2011-02-25T03:26:34.467 | 183 | null | [
"logistic",
"spss"
] |
6610 | 2 | null | 6581 | 56 | null | Deviance and GLM
Formally, one can view deviance as a sort of distance between two probabilistic models; in GLM context, it amounts to two times the log ratio of likelihoods between two nested models $\ell_1/\ell_0$ where $\ell_0$ is the "smaller" model; that is, a linear restriction on model parameters (cf. the [Neyma... | null | CC BY-SA 4.0 | null | 2011-01-27T09:05:42.833 | 2019-09-15T17:04:48.410 | 2019-09-15T17:04:48.410 | 129149 | 930 | null |
6611 | 2 | null | 6540 | 1 | null | The question does not state the precise intervals or yields so the $H_{0}$ hypothesis must be conservative with infinite intervals and yields with pessimist approximations, `.*logic`'s suggestion won't qualify. Confidence interval not calculated. So:
$lim_{ m \rightarrow \infty } \left[ 1+\frac{r}{m} \right] ^{mt}=e^{r... | null | CC BY-SA 2.5 | null | 2011-01-27T10:46:01.347 | 2011-01-28T21:04:15.727 | 2011-01-28T21:04:15.727 | 2914 | 2914 | null |
6612 | 2 | null | 6604 | 3 | null | This first part of my response won't address your two questions directly since what I am suggesting departs from your correlational approach. If I understand you correctly, you have two blocks of variables, and they play an asymmetrical role in the sense that one of them is composed of response variables (performance o... | null | CC BY-SA 2.5 | null | 2011-01-27T10:46:55.900 | 2011-01-27T10:46:55.900 | 2017-04-13T12:44:39.283 | -1 | 930 | null |
6613 | 1 | null | null | 2 | 4178 | ```
x <- read.table('file_name',header=TRUE, row.names=1)
y <- t(x)
y <- data.frame(y)
row.names(y) <- names(x)
names(y) <- row.names(x)
library(corrplot)
corr <- cor(y)
par(ask = TRUE)
corrplot(corr, order = "hclust")
```
I'm trying use corrplot on my dataset. The original dataset has 25000 rows and 100 columns. I t... | Error in cor(y): allocMatrix: too many elements specified | CC BY-SA 2.5 | null | 2011-01-27T11:03:38.850 | 2011-01-27T17:53:25.883 | 2011-01-27T17:53:25.883 | null | null | [
"r",
"correlation"
] |
6614 | 2 | null | 6601 | 8 | null | From my personal experience the concept of [likelihood](http://en.wikipedia.org/wiki/Likelihood_function) can also cause quite a lot of stir, especially for non-statisticians. As wikipedia says, it is very often mixed up with the concept of probability, which is not exactly correct.
| null | CC BY-SA 2.5 | null | 2011-01-27T11:08:52.113 | 2011-01-27T11:08:52.113 | null | null | 22 | null |
6615 | 2 | null | 6557 | 9 | null | Here's my version with your simulated data set:
```
x1 <- rnorm(100,2,10)
x2 <- rnorm(100,2,10)
y <- x1+x2+x1*x2+rnorm(100,1,2)
dat <- data.frame(y=y,x1=x1,x2=x2)
res <- lm(y~x1*x2,data=dat)
z1 <- z2 <- seq(-1,1)
newdf <- expand.grid(x1=z1,x2=z2)
library(ggplot2)
p <- ggplot(data=transform(newdf, yp=predict(res, newdf... | null | CC BY-SA 2.5 | null | 2011-01-27T11:45:40.877 | 2011-01-27T11:45:40.877 | null | null | 930 | null |
6617 | 2 | null | 6601 | 5 | null | I think the question is interpretable in two ways, which will give very different answers:
1) For people studying statistics, particularly at a relatively advanced level, what is the hardest concept to grasp?
2) Which statistical concept is misunderstood by the most people?
For 1) I don't know the answer at all. S... | null | CC BY-SA 2.5 | null | 2011-01-27T13:22:27.327 | 2011-01-27T13:22:27.327 | null | null | 686 | null |
6618 | 2 | null | 6601 | 9 | null | I think that very few scientists understand this basic point: It is only possible to interpret results of statistical analyses at face value, if every step was planned in advance. Specifically:
- Sample size has to be picked in advance. It is not ok to keep analyzing the data as more subjects are added, stopping when ... | null | CC BY-SA 2.5 | null | 2011-01-27T13:29:56.687 | 2011-01-28T14:51:32.457 | 2011-01-28T14:51:32.457 | 25 | 25 | null |
6619 | 1 | 6620 | null | 4 | 281 | ...2 to 5 questions answered correctly, out of 20 of them? Each question has 5 choices. Probability of getting one right is 1/5. Probability of getting exactly 1 right is ${20 \choose 1} p^1 q^{19}$, with $p=P(\mathrm{right})$ and $q=P(\mathrm{wrong})$ (which I managed to understand and calculate). However how do I c... | Probability of getting between | CC BY-SA 2.5 | null | 2011-01-27T13:40:46.323 | 2011-01-27T16:41:24.930 | 2011-01-27T16:41:24.930 | 919 | 1833 | [
"probability",
"self-study",
"binomial-distribution"
] |
6620 | 2 | null | 6619 | 5 | null | Hint: sum up the probabilities. The probability that exactly $k$ answers are answered correctly is $${20 \choose k}\left(\frac{1}{5}\right)^k\left(\frac{4}{5}\right)^{20-k}.$$ In your case you have $k=2,3,4,5$.
| null | CC BY-SA 2.5 | null | 2011-01-27T13:56:41.140 | 2011-01-27T13:56:41.140 | null | null | 2116 | null |
6621 | 2 | null | 6577 | 1 | null | Why not display the raw data?
```
df <- data.frame(group=sample(LETTERS, 500, T, log(2:27)),
x=sample(0:20, 500, T),
y=sample(0:20, 500, T))
df$ratio <- with(df, (x-y)/(x+y))
library(ggplot2)
qplot(group, ratio, data = df) +
stat_summary(fun.y = mean, colour = "red", size = 2, ge... | null | CC BY-SA 2.5 | null | 2011-01-27T14:51:14.593 | 2011-01-27T14:51:14.593 | null | null | 46 | null |
6622 | 2 | null | 6613 | 0 | null | I would guess that you don't have enough memory. The correlation matrix for 25,000 columns will be 25,000 x 25000 which is about 4 gig. `(25000 ^ 2 * 8 ) / 1024 ^ 3)`
| null | CC BY-SA 2.5 | null | 2011-01-27T14:53:11.207 | 2011-01-27T14:53:11.207 | null | null | 46 | null |
6623 | 2 | null | 4466 | 1 | null | Good suggestions, I've got plenty of things to look into now.
Remember, one extremely important consideration is making sure that the work is "correct" in the first place. This is the role that tools like [Sweave](http://en.wikipedia.org/wiki/Sweave) play, by increasing the chances that what you did, and what you sa... | null | CC BY-SA 2.5 | null | 2011-01-27T14:57:16.553 | 2011-01-27T14:57:16.553 | null | null | 1434 | null |
6624 | 1 | 6631 | null | 7 | 3321 | I've asked the [same question](https://math.stackexchange.com/questions/19180/detect-abnormal-points-in-point-cloud) at Math SE, but the suggestion is that probably this question belongs here.
Given a list of [point cloud](http://en.wikipedia.org/wiki/Point_cloud) in terms of $(x,y,z)$ how to determine abnormal points... | Detecting abnormal points in point cloud | CC BY-SA 2.5 | null | 2011-01-27T15:04:03.230 | 2011-01-28T09:14:13.320 | 2017-04-13T12:19:38.800 | -1 | 175 | [
"outliers",
"spatial"
] |
6626 | 2 | null | 6252 | 5 | null | As others have pointed out, there are many measures of clustering "quality";
most programs minimize SSE.
No single number can tell much about noise in the data,
or noise in the method,
or flat minima — low points in Saskatchewan.
So first try to visualize, get a feel for,
a given clustering, before reducing it to "41".... | null | CC BY-SA 2.5 | null | 2011-01-27T15:37:09.260 | 2011-01-28T18:28:55.360 | 2011-01-28T18:28:55.360 | 557 | 557 | null |
6627 | 2 | null | 6613 | 3 | null | The "allocMatrix: too many elements specified" error is thrown in on line 170 of `R/src/main/array.c` when nrow x ncol is greater than `INT_MAX` (+2,147,483,647). `INT_MAX` is defined in the C standard library file "limits.h" and it is the same in the 32-bit and 64-bit toolchain used to build R, so no amount of RAM on... | null | CC BY-SA 2.5 | null | 2011-01-27T15:42:03.393 | 2011-01-27T15:42:03.393 | null | null | 1657 | null |
6628 | 2 | null | 6570 | 7 | null | I think this whole "force people to choose" thing is just a complete red herring. People say it to me all the time. To me it sounds like "force people to state the capital of Uzbekistan". They don't know, and forcing them won't make them know any better.
With that mini-rant over, my only sensible contribution is to say... | null | CC BY-SA 2.5 | null | 2011-01-27T16:04:35.303 | 2011-01-27T16:04:35.303 | null | null | 199 | null |
6629 | 2 | null | 6624 | 1 | null | Are the points relatively dense on your surface? Then I would suggest counting the number of points in a sphere around every point. Choose the radius of your sphere to be a bit less than the distance the "abnormal" points have to the regular surface - maybe half of what they typically have. Then throw out the points wh... | null | CC BY-SA 2.5 | null | 2011-01-27T16:10:51.673 | 2011-01-27T16:10:51.673 | null | null | 2898 | null |
6630 | 1 | null | null | 9 | 1067 | I need some guidance on the appropriate level of pooling to use for difference of means tests on time series data. I am concerned about temporal and sacrificial pseudo-replication, which seem to be in tension on this application. This is in reference to a mensural study rather than a manipulative experiment.
Consider... | What temporal resolution for time series significance test? | CC BY-SA 2.5 | null | 2011-01-27T16:18:30.880 | 2011-05-06T12:32:01.043 | null | null | null | [
"time-series"
] |
6631 | 2 | null | 6624 | 8 | null | An outlier detector for your irregular ("vector") point data is available in GRASS as [v.outlier](http://grass.osgeo.org/grass64/manuals/html64_user/v.outlier.html).
An overview of spatial outlier detection methods appears in a [2004 paper by Cheng and Li](http://www.geo.upm.es/postgrado/CarlosLopez/papers/AHybridAppro... | null | CC BY-SA 2.5 | null | 2011-01-27T16:39:42.483 | 2011-01-27T16:39:42.483 | null | null | 919 | null |
6632 | 2 | null | 6624 | 2 | null | You could fit some sort of smooth function for $z(x,y)$, perhaps using [locally weighted scatterplot smoothing](http://en.wikipedia.org/wiki/Local_regression) (LOWESS or LOESS), then look for points where the residual for $z$ (i.e. the difference between the observed and fitted values) is greater than some fixed multip... | null | CC BY-SA 2.5 | null | 2011-01-27T16:41:47.253 | 2011-01-28T09:14:13.320 | 2011-01-28T09:14:13.320 | 449 | 449 | null |
6634 | 2 | null | 6624 | 1 | null | I think this problem relies only in the outliers of variable $z$.
The surveyor scans a grid of $x$,$y$ points that are "well-behaved". On the other hand $z$ points may contain abnormal values (in statistics we call them outliers).
I would suggest to explore the values of $z$, and the plot of $(x,y,z)$.
From those plots... | null | CC BY-SA 2.5 | null | 2011-01-27T17:48:28.370 | 2011-01-27T17:48:28.370 | null | null | 2902 | null |
6635 | 2 | null | 6601 | 9 | null | Tongue firmly in cheek: For frequentists, the Bayesian concept of probability; for Bayesians, the frequentist concept of probability. ;o)
Both have merit of course, but it can be very difficult to understand why one framework is interesting/useful/valid if your grasp of the other is too firm. Cross-validated is a goo... | null | CC BY-SA 3.0 | null | 2011-01-27T17:57:12.117 | 2015-05-23T23:01:52.830 | 2015-05-23T23:01:52.830 | 22047 | 887 | null |
6636 | 1 | 6666 | null | 6 | 1521 | (redirected here from mathoverflow.net)
Hello,
At work I was asked the probability of a user hitting an outage on the website. I have some following metrics. Total system downtime = 500,000 seconds a year. Total amount of seconds a year = 31,556,926 seconds. Thus, p of system down = 0.159 or 1.59%
We can also assume t... | Probability calculation, system uptime, likelihood of occurence | CC BY-SA 2.5 | null | 2011-01-27T18:42:03.703 | 2011-04-29T00:51:46.293 | 2017-04-13T12:58:32.177 | -1 | 2950 | [
"probability",
"odds-ratio"
] |
6637 | 1 | 6639 | null | 6 | 3007 | Let us take two formulations of the $\ell_{2}$ SVM optimization problem, one constrained:
$\min_{\alpha,b} ||w||_2^2 + C \sum_{i=1}^n {\xi_{i}^2}$
s.t $ y_i(w^T x_i +b) \geq 1 - \xi_i$
and $\xi_i \geq 0 \forall i$
and one unconstrained:
$\min_{\alpha,b} ||w||_2^2 + C \sum_{i=1}^n \max(0,1 - y_i (w^T x_i + b))^2$
Wh... | Constrained versus unconstrained formulation of SVM optimisation | CC BY-SA 3.0 | null | 2011-01-27T19:15:38.773 | 2021-12-31T02:54:02.783 | 2011-08-10T14:58:28.420 | 2513 | 1320 | [
"optimization",
"svm"
] |
6638 | 1 | 6641 | null | 5 | 2686 | I have been running a linear regression where my dependent variable is a composite. By this I mean that it is built up of components that are added and multiplied together. Specifically, for the composite variable A:
```
A = (B*C + D*E + F*G + H*I + J*K + L*M)*(1 - N)*(1 + O*P)
```
None of the component variables are ... | Composite dependent variable | CC BY-SA 2.5 | null | 2011-01-27T19:27:56.060 | 2011-01-27T20:29:25.263 | 2011-01-27T19:39:57.683 | null | 1090 | [
"regression"
] |
6639 | 2 | null | 6637 | 7 | null | It seems to me that at the solution of the first problem, the inequality constraint becomes an equality, i.e. $1 - \xi_i = y_i(w^Tx_i + b)$, because we are minimising the $\xi_i$s and the smallest value that satisfies the constraint occurs at equality. So as $\xi_i \geq 0$, $\xi_i = max(0, 1 - y_i(w^Tx_i+b))$, which s... | null | CC BY-SA 4.0 | null | 2011-01-27T19:48:05.287 | 2018-05-25T18:38:34.540 | 2018-05-25T18:38:34.540 | 168251 | 887 | null |
6640 | 1 | null | null | 1 | 1640 | I have what seems like a fairly common business statistics scenario: I need to compare one group of stores to another group of stores and be able to say if their difference in sales is statistically different.
For example:
Group A ($n_A$ = 30 stores) participated in a promotion and saw an avg sales increase for this mo... | How should I compare average store sales change across time? | CC BY-SA 2.5 | null | 2011-01-27T19:52:14.727 | 2011-01-27T22:45:31.127 | null | null | null | [
"multiple-comparisons",
"mean",
"business-intelligence"
] |
6641 | 2 | null | 6638 | 4 | null | (1) Should I expect to obtain the same fits using the two models? No. Let's look at what's going on.
(a) In the regression of $A$ directly--I'll call it the "monolithic model," the model is
$$A_j = \sum{\beta_i X_{ij}} + \epsilon_j,$$
with the cases indexed by $j$, the variables (including a constant, if any) indexe... | null | CC BY-SA 2.5 | null | 2011-01-27T20:29:25.263 | 2011-01-27T20:29:25.263 | null | null | 919 | null |
6642 | 1 | 6676 | null | 7 | 439 | Let $S_n = \frac{1}{n}\sum_{i=1}^n X_i$, and $T_n = \frac{1}{n}\sum_{j=1}^nY_i$, where
The $X_i$ are iid, the $Y_i$ are iid (with a different law)
$X_i$, and $Y_i$ are dependent
For $i\neq j$, $X_i$ and $Y_j$ are independent.
Is there a central limit type result for $S_n^2 - T_n^2$?
| Limiting distribution of a squared sum of random variables | CC BY-SA 2.5 | null | 2011-01-27T20:52:04.727 | 2011-01-28T18:48:44.800 | 2011-01-28T13:05:46.070 | 2116 | 2952 | [
"central-limit-theorem",
"delta-method"
] |
6643 | 1 | 6645 | null | 7 | 1132 | Is there a closed form solution for this inverse CDF?
| What is the closed form solution for the inverse CDF for Epanechnikov | CC BY-SA 2.5 | null | 2011-01-27T21:09:18.177 | 2017-09-12T18:41:50.350 | 2015-04-23T05:54:35.603 | 9964 | 2808 | [
"distributions",
"cumulative-distribution-function",
"kernel-smoothing"
] |
6644 | 2 | null | 6640 | 3 | null | If all of the stores were included in the study rather than a sample, then you could make conclusions without using probability statements or statistics.
But if you want to use a subsample to make inference about the larger population or make forecasts, then the use of statistics is appropriate.
A standard comparison o... | null | CC BY-SA 2.5 | null | 2011-01-27T21:51:47.840 | 2011-01-27T22:45:31.127 | 2011-01-27T22:45:31.127 | 1381 | 1381 | null |
6645 | 2 | null | 6643 | 7 | null | You mean for a random variable with a single Epanechnikov kernel as PDF? Well, the PDF is $\frac{3}{4}(1-u^2)$, so the CDF is $\frac{1}{4}(2 + 3 u - u^3)$. Inverting this in Maple leads to three solutions, of which $$u = -1/2\,{\frac { \left( 1-2\,t+2\,i\sqrt {t}\sqrt {1-t} \right) ^{2/3}+1 +i\sqrt {3} \left( 1-2\,t+2... | null | CC BY-SA 3.0 | null | 2011-01-27T22:41:50.163 | 2017-09-12T18:41:50.350 | 2017-09-12T18:41:50.350 | 22311 | 2898 | null |
6646 | 2 | null | 6601 | 21 | null | What is the meaning of "degrees of freedom"? How about df that are not whole numbers?
| null | CC BY-SA 2.5 | null | 2011-01-27T23:29:07.293 | 2011-01-29T19:13:03.407 | 2011-01-29T19:13:03.407 | -1 | null | null |
6648 | 1 | 6649 | null | 3 | 240 | An average of n birds fly through an area, in an hour, following a Poisson process. (I think this means the hours don't matter; there's no influence in the number of birds that fly through, at different parts of the day, hypothetically. Correct me if I'm wrong.)
P1 is the probability that exactly m birds fly through be... | Poisson process, time and probabilities | CC BY-SA 2.5 | null | 2011-01-27T23:34:01.300 | 2011-01-27T23:42:22.013 | null | null | 1833 | [
"probability",
"poisson-distribution"
] |
6649 | 2 | null | 6648 | 5 | null | Yes, they are the same. Another crucial assumption in the Poisson process is that what happens now is independent of what happened a moment ago or what will happen in the next moment (or at any other moment, for that matter). Therefore the distribution of events during any (measurable) period of time depends only on ... | null | CC BY-SA 2.5 | null | 2011-01-27T23:42:22.013 | 2011-01-27T23:42:22.013 | null | null | 919 | null |
6650 | 2 | null | 6601 | 7 | null | [Fiducial inference](http://en.wikipedia.org/wiki/Fiducial_inference). Even Fisher admitted he didn't understand what it does, and he invented it.
| null | CC BY-SA 2.5 | null | 2011-01-27T23:45:50.293 | 2011-01-27T23:45:50.293 | null | null | 449 | null |
6651 | 2 | null | 6347 | 3 | null | PCA depends on the scaling of your columns. If you perform a PCA on matrix $X$, then rescale each column to be norm 1 (i.e. divide by the two norm of each column), then perform a PCA on the transformed $X$, you will get different answers. I believe this is part of what 'small w.r.t. a particular row/column' is referrin... | null | CC BY-SA 2.5 | null | 2011-01-28T00:18:46.790 | 2011-01-28T00:18:46.790 | null | null | 795 | null |
6652 | 1 | 6801 | null | 104 | 17955 | I know roughly and informally what a confidence interval is. However, I can't seem to wrap my head around one rather important detail: According to Wikipedia:
>
A confidence interval does not predict that the true value of the parameter has a particular probability of being in the confidence interval given the data ... | What, precisely, is a confidence interval? | CC BY-SA 2.5 | null | 2011-01-28T00:23:50.893 | 2021-01-25T11:56:53.507 | null | null | 1347 | [
"confidence-interval",
"definition"
] |
6653 | 1 | 6839 | null | 18 | 1190 | I am hoping that I can ask this question the correct way. I have access to play-by-play data, so it's more of an issue with best approach and constructing the data properly.
What I am looking to do is to calculate the probability of winning an NHL game given the score and time remaining in regulation. I figure I coul... | Logistic Regression and Dataset Structure | CC BY-SA 2.5 | null | 2011-01-28T00:24:32.027 | 2011-02-21T19:22:09.767 | 2011-02-02T11:01:33.630 | 264 | 569 | [
"time-series",
"probability",
"logistic"
] |
6654 | 2 | null | 6652 | 47 | null | There are many issues concerning confidence intervals, but let's focus on the quotations. The problem lies in possible misinterpretations rather than being a matter of correctness. When people say a "parameter has a particular probability of" something, they are thinking of the parameter as being a random variable. ... | null | CC BY-SA 2.5 | null | 2011-01-28T03:47:24.143 | 2011-01-28T03:47:24.143 | null | null | 919 | null |
6655 | 1 | 6661 | null | 12 | 6521 | There are quite a few methods for parameter estimation out there. MLE, UMVUE, MoM, decision-theoretic, and others all seem like they have a fairly logical case for why they are useful for parameter estimation. Is any one method better than the others, or is it just a matter of how we define what the "best fitting" esti... | How do I know which method of parameter estimation to choose? | CC BY-SA 2.5 | null | 2011-01-28T06:12:21.280 | 2012-08-07T15:35:30.410 | 2012-08-07T15:35:30.410 | null | 1118 | [
"estimation",
"mathematical-statistics",
"maximum-likelihood",
"method-of-moments",
"umvue"
] |
6656 | 1 | null | null | 2 | 292 | I am studying a dynamical system that takes as an initial condition a list. I want to analyze the evolution of Shannon's entropy in this system. I know the maximum entropy (50) and the minimum (0). Pure random conditions have almost maximum entropy, and so it is hard to analyze changes in it unless it decreases. I set ... | Median entropy to observe evolution of system? | CC BY-SA 2.5 | null | 2011-01-28T06:51:57.627 | 2012-07-23T19:45:09.440 | null | null | null | [
"entropy"
] |
6657 | 2 | null | 6371 | 2 | null | So, as said in the comments, the Markov chain you consider has some absorbing states (and is irreducible, presumably), hence its stationary distribution is concentrated on these absorbing states. Therefore the issue is to compute some confidence intervals for the only two non zero coordinates of the stationary vector, ... | null | CC BY-SA 2.5 | null | 2011-01-28T06:54:14.890 | 2011-01-29T21:39:40.393 | 2011-01-29T21:39:40.393 | 2592 | 2592 | null |
6658 | 1 | null | null | 7 | 13300 | I want to compare sixteen Case Fatality Rates (deaths per 100 cases) of a particular disease from sixteen different populations across 7 years. Each population received the same treatment but some regions did not implement it properly. As a result, I am trying to show the effectiveness the treatment had in each of the ... | How can we compare multiple proportions from multiple independent populations to evaluate implementation of a treatment? | CC BY-SA 2.5 | null | 2011-01-28T06:55:48.787 | 2015-02-19T14:19:26.490 | 2011-01-30T06:01:05.940 | 2956 | 2956 | [
"hypothesis-testing",
"spss",
"proportion"
] |
6660 | 1 | null | null | 9 | 1664 | as question, since we can do the conversion from odds ratio `(p1/q1)/(p2/q2)` to relative risk `(p1/(p1+q1))/(p2/(p2+q2))` fairly easily, I wonder if there is anything that I need to pay attention before doing this?
It is obvious that if I am doing a case-control study, I shouldn't do a conversion, because I never know... | Prerequisite for conversion from odds ratio to relative risk to be valid | CC BY-SA 2.5 | null | 2011-01-28T08:24:29.473 | 2011-10-01T03:35:32.033 | 2011-08-31T23:18:21.180 | 5836 | 588 | [
"epidemiology",
"relative-risk",
"odds"
] |
6661 | 2 | null | 6655 | 12 | null | There's a slight confusion of two things here: methods for deriving estimators, and criteria for evaluating estimators. Maximum likelihood (ML) and method-of-moments (MoM) are ways of deriving estimators; Uniformly minimum variance unbiasedness (UMVU) and decision theory are criteria for evaluating different estimators... | null | CC BY-SA 2.5 | null | 2011-01-28T09:10:11.987 | 2011-01-28T09:26:27.437 | 2011-01-28T09:26:27.437 | 449 | 449 | null |
6662 | 2 | null | 6601 | 5 | null | Confidence interval in non-Bayesian tradition is a difficult one.
| null | CC BY-SA 2.5 | null | 2011-01-28T11:07:15.030 | 2011-01-28T11:07:15.030 | null | null | 1966 | null |
6664 | 1 | null | null | 8 | 4374 | I have serial hematological measurements data and I have plotted their means and SE in Stata. On the y-axis I have for example hemoglobin and time (visit days) on the x-axis hence I can visualize hemoglobin levels with time (whether it is decreasing or in increasing). The level decreases up to sometime and increases ag... | Statistical test for trend (continuous variable) in Stata or R | CC BY-SA 2.5 | null | 2011-01-28T13:07:55.577 | 2011-01-28T13:49:56.673 | 2011-01-28T13:09:32.870 | 2116 | 2961 | [
"r",
"stata"
] |
6665 | 2 | null | 6599 | 11 | null | This is a topic of practical interest to me as well so I did a little research. Here are two papers by an author that is often listed as a reference in these matters.
- Transforming classifier scores into
accurate multiclass probability
estimates
- Reducing multiclass
to binary by coupling probability
estimates
Th... | null | CC BY-SA 2.5 | null | 2011-01-28T13:21:02.197 | 2011-01-28T13:21:02.197 | null | null | 2040 | null |
6666 | 2 | null | 6636 | 4 | null | Okay, so here is my answer that I promised. I initially thought it would be quickish, but my answer has become quite large, so at the begining, I state my general results first, and leave the gory details down the bottom for those who want to see it.
I must thank @terry felkrow for this fascinating question - if I cou... | null | CC BY-SA 2.5 | null | 2011-01-28T13:24:37.943 | 2011-01-28T16:22:23.360 | 2011-01-28T16:22:23.360 | 2392 | 2392 | null |
6667 | 2 | null | 6664 | 3 | null | It seems that your problem can be stated as change-point problem. R packages dealing with such type of problems are [segmented](http://cran.r-project.org/web/packages/segmented/index.html) and [strucchange](http://cran.r-project.org/web/packages/strucchange/index.html). Since you want to look into changes in time trend... | null | CC BY-SA 2.5 | null | 2011-01-28T13:49:56.673 | 2011-01-28T13:49:56.673 | 2017-04-13T12:44:46.680 | -1 | 2116 | null |
6668 | 1 | null | null | 5 | 423 | I calculated the quantiles for an Epanechnikov kernel which I'm using to estimate the density of a sample. What I need is to find the sample quantiles knowing that it is composed of many Epanechnikov kernels. Is there a way to calculate at wich data points the different quantiles are using the kernel inverse CDF formul... | How can I convert kernel quantiles into sample quantiles? | CC BY-SA 2.5 | null | 2011-01-28T15:13:59.893 | 2015-04-23T05:57:46.607 | 2015-04-23T05:57:46.607 | 9964 | 2953 | [
"quantiles",
"cumulative-distribution-function",
"kernel-smoothing"
] |
6669 | 2 | null | 6652 | 5 | null | From a theoretical perspective Questions 2 and 3 are based on the incorrect assumption that the definitions are wrong. So I am in agreement with @whuber's answer in that respect, and @whuber's answer to question 1 does not require any additional input from me.
However, from a more practical perspective a confidence in... | null | CC BY-SA 2.5 | null | 2011-01-28T15:32:33.077 | 2011-01-28T15:32:33.077 | null | null | 2392 | null |
6670 | 1 | null | null | 9 | 8971 | I want to calculate a better bandwidh for my kernel density estimator, which is an Epanechnikov. I use Silverman's formula which involves the standard deviation of the sample, the sample size and a constant, but I'm getting a very smooth curve in most cases and I would prefer if it were more balanced. Thank you for any... | Which is the formula from Silverman to calculate the bandwidth in a kernel density estimation? | CC BY-SA 2.5 | null | 2011-01-28T15:37:17.283 | 2015-04-27T05:39:17.940 | 2015-04-27T05:39:17.940 | 9964 | 2953 | [
"estimation",
"smoothing",
"kernel-smoothing"
] |
6671 | 2 | null | 6670 | 11 | null | To shamelessly quote the Stata manual entry for [kdensity](http://www.stata.com/help.cgi?kdensity):
>
The optimal width is the width that would minimize the mean integrated squared error if the data were Gaussian and a Gaussian kernel were used, so it is not optimal in any global sense. In fact, for multimodal and hig... | null | CC BY-SA 2.5 | null | 2011-01-28T16:00:09.583 | 2011-01-28T16:22:45.787 | 2011-01-28T16:22:45.787 | 449 | 449 | null |
6672 | 2 | null | 6652 | 2 | null | Okay, I realize that when you calculate a 95% confidence interval for a parameter using classical frequentist methods, it doesn't mean that there is a 95% probability that the parameter lies within that interval. And yet ... when you approach the problem from a Bayesian perspective, and calculate a 95% credible interva... | null | CC BY-SA 2.5 | null | 2011-01-28T17:14:04.500 | 2011-01-28T17:14:04.500 | null | null | 2617 | null |
6674 | 2 | null | 726 | 16 | null | I just can't help myself, this is a provocative quote from E. T. Jaynes:
>
Many of us have already explored the road you are following, and we
know what you will find at the end of it. It doesn't matter how many
new words you drag into the discussion to avoid having to utter the
word 'probability' in a sense di... | null | CC BY-SA 3.0 | null | 2011-01-28T18:01:15.537 | 2011-08-15T04:14:01.257 | 2011-08-15T04:14:01.257 | 1381 | 2392 | null |
6675 | 2 | null | 5077 | 4 | null | I came across your question when I was looking for the original reference for Hit-and-Run. Thanks for that! I just put together a proof-of-concept implementation of hit-and-run for PyMC at the end of [this recent blog](http://healthyalgorithms.wordpress.com/2011/01/28/mcmc-in-python-pymc-step-methods-and-their-pitfal... | null | CC BY-SA 2.5 | null | 2011-01-28T18:42:16.323 | 2011-01-28T18:42:16.323 | null | null | 2498 | null |
6676 | 2 | null | 6642 | 5 | null | If $X_i$ and $Y_j$ are dependent for $i=j$, but independent for $i\neq j$ we have an iid sample from bivariate distribution $Z_i=(X_i,Y_i)$. Then central limit theorem gives us
\begin{align}
\sqrt{n}\left(\frac{1}{n}\sum_{i=1}^nZ_i -EZ_1\right)\xrightarrow{D}N(0,\Sigma)
\end{align}
with $\Sigma=cov(Z_1)$ and $\xrighta... | null | CC BY-SA 2.5 | null | 2011-01-28T18:42:37.477 | 2011-01-28T18:48:44.800 | 2011-01-28T18:48:44.800 | 2116 | 2116 | null |
6677 | 2 | null | 6670 | 2 | null | I second @onestop, but quote Wilcox, 'Introduction to Robust Estimation and Hypothesis Testing', 2nd edition, page 50:
$$
h = 1.06\frac{A}{n^{1/5}}, \qquad A = \min{\left(s,\frac{IQR(x)}{1.34}\right)},
$$
where $s$ is the sample standard deviation.
| null | CC BY-SA 2.5 | null | 2011-01-28T19:00:00.760 | 2011-01-28T19:00:00.760 | null | null | 795 | null |
6678 | 2 | null | 726 | 7 | null | >
Everybody knows that probability and statistics are the same thing, and statistics is nothing but correlation. Now the correlation is just the cosine of an angle, thus all is trivial.
-- Emil Artin, according to Kai Lai Chung in
[Elementary probability theory](http://books.google.com/books?id=safNnEOICL8C) (right... | null | CC BY-SA 2.5 | null | 2011-01-28T19:20:55.060 | 2011-01-28T19:29:25.247 | 2011-01-28T19:29:25.247 | 2592 | 2592 | null |
6679 | 2 | null | 6492 | 7 | null | On page 372 of [ARM](http://www.stat.columbia.edu/~gelman/arm/), Gelman and Hill mention using a uniform distribution on the inverse of DF between 1/DF = .5 and 1/DF = 0.
Specifically, in BUGS, they use:
```
nu.y <- 1/nu.inv.y
nu.inv.y ~ dunif(0,.5)
```
| null | CC BY-SA 2.5 | null | 2011-01-28T19:39:49.070 | 2011-01-28T19:39:49.070 | null | null | 1146 | null |
6680 | 1 | 6685 | null | 3 | 1824 | I need to convert the table A to table B. How can I do that using R?
TABLE A
```
Y 10
Y 12
Y 18
X 22
X 12
Z 11
Z 15
```
TABLE B
```
X 22 12
Y 10 12 18
Z 11 15
```
| Need to convert duplicate column elements to a unique element in R | CC BY-SA 2.5 | null | 2011-01-28T19:46:47.740 | 2011-01-28T23:08:18.223 | 2011-01-28T22:23:20.910 | 919 | 2725 | [
"r"
] |
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