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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5464 | 2 | null | 5462 | 10 | null | For a high school project it will probably be difficult to gain a representative sample with your limited time and budget. I'd be somewhat skeptical an online vendor can achieve a random sample of anything. If I had to do it I think I would use the phone book and either call or go to the addresses to distribute the sur... | null | CC BY-SA 2.5 | null | 2010-12-14T06:13:02.323 | 2010-12-14T06:13:02.323 | null | null | 1036 | null |
5465 | 1 | 5470 | null | 69 | 89945 | I am looking for some statistics (and probability, I guess) interview questions, from the most basic through the more advanced. Answers are not necessary (although links to specific questions on this site would do well).
| Statistics interview questions | CC BY-SA 2.5 | null | 2010-12-14T06:20:48.903 | 2022-05-21T08:18:45.927 | 2017-06-05T18:47:39.890 | 11887 | 795 | [
"intuition",
"careers"
] |
5466 | 2 | null | 5465 | 6 | null | I often ask "how would you define/explain what forecasting is?"
Answer to that type of very general question helps me to see if people are connected to a particular case of forecasting. There is not a right answer but answering this synthetically during an interview is not always easy:)
| null | CC BY-SA 2.5 | null | 2010-12-14T07:08:35.253 | 2010-12-14T23:00:17.420 | 2010-12-14T23:00:17.420 | 795 | 223 | null |
5467 | 2 | null | 5462 | 1 | null | a sample representative of population cannot be obtained through internet as you will only get people interested in answering your survey online, which will give you a biased sample.
| null | CC BY-SA 2.5 | null | 2010-12-14T07:17:58.210 | 2010-12-14T07:17:58.210 | null | null | 1709 | null |
5468 | 2 | null | 5448 | 6 | null | What is the rationale of applying an exploratory/unsupervised method (PCA or FA with VARIMAX rotation) after having tested a confirmatory model, especially if this is done on the same sample?
In your CFA model, you impose constraints on your pattern matrix, e.g. some items are supposed to load on one factor but not on... | null | CC BY-SA 2.5 | null | 2010-12-14T08:01:23.333 | 2010-12-14T09:50:58.897 | 2010-12-14T09:50:58.897 | 930 | 930 | null |
5469 | 2 | null | 5457 | 3 | null | I'm not a statistician, but a lot of my work involves statistics, and I work in health care.
The two things that I spend most of my time doing are:
a) examining the sizes of effects and trends and seeing if they are "real"
b) presenting very large datasets in a simple way so that managers and users of our services can ... | null | CC BY-SA 2.5 | null | 2010-12-14T08:10:11.293 | 2010-12-14T08:10:11.293 | null | null | 199 | null |
5470 | 2 | null | 5465 | 41 | null | Not sure what the job is, but I think "Explain x to a novice" would probably be good-
a) because they will probably need to do this in the job
b) it's a good test of understanding, I reckon.
| null | CC BY-SA 2.5 | null | 2010-12-14T08:12:55.613 | 2010-12-14T08:12:55.613 | null | null | 199 | null |
5471 | 2 | null | 5452 | 10 | null | It is a mathematical trick. We have
\begin{align*}
\log\frac{p_i}{1-p_i}=f(x_i)
\end{align*}
and from this we get
\begin{align*}
\frac{1}{1-p_i}=1+\exp(f(x_i))
\end{align*}
The log likelihood is
\begin{align*}
\sum_{i=1}^n\left[y_i\log(p_i)+(1-y_i)\log(1-p_i)\right]&=\sum_{i=1}^n\left[y_i\log\frac{p_i}{1-p_i}+\log(1-p... | null | CC BY-SA 2.5 | null | 2010-12-14T09:16:36.017 | 2010-12-14T13:20:06.913 | 2010-12-14T13:20:06.913 | 2116 | 2116 | null |
5472 | 2 | null | 5465 | 21 | null | Standard Q where I work is along the lines of:
>
Have a look at this output of a multiple logistic regression from a statistical package you claim to have used (preferably one we use too). XXX is the independent variable of principal interest. How woud you interpret the results for a colleague with knowledge of the su... | null | CC BY-SA 2.5 | null | 2010-12-14T09:17:59.400 | 2010-12-14T14:52:25.680 | 2010-12-14T14:52:25.680 | 919 | 449 | null |
5473 | 2 | null | 5450 | 5 | null | In a regular multiple regression with two quantitative predictor variables, including their interaction just means including their observation-wise product as an additional predictor variable: $Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 (X_1 \cdot X_2) = (b_0 + b_2 X_2) + (b_1 + b_3 X_2) X_1$
This typically int... | null | CC BY-SA 2.5 | null | 2010-12-14T11:41:45.440 | 2010-12-14T11:41:45.440 | null | null | 1909 | null |
5474 | 2 | null | 5452 | 0 | null | I'm also studying the GBM package!
- mpiktas, i think you forgot a log on the left hand side in the 2nd equation? I assume you substitute p_i with 1/(1+exp(-f(x_i))), but then in the 2nd row above there is log(1/...) = log(1+...), or am i wrong? Anyway, i think then you did it right in the third row...
- Can you tell... | null | CC BY-SA 2.5 | null | 2010-12-14T12:41:25.990 | 2010-12-14T12:41:25.990 | null | null | null | null |
5475 | 2 | null | 5465 | 9 | null | I was asked once how I would explain the relevance of the central limit theorem to a class of freshmen in the social sciences that barely have knowledge about statistics.
| null | CC BY-SA 2.5 | null | 2010-12-14T12:57:48.713 | 2010-12-14T12:57:48.713 | null | null | 1934 | null |
5476 | 2 | null | 5465 | 2 | null | While doing the variance analysis of quantitative variable, sometimes it found that frequency of the variable are very high (>5) then we use the Fisher's exact test to find independence of the variable.
| null | CC BY-SA 3.0 | null | 2010-12-14T13:12:15.970 | 2011-12-24T09:40:02.100 | 2011-12-24T09:40:02.100 | -1 | 5792 | null |
5477 | 2 | null | 5462 | 1 | null | Marketing people are using self-selected samples in online-surveys all the time, so they probably have methods for cleaning their data. Sadly, I don't have a good pointer right now to look for their methods.
| null | CC BY-SA 2.5 | null | 2010-12-14T13:13:52.900 | 2010-12-14T13:13:52.900 | null | null | 1766 | null |
5478 | 2 | null | 5465 | 17 | null | You might also want to reflect on whether the interview is the best medium for measuring the construct of interest.
If you want to measure prior knowledge of probability or statistics, you might be better off relying more on a written test.
You can ask more questions, and thus increase reliability of measurement. It's ... | null | CC BY-SA 2.5 | null | 2010-12-14T14:01:47.267 | 2010-12-14T14:01:47.267 | null | null | 183 | null |
5479 | 1 | 5505 | null | 5 | 2240 | I am teaching myself DLM's using R's `dlm` package and have two strange results. I am modeling a time series using three combined elements: a trend (`dlmModPoly`), seasonality (`dlmModTrig`), and moving seasonality (`dlmModReg`).
The first strange result is with the `$f` (one-step-ahead foreacast) result. Most of this ... | DLM results looking wonky | CC BY-SA 2.5 | null | 2010-12-14T14:05:31.890 | 2011-09-16T18:35:35.160 | 2010-12-14T20:13:14.577 | 1764 | 1764 | [
"r",
"time-series",
"dlm"
] |
5480 | 2 | null | 5457 | 2 | null | My attempt at a simple answer that is both applicable across sub-domains and understandable (in gist) to the lay-person: When science develops theories about the world, these theories are compared to real-world data. The role of the statistician is to assess how well one or more competing theories account for the data.... | null | CC BY-SA 2.5 | null | 2010-12-14T14:12:47.083 | 2010-12-14T14:12:47.083 | null | null | 364 | null |
5481 | 2 | null | 5450 | 13 | null | Are you sure the variables have been appropriately expressed? Consider two independent variables $X_1$ and $X_2$. The problem statement asserts that you are getting a good fit in the form
$$Y = \beta_0 + \beta_{12} X_1 X_2 + \epsilon$$
If there is some evidence that the variance of the residuals increases with $Y$, ... | null | CC BY-SA 2.5 | null | 2010-12-14T15:21:58.523 | 2010-12-14T15:21:58.523 | null | null | 919 | null |
5482 | 2 | null | 5347 | 10 | null | (Because this is approach is independent of the other solutions posted, including one that I have posted, I'm offering it as a separate response).
You can compute the exact distribution in seconds (or less) provided the sum of the p's is small.
We have already seen suggestions that the distribution might approximately ... | null | CC BY-SA 3.0 | null | 2010-12-14T16:46:07.687 | 2012-11-12T23:15:33.837 | 2012-11-12T23:15:33.837 | 919 | 919 | null |
5483 | 1 | 5485 | null | 3 | 164 | Lets say for example a class of students and their grades are the data set.
Lets say there are around 35 students.
Here is what you know:
```
Your mark
The class average
The median mark
The standard deviation
```
Are there any other conclusions about this data that can be made given this information?
Thanks
| What else can be deduced from the following class grade summary information? | CC BY-SA 2.5 | null | 2010-12-14T17:33:16.430 | 2010-12-15T04:35:56.323 | 2010-12-15T04:35:56.323 | 183 | 2380 | [
"r",
"distributions",
"dataset"
] |
5484 | 2 | null | 5457 | 2 | null | A statistician tells you what conclusions can be reached from a data set and, maybe even more important, what conclusions cannot be reached.
| null | CC BY-SA 2.5 | null | 2010-12-14T17:34:59.467 | 2010-12-14T17:34:59.467 | null | null | 666 | null |
5485 | 2 | null | 5483 | 4 | null | You can probably also compute upper bounds on the number of students who did better or worse than you did. The population version would be via [Chebyshev's inequality](http://en.wikipedia.org/wiki/Chebyshev%27s_inequality).
For example, if $X_{me}$ is 'my' score, $s^2$ is the sample variance, $\bar{X}$ is the sample me... | null | CC BY-SA 2.5 | null | 2010-12-14T17:40:38.690 | 2010-12-14T22:52:58.770 | 2010-12-14T22:52:58.770 | 795 | 795 | null |
5487 | 1 | null | null | 3 | 2401 | I need to calculate the sample size required for an observational study in which incidence of disease is 19-29%. Population affected is 600,000 people.
The study has two samples similar on baseline characteristics treated
with two different drugs.
Statistical analysis will be chi-square and Fisher's test.
I have to d... | Sample size calculation for study aiming to demonstrate non-inferiority of a drug | CC BY-SA 2.5 | null | 2010-12-14T18:30:49.127 | 2012-05-28T16:05:36.517 | 2010-12-15T04:32:31.740 | 183 | null | [
"sample-size",
"epidemiology"
] |
5489 | 2 | null | 5453 | 4 | null | As per whuber's suggestion, I am posting the summary of mistakes in a separate answer.
- The distribution of $t$ is not stated. If $t$ is not a random variable, then the mean of $r$ is $\sin t$ and unconditional variance hence is $V$, not $\frac{1}{2}+V$.
- If we assume that $t$ is uniformly distributed in interval ... | null | CC BY-SA 2.5 | null | 2010-12-14T20:04:37.797 | 2010-12-14T20:04:37.797 | null | null | 2116 | null |
5490 | 1 | null | null | 2 | 893 | Assume we have a sensor field with dimension M*M. In order to apply any data compression technique, first I want to know what is the compression limit or minimum entropy of the entire sensor field. How could I compute the minimum entropy or compression limit for the sensor field?
or
Actually I want to have the theore... | How to compute theoretical compression limit? | CC BY-SA 2.5 | null | 2010-12-14T20:08:27.367 | 2010-12-14T21:36:25.123 | 2010-12-14T21:36:25.123 | null | 2384 | [
"entropy",
"compression"
] |
5491 | 2 | null | 4991 | 5 | null | If you have time varying parameters and want to do things sequentially (filtering), then SMC makes the most sense. MCMC is better when you want to condition on all of the data, or you have unknown static parameters that you want to estimate. Particle filters have issues with static parameters (degeneracy).
| null | CC BY-SA 2.5 | null | 2010-12-14T20:29:05.030 | 2010-12-14T20:29:05.030 | null | null | 643 | null |
5492 | 2 | null | 5490 | 2 | null | The "entropy" is defined only within the [context of a probabilistic model](http://en.wikipedia.org/wiki/Entropy_%28information_theory%29#Data_compression) for the data. If you characterize the image as a set of $M^2$ distinct "characters" and assume the frequencies of those characters adequately reflect their probabi... | null | CC BY-SA 2.5 | null | 2010-12-14T20:33:56.257 | 2010-12-14T20:33:56.257 | null | null | 919 | null |
5493 | 2 | null | 5487 | 5 | null | [G*Power](http://www.psycho.uni-duesseldorf.de/aap/projects/gpower/) is a commonly-recommended program for sample size calculations. I've only dabbled with it a couple of times in the past, but it's more than capable of handling the situation you describe.
| null | CC BY-SA 2.5 | null | 2010-12-14T20:42:14.083 | 2010-12-14T20:42:14.083 | null | null | 71 | null |
5494 | 2 | null | 5115 | 7 | null | [Leland Wilkinson](http://www.cs.uic.edu/~wilkinson/) for his contribution to statistical graphics.
| null | CC BY-SA 2.5 | null | 2010-12-14T21:11:31.903 | 2010-12-14T21:11:31.903 | null | null | 609 | null |
5495 | 2 | null | 5434 | 3 | null | If you can't get satisfaction with R you can fit this model and more complicated
ones with AD Model Builder which is free software available at [http://admb-project.org](http://admb-project.org). ADMB permits you to model the over dispersion in a variety of ways,
rather than being confined to the GLM paradigm. I can a... | null | CC BY-SA 2.5 | null | 2010-12-14T22:23:25.407 | 2010-12-14T22:23:25.407 | null | null | 1585 | null |
5496 | 2 | null | 5457 | 9 | null | A statistician is a numerical detective, uncovering the stories hidden in a mass of data.
| null | CC BY-SA 2.5 | null | 2010-12-14T23:13:34.167 | 2010-12-14T23:13:34.167 | null | null | 159 | null |
5497 | 2 | null | 5457 | 3 | null | The TV show Numb3rs is useful as many people have seen it. I tell them that I'm like the guys on Numb3rs except I deal with solving business problems rather than crimes. (Substitute "business problems" for whatever field you work in.) That usually gets the response "Wow, cool!" which is better than what I used to get.
| null | CC BY-SA 2.5 | null | 2010-12-14T23:17:09.983 | 2010-12-14T23:17:09.983 | null | null | 159 | null |
5498 | 2 | null | 5479 | 6 | null | So you have monthly data with trend and seasonality and you want to both analyse the trend/seasonal components and produce forecasts. These are two separate tasks. While you can do both with `dlm`, there are simpler approaches if you separate the tasks.
For studying the trend and seasonality, I suggest using STL via th... | null | CC BY-SA 2.5 | null | 2010-12-14T23:41:45.517 | 2010-12-14T23:41:45.517 | null | null | 159 | null |
5499 | 2 | null | 5483 | 4 | null | Since the data set is grades, you probably also know the minimum and maximum possible scores, e.g. 0 and 100. Given the median and first two moments, you can fit a variety of models to the sample statistics, say a scaled beta distribution. This would give you tighter estimates than Chebyshev's inequality, although yo... | null | CC BY-SA 2.5 | null | 2010-12-15T03:00:22.257 | 2010-12-15T03:00:22.257 | null | null | 5792 | null |
5500 | 2 | null | 5461 | 3 | null | From what I can tell, your problems seem to be:
1) smooth the time series data to remove correlated fluctuations.
2) Identify which of the inputs differs, using the smoothed data.
You're seem to be worried about not being able to solve (2) once you solve (1). But let's solve 1 first and then worry about 2, right?
He... | null | CC BY-SA 2.5 | null | 2010-12-15T03:04:59.567 | 2010-12-15T03:04:59.567 | null | null | 2073 | null |
5501 | 2 | null | 5462 | 7 | null | Your best bet is mechanical turk, [https://www.mturk.com/mturk/welcome](https://www.mturk.com/mturk/welcome). It will cost you some money, but not much. If your questions are short and can be done in ~ 1 min you can easily charge 10 cents or so per answer, so if you want 50 responses it will cost only $5. You can get t... | null | CC BY-SA 2.5 | null | 2010-12-15T03:22:35.440 | 2010-12-15T03:22:35.440 | null | null | 2073 | null |
5502 | 1 | 5521 | null | 13 | 5236 | Is there a principled way to estimate factor scores when you have ordinal, discrete variables.
I have $n$ ordinal, discrete, variables. If I make the assumption that underlying each response is a continuous, normally distributed variable, then I can calculate an $n\times n$ polychoric correlation matrix. I can then run... | Factor scores from discrete, ordinal responses | CC BY-SA 2.5 | null | 2010-12-15T04:04:07.567 | 2012-03-15T12:13:35.497 | 2010-12-16T05:25:16.043 | 82 | 82 | [
"factor-analysis",
"ordinal-data"
] |
5503 | 1 | 5522 | null | 4 | 357 | I think, I read this quote some where:
>
For every field "x" there exists a field "computational x"
Has anyone else read this or remembers reading anything close to this?
If I remember correctly, it was by Dr. Jan de Leeuw.
Can anyone please tell if my memory fails me here? (I could not find any link after a lot of ... | Is there a quote like this from some statistician? | CC BY-SA 2.5 | null | 2010-12-15T04:40:11.687 | 2010-12-15T19:22:15.603 | null | null | 1307 | [
"reproducible-research"
] |
5504 | 1 | 5516 | null | 4 | 3657 | The annual returns on stocks and treasury bonds over the next 12 months are uncertain. Suppose that these returns can be described by normal distributions with stocks having a mean of 15% and a standard deviation of 20%, and bonds having a mean of 6% and a standard deviation of 9%. Which asset is more likely to have ... | Normal distribution probability | CC BY-SA 2.5 | null | 2010-12-15T06:00:52.117 | 2010-12-15T17:16:05.057 | null | null | 2385 | [
"self-study"
] |
5505 | 2 | null | 5479 | 0 | null | Difficult to diagnose without looking at the results, but here is a wild guess: if in the fit the variance associated to the level component is large, that component will "follow" your data. The filtered estimates will nearly coincide with observations, and the forecasts will appear to lag them --which, as I understand... | null | CC BY-SA 2.5 | null | 2010-12-15T06:14:58.613 | 2010-12-15T06:14:58.613 | null | null | 892 | null |
5506 | 2 | null | 5504 | 6 | null | Here is the R code to quickly solve this:
```
> pnorm(0, mean=15, sd=20)
[1] 0.2266274
> pnorm(0, mean=6, sd=9)
[1] 0.2524925
```
So bonds will be more likely to have a negative return
| null | CC BY-SA 2.5 | null | 2010-12-15T06:37:51.053 | 2010-12-15T06:37:51.053 | null | null | 2144 | null |
5507 | 2 | null | 5504 | 5 | null | Stock: P( (X-15)/20 < (0-15)/20 ) = P(Z<-3/4) = .2266,
Bond: P( (Y-6)/9 < (0-6)/9 ) = P(Z<-2/3) = .2525
More likely the bond.
Note that the price is log-normally distributed.
| null | CC BY-SA 2.5 | null | 2010-12-15T07:24:41.860 | 2010-12-15T07:24:41.860 | null | null | 2387 | null |
5508 | 1 | 5540 | null | 3 | 336 | I'm using multinomial probit to estimate some parameters, and I keep seeing references to the fact that MNP was considered computationally "intractible" relative to binomial probit up until the early 21st century. The question is: why? I get that adding variables makes things take longer (I've got a background in CS), ... | Computational considerations of multinomial probit versus binomial probit | CC BY-SA 2.5 | null | 2010-12-15T07:57:00.543 | 2010-12-15T23:46:54.900 | null | null | 53 | [
"maximum-likelihood",
"multinomial-distribution"
] |
5509 | 2 | null | 5503 | 5 | null | Maybe you are after this talk?
>
Tutorial: Methods for Reproducible
Research, by Roger D. Peng (slide
3)
Also, papers on Reproducible research written by de Leeuw that I am aware of are [Reproducible Research: the Bottom Line](http://preprints.stat.ucla.edu/301/301.pdf), and [Statistical Software -- Overview](ht... | null | CC BY-SA 2.5 | null | 2010-12-15T08:05:29.210 | 2010-12-15T17:44:09.527 | 2010-12-15T17:44:09.527 | 919 | 930 | null |
5510 | 2 | null | 3313 | 0 | null | did you convert the aforementioned library to R? I need to convert the same library into R and wanted to ask if you might share your results?
Thanks
| null | CC BY-SA 2.5 | null | 2010-12-15T08:30:42.657 | 2010-12-15T08:30:42.657 | null | null | null | null |
5511 | 2 | null | 5115 | 13 | null | [W. Edwards Deming](http://en.wikipedia.org/wiki/W._Edwards_Deming) for promoting statistical process control
| null | CC BY-SA 2.5 | null | 2010-12-15T08:53:41.307 | 2010-12-15T08:53:41.307 | null | null | 74 | null |
5512 | 2 | null | 5114 | 1 | null | You could also try the triangular distribution. To fit this, you basically specify a lower bound (this would be X=2), an upper bound (this would be X=8), and a "most likely" value. The wikepedia page [http://en.wikipedia.org/wiki/Triangular_distribution](http://en.wikipedia.org/wiki/Triangular_distribution) has more ... | null | CC BY-SA 2.5 | null | 2010-12-15T12:27:20.673 | 2010-12-15T12:27:20.673 | null | null | 2392 | null |
5513 | 2 | null | 1906 | 1 | null | Predictive Analytics World: [pawcon.com](http://www.predictiveanalyticsworld.com/).
| null | CC BY-SA 3.0 | null | 2010-12-15T12:47:51.133 | 2011-09-20T21:34:25.253 | 2011-09-20T21:34:25.253 | 930 | null | null |
5514 | 1 | null | null | 4 | 1451 | Could anyone kindly provide an explanation (mathematically or non-mathematically) about the non-existence of the intercept term in [conditional logistic regression](http://www.ats.ucla.edu/stat/sas/library/logistic.pdf)? Is the interpretation of the coefficients similar to that of (unconditional) [logistic regression](... | Queries on conditional logistic regression | CC BY-SA 2.5 | null | 2010-12-15T14:18:11.087 | 2013-09-03T09:53:10.170 | 2013-09-03T09:53:10.170 | 21599 | null | [
"logistic",
"survival",
"epidemiology",
"clogit"
] |
5515 | 2 | null | 5502 | 8 | null | It's commonplace to extract factor scores from ordinal-variable indicators. Researchers using likert measures do it all the time. Because factor scores are based on covariance, it's usually not that big a deal that the "intervals" might not be uniform within and across items, particularly if the items are comparable & ... | null | CC BY-SA 2.5 | null | 2010-12-15T14:54:08.093 | 2010-12-15T14:54:08.093 | null | null | 11954 | null |
5516 | 2 | null | 5504 | 8 | null | The objective of this exercise is to help you develop your ability to reason with probability distributions. You would like to get to the point where your reflex is to think through such problems this way:
"A negative return is anything less than 0%.
"For stocks, 0% is three-quarters of a standard deviation (i.e., thr... | null | CC BY-SA 2.5 | null | 2010-12-15T15:14:55.810 | 2010-12-15T17:16:05.057 | 2010-12-15T17:16:05.057 | 919 | 919 | null |
5517 | 1 | 9680 | null | 12 | 6628 | The library languageR provides a method (pvals.fnc) to do MCMC significance testing of the fixed effects in a mixed effect regression model fit using lmer. However, pvals.fnc gives an error when the lmer model includes random slopes.
Is there a way to do an MCMC hypothesis test of such models?
If so, how? (To be ac... | How can one do an MCMC hypothesis test on a mixed effect regression model with random slopes? | CC BY-SA 2.5 | null | 2010-12-15T16:18:49.643 | 2013-08-24T15:05:00.227 | 2017-04-13T12:44:52.277 | -1 | 196 | [
"r",
"mixed-model",
"statistical-significance",
"monte-carlo"
] |
5518 | 2 | null | 1906 | 2 | null | SIAM's [Data Mining Conference](http://www.siam.org/meetings/sdm11/), SDM11.
| null | CC BY-SA 2.5 | null | 2010-12-15T17:28:47.470 | 2010-12-15T17:28:47.470 | null | null | 795 | null |
5519 | 2 | null | 5514 | 3 | null | Conditional logistic regression compares cases to controls. The coefficients multiply differences in factor values between cases and controls. The intercept terms cancel in the likelihood and therefore play no identifiable role in the model. See the section on "Conditional Logistic Regression" in [Ying So's tutorial... | null | CC BY-SA 2.5 | null | 2010-12-15T17:31:04.437 | 2010-12-15T17:31:04.437 | null | null | 919 | null |
5520 | 1 | 5568 | null | 7 | 1598 | I am wondering if there is any reasonably simple way of calculating the following problem:
Drawing, with replacement, $n$ balls from a bin of $N$ different colored balls, with a known probability of drawing each color of ball, what is the expected number of "unique" balls, i.e., balls with no other ball of the same col... | Expected number of uniques in a non-uniformly distributed population | CC BY-SA 2.5 | null | 2010-12-15T18:08:15.290 | 2010-12-21T15:01:39.427 | 2010-12-16T22:12:01.823 | 919 | 2395 | [
"expected-value",
"multinomial-distribution"
] |
5521 | 2 | null | 5502 | 8 | null | The 'principled' approach (that is to say the a priori defensible approach that may not empirically make much difference) is to use a graded response model, a rather useful member of the IRT family often used for Likert type items. The R package ltm makes this very straightforward.
You're then assuming there is a ordi... | null | CC BY-SA 2.5 | null | 2010-12-15T19:05:19.570 | 2010-12-15T19:05:19.570 | null | null | 1739 | null |
5522 | 2 | null | 5503 | 4 | null | Well here's one place de Leeuw says it: [http://preprints.stat.ucla.edu/491/useR.pdf](http://preprints.stat.ucla.edu/491/useR.pdf)
It might also be found in a more formal document, but nothing in my collection...
| null | CC BY-SA 2.5 | null | 2010-12-15T19:22:15.603 | 2010-12-15T19:22:15.603 | null | null | 1739 | null |
5524 | 2 | null | 5465 | 5 | null | For an observational data context:
Consider this regression model applied to this substantive problem. What, if anything, in it can be interpreted causally? [Further probe] What would you need to learn to change your opinion?
| null | CC BY-SA 2.5 | null | 2010-12-15T19:33:16.230 | 2010-12-15T19:33:16.230 | null | null | 1739 | null |
5525 | 1 | 5526 | null | 5 | 25594 | Is there a way to get the number of parameters of a linear model like that?
```
model <- lm(Y~X1+X2)
```
I would like to get the number 3 somehow (intercept + X1 + X2). I looked for something like this in the structures that `lm`, `summary(model)` and `anova(model)` return, but I didn't figure it out. In case I don't ... | Get the number of parameters of a linear model | CC BY-SA 2.5 | null | 2010-12-15T19:41:44.733 | 2016-05-02T22:14:57.473 | null | null | 632 | [
"r",
"regression"
] |
5526 | 2 | null | 5525 | 11 | null | Try something like:
```
> x <- replicate(2, rnorm(100))
> y <- 1.2*x[,1]+rnorm(100)
> summary(lm.fit <- lm(y~x))
> length(lm.fit$coefficients)
[1] 3
> # or
> length(coef(lm.fit))
[1] 3
```
You can have a better idea of what an R object includes with
```
> str(lm.fit)
```
| null | CC BY-SA 2.5 | null | 2010-12-15T19:56:17.260 | 2010-12-15T19:56:17.260 | null | null | 930 | null |
5527 | 2 | null | 5508 | 3 | null | The currently popular method of fitting multinomial probit models is [maximum simulated likelihood](http://jblevins.org/notes/msl) using the Geweke–Hajivassiliou–Keane algorithm ([Geweke 1989](http://www.jstor.org/stable/1913710); [Hajivassiliou and McFadden 1998](http://www.jstor.org/stable/2999576); [Keane and Wolpin... | null | CC BY-SA 2.5 | null | 2010-12-15T20:02:45.727 | 2010-12-15T20:02:45.727 | null | null | 449 | null |
5528 | 2 | null | 5465 | 8 | null | >
How do you numericize something that
is not numerical?
Example, ["Automatic Feature Extraction for Classifying Audio Data"](https://doi.org/10.1007/s10994-005-5824-7)
Rationale: Can they figure out how to analyze something statistically that is not already in a big table?
| null | CC BY-SA 4.0 | null | 2010-12-15T20:06:07.013 | 2022-05-21T08:18:45.927 | 2022-05-21T08:18:45.927 | 79696 | 74 | null |
5529 | 2 | null | 5465 | 9 | null | >
How do you prevent over-fitting when
you are creating a statistical model?
Good answer: cross-validation
| null | CC BY-SA 2.5 | null | 2010-12-15T20:08:32.143 | 2010-12-15T20:08:32.143 | null | null | 74 | null |
5530 | 2 | null | 5465 | 11 | null | >
Here is a big data set. What is your
plan for dealing with outliers? How
about missing values? How about transformations?
Can they deal with real-world data?
| null | CC BY-SA 2.5 | null | 2010-12-15T20:10:26.060 | 2010-12-15T20:10:26.060 | null | null | 74 | null |
5531 | 2 | null | 5465 | 3 | null | >
Here is a TinkerToy set. Show me
how Euclidean distance works in three
dimensions. Now show me how multiple regression works.
Can they explain how statistics works in the physical world?
| null | CC BY-SA 2.5 | null | 2010-12-15T20:14:20.347 | 2010-12-15T20:14:20.347 | null | null | 74 | null |
5532 | 1 | 5541 | null | 2 | 324 | Working off a fairly limited statistical understanding, so apologies in advance if this question is ludicrously basic. This has to have happened to other people who have successfully solved this issue, but searching for what I think are relevant terms is getting me things like "Karlin's Conjecture for Random Replaceme... | Survey Sampling: What am I missing with this plan? | CC BY-SA 2.5 | null | 2010-12-15T20:21:02.263 | 2010-12-16T02:22:22.583 | null | null | 2398 | [
"sampling",
"survey"
] |
5534 | 1 | 5607 | null | 8 | 2045 | I was reading Robert Serfling's 1980 book "Approximation Theorems of Mathematical Statistics" and came across the following construction of the Dvoretzky–Kiefer–Wolfowitz inequality for arbitrary distributions $F$, which DKW prove for distributions on $[0,1]$.
>
Given independent $X_i$ with d.f. F and defined on a co... | Transforming arbitrary distributions to distributions on $[0,1]$ | CC BY-SA 3.0 | null | 2010-12-15T20:52:37.257 | 2013-10-05T15:08:23.810 | 2013-10-05T15:08:23.810 | 919 | 2399 | [
"distributions"
] |
5535 | 2 | null | 5462 | 1 | null | Just a thought, but it might make sense to implement an inexpensive Google Ad Words campaign where at least the participants would be coming directly from Google search and you can somewhat control some sort of stratification. Of course, it is never possible to get a true sample.
Along these lines, there is some work ... | null | CC BY-SA 2.5 | null | 2010-12-15T21:15:14.057 | 2010-12-15T21:15:14.057 | null | null | null | null |
5536 | 2 | null | 5525 | 1 | null | I think you could use the component `lm.fit$rank` or else subtract `lm.fit$df.residual` from the sample size to get what you want. (I assume you want the number of free parameters.)
| null | CC BY-SA 2.5 | null | 2010-12-15T21:19:18.657 | 2010-12-16T16:11:57.860 | 2010-12-16T16:11:57.860 | 892 | 892 | null |
5538 | 2 | null | 5534 | 14 | null | This is merely saying that $F(x) = \Pr[X \le x] = \Pr[F(X) \le F(x)]$ which is exactly what it means for $F(X)$ to have a uniform distribution.
OK, let's go a little slower.
For continuous distributions, forget for a moment that the CDF $F$ is a CDF and think of it as just a nonlinear way to re-express the values of $X... | null | CC BY-SA 2.5 | null | 2010-12-15T22:09:06.303 | 2010-12-15T22:09:06.303 | null | null | 919 | null |
5539 | 2 | null | 5525 | 3 | null | May be it's a little bit hackish but you can do :
```
n <- length(coefficients(model))
```
| null | CC BY-SA 2.5 | null | 2010-12-15T22:24:19.247 | 2010-12-15T22:24:19.247 | null | null | 2028 | null |
5540 | 2 | null | 5508 | 4 | null | It boils down to how you feel about assuming the Independence of Irrelevant Alternatives (IIA) as an assumption about choice behaviour. So the first thing to do is look that up.
Multinomial logit assumes IIA and multinomial probit does not. The computational price of not assuming it is what gets expensive. Almost an... | null | CC BY-SA 2.5 | null | 2010-12-15T23:46:54.900 | 2010-12-15T23:46:54.900 | null | null | 1739 | null |
5541 | 2 | null | 5532 | 6 | null | This is a very common situation, and usually one just plans for a larger initial sample size, so that once ineligible subjects are thrown out, the desired sample size is reached. In your case it appears that ~65% of the original list qualifies. So to get 350 more qualifying subjects you need to draw a random sample of ... | null | CC BY-SA 2.5 | null | 2010-12-16T00:48:28.910 | 2010-12-16T00:48:28.910 | null | null | 279 | null |
5542 | 1 | 5548 | null | 5 | 753 |
## Background
I am generally interested in learning appropriate methods of using data to specify priors. A [previous question](https://stats.stackexchange.com/q/1/1381) asks how to elicit priors from experts and received some good recommendations. Here, I am interested in learning how to specify a prior using data. ... | What methods can be used to specify priors from data? | CC BY-SA 2.5 | null | 2010-12-16T01:07:58.040 | 2014-04-08T21:43:23.227 | 2017-04-13T12:44:41.607 | -1 | 1381 | [
"probability",
"bayesian",
"meta-analysis",
"prior"
] |
5543 | 1 | 5564 | null | 31 | 7110 | I have found some distributions for which BUGS and R have different parameterizations: Normal, log-Normal, and Weibull.
For each of these, I gather that the second parameter used by R needs to be inverse transformed (1/parameter) before being used in BUGS (or JAGS in my case).
Does anyone know of a comprehensive list ... | For which distributions are the parameterizations in BUGS and R different? | CC BY-SA 3.0 | null | 2010-12-16T01:48:12.853 | 2014-03-19T15:51:42.810 | 2011-11-18T05:50:51.333 | 1381 | 1381 | [
"r",
"distributions",
"bugs",
"jags",
"parameterization"
] |
5544 | 2 | null | 5502 | 4 | null | Can I just clarify something here please, do you have items scored on different scales you need to pre-process and combine (interval, ordinal, nominal), or are you looking to do a factor analysis on just ordinal scale variables?
If it is the latter - here is one approach.
[http://cran.r-project.org/web/packages/Zelig/v... | null | CC BY-SA 3.0 | null | 2010-12-16T02:03:23.547 | 2012-03-15T12:13:35.497 | 2012-03-15T12:13:35.497 | 4884 | 2238 | null |
5545 | 1 | null | null | 5 | 883 | I have to run repeated correlations. If one of them reach the significant value (as given in the Pearson table), how can I be sure that it isn't actually a false positive?
I'm pretty new in the statistical analysis, so I apologize if my question seems naive.
Thanks for any answers.
| How to prevent false positive findings with repeated correlations? | CC BY-SA 2.5 | null | 2010-12-16T02:04:39.983 | 2010-12-16T09:59:10.713 | 2010-12-16T09:59:10.713 | 930 | 2402 | [
"correlation",
"multiple-comparisons"
] |
5546 | 2 | null | 5532 | 2 | null | I hate answering a question with a list of questions, but I hope thinking about some of these issues will give you some insight as to the best way to proceed. The first two questions are mainly for clarification as it wasn't clear from reading your question:
- Have you already obtained a list of 1000 people?
- Have y... | null | CC BY-SA 2.5 | null | 2010-12-16T02:22:22.583 | 2010-12-16T02:22:22.583 | null | null | 696 | null |
5547 | 2 | null | 5443 | 1 | null | Tal,
My understanding based on this article, is that you cannot obtain the individual variables related to each separate class when the class is a factor. However, using rpart.control, you are able to identify the variables that are important at each node.
[http://cran.r-project.org/web/packages/caret/vignettes/caret... | null | CC BY-SA 2.5 | null | 2010-12-16T02:34:59.040 | 2010-12-16T02:34:59.040 | null | null | 2238 | null |
5548 | 2 | null | 5542 | 8 | null | If you have all this data, I think the best answer is to actually fit a single large model, using Hierarchical Modeling rather than do it in two steps (generating a prior then fitting a model). This is basically the [answer I gave to this question](https://stats.stackexchange.com/questions/5181/estimating-distribution-... | null | CC BY-SA 2.5 | null | 2010-12-16T03:31:59.010 | 2010-12-16T03:31:59.010 | 2017-04-13T12:44:33.977 | -1 | 1146 | null |
5549 | 2 | null | 5520 | 1 | null | Maybe I'm being naive here, but would the Multinomial Distribution not work for this?
P(1 unique) = P(1 Blue) + P(1 Red) + P(1 Blue), there are probably a lot of details that would need to be fill in, like P(1 Blue) = The multinomial distribution for all possible combinations of the other ball combinations where there'... | null | CC BY-SA 2.5 | null | 2010-12-16T05:25:07.710 | 2010-12-16T05:25:07.710 | null | null | 2387 | null |
5550 | 1 | 5553 | null | 3 | 608 | I want to perform model comparison according to several criteria using R.
My dataframe's name is `df`
```
head(df)
Y X1 X2 X3 X4
1 18 307 130 3504 12.0
2 15 350 165 3693 11.5
3 18 318 150 3436 11.0
4 16 304 150 3433 12.0
5 17 302 140 3449 10.5
6 15 429 198 4341 10.0
```
I want to compare all possible combin... | Automating model selection criteria production | CC BY-SA 2.5 | null | 2010-12-16T07:56:16.983 | 2011-02-15T11:31:07.203 | 2010-12-16T08:18:23.023 | 632 | 632 | [
"r",
"model-selection"
] |
5551 | 2 | null | 5520 | 2 | null | I will give the outline of the solution. Numbers of each coloured ball in a draw follows multinomial distribution as tshauck pointed out. Let $R$ denote the number of red balls, $G$ - number of green balls, $B$ - number of blue balls and $Y$ - number of yellow balls in the draw of the size $n$. Then the probability th... | null | CC BY-SA 2.5 | null | 2010-12-16T07:57:26.163 | 2010-12-16T18:46:12.313 | 2010-12-16T18:46:12.313 | 2116 | 2116 | null |
5552 | 2 | null | 4267 | 2 | null | At this year's [NIPS](http://nips.cc/) there was a short paper on [distributed, very large-scale SVD that works in a single pass over a streaming input matrix](http://nlp.fi.muni.cz/~xrehurek/nips/rehurek_nips.pdf).
The paper's more implementation-oriented but puts things into perspective with real wall-clock times and... | null | CC BY-SA 2.5 | null | 2010-12-16T08:03:12.553 | 2010-12-16T08:38:52.443 | 2010-12-16T08:38:52.443 | 223 | null | null |
5553 | 2 | null | 5550 | 5 | null | I think package [leaps](http://cran.r-project.org/web/packages/leaps/index.html) will help you. It is designed specifically to select a "best" subset of variables for linear regression.
As for the formula creation, you can use foreach package like this:
```
#Create the list where each list element contains one combina... | null | CC BY-SA 2.5 | null | 2010-12-16T08:10:19.380 | 2010-12-16T13:20:56.190 | 2010-12-16T13:20:56.190 | 2116 | 2116 | null |
5554 | 2 | null | 5545 | 5 | null | I think you should consider [Bonferroni](http://en.wikipedia.org/wiki/Bonferroni_correction) correction. This seems a [multiple comparison](http://en.wikipedia.org/wiki/Multiple_comparisons) problem, so Bonferroni is only one of the solutions, but it is the easiest to implement.
| null | CC BY-SA 2.5 | null | 2010-12-16T08:17:59.500 | 2010-12-16T08:17:59.500 | null | null | 2116 | null |
5555 | 1 | 5557 | null | 0 | 277 | I've been having some trouble with this story problem. Any help you could give me would be really appreciated.
A store manager monitors the store's temperature by taking 4 independent temperature readings at 4 locations where, if the system is working correctly, the temp. is normally distributed with a mean of 68 de... | Probability and limits | CC BY-SA 2.5 | null | 2010-12-16T09:44:12.623 | 2010-12-16T11:12:31.337 | null | null | 2385 | [
"distributions",
"standard-deviation",
"self-study",
"mean"
] |
5556 | 2 | null | 5550 | 3 | null | This is another way to get the formulas
```
comb_list_forms <- unlist(lapply(1:length(comb_list), function(i)
lapply(1:dim(comb_list[[i]])[2], function(x)
as.formula(paste("Y ~ ", paste(names(df[1+comb_list[[i]][,x]]), collapse= "+"))))))
```
| null | CC BY-SA 2.5 | null | 2010-12-16T10:29:14.630 | 2010-12-16T10:29:14.630 | null | null | 339 | null |
5557 | 2 | null | 5555 | 5 | null | The two key elements that you need to be familiar with before working through this exercise are:
- The sampling distribution of a mean: in your case, the average of your four measurements will follow a normal distribution, with a mean that equals that of the parent distribution but with a standard deviation of $3/\sqr... | null | CC BY-SA 2.5 | null | 2010-12-16T11:07:02.010 | 2010-12-16T11:12:31.337 | 2010-12-16T11:12:31.337 | 930 | 930 | null |
5558 | 2 | null | 5550 | 2 | null | You could consider using `step` on the full model. This uses AIC rather than $R^2$ as the criterion for selection as this is considered to be superior.
In any case, the functions `add1` and `drop1` that `step` uses may be of use.
| null | CC BY-SA 2.5 | null | 2010-12-16T12:46:10.393 | 2010-12-16T12:46:10.393 | null | null | 229 | null |
5559 | 1 | null | null | 2 | 3236 | What is the best way to divide Cross Validated/Stack Overflow/Server fault reputations into separate bins, considering reputation systems tend to be highly skewed? For example: Suppose I want a set of 64 bins - 0-250,250-500,500-100...10,000-11,000. Is there a method that takes into account the number of users that ha... | How to divide ordinal set into bins? | CC BY-SA 2.5 | null | 2010-12-16T13:15:13.730 | 2020-01-13T00:40:09.660 | 2020-01-13T00:40:09.660 | 11887 | 2405 | [
"ordinal-data",
"binning"
] |
5560 | 1 | 5569 | null | 3 | 461 | I have a random events generator. I know in advance the set of event that can be generated (in my case I have only three possible events). The probabilities of the events are not known. I need to estimate these probabilities. For that I run an experiment. For example I generate 20 events. So, I have a sequence of event... | How to estimate the likelihood function for random generator of three events? | CC BY-SA 2.5 | null | 2010-12-16T14:57:00.873 | 2010-12-16T16:39:56.680 | null | null | 2407 | [
"distributions",
"probability",
"measurement",
"likelihood"
] |
5561 | 2 | null | 5550 | 4 | null | Ok, here is another way (which is certainly not as elegant or concise than the other ones, but it has the merit of not requiring Emacs to check parenthesis matches :-)
Let's say we have a vector of predictors of interest like this
```
> Xs <- paste("X", 1:4, sep="")
```
Then, we can just use
```
> allXs <- lapply(seq(... | null | CC BY-SA 2.5 | null | 2010-12-16T15:04:03.347 | 2010-12-16T15:04:03.347 | null | null | 930 | null |
5563 | 1 | 5565 | null | 5 | 9394 | Is there a 'limit' function in R? `help(lim)` or `help(limit)` weren't useful.
And in general, is R suitable for mathematical computations?
| Is there a limit function in R? | CC BY-SA 2.5 | null | 2010-12-16T15:13:16.647 | 2023-05-19T21:45:49.777 | 2010-12-16T15:20:42.767 | 930 | 1700 | [
"r",
"mathematical-statistics"
] |
5564 | 2 | null | 5543 | 36 | null | I don't know of a canned list.
update: this list (plus additional information) is now published as [Translating Probability Density Functions: From R to BUGS and Back Again](http://journal.r-project.org/archive/2013-1/lebauer-dietze-bolker.pdf) (2013), DS LeBauer, MC Dietze, BM Bolker R Journal 5 (1), 207-209.
Here is... | null | CC BY-SA 3.0 | null | 2010-12-16T15:25:54.663 | 2014-03-19T15:51:42.810 | 2017-04-13T12:44:25.243 | -1 | 2126 | null |
5565 | 2 | null | 5563 | 4 | null | All links in this answer are dead.
Following the advice of [this answer](https://web.archive.org/web/20170924085650/http://tolstoy.newcastle.edu.au/R/e11/help/10/07/2296.html) on the r-help mailing list, you can find examples in the sympy help file in the [rSymPy](https://web.archive.org/web/20100827101138/http://code.... | null | CC BY-SA 4.0 | null | 2010-12-16T15:29:34.803 | 2023-05-19T21:45:49.777 | 2023-05-19T21:45:49.777 | 362671 | 1381 | null |
5566 | 1 | 5570 | null | 5 | 897 | I flip a coin 20 times and get 14 heads. I want to calculate the p-value of the hypothesis that my coin is fair.
What probability should I calculate?
Wikipedia says that I need to calculate the probability to get 14 or more heads in 20 flips.
Why is it 14 "or more"? Why not 14 or less?
| Testing if a coin is fair | CC BY-SA 4.0 | null | 2010-12-16T15:36:19.483 | 2018-06-12T19:04:40.093 | 2018-06-12T19:04:40.093 | 34826 | 2407 | [
"maximum-likelihood",
"p-value"
] |
5568 | 2 | null | 5520 | 9 | null | Let $X_i$ be the random variable equal to $1$ when there is exactly one ball of color $i$ ($i = 1, 2, \ldots, m$; to avoid confusion I write $m$ instead of $N$). The count of color $i$ follows a Binomial($p_i$, $n$) distribution, implying the expectation of $X_i$ is
$$\eqalign{
\mathbb{E}[X_i] = &\sum_{j=0}^{n} \bino... | null | CC BY-SA 2.5 | null | 2010-12-16T16:20:37.650 | 2010-12-21T15:01:39.427 | 2010-12-21T15:01:39.427 | 919 | 919 | null |
5569 | 2 | null | 5560 | 4 | null | You should look at the [multinomial distribution](http://en.wikipedia.org/wiki/Multinomial_distribution).
| null | CC BY-SA 2.5 | null | 2010-12-16T16:39:56.680 | 2010-12-16T16:39:56.680 | null | null | null | null |
5570 | 2 | null | 5566 | 12 | null | It's neither because the alternative to being fair is that the coin favors heads or tails.
You are free to invent any test you like. For example, I could (idiosyncratically) decide the coin is unfair if and only if the number of heads is either 6 or 15 (the "critical region"), because this event occurs with only 5% ch... | null | CC BY-SA 2.5 | null | 2010-12-16T16:42:57.647 | 2010-12-16T17:54:03.310 | 2010-12-16T17:54:03.310 | 919 | 919 | null |
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