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8347 | 1 | null | null | 21 | 3619 | While attending conferences, there has been a bit of a push by advocates of Bayesian statistics for assessing the results of experiments. It is vaunted as both more sensitive, appropriate, and selective towards genuine findings (fewer false positives) than frequentist statistics.
I have explored the topic somewhat, an... | Is Bayesian statistics genuinely an improvement over traditional (frequentist) statistics for behavioral research? | CC BY-SA 2.5 | null | 2011-03-11T19:47:33.830 | 2011-03-18T11:36:47.960 | 2011-03-16T07:50:42.073 | 449 | null | [
"bayesian",
"statistical-power",
"frequentist"
] |
8348 | 2 | null | 8347 | 2 | null | I am not familiar with Bayesian Statistics myself but I do know that Skeptics Guide to the Universe Episode 294 has and interview with Eric-Jan Wagenmakers where they discuss Bayesian Statistics. Here is a link to the podcast:
[http://www.theskepticsguide.org/archive/podcastinfo.aspx?mid=1&pid=294](http://www.theskepti... | null | CC BY-SA 2.5 | null | 2011-03-13T04:16:06.883 | 2011-03-13T04:16:06.883 | null | null | null | null |
8349 | 1 | 8488 | null | 2 | 185 | I'm a complete noob when it comes to statistics, so please excuse my lack of standard terminology. I think my question might be related to normalization, but please re-tag if I'm wrong.
I'm a programmer by trade, and I've been tasked with creating a chart which shows how the number of errors in a data set change from ... | Charting errors based on number of items per month | CC BY-SA 2.5 | null | 2011-03-16T09:06:15.457 | 2011-03-19T10:40:15.893 | 2011-03-16T11:55:43.973 | 3735 | 3735 | [
"data-visualization",
"descriptive-statistics"
] |
8350 | 2 | null | 8347 | 5 | null | Bayesian statistics can be derived from a few Logical principles. Try Searching "probability as extended logic" and you will find more in depth analysis of the fundamentals. But basically, Bayesian statistics rests on three basic "desiderata" or normative principles:
- The plausability of a proposition is to be repr... | null | CC BY-SA 2.5 | null | 2011-03-16T09:23:16.897 | 2011-03-18T11:36:47.960 | 2017-04-13T12:44:44.530 | -1 | 2392 | null |
8351 | 1 | 8359 | null | 8 | 7389 | I have a quarterly time series and test for stationarity with an augmented Dickey-Fuller test using R.
```
adf.test(myseries)
# returns
# Dickey-Fuller = -3.9828, Lag order = 4, p-value = 0.01272
# alternative hypothesis: stationary
```
so the H0 is rejected. I tried to validate this intuitively and regressed the sam... | Trend or no trend? | CC BY-SA 2.5 | null | 2011-03-16T09:29:27.813 | 2011-03-16T22:55:24.480 | 2011-03-16T15:06:42.183 | 704 | 704 | [
"r",
"time-series",
"loess"
] |
8352 | 1 | null | null | 2 | 634 | I would like to analyse the following type of experiment using a two-level fully nested ANOVA. We have mice of two genotypes (Group factor) and for each of the genotypes we take a certain number of mice (sub-groups) and measure several identical samples per each mouse. Thus it looks like a classic two-level nested desi... | What is the proper ratio of mean squares for a two-level nested ANOVA? | CC BY-SA 3.0 | null | 2011-03-16T10:00:26.227 | 2011-06-22T08:48:31.287 | 2011-06-21T20:45:01.260 | 930 | 1727 | [
"anova",
"experiment-design",
"genetics"
] |
8353 | 2 | null | 8335 | 3 | null | Using the suggested corrected data we have:
The model to be tested is : Y(T)=B0 + B1*X(T) + A(T)
The null hypothesis is that the set B0 and B1 are the same over the two states
step 1 : Estimate this for STATE1 obtaining an error sum of squares SOS1 =.789
step 2 : Estimate this for STATE2 obtaining an error sum of squar... | null | CC BY-SA 2.5 | null | 2011-03-16T10:25:22.210 | 2011-03-17T11:37:41.620 | 2011-03-17T11:37:41.620 | 2116 | 3382 | null |
8354 | 2 | null | 8344 | 22 | null | Influence functions are basically an analytical tool that can be used to assess the effect (or "influence") of removing an observation on the value of a statistic without having to re-calculate that statistic. They can also be used to create asymptotic variance estimates. If influence equals $I$ then asymptotic varia... | null | CC BY-SA 2.5 | null | 2011-03-16T11:27:17.790 | 2011-03-16T11:35:25.490 | 2011-03-16T11:35:25.490 | 2392 | 2392 | null |
8355 | 2 | null | 8351 | 5 | null | [Larry Bretthorst's extended phd](http://bayes.wustl.edu/glb/book.pdf) will greatly help you I think. You should take the discrete fourier transform of the data. This will give you a look at your series in the frequency domain. Trend is represented by low frequency. Ultimate modeling book. It's 200 pages, but well... | null | CC BY-SA 2.5 | null | 2011-03-16T11:46:52.670 | 2011-03-16T11:46:52.670 | null | null | 2392 | null |
8357 | 2 | null | 8334 | 6 | null | Your question is most interesting to me and it's solution has been my primary research for a number of years.
There are a number of ways that "a structural break" may occur.
If there is a change in the Intercept or a change in Trend in "the latter portion of the time series" then one would be better suited to perform ... | null | CC BY-SA 2.5 | null | 2011-03-16T12:27:38.923 | 2011-03-16T13:50:24.793 | 2011-03-16T13:50:24.793 | 3382 | 3382 | null |
8358 | 1 | 8543 | null | 4 | 679 | I am a beginner in statistics with just basic knowledge. I have these data: cases, deaths and CFR (Case Fatality Rate-deaths per 100 cases) of a disease for 17 years (1994-2010) from 2 neighbouring states where people can walk across the states freely. This is a population based cohort study.
Data are available from 19... | How to assess effect of intervention in one state versus another using annual case fatality rate? | CC BY-SA 2.5 | null | 2011-03-16T12:31:39.013 | 2011-03-20T20:31:57.940 | 2011-03-16T13:00:03.317 | null | 2956 | [
"time-series",
"statistical-significance",
"spss",
"relative-risk"
] |
8359 | 2 | null | 8351 | 7 | null | The answer to your first question is no. If the null hypothesis of unit root is rejected, the alternative in its most general form is stationary series with [time trend](http://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test). Here is the example:
```
> rr <- 1+0.01*(1:100)+rnorm(100)
> plot(rr)
> adf.test(r... | null | CC BY-SA 2.5 | null | 2011-03-16T12:44:21.670 | 2011-03-16T12:44:21.670 | null | null | 2116 | null |
8361 | 5 | null | null | 0 | null | [ggplot2](http://had.co.nz/ggplot2/) is an [actively maintained, open-source](https://github.com/hadley/ggplot2) chart-drawing library for [r](/questions/tagged/r), written by [Hadley Wickham](http://stackoverflow.com/users/16632/hadley), based upon the principles of ["The Grammar of Graphics"](https://rads.stackoverfl... | null | CC BY-SA 4.0 | null | 2011-03-16T14:34:23.620 | 2020-07-02T00:46:57.570 | 2020-07-02T00:46:57.570 | 7290 | 3376 | null |
8362 | 4 | null | null | 0 | null | ggplot2 is an enhanced plotting library for R based upon the principles of "The Grammar of Graphics". Use this tag for *on topic* questions that (a) involve `ggplot2` as a critical part of the question &/or expected answer, & (b) are not just about how to use `ggplot2`. | null | CC BY-SA 4.0 | null | 2011-03-16T14:34:23.620 | 2020-07-02T00:44:16.790 | 2020-07-02T00:44:16.790 | 7290 | null | null |
8363 | 2 | null | 8342 | 1 | null | Initially I suggested to try one of the constrained ordination techniques from the `vegan` package, but on a second thought I doubt that this would be useful, as you actually have 2 contingency tables. I hope that the second part of [this example](http://is.gd/CSS9QV) [PDF: R Demonstration – Categorical Analysis] will ... | null | CC BY-SA 2.5 | null | 2011-03-16T14:47:55.757 | 2011-03-17T08:06:04.890 | 2011-03-17T08:06:04.890 | 3467 | 3467 | null |
8364 | 2 | null | 8347 | 15 | null | A quick response to the bulleted content:
1) Power / Type 1 error in a Bayesian analysis vs. a frequentist analysis
Asking about Type 1 and power (i.e. one minus the probability of Type 2 error) implies that you can put your inference problem into a repeated sampling framework. Can you? If you can't then there isn't ... | null | CC BY-SA 2.5 | null | 2011-03-16T14:54:27.780 | 2011-03-16T14:54:27.780 | null | null | 1739 | null |
8367 | 2 | null | 8303 | 6 | null | One idea would be to use a random forest and then use the variable importance measures it outputs to choose your best 8 variables. Another idea would be to use the "boruta" package to repeat this process a few hundred times to find the 8 variables that are consistently most important to the model.
| null | CC BY-SA 2.5 | null | 2011-03-16T16:04:09.750 | 2011-03-16T16:04:09.750 | null | null | 2817 | null |
8369 | 2 | null | 8342 | 1 | null | Logistic regression seems appropriate for your problem. The variable you are trying to predict is the probability that an observation (which is either species A or species B) is species A. The covariates are $host$, $rain$ and optionally $host*rain$.
The R command would be:
>
glm(formula = species ~ host + rain, fam... | null | CC BY-SA 2.5 | null | 2011-03-16T16:38:09.173 | 2011-03-16T18:38:27.713 | 2011-03-16T18:38:27.713 | 3567 | 3567 | null |
8370 | 1 | 8372 | null | 6 | 2304 | I have a matrix of positive real numbers between 0 and 1; the rows represent genes and columns represent samples. Number of rows is greater than the number of columns by a magnitude of $10^4$. I am wondering how to visualize this in `R`. I know heatmap is one of the ways to do this, but are there other ideas. Here are ... | How to visualize/summarize a matrix with number of rows $\gg$ number of columns? | CC BY-SA 2.5 | null | 2011-03-16T17:04:50.353 | 2011-03-16T20:14:48.487 | 2011-03-16T17:44:54.310 | 930 | 1307 | [
"r",
"data-visualization",
"genetics"
] |
8371 | 1 | 9657 | null | 10 | 1792 | French polling institutes are currently facing a major crisis after they recently published what can only be called [the most ridiculous poll so far](http://www.bbc.co.uk/news/world-europe-12660329) on the 2012 presidential election horse race. The French Senate is now considering to legislate on the issue by forcing p... | Do confidence intervals apply to quota sampling? | CC BY-SA 2.5 | null | 2011-03-16T17:38:34.997 | 2019-07-17T12:03:09.373 | null | null | 3582 | [
"sampling",
"polling"
] |
8372 | 2 | null | 8370 | 6 | null | Find one-dimensional [multidimensional scaling solutions](http://en.wikipedia.org/wiki/Multidimensional_scaling) for the rows and for the columns (separately), using whatever similarity measures you like (such as correlation). Sort the rows and columns according to their MDS positions. This will bring similar genes t... | null | CC BY-SA 2.5 | null | 2011-03-16T18:29:31.027 | 2011-03-16T18:29:31.027 | null | null | 919 | null |
8373 | 1 | null | null | 15 | 12360 | I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean of risk-free bonds and corporate bond yields), YoY Inflation rate and YoY index of industrial production.
It looks thus (... | Time series regression with overlapping data | CC BY-SA 3.0 | null | 2011-03-16T18:40:35.840 | 2023-03-12T14:27:22.750 | 2023-03-12T14:27:22.750 | 53690 | 1349 | [
"regression",
"time-series",
"autocorrelation",
"overlapping-data"
] |
8374 | 1 | null | null | 8 | 266 | I am trying to correlate age (6-90 yrs) with loudness of voice (in dB). However, my data do not contain any data points in the range of 20-50 yrs.
What correlation measure is most appropriate with such a considerable gap, and why? I have been using Kendall Tau so far.
Note that we are not dealing with bimodally distri... | Which correlation measure should be used with a large gap (missing data)? | CC BY-SA 2.5 | null | 2011-03-16T18:52:37.840 | 2011-03-16T22:59:14.990 | 2011-03-16T22:44:11.883 | 919 | null | [
"distributions",
"correlation",
"missing-data"
] |
8375 | 1 | 8806 | null | 17 | 9270 | I am looking to build a predictive model for predicting churn and looking to use a discrete time survival model fitted to a person-period training dataset (one row for each customer and discrete period they were at risk, with an indicator for event – equaling 1 if the churn happened in that period, else 0).
- I am fi... | Survival Model for Predicting Churn - Time-varying predictors? | CC BY-SA 4.0 | null | 2011-03-16T19:58:45.120 | 2018-07-01T11:28:47.083 | 2018-06-04T07:11:26.640 | 128677 | 2040 | [
"survival",
"predictive-models",
"churn"
] |
8376 | 2 | null | 8370 | 6 | null | I was about to suggest something along @whuber's answer (I used this reordering technique but in a context of feature selection, so I was mainly concerned with the "variables view"). So let me suggest two other directions (but the first one is close to the already proposed one).
As far as heatmaps are concerned, you ca... | null | CC BY-SA 2.5 | null | 2011-03-16T20:14:48.487 | 2011-03-16T20:14:48.487 | null | null | 930 | null |
8377 | 1 | 8383 | null | 7 | 1571 | I am a first year psychology student. I am doing some research work with a prof, unfortunately the material that I need to use right now is covered only in my second year. But I need to already know it now. So I am burning through any resources I can find to quickly come up to speed. I need help to understand this part... | Understanding multiple regression output | CC BY-SA 2.5 | null | 2011-03-16T20:55:08.437 | 2011-03-17T16:58:37.463 | 2011-03-17T16:58:37.463 | 919 | 3745 | [
"regression",
"anova",
"sas"
] |
8378 | 2 | null | 8342 | 11 | null | There are two ways to interpret your first question, which are reflected in the two ways you asked it: “Are species associated with host plants?” and, “Are species independent to host plants, given the effect of rain?”
The first interpretation corresponds to a model of joint independence, which states that species and... | null | CC BY-SA 2.5 | null | 2011-03-16T21:16:26.233 | 2011-03-16T21:21:45.497 | 2011-03-16T21:21:45.497 | 3432 | 3432 | null |
8379 | 2 | null | 8374 | 8 | null | Create a scatterplot to check whether it makes any sense to suppose that a single correlation coefficient is an adequate description of the association between the variables.
For example, in these (simulated) data the correlation for ages 6-20 is 90%, for ages 50+ it's -70%, and overall it's 15%. In such a situation r... | null | CC BY-SA 2.5 | null | 2011-03-16T22:15:34.437 | 2011-03-16T22:15:34.437 | null | null | 919 | null |
8380 | 1 | null | null | 4 | 396 | I want to build a linear model to predict a scalar output from a vector of noisy scalar variable measurements.
I have two separate training data sets. One has output data and corresponding exact variable measurements. The other has exact variable measurements and corresponding noisy variable measurements. The noisy ... | Building linear model from exact variable measurements for use with noisy variable measurements | CC BY-SA 2.5 | null | 2011-03-16T22:15:53.453 | 2011-03-25T16:48:20.410 | 2011-03-21T16:06:20.593 | 3744 | 3744 | [
"r",
"regression",
"predictive-models",
"errors-in-variables"
] |
8381 | 2 | null | 652 | 1 | null | Agresti & Finlay's Statistical Methods for the Social Sciences is quite good, though I'd like to believe there is a good open source alternative. Is it wrong to use an amazon affiliate link here?
| null | CC BY-SA 2.5 | null | 2011-03-16T22:34:19.733 | 2011-03-16T22:34:19.733 | null | null | 3748 | null |
8382 | 1 | 8438 | null | 6 | 611 | So I have data that has been quantized by an analogue to digital converter. (continuous data has been turned into discrete data and the values range from 0 to the saturation value , which is 127 in this case).
This particular instrument I used to gather the data is quite noisy, let's say there is added Gaussian noise... | How to average quantized and truncated data? | CC BY-SA 3.0 | null | 2011-03-16T22:52:36.403 | 2017-04-23T16:16:25.247 | 2017-04-23T16:16:25.247 | 11887 | 3749 | [
"mean",
"discrete-data",
"signal-processing",
"winsorizing"
] |
8383 | 2 | null | 8377 | 6 | null | It seems like you need an introduction to regression. People made book recommendations [here](https://stats.stackexchange.com/questions/652/best-books-for-an-introduction-to-statistical-data-analysis). Free book recommendations [here](https://stats.stackexchange.com/questions/170/free-statistical-textbooks).
It's har... | null | CC BY-SA 2.5 | null | 2011-03-16T22:54:57.350 | 2011-03-17T00:22:58.507 | 2017-04-13T12:44:35.347 | -1 | 3748 | null |
8384 | 2 | null | 8351 | 1 | null | The ADF test has weak power and, as Dmitrij Celov mentioned, you should probably also check the results of PP and KPSS tests. If you find that your results are on the margin of detecting a unit root, it's possible your series is fractionally integrated. I would also check ACF and PACF plots of the series, looking for s... | null | CC BY-SA 2.5 | null | 2011-03-16T22:55:24.480 | 2011-03-16T22:55:24.480 | null | null | 3265 | null |
8386 | 2 | null | 290 | 2 | null | Alex Tabarrok posted a great list of resources here on [marginalrevolution.com](http://marginalrevolution.com/marginalrevolution/2011/01/stata-resources.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3a+marginalrevolution/hCQh+%28Marginal+Revolution%29)
Gabriel Rossman shares a good introductory guide [he... | null | CC BY-SA 2.5 | null | 2011-03-16T23:45:24.433 | 2011-03-17T00:16:14.973 | 2011-03-17T00:16:14.973 | 3748 | 3748 | null |
8387 | 2 | null | 103 | 4 | null | Andrew Gelman doesn't limit himself to visualization, but he comments on it frequently.
[Statistical Modeling, Causal Inference, and Social Science](http://www.stat.columbia.edu/~gelman/blog/)
| null | CC BY-SA 3.0 | null | 2011-03-16T23:59:43.900 | 2012-10-24T14:54:19.153 | 2012-10-24T14:54:19.153 | 615 | 3748 | null |
8388 | 2 | null | 363 | 8 | null | Andrew Gelman's interesting book recommendations are here:
[http://thebrowser.com/interviews/andrew-gelman-on-statistics](http://thebrowser.com/interviews/andrew-gelman-on-statistics)
| null | CC BY-SA 2.5 | null | 2011-03-17T00:09:23.670 | 2011-03-17T00:09:23.670 | null | null | 3748 | null |
8389 | 1 | null | null | 5 | 654 | I need a closed-form expression for the probability that 2 values drawn at random from a normal distribution are separated by at most T, as a function of T and the variance of the distribution. I can formulate the problem as an integration, but can't solve it. I'd be happy with a well-motivated approximation. I'd be am... | Probability that two values drawn at random from a normal distribution are separated by at most T | CC BY-SA 2.5 | null | 2011-03-17T00:30:01.570 | 2011-03-17T02:07:17.637 | null | null | null | [
"normal-distribution",
"sample"
] |
8390 | 2 | null | 7562 | 4 | null | Looking at the example for `postStratify` in the [manual](http://cran.r-project.org/web/packages/survey/survey.pdf), you are correct: you seem to be required to give a `svydesign` object (though you can if needed use `svrepdesign` to specify it instead).
The `svydesign` object must have `ids`; all the others are opti... | null | CC BY-SA 2.5 | null | 2011-03-17T00:40:10.997 | 2011-03-17T00:40:10.997 | null | null | 2958 | null |
8391 | 2 | null | 8389 | 10 | null | The distribution of the difference between two single independent samples from normal distributions has a mean which is the difference between the means of the original distributions and a variance which is the sum of the variances of the original distributions.
So in your case, if the normal distribution of $X$ has ... | null | CC BY-SA 2.5 | null | 2011-03-17T00:47:18.960 | 2011-03-17T02:07:17.637 | 2011-03-17T02:07:17.637 | 919 | 2958 | null |
8392 | 1 | 8431 | null | 5 | 144 | I have logs of which users run which programs on our systems at a university. The data shows username, process time, and the time stamp for the sample. I also know which classes each user is in. Given this data, is it possible to reliably tell which professors/courses use a particular piece of software? I figure this m... | How to determine course-based usage of software? | CC BY-SA 2.5 | 0 | 2011-03-17T01:02:41.093 | 2011-03-19T09:46:11.220 | 2011-03-17T21:59:53.503 | 3369 | 2814 | [
"r",
"modeling"
] |
8393 | 1 | 8403 | null | 2 | 434 | I have three questions.
- I want to conduct a planned comparison in ANCOVA which compares three groups (n=2, n=7 and n=5) with two groups (n=11 and n=25). Is it ok to include the group with n=2 if I am only conducting a planned comparison as described above?
- Is it ok to conduct an ANCOVA when I am only comparing tw... | Questions about number of groups and group size in planned comparisons in ANCOVA | CC BY-SA 2.5 | null | 2011-03-17T01:17:24.617 | 2011-03-17T10:39:06.427 | null | null | 2025 | [
"data-transformation",
"ancova",
"small-sample"
] |
8394 | 2 | null | 2917 | 0 | null | You may consider trying the R package [compute.es](http://cran.r-project.org/web/packages/compute.es/index.html). There are several functions for deriving effect size estimates and the variance of the effect size.
| null | CC BY-SA 2.5 | null | 2011-03-17T01:53:03.130 | 2011-03-17T02:47:49.370 | 2011-03-17T02:47:49.370 | 1381 | null | null |
8395 | 2 | null | 8157 | 4 | null | If I understand you correctly, only points in some small volume of n-dimensional space meet your constraints.
Your first constraint constrains it to the interior of a hypersphere,
which reminds me of the comp.graphics.algorithms FAQ ["Uniform random points on sphere"](http://www.cgafaq.info/wiki/Uniform_random_points_o... | null | CC BY-SA 2.5 | null | 2011-03-17T02:56:04.293 | 2011-03-17T02:56:04.293 | 2017-04-13T12:44:46.680 | -1 | 3754 | null |
8396 | 1 | null | null | 5 | 5108 | I do not know why package forecast 2.16 in R does not produce Theil's U?
I really appreciate your efforts.
| How to produce Theil's U with package forecast 2.16 in R? | CC BY-SA 2.5 | null | 2011-03-17T03:27:07.263 | 2015-07-21T20:22:14.807 | 2011-03-17T07:22:09.587 | 2116 | null | [
"r",
"forecasting",
"goodness-of-fit"
] |
8397 | 2 | null | 8377 | 2 | null | You may be interested by [this](http://joyeuserrance.wordpress.com/2010/11/25/the-linear-model-2-simple-linear-regression/) introduction to the linear model (basis of almost any statistical analyses), and linear regression in particular:
- it thoroughly explains lots of the mathematical aspects of linear regression, b... | null | CC BY-SA 2.5 | null | 2011-03-17T04:08:33.860 | 2011-03-17T04:08:33.860 | null | null | 3459 | null |
8398 | 2 | null | 8396 | 12 | null | It does. Use the `accuracy()` command.
Update: here is an example.
```
library(forecast)
x <- EuStockMarkets[1:200,1]
f <- EuStockMarkets[201:300,1]
fit1 <- ses(x,h=100)
accuracy(fit1,f)
ME RMSE MAE MPE MAPE MASE ACF1 Theil's U
0.8065983 78.1801986 63.2728352 -0.1725009 ... | null | CC BY-SA 2.5 | null | 2011-03-17T05:01:07.543 | 2011-03-17T09:28:55.800 | 2011-03-17T09:28:55.800 | 159 | 159 | null |
8399 | 1 | null | null | 3 | 2532 | I posted [this](https://electronics.stackexchange.com/q/10632/2939) question on Electronics.Stackexchange and someone told me I'll be better off posting it here.
Its an implementation of the Particle Filter using MATLAB but the results never follow the observations. I changed the weighting function to be Gaussian but ... | Particle filter in Matlab - what is going wrong? | CC BY-SA 2.5 | null | 2011-03-17T05:09:45.780 | 2011-06-15T13:00:42.117 | 2017-04-13T12:33:11.210 | -1 | null | [
"correlation",
"matlab",
"monte-carlo",
"particle-filter"
] |
8401 | 1 | null | null | 11 | 2566 | I am working on a research project that is related to optimization and recently had an idea to use MCMC in this setting. Unfortunately, I am fairly new to MCMC methods so I had several questions. I'll start by describing the problem and then asking my questions.
Our problem boils down to estimating the expected value o... | Using MCMC to evaluate the expected value of a high-dimensional function | CC BY-SA 2.5 | 0 | 2011-03-17T06:41:46.460 | 2014-09-22T20:13:17.627 | 2011-03-17T08:51:51.883 | 3572 | 3572 | [
"sampling",
"markov-chain-montecarlo",
"matlab",
"expected-value"
] |
8402 | 2 | null | 8377 | 2 | null | If you want a book specifically on this sort of regression - as opposed to data analysis in general - I recommend [Regression Analysis by Example by Chatterjee and Price](http://rads.stackoverflow.com/amzn/click/0471746967). Good, not technical, but it doesn't oversimplify.
| null | CC BY-SA 2.5 | null | 2011-03-17T10:33:44.997 | 2011-03-17T10:33:44.997 | null | null | 686 | null |
8403 | 2 | null | 8393 | 4 | null | First off, with such small N, I think anything that comes close to statistical significance is going to pass the inter-ocular trauma test - it will hit you between the eyes.
I am not sure what you mean by compare 3 groups with 2 groups. Do you mean you want to combine the three groups into one, and the 2 groups into a... | null | CC BY-SA 2.5 | null | 2011-03-17T10:39:06.427 | 2011-03-17T10:39:06.427 | null | null | 686 | null |
8404 | 2 | null | 726 | 35 | null | >
The plural of anecdote is not data.
-- Roger Brinner
(in the context of [Anecdotal_evidence](http://en.wikipedia.org/wiki/Anecdotal_evidence))
| null | CC BY-SA 2.5 | null | 2011-03-17T10:53:40.767 | 2011-03-17T10:53:40.767 | null | null | 264 | null |
8405 | 1 | 8410 | null | 3 | 5488 | I have continuous data $A$ and categorical data $O$. I need counts of $A$ in bins by group $O$. I'm working in R.
I know how to bin data (using `cut2`) and how to get the counts of $O$ (using `aggregate` or `by` or `describe` or `summaryBy`). I could also get what I need by running one of these functions with a subset ... | Counts of binned data by group | CC BY-SA 2.5 | null | 2011-03-17T12:03:05.233 | 2011-03-28T06:07:52.767 | 2011-03-22T09:04:58.307 | 2824 | 2824 | [
"r",
"descriptive-statistics",
"binning"
] |
8406 | 2 | null | 726 | 7 | null | >
The researcher armed with a confidence
interval, but deprived of the false
respectability of statistical
significance, must work harder to
convince himself and others of the
importance of his findings. This can
only be good.
Michael Oakes, Statistical inference: A commentary for the social and behaviou... | null | CC BY-SA 2.5 | null | 2011-03-17T12:05:58.503 | 2011-03-17T12:05:58.503 | null | null | 2669 | null |
8407 | 1 | 8417 | null | 26 | 34327 | I would like to import the "Last Trade" stock price from Yahoo finance into R. The intention is to work with (almost) real time data. Are there any solutions?
Thanks in advance for any helpful comment.
| Import stock price from Yahoo Finance into R? | CC BY-SA 2.5 | null | 2011-03-17T12:11:10.293 | 2016-03-01T17:19:29.597 | 2011-03-18T16:15:41.810 | null | 3757 | [
"r"
] |
8408 | 2 | null | 8407 | 15 | null | That is pretty easy given that [R](http://www.r-project.org) can read directly off a given URL. The key is simply to know how to form the URL. Here is a quick and dirty example based on code Dj Padzensky wrote in the late 1990s and which I have been maintaining in the [Perl](http://www.perl.org) module [Yahoo-FinanceQ... | null | CC BY-SA 2.5 | null | 2011-03-17T12:29:03.407 | 2011-03-17T12:29:03.407 | null | null | 334 | null |
8409 | 2 | null | 8401 | 4 | null | I would always remember, that MCMC is just a numerical integration tool (and a rather inefficient one at that). It is not some magic/mystical thing. It is very useful because it is reasonably easy to apply. It does not require much thinking compared to some other numerical integration techniques. For instance, you ... | null | CC BY-SA 2.5 | null | 2011-03-17T12:40:34.437 | 2011-03-17T12:40:34.437 | null | null | 2392 | null |
8410 | 2 | null | 8405 | 6 | null | I really like using the `summary.formula` function in the `Hmisc` package for these tasks.
Some artificial data:
```
A <- rnorm(100, mean=50, sd=20)
G <- gl(2, 50)
B <- unclass(G)*10 + rnorm(100, sd=3)
```
Descriptives by `G`:
```
summary( G ~ cut2(A,g=4) + B, method="reverse")
Descriptive Statistics by G
+-----... | null | CC BY-SA 2.5 | null | 2011-03-17T13:04:36.523 | 2011-03-17T15:52:42.263 | 2011-03-17T15:52:42.263 | 279 | 279 | null |
8413 | 2 | null | 8377 | 4 | null | The two together don't tell you anything more than the second one would alone! The main effects are uninteresting and misleading when there is interaction present. The second model tells you all you need to know. Here are a couple of plots, with R code, to help you understand what that second model looks like...
```
l... | null | CC BY-SA 2.5 | null | 2011-03-17T14:42:00.950 | 2011-03-17T14:42:00.950 | null | null | 485 | null |
8415 | 1 | 8416 | null | 5 | 658 | I have a time series, let's call it $y(t)$. Augmented Dickey-Fuller and Phillips-Perron tests indicate no unit root but a deterministic trend. The series is strongly autocorrelated. I estimated the following short-run dynamic model:
$$y(t) = \alpha + \beta\cdot y(t-1) + \gamma\cdot t + \varepsilon_t$$
where $\alpha$, ... | Short and long-run trend | CC BY-SA 2.5 | null | 2011-03-17T16:09:46.423 | 2011-03-17T16:46:02.370 | 2011-03-17T16:30:57.613 | 919 | 3762 | [
"time-series",
"model-selection",
"trend"
] |
8416 | 2 | null | 8415 | 3 | null | There is no quick solution to the problem, since the deterministic trend is just a function of $t$. We may denote this trend by $f(t)$ and it is not evident that the trend is linear.
So some quick tips what to do in this case:
- Plot the original data, though the noise ratio could be huge therefore...
- Make technica... | null | CC BY-SA 2.5 | null | 2011-03-17T16:46:02.370 | 2011-03-17T16:46:02.370 | null | null | 2645 | null |
8417 | 2 | null | 8407 | 14 | null | This really isn't a statistics question (perhaps this could be moved to SO?), but there's a nice function in [quantmod](http://www.quantmod.com) that does what Dirk has done by hand. See `getQuote()` and `yahooQF()`. Typing `yahooQF()` will bring up a menu of all the possible quote formats you can use.
```
> require(... | null | CC BY-SA 3.0 | null | 2011-03-17T16:48:37.487 | 2011-07-01T07:25:32.483 | 2011-07-01T07:25:32.483 | 3651 | 1657 | null |
8418 | 1 | null | null | 1 | 2917 | I have a dataset with repeated measures at different speeds. I've binned the speed ranges into 0-0.5, 0.5-1.5, and 1.5-infinity.
```
mydata`$`fact<−cut(mydata$section, breaks=c(0,0.5,1.5,Inf))
```
which gives the levels (0,0.5] (0.5,1.5] (1.5,Inf]
I first used the lme to test all speeds together to see if x and y ... | How can I specify a level of a factor while in an lme? | CC BY-SA 2.5 | null | 2011-03-17T16:52:45.347 | 2011-03-17T18:10:24.983 | 2011-03-17T16:56:34.597 | 919 | null | [
"r",
"mixed-model"
] |
8419 | 1 | 8434 | null | 3 | 7056 | Supposing a Bayesian classifier with multivariate normal densities, how do I find the error rate of the classifier when we have two classes?
I am using this:
When dimension $d = 1$:
$$P(x | \mu , \sigma^2) = N(x, \mu, \sigma^2) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}} $$
In $d$ dimensions:
$$P(... | Bayesian classifier with multivariate normal densities | CC BY-SA 2.5 | null | 2011-03-17T17:18:25.883 | 2011-04-29T00:57:16.927 | 2011-04-29T00:57:16.927 | 3911 | 3681 | [
"machine-learning",
"naive-bayes"
] |
8420 | 1 | 8450 | null | 4 | 196 | Other then gapminder.org, could you please direct me to any good examples (interactive or static) of visualizations that compare between countries?
| Examples of visualization featuring comparisons between countries? | CC BY-SA 3.0 | null | 2011-03-17T17:33:20.797 | 2017-10-06T09:42:48.480 | 2017-10-06T09:42:48.480 | 11887 | 253 | [
"data-visualization"
] |
8421 | 2 | null | 7720 | 2 | null | To 'test' (i.e., evaluate) the proportional odds assumption in R, you can use residuals.lrm() in Frank Harrell Jr.'s Design package. If you type ?residuals.lrm , there is a quick-to-replicate example of how Frank Harrell recommends evaluating the proportional odds assumption (i.e., visually, rather than by a push-butto... | null | CC BY-SA 3.0 | null | 2011-03-17T17:52:24.270 | 2017-01-29T21:05:53.147 | 2017-01-29T21:05:53.147 | 145102 | 3763 | null |
8422 | 2 | null | 8206 | 2 | null | Here is mine implementation of your solutions. I've decided not to map the mean value, there is not to much space left. Also the line from 0 to 1 seems odd.
Thank's a lot everyone.
```
data <- read.table("roc_average.txt")
bxp <- boxplot(data, horizontal = TRUE, range = 0, axes = FALSE, col = "grey", add = TRUE, at = 0... | null | CC BY-SA 2.5 | null | 2011-03-17T17:58:12.653 | 2011-03-19T12:26:25.473 | 2011-03-19T12:26:25.473 | 3345 | 3345 | null |
8424 | 2 | null | 8418 | 1 | null | If you just want to fit separate models for each level of your factor, then probably the easiest way is to use the `subset=` argument to `lme` (or any other GLM in R, btw). For example,
```
lme(y ~ x + factor(repeatedmeasures) + fact,
random = ~1 | z, data=mydata, subset=as.numeric(fact) == 1)
```
should subset o... | null | CC BY-SA 2.5 | null | 2011-03-17T18:10:24.983 | 2011-03-17T18:10:24.983 | null | null | 930 | null |
8425 | 1 | null | null | 5 | 291 | I hope this is an appropriate forum for this question...if not, any pointers on a place to ask would be great. If my questions is not clear, please just let me know and I'll try to add information/explanation where I can.
Say I have a set of data points that I run through a [FLAME clustering algorithm](http://en.wikip... | Bias from increased information in FLAME clustering | CC BY-SA 2.5 | null | 2010-08-27T18:14:14.280 | 2012-06-10T14:34:20.997 | 2011-03-17T21:59:03.863 | 3369 | 338 | [
"clustering",
"online-algorithms"
] |
8426 | 2 | null | 8425 | 1 | null | I think it is possible to introduce the new set of data periodically by assigning its initial value with the interpolated value of its neighbor while the possible set of neighbor for each new data point could be the points in the triangle that data point resides in.
I think what is more important is the order of assig... | null | CC BY-SA 2.5 | null | 2010-12-17T07:46:14.033 | 2011-03-18T08:01:26.373 | 2011-03-18T08:01:26.373 | null | null | null |
8428 | 1 | null | null | 11 | 6107 | I am finishing up some analysis on a large set of data. I would like to take the linear model used in the first part of the work and re-fit it using an linear mixed model (LME). The LME would be very similar with the exception that one of the variables used in the model would be used as a random effect. This data comes... | Comparing a mixed model (subject as random effect) to a simple linear model (subject as a fixed effect) | CC BY-SA 3.0 | null | 2011-03-17T19:28:38.270 | 2018-03-12T11:19:53.597 | 2018-03-12T11:19:53.597 | 28666 | 3727 | [
"r",
"regression",
"hypothesis-testing",
"mixed-model",
"lme4-nlme"
] |
8429 | 2 | null | 8428 | 2 | null | I am not totally sure to figure out what model is fitted when you use the lme function. (I guess the random effect is supposed to follow a normal distribution with zero mean?). However, the linear model is a special case of the mixed model when the variance of the random effect is zero. Although some technical difficul... | null | CC BY-SA 2.5 | null | 2011-03-17T20:08:02.357 | 2011-03-17T20:34:47.713 | 2011-03-17T20:34:47.713 | 3019 | 3019 | null |
8430 | 2 | null | 8428 | 8 | null | This is to add to @ocram's answer because it is too long to post as a comment. I would treat `A ~ B + C` as your null model so you can assess the statistical significance of a `D`-level random intercept in a nested model setup. As ocram pointed out, regularity conditions are violated when $H_0: \sigma^2 = 0$, and the l... | null | CC BY-SA 2.5 | null | 2011-03-17T20:43:54.430 | 2011-03-17T20:50:30.897 | 2011-03-17T20:50:30.897 | 3432 | 3432 | null |
8431 | 2 | null | 8392 | 1 | null | Purely from a machine learning perspective, I think it is possible to do the inference you want from this data. How reliable it will be will depend on how much data you have (the more the better) and on usage patterns (do students mostly do course-related or course-unrelated stuff on these computers). However, this is ... | null | CC BY-SA 2.5 | null | 2011-03-17T21:11:16.093 | 2011-03-17T21:11:16.093 | null | null | 3369 | null |
8432 | 2 | null | 8392 | 0 | null | Considering you observe co-occurences of discrete variables, I would do the following:
```
num_programs = 3
num_students = 10
num_courses = 5
# How long a given student uses a given program
random_exponential = rexp(num_programs*num_students)
P = matrix(random_exponential, nrow = num_programs)
# Given a student, proba... | null | CC BY-SA 2.5 | null | 2011-03-17T22:52:13.613 | 2011-03-19T09:46:11.220 | 2011-03-19T09:46:11.220 | 1351 | 1351 | null |
8433 | 1 | 8437 | null | 6 | 1274 | I have data on 70,000 students, nested in 120 schools. I'm starting with fixed effects for the schools, but at some point I might start letting intercepts and slopes vary.
Some key predictors (e.g. gpa, test scores) have non-linear relationships with some outcomes. I definitely want to interact these predictors wit... | Interactions between non-linear predictors | CC BY-SA 2.5 | null | 2011-03-17T23:33:22.827 | 2011-10-08T15:32:08.783 | 2011-10-08T15:32:08.783 | 3748 | 3748 | [
"r",
"mixed-model",
"interaction",
"splines",
"generalized-additive-model"
] |
8434 | 2 | null | 8419 | 5 | null | I would have thought the "best" (most intuitive) estimate of error would be the probability that the classification is incorrect. Or alternatively/equivalently the odds against the class. Using the [wikipedia page](http://en.wikipedia.org/wiki/Naive_Bayes_classifier), you classify as Male or female. I would have tho... | null | CC BY-SA 2.5 | null | 2011-03-17T23:34:51.363 | 2011-03-19T01:49:46.053 | 2011-03-19T01:49:46.053 | 2392 | 2392 | null |
8435 | 1 | null | null | 0 | 665 | When to use multivariate logistic regression versus generalized linear mixed-effects models? What is the difference between the two?
Edit (in response to comments):
I was hoping to see multivariate as more than one covariate.
My problemset is analysis for a longitudinal prospective cohort study where im measuring vita... | Generalized Linear Mixed Effects | CC BY-SA 2.5 | null | 2011-03-18T01:21:08.357 | 2011-03-29T06:32:50.853 | 2011-03-29T06:32:50.853 | 930 | null | [
"regression",
"mixed-model",
"panel-data"
] |
8436 | 1 | 8440 | null | 8 | 2166 | I have some data; it's a proportion $y$ of some stuff relative to everything, so it's bounded between 0 and 1 by definition. The proportion changes over time. Besides fairly high variance there is a step-like change about the middle of the time period; the step isn't very large, but it's there, and it happens pretty f... | Logistic regression for bounds different from 0 and 1 | CC BY-SA 2.5 | null | 2011-03-18T02:30:48.143 | 2019-10-06T08:36:32.147 | 2011-03-19T17:35:35.033 | 2658 | 2658 | [
"logistic"
] |
8437 | 2 | null | 8433 | 7 | null | You could try generalized additive mixed models, handily implemented in the [gamm4](http://cran.r-project.org/web/packages/gamm4/index.html) package. The way I've used them, you can do something like:
```
fit1 = gamm4(
formula = V1 ~ V2 + s(V3)
, random = ~ (1|V4)
)
fit2 = gamm4(
formula = V1 ~ V2 + s(V3,by... | null | CC BY-SA 2.5 | null | 2011-03-18T02:30:58.790 | 2011-03-18T02:30:58.790 | null | null | 364 | null |
8438 | 2 | null | 8382 | 1 | null | [http://en.wikipedia.org/wiki/Truncation_%28statistics%29](http://en.wikipedia.org/wiki/Truncation_%28statistics%29)
This is not much help, but at least it gives the correct buzzword (truncated, not quantized; quantization is not your problem) and one pointer to a paper. This should do as a starting point for further s... | null | CC BY-SA 2.5 | null | 2011-03-18T02:54:43.513 | 2011-03-18T02:54:43.513 | null | null | 2658 | null |
8439 | 1 | null | null | 2 | 278 | I would like to validate a commonly used questionnaire for internet-based administration. What statistical tests should I be using?
| Validating a paper questionnaire in a web-based format | CC BY-SA 2.5 | null | 2011-03-18T03:00:31.760 | 2011-03-18T11:38:41.063 | 2011-03-18T08:16:00.613 | 183 | null | [
"validation",
"survey"
] |
8440 | 2 | null | 8436 | 3 | null | To begin with, I think we have to distinguish between [logistic regression](http://en.wikipedia.org/wiki/Logistic_regression) and (generalized) [logistic function](http://en.wikipedia.org/wiki/Generalised_logistic_curve). Though the latter may be viewed as a separate case of a the former taking the time as the only exp... | null | CC BY-SA 2.5 | null | 2011-03-18T05:48:45.150 | 2011-03-18T09:15:16.640 | 2011-03-18T09:15:16.640 | 2645 | 2645 | null |
8441 | 2 | null | 8439 | 5 | null | The following are two complementary strategies that you could adopt.
### 1. Conduct your own empirical validation:
A simple strategy would be to run some form of experiment.
Randomly assign half of your participants to web based completion and the other half to paper based completion.
Then, examine whether statistic... | null | CC BY-SA 2.5 | null | 2011-03-18T08:13:31.180 | 2011-03-18T11:38:41.063 | 2011-03-18T11:38:41.063 | 183 | 183 | null |
8442 | 1 | null | null | 4 | 212 | My goal is to create CI for the CART prediction of new_x
Consider the following code:
```
require(rpart)
set.seed(147830)
n <- 100
x1 <- runif(n)
x2 <- rnorm(n)
y <- x1 + 2*x2 + rnorm(n, 0, .5)
DAT <- data.frame(y,x1,x2)
fit <- rpart(y ~ x1 + x2, data = DAT)
new_x <- data.frame(x1 = .5 , x2 = .25)
predict(fit, newd... | How to produce a CI for a value predicted in CART? | CC BY-SA 2.5 | null | 2011-03-18T08:46:16.270 | 2011-03-18T09:58:04.173 | null | null | 253 | [
"r",
"predictive-models",
"cart",
"rpart"
] |
8443 | 2 | null | 7236 | 0 | null | What I would do is provide `prob` argument with weights for each data point based on number of levels in your variable. Example:
```
df <- data.frame(oks = sample(100),
grp = c(rep("trt1", times = 30), rep("trt2", times = 70)))
> head(df)
oks grp
1 40 trt1
2 29 trt1
3 12 trt1
4 25 trt1
5 19 trt1
6 45 ... | null | CC BY-SA 2.5 | null | 2011-03-18T09:21:26.243 | 2011-03-18T09:21:26.243 | null | null | 144 | null |
8444 | 2 | null | 8442 | 1 | null | Couldn't you use the distribution of the observed `y` in the leafs/terminal nodes to get an idea about the precision of the estimated mean for new observations falling into that leaf? For CIs with large coverage you'd probably need a lot of data / a small tree so that each terminal node contains a lot of observations, ... | null | CC BY-SA 2.5 | null | 2011-03-18T09:58:04.173 | 2011-03-18T09:58:04.173 | null | null | 1979 | null |
8446 | 1 | null | null | 4 | 672 | My supervisor asked me to find out which distribution represents a particular situation.
I have a VoIP generator that generates calls "uniformly" distributed between callers. This means that the volume per caller distribution is "almost" uniformly distributed between a minimum and maximum. So by running a test with 100... | What kind of distribution is this "almost" uniformly distributed data for calls/week? | CC BY-SA 4.0 | 0 | 2011-03-18T10:03:48.483 | 2018-08-24T06:34:50.247 | 2018-08-20T06:45:47.427 | 176202 | 3342 | [
"hypothesis-testing",
"distributions",
"numerics",
"distribution-identification"
] |
8447 | 1 | null | null | 4 | 4030 | I want to graphically show the change in relative risk along with confidence limits over 17 years.
Can I use a forest plot without meta-analysis because it will have the added advantage to tabulate Z-values and p-values also? Here I want to show the progressive change in the relative risk and NOT meta-analysis.
Or is... | Visualizing change in risk ratio along with confidence limits | CC BY-SA 2.5 | null | 2011-03-18T10:55:33.273 | 2011-09-05T06:24:41.813 | 2011-03-18T16:01:58.567 | null | 2956 | [
"confidence-interval",
"relative-risk",
"graphical-model"
] |
8448 | 2 | null | 8405 | 2 | null | @SabreWolfy: I've edited my answer to add an additional factor by binning data:
```
library(Hmisc)
library(doBy)
set.seed(123)
A=rnorm(100, mean=50, sd=20)
bin.data<-data.frame(bins.A=cut2(A, g=4),G=gl(2,50), A)
summaryBy(A~bins.A+G,data=bin.data,FUN=each(length,mean,median))
```
will give output for age:
```
... | null | CC BY-SA 2.5 | null | 2011-03-18T11:12:23.097 | 2011-03-18T18:19:36.817 | 2011-03-18T18:19:36.817 | 3774 | 3774 | null |
8449 | 2 | null | 8446 | 1 | null | Try Q-Q plots (or P-P plots, only they are somewhat less widely used) of your empirical distribution against each of hypothetical type of distribution. The parameters for the latter are usually deduced by software from your empirical distribution although you could input parameter values you wish to check.
| null | CC BY-SA 2.5 | null | 2011-03-18T11:24:22.680 | 2011-03-18T11:24:22.680 | null | null | 3277 | null |
8450 | 2 | null | 8420 | 2 | null | Although I haven't seen international examples, I think [InstantAtlas](http://www.instantatlas.com/) offers quite a lot of nice features that could be useful in your case:
[StatPlanet](http://www.sacmeq.org/statplanet/) might be another (free) option:... | null | CC BY-SA 2.5 | null | 2011-03-18T11:48:32.543 | 2011-03-18T11:48:32.543 | null | null | 22 | null |
8451 | 2 | null | 8436 | 5 | null | You are looking for the wrong keywords. Logistic regression is for 0-1 outcomes, where the probability of being 1 is modeled with an S-shaped (logistic) function, not the actual data points themselves. Looking for nonlinear regression, specifically the four-parameter logistic model: $y = A + (B-A)/(1+\exp(-(a+bx))) + \... | null | CC BY-SA 2.5 | null | 2011-03-18T13:04:44.530 | 2011-03-18T13:04:44.530 | null | null | 279 | null |
8452 | 2 | null | 8435 | 2 | null | This depends on what you mean by "multivariate". Doing a quick google search, the term seems to refer to "more than 1 co variate". So the model equation is for "ordinary logistic regression" you have:
$$log(\frac{\pi}{1-\pi})=\beta_0 + \beta_1 X_1$$
For "multivariate logistic regression" you have:
$$logit(\frac{\pi}{... | null | CC BY-SA 2.5 | null | 2011-03-18T13:08:03.853 | 2011-03-18T13:08:03.853 | null | null | 2392 | null |
8453 | 1 | null | null | 2 | 340 | Does $R^2 * \mbox{slope}$ provide an estimation of cause and effect? For example, if $R^2$ of miles driven and coffee consumed is $x\%$ and the slope of the coffee-and-miles dataset is $y$ (miles/coffee), can you say "$z$ of each cup of coffee drunk is related to the number of miles driven"?
| Can you use $R^2$ and regression to estimate cause and effect? | CC BY-SA 3.0 | null | 2011-03-18T13:17:29.973 | 2014-05-25T15:29:49.563 | 2014-05-25T15:29:49.563 | 27403 | null | [
"regression",
"correlation"
] |
8455 | 1 | 8531 | null | 10 | 1265 | The recent events in Japan have made me think about the following.
Nuclear Plants are usually designed to limit risk of serious accidents
to a 'design basis probability' for example, say, 10E-6/year.
This is the criteria for a single plant.
However, when there is a population of hundreds of reactors, how do we combine ... | Combining probabilities of nuclear accidents | CC BY-SA 2.5 | null | 2011-03-18T13:59:43.307 | 2011-03-21T17:36:23.963 | null | null | null | [
"probability"
] |
8456 | 1 | null | null | 5 | 46489 | I am a senior doing a science fair project on the efficiency of fuels. For each fuel I tested, I have a time series of temperature.
I want to plot these into an excel xy chart, how do I plot them if the constants (time) are not the same for each set of data?
| Excel xy chart with unequal x values in series | CC BY-SA 2.5 | null | 2011-03-18T14:03:39.683 | 2023-01-25T10:51:48.917 | 2011-03-18T16:07:45.020 | null | null | [
"data-visualization",
"excel"
] |
8457 | 2 | null | 8455 | 1 | null | As commentators pointed out, this has the very strong independency assumption.
Let the probability that a plant blows up be $p$. Then the probability that a plant does not blow up is $1-p$. Then the probability that $n$ plants do not blow up is $(1-p)^n$. The expected number of plants blown up per year is $np$.
In case... | null | CC BY-SA 2.5 | null | 2011-03-18T14:09:25.707 | 2011-03-18T16:04:05.963 | 2011-03-18T16:04:05.963 | 2860 | 2860 | null |
8458 | 2 | null | 8453 | 5 | null | Regression gives you, essentially, a correlation. Correlation is not causality -- or else global warming would be caused by a decline in global number of pirates.
| null | CC BY-SA 2.5 | null | 2011-03-18T14:17:10.117 | 2011-03-18T14:17:10.117 | null | null | 2044 | null |
8459 | 1 | null | null | 2 | 131 | I recently was asked to help characterize some surfaces (raised ink dots on paper) that were scanned with a profilometer, yielding height data across a grid of x & y coordinates. The critical question was to characterize the average dot height. The data were rather noisy as the paper was not perfectly flat, so I played... | Does this de-noising algorithm have a name? | CC BY-SA 2.5 | null | 2011-03-18T15:26:07.260 | 2011-03-19T15:56:45.143 | null | null | 364 | [
"smoothing"
] |
8460 | 2 | null | 8420 | 0 | null | I like Visualizing data patterns with micromaps by daniel carr and linda pickle, specially the concept of linked micromaps, which is influenced by the idea of small multiples (Tufte) and the idea of dot - charts (Cleveland). I strong recommend to read it.
| null | CC BY-SA 2.5 | null | 2011-03-18T15:49:52.650 | 2011-03-18T15:49:52.650 | null | null | null | null |
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