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11256 | 1 | 11260 | null | 7 | 4919 | I need to do a regression with a non-normal DV for which no proper non-linear transformation (that I know of) exists:
It is a score ranging from 10 to 50, with a high peak at 10, a drop at 11 and a regular decline from 11 to 50. The distribution of re... | Regression on a non-normal dependent variable | CC BY-SA 3.0 | null | 2011-05-26T04:36:57.507 | 2011-05-26T12:42:05.990 | 2011-05-26T06:03:45.123 | 2116 | 4754 | [
"regression",
"ordinal-data"
] |
11257 | 1 | null | null | 4 | 5074 |
### Context:
I am analysing some impact assessment data (measuring invertebrate richness in response to pollution), but they are unbalanced - there are not data for every site at sampling occasion, and there were more datapoints recorded after the impact than before the impact.
I am a new user to R, and have gather... | How do I set up an unbalanced repeated measures analysis in R? | CC BY-SA 3.0 | null | 2011-05-26T04:38:23.123 | 2011-10-24T01:00:49.183 | 2011-07-25T22:05:14.627 | 930 | 4758 | [
"r",
"mixed-model",
"repeated-measures"
] |
11258 | 2 | null | 11253 | 8 | null | Are you familiar with [Simpson's paradox](http://en.wikipedia.org/wiki/Simpson%27s_paradox)? This would seem to be what you're observing here.
Edit: I didn't answer your question :) What exactly you should do is to some degree context dependent (Are the groups meaningful? Does this represent a problem in the study desi... | null | CC BY-SA 3.0 | null | 2011-05-26T05:12:45.137 | 2011-05-26T05:12:45.137 | null | null | 26 | null |
11259 | 1 | null | null | 8 | 237 | [Related to my earlier question](https://stats.stackexchange.com/questions/11256/regression-on-a-non-normal-dependent-variable), I need to perform regression on a skewed dependent variable (n = 500). Since the residuals weren't normally distributed, I was able to transform the DV non-linearly in a way that it now appro... | Does similarity of coefficients and p-values regardless of whether dependent variable is transformed suggest untransformed model is reliable? | CC BY-SA 3.0 | null | 2011-05-26T05:17:10.670 | 2011-09-28T15:44:29.120 | 2017-04-13T12:44:52.660 | -1 | 4754 | [
"regression"
] |
11260 | 2 | null | 11256 | 7 | null | The normality assumption is a convenient property of model's residuals, since it enables correct inferences about the estimated parameters and critical values of many other tests are also dependent on this assumption (therefore some corrections should be made, or you may roughly take more strict rule-of-thumb criteria,... | null | CC BY-SA 3.0 | null | 2011-05-26T05:26:42.400 | 2011-05-26T05:26:42.400 | null | null | 2645 | null |
11262 | 1 | null | null | 3 | 33 | This question is motivated by a issue regarding network motifs. To determine if a (connected, induced) subgraph $H$ occurs with significantly high frequency in an input network $G$, we generate an ensemble of comparison networks similar to $G$ and count the number of occurences of $H$ in them. Thus we obtain the numb... | Methods for checking if the number of comparisons is sufficient | CC BY-SA 3.0 | null | 2011-05-26T05:50:38.113 | 2021-11-12T22:32:35.583 | 2021-11-12T22:32:35.583 | 11887 | 386 | [
"statistical-significance",
"networks"
] |
11263 | 1 | 11295 | null | 1 | 2345 | I'm sure this is a pretty standard statistics question, but I'm no expert... I'm running an A/B test on my website to see if a change results in users adding more content. So there are 2 basic things I'm looking at; the # of users adding at least 1 piece of content and the total # of pieces of content added by all us... | Statistical significance when A/B test has multiple values | CC BY-SA 3.0 | null | 2011-05-26T06:28:43.387 | 2012-11-11T21:32:33.883 | null | null | 4759 | [
"confidence-interval",
"statistical-significance",
"sample-size",
"chi-squared-test",
"ab-test"
] |
11264 | 2 | null | 11263 | -1 | null | Applying binomial distribution you can calculate variance out of number of events.
Formula for standard deviation from binomial distribution is
```
sigma = sqrt( n * p * (1-p) )
```
where n is number of all events and p is probability of event you observing.
To be sure 95% you have to be at lest 2 sigma away from pr... | null | CC BY-SA 3.0 | null | 2011-05-26T07:27:25.430 | 2011-05-26T07:33:00.320 | 2011-05-26T07:33:00.320 | 4738 | 4738 | null |
11265 | 2 | null | 11246 | 6 | null | Just to add to Frank's points and paint a somewhat finer picture: CART/RPART is indeed highly exploratory and adding a p-value is difficult. I have seen some rare cases where people tried to use bootstrapping to get such p-value but I agree with Frank that it's not worth the effort.
As for combining statistical inferen... | null | CC BY-SA 3.0 | null | 2011-05-26T07:49:23.200 | 2011-05-26T07:49:23.200 | null | null | 4760 | null |
11266 | 1 | 11272 | null | 14 | 1031 | I would like (in the distant future) to teach statistics to kids. For that matter, I'd be happy to know of software (obviously I am tending towards FOSS), or webapps, that are helpful in explaining statistical/probabilistic ideas to kids (or adults for that matter).
This can be used either by the instructor, the kids,... | Software (or webapps) for teaching kids statistics or probability? | CC BY-SA 3.0 | null | 2011-05-26T08:32:34.833 | 2016-12-21T01:56:36.627 | 2016-12-21T01:56:36.627 | 22468 | 253 | [
"probability",
"references",
"software",
"teaching"
] |
11267 | 2 | null | 11266 | 5 | null | GGobi
Help teach interactive data visualization. Including - histograms, scatter plots (2d, 3d, multi-d), with brushing/linking etc.
Mostly for the teacher - less for the children (but still possible)
[http://www.ggobi.org/](http://www.ggobi.org/)
| null | CC BY-SA 3.0 | null | 2011-05-26T08:34:15.173 | 2011-05-26T08:34:15.173 | null | null | 253 | null |
11268 | 2 | null | 11266 | 7 | null | RcmdrPlugin.TeachingDemos: Rcmdr Teaching Demos Plug-In
Extending R with Rcmdr and give demos for probability and statistics ideas.
- Interactive: correlation and linear regression.
- Static: power of a test, confidence interval, central limit theorem.
Mostly for the teacher - less for the children
[http://cran.r-p... | null | CC BY-SA 3.0 | null | 2011-05-26T08:42:20.983 | 2011-05-26T09:06:15.867 | 2011-05-26T09:06:15.867 | 253 | 253 | null |
11269 | 2 | null | 11266 | 4 | null | animation: A Gallery of Animations in Statistics and Utilities to Create Animations
An R package. Enables the teacher to create many animation that can be made into webapps.
Great for the teacher to create a children webapp.
[http://cran.r-project.org/web/packages/animation/index.html](http://cran.r-project.org/web/pa... | null | CC BY-SA 3.0 | null | 2011-05-26T08:43:59.467 | 2011-05-26T08:43:59.467 | null | null | 253 | null |
11270 | 1 | null | null | 2 | 265 | My study is about customers’ perception of the effectiveness for a Malaysia corporate Weblog. In my study, customers’ perception of the effectiveness defined as perceived ease of use, perceived interactivity, and perceived trustworthiness; whereas the corporate Weblog defined as Weblog publishing software, Weblog comme... | How to analyse a study looking at relationship between one set of five items (predictors) and a second set of five items (outcomes) | CC BY-SA 3.0 | null | 2011-05-26T10:13:55.363 | 2017-03-06T16:52:53.847 | 2017-03-06T16:52:53.847 | 101426 | 4762 | [
"regression",
"spearman-rho"
] |
11271 | 2 | null | 11257 | 3 | null | I believe that your scenario is generally described as one of missing data, not as an unbalanced design, which is usually reserved for cases of unequal numbers of observations between independent groups. `ezANOVA()` from the [ez package](http://cran.r-project.org/web/packages/ez/index.html) can handle unbalanced design... | null | CC BY-SA 3.0 | null | 2011-05-26T11:05:46.140 | 2011-05-26T11:05:46.140 | null | null | 364 | null |
11272 | 2 | null | 11266 | 3 | null | [Videos](http://understandinguncertainty.org/view/videos) and [animations](http://understandinguncertainty.org/view/animations) from Understanding Uncertainty website.
| null | CC BY-SA 3.0 | null | 2011-05-26T12:05:30.677 | 2011-05-26T12:05:30.677 | null | null | 22 | null |
11273 | 1 | null | null | 6 | 9242 | In principle a simple question: What is the pull distribution?
(All I could find out is that it is the error-weighted distribution of estimators around the true value.)
I'd be interested in the precise mathematical definition, how, why, and when to use it, what is it expected to look like, and if both estimator values ... | What is the pull distribution? | CC BY-SA 3.0 | null | 2011-05-26T12:31:52.523 | 2012-04-21T19:07:03.437 | 2011-05-26T12:54:14.570 | 1512 | 1512 | [
"distributions",
"data-mining"
] |
11274 | 2 | null | 11270 | 3 | null | About your first question (using Spearman rank correlation with ordinal scales), I think you will find useful responses on this site (search for spearman, likert, ordinal or scale).
About your second question: As I understand the situation, for each dimension (what you call a "section"), you have a set of five question... | null | CC BY-SA 3.0 | null | 2011-05-26T12:38:05.190 | 2011-05-26T12:38:05.190 | 2017-04-13T12:44:41.493 | -1 | 930 | null |
11275 | 2 | null | 11256 | 8 | null | Ordinal regression is not affected by empty cells of Y. Quantile grouping is not required unless you just want to reduce computational burden. Proportional odds or continuation ratio ordinal logistic models are likely to be able to handle the distribution of Y you plotted (with no grouping of Y).
| null | CC BY-SA 3.0 | null | 2011-05-26T12:42:05.990 | 2011-05-26T12:42:05.990 | null | null | 4253 | null |
11276 | 2 | null | 6723 | 7 | null | Stepwise regression in the absence of penalization is frought with so many difficulties that I'm surprised people are still using it. The web has long lists of problems, starting with the extremely low probability of finding the "right" model.
| null | CC BY-SA 3.0 | null | 2011-05-26T12:43:37.610 | 2011-05-26T12:43:37.610 | null | null | 4253 | null |
11277 | 1 | 11324 | null | 5 | 176 | In a nutshell, here's what I have:
- Annual population estimates for the State
- Periodical (5 years) age, population, and basic census data per zones
Here's what I want to do:
- Create a simplistic model to generate the data for the missing years between the period for each zone, and have the total sums add up to... | Is there a simplistic model to disaggregate census data based on years and smaller zones? | CC BY-SA 3.0 | null | 2011-05-26T12:51:55.790 | 2011-05-27T23:41:14.597 | 2011-05-26T13:09:38.747 | 59 | 59 | [
"estimation",
"census"
] |
11278 | 2 | null | 11253 | 7 | null | I agree with JMS advice, that the answer is totally context dependent.
But what you are looking at may also be considered a [moderation effect](http://en.wikipedia.org/wiki/Moderation_%28statistics%29).
>
In statistics, moderation occurs when
the relationship between two variables
depends on a third variable.
... | null | CC BY-SA 3.0 | null | 2011-05-26T13:45:16.910 | 2011-05-26T13:45:16.910 | null | null | 442 | null |
11279 | 2 | null | 11249 | 10 | null | Although I would always recommend to use R, you could nevertheless achieve what you want with python.
There is at least a package for reading [dbf files](http://pypi.python.org/pypi/dbf/).
Furthermore, [scipy](http://www.scipy.org/) offers a great range of functions for statistical analysis. For example the library [Sc... | null | CC BY-SA 3.0 | null | 2011-05-26T13:55:58.253 | 2011-05-26T13:55:58.253 | null | null | 442 | null |
11280 | 1 | null | null | 3 | 1653 | I am new to R and some help would be of great use to me.
Basically, I need to perform a GLM analysis with negative binomial errors and with fixed factors, no covariates and no random effects. My factors are of type: year (1-4), site (1-3), sex (1-2), age (1-3), with a sample size of around 5000.
Currently I am fitting ... | Automatisation of GLM analysis with negative binomial errors | CC BY-SA 3.0 | null | 2011-05-26T14:51:26.587 | 2011-05-27T09:30:47.783 | 2011-05-26T16:02:15.300 | 930 | 4766 | [
"r",
"model-selection",
"generalized-linear-model"
] |
11281 | 2 | null | 4753 | 2 | null | One has to be careful about the meaning of the word sparse. Your matrix contains many zeroes and one may represent such a matrix in a sparse way (to save on storage). But since the figures represent co-occurrences these zeroes are still to be considered informative (they are not missing; they are not structurally zero)... | null | CC BY-SA 3.0 | null | 2011-05-26T15:05:23.787 | 2011-05-26T15:05:23.787 | null | null | 4767 | null |
11282 | 2 | null | 11280 | 3 | null | The `stepAIC` function in MASS can perform the kinds of variable selection you are looking for.
In addition, the `leaps` package appears to have similar capacities. That being said, I have not used it, so cannot speak directly on its efficacy.
| null | CC BY-SA 3.0 | null | 2011-05-26T15:41:30.293 | 2011-05-26T15:41:30.293 | null | null | 656 | null |
11283 | 1 | 11285 | null | 3 | 270 | I am running a logistic model on insurance data. I have a field agent gender which matters for a channel A (say) and doesn't matter for B. I want to put null values in case of B. The only thing I risk is exclusion by SAS (as SAS excludes every missing case by default). I heard that pairwise exclusion can solve my probl... | Pairwise exclusions | CC BY-SA 3.0 | null | 2011-05-26T16:26:07.750 | 2011-05-27T06:45:52.127 | 2011-05-27T06:45:52.127 | 2116 | 1763 | [
"modeling",
"dataset",
"sas"
] |
11284 | 2 | null | 11283 | 4 | null | There are two main types of traditional treatments of missing data.
These are:
1) listwise
2) pairwise
Listwise is (from what you have said) the default in SAS. It means that you exclude any observation that has missing values in any of the terms in your model.
The advantage of this is that it ensures that all variable... | null | CC BY-SA 3.0 | null | 2011-05-26T16:51:15.040 | 2011-05-26T16:51:15.040 | null | null | 656 | null |
11285 | 2 | null | 11283 | 6 | null | This doesn't sound like a missing data problem to me: it sounds like a question of model structure.
Distilling it to its essence, it seems you have two independent categorical variables gender ($X$, say) and "channel" ($Y$) and a binary response ($Z$). Conceptually the model is
$$logit(\Pr(Z=1)) = \beta_0 + \beta_1 X ... | null | CC BY-SA 3.0 | null | 2011-05-26T18:11:50.257 | 2011-05-26T18:11:50.257 | null | null | 919 | null |
11286 | 2 | null | 10890 | 15 | null | I agree with @Michael's description of endogeneity---this is about a problem with the variables that you include and their relationship to the variables that you do not (i.e., the stuff in the error term).
Unobserved heterogeneity is typically about unobservable componenents of the effects that you are estimating. Con... | null | CC BY-SA 3.0 | null | 2011-05-26T18:19:51.773 | 2011-05-26T18:19:51.773 | null | null | 401 | null |
11287 | 2 | null | 11072 | 2 | null | The $R^2$ in regression is given that name because it is the correlation between $y_i$ and $\hat{y}_i$ squared. You could calculate the correlation between $z_i$ and $\hat{z}_i$ in your case, square it, and use that as a measure of goodness-of-fit. I can't say what the statistical properties of this measure will be for... | null | CC BY-SA 3.0 | null | 2011-05-26T18:25:22.063 | 2011-05-26T18:25:22.063 | null | null | 401 | null |
11288 | 2 | null | 10613 | 35 | null | Under the null hypothesis, your test statistic $T$ has the distribution $F(t)$ (e.g., standard normal). We show that the p-value $P=F(T)$ has a probability distribution
$$\begin{equation*} \Pr(P < p) = \Pr(F^{-1}(P) < F^{-1}(p)) = \Pr(T < t) \equiv p; \end{equation*}$$
in other words, $P$ is distributed uniformly. This... | null | CC BY-SA 3.0 | null | 2011-05-26T18:50:27.493 | 2011-05-27T00:19:15.937 | 2011-05-27T00:19:15.937 | 401 | 401 | null |
11289 | 1 | null | null | 37 | 2603 | I am looking for some probability inequalities for sums of unbounded random variables. I would really appreciate it if anyone can provide me some thoughts.
My problem is to find an exponential upper bound over the probability that the sum of unbounded i.i.d. random variables, which are in fact the multiplication of two... | Probability inequalities | CC BY-SA 3.0 | null | 2011-05-26T19:27:05.283 | 2019-10-06T16:06:34.740 | 2018-09-26T08:43:57.307 | 11887 | 4770 | [
"probability",
"mathematical-statistics",
"probability-inequalities",
"moment-generating-function"
] |
11290 | 1 | 11314 | null | 10 | 6930 | I have come across the sampling method called "Propensity Weighting Sampling/RIM", but I do not have a good idea of what these survey methods are all about.
What references in the literature cover this topic?
| What is a propensity weighting sampling / RIM? | CC BY-SA 3.0 | null | 2011-05-26T20:06:04.120 | 2016-07-10T21:14:22.153 | 2012-12-16T09:36:23.267 | 3826 | 4278 | [
"sampling",
"weighted-sampling"
] |
11291 | 2 | null | 11236 | 5 | null | From the [documentation for qqmath](http://stat.ethz.ch/R-manual/R-devel/library/lattice/html/qqmath.html) it seems that the default behavior is to compare the empirical quantiles to those of a normal distribution. So what the QQ plot for $\sigma^2$ (which is the error variance) is telling you is that its marginal post... | null | CC BY-SA 3.0 | null | 2011-05-26T20:15:53.020 | 2011-05-26T20:15:53.020 | null | null | 26 | null |
11292 | 1 | null | null | 3 | 482 | I am performing a retrospective study on patients looking at the size of their nostril (continuous variable measured in millimetres) and the need for treatment which is either conservative or surgical (this is a categorical variable).
Sample size is only 15.
What would be the right test to compare groups to determine i... | How to compare two groups of patients with a continuous outcome? | CC BY-SA 3.0 | null | 2011-05-26T21:13:05.773 | 2011-05-27T12:59:13.753 | 2011-05-27T12:59:13.753 | 183 | 4772 | [
"statistical-significance"
] |
11293 | 2 | null | 11253 | 5 | null | The previous comments are all good, but with group sample sizes of 5, 7, and 11, I wouldn't trust any of their correlations as far as I could throw them. You'll need to give the overall r a wide confidence interval as well. btw Nice job on the graph.
| null | CC BY-SA 3.0 | null | 2011-05-26T21:28:02.027 | 2011-05-26T21:28:02.027 | null | null | 2669 | null |
11294 | 2 | null | 11292 | 8 | null | Note that the sample size is very small and it is therefore higly likely that you run into power problems if you get a nonsignificant result. Therefore, definitely report the effect size whatever the result of your test will be (i.e., the differences in nostril size between the two treatment groups, in relation to the ... | null | CC BY-SA 3.0 | null | 2011-05-26T21:36:45.680 | 2011-05-26T21:36:45.680 | null | null | 442 | null |
11295 | 2 | null | 11263 | 3 | null | The perspective Ralu is using is basically p is the probability of A and for the binomial he's saying you have the events A and not A which for you is B and that's your event space. Since you don't know your actual value for P(A) and assuming you don't have a good guess for it you'll want to use a conservative estimate... | null | CC BY-SA 3.0 | null | 2011-05-26T21:39:40.440 | 2011-05-26T21:39:40.440 | null | null | 4325 | null |
11296 | 1 | null | null | 20 | 64366 | I have a table with four groups (4 BMI groups) as the independent variable (factor). I have a dependent variable that is "percent mother smoking in pregnancy".
Is it permissible to use ANOVA for this or do I have to use chi-square or some other test?
| Using ANOVA on percentages? | CC BY-SA 3.0 | null | 2011-05-27T00:39:52.903 | 2021-02-18T14:43:35.973 | 2021-02-18T14:43:35.973 | 11887 | 4774 | [
"anova",
"percentage"
] |
11297 | 2 | null | 11296 | 21 | null | It depends on how close the responses within different groups are to 0 or 100%. If there are a lot of extreme values (i.e. many values piled up on 0 or 100%) this will be difficult. (If you don't know the "denominators", i.e. the numbers of subjects from which the percentages are calculated, then you can't use contin... | null | CC BY-SA 3.0 | null | 2011-05-27T01:05:52.183 | 2011-05-27T01:05:52.183 | null | null | 2126 | null |
11298 | 2 | null | 11296 | 23 | null | There is a difference between having a binary variable as your dependent variable and having a proportion as your dependent variable.
- Binary dependent variable:
This sounds like what you have. (i.e., each mother either smoked or she did not smoke)
In this case I would not use ANOVA. Logistic regression with some f... | null | CC BY-SA 3.0 | null | 2011-05-27T02:23:47.557 | 2011-05-27T02:23:47.557 | null | null | 183 | null |
11299 | 1 | null | null | 6 | 170 |
### Context:
I'm investigating behaviour in a clinical study involving children. I had both parents and teachers completing questionnaires to inform an understanding of the same underlying constructs, for example reactive aggression.
At the conclusion of data collection I have parent data in all cases, n=55, and t... | Getting an average measurement based on two raters for cases where data is missing for one rater | CC BY-SA 3.0 | null | 2011-05-27T04:08:42.267 | 2011-05-27T10:02:06.747 | 2011-05-27T06:25:27.220 | 183 | 4775 | [
"spss",
"missing-data",
"data-imputation"
] |
11300 | 1 | 11306 | null | 3 | 62 | I have a particular semiparametric model which I'm fitting via MCMC. One of the model parameters I have "semiparametric'ed" away (say $\alpha$) is known to lie between two other parameters, $\theta_1$ and $\theta_2$. Since I have a series of samples $(\theta_1^t, \theta_2^t)$ I also have a series of interval estimates ... | Summarizing samples of an interval | CC BY-SA 3.0 | null | 2011-05-27T04:59:18.913 | 2011-05-27T09:12:29.650 | null | null | 26 | [
"estimation"
] |
11302 | 2 | null | 8903 | 2 | null | According to [Wikipedia's article of tf-idf](http://en.wikipedia.org/wiki/Tf-idf):
>
The term count in the given document is simply the number of times a given term appears in
that document. This count is usually normalized to prevent a bias towards longer documents
(which may have a higher term count regardless ... | null | CC BY-SA 3.0 | null | 2011-05-27T06:31:45.607 | 2011-05-27T06:31:45.607 | null | null | 4777 | null |
11303 | 2 | null | 11231 | 2 | null | The classic problem with PCR is that principal components corresponding to small eigenvalues (and hence discarded) can be significant for explaining the dependent variable. One of the solutions to this problem is to use [PLS regression](http://en.wikipedia.org/wiki/Partial_least_squares_regression). In PLS regression t... | null | CC BY-SA 3.0 | null | 2011-05-27T06:59:20.680 | 2011-05-27T06:59:20.680 | null | null | 2116 | null |
11304 | 2 | null | 11255 | 13 | null | Disclaimer: I consider myself an experiemtal psychologist with an emphasis on experimental. Hence, I have a natural unease with designs like this.
To answer your first and second question: I think for a design like this a SEM or, depending on the number of variables involved, mediation or moderation analyses is the nat... | null | CC BY-SA 3.0 | null | 2011-05-27T08:08:38.927 | 2011-05-27T08:08:38.927 | null | null | 442 | null |
11305 | 2 | null | 726 | 32 | null | >
"The first time I was in a statistics course, I was there to teach it"
John Tukey ([link](http://www.stat.berkeley.edu/~brill/Papers/life.pdf))
| null | CC BY-SA 3.0 | null | 2011-05-27T08:11:49.203 | 2011-05-27T08:11:49.203 | null | null | 74 | null |
11306 | 2 | null | 11300 | 1 | null | Let's see:
$$
P(\alpha) = \int P(\alpha, \theta_1, \theta_2) d\theta_1 d\theta_2
= \int P(\alpha | \theta_1, \theta_2) P(\theta_1, \theta_2) d\theta_1 d\theta_2
$$
With the good help of Monte Carlo, we can approximate this as
$$
\frac{1}{n} \sum_t P(\alpha | \theta_1^t, \theta_2^t)
$$
With the grid trick, you are... | null | CC BY-SA 3.0 | null | 2011-05-27T09:12:29.650 | 2011-05-27T09:12:29.650 | null | null | 4257 | null |
11307 | 2 | null | 11280 | 2 | null | See also the [glmulti](http://cran.r-project.org/web/packages/glmulti/index.html) package on CRAN and the accompanying [JSS paper](http://www.jstatsoft.org/v34/i12/paper):
`glmulti` provides a wrapper for `glm` and similar functions (`glm.nb`, etc.), automatically generating all possible models (under constraints set b... | null | CC BY-SA 3.0 | null | 2011-05-27T09:30:47.783 | 2011-05-27T09:30:47.783 | null | null | 103 | null |
11308 | 2 | null | 11299 | 4 | null | The idea above sounds rather like single imputation. This is a better idea when faced with missing data than either list-wise or pair-wise deletion. However, its still not a good approach.
A better approach could be multiple imputation. Essentially, you simulate from 3-10 datasets conditional on your observed data. Yo... | null | CC BY-SA 3.0 | null | 2011-05-27T10:02:06.747 | 2011-05-27T10:02:06.747 | null | null | 656 | null |
11309 | 1 | null | null | 2 | 235 | I am wondering what the proper term is, for when a table like this (where values that did not occur are omitted entirely):
```
________ _______
| Length | Count |
|--------|-------|
| 1 | 5 |
| 3 | 2 |
| 6 | 12 |
|________|_______|
```
Is rewritten like this (where values that did not occur ... | What do you call adding zeros to a table of frequency counts of consecutive integers where the given integer does not occur | CC BY-SA 3.0 | null | 2011-05-27T12:17:11.043 | 2016-04-07T11:43:22.660 | 2016-04-07T11:43:22.660 | 22228 | 4781 | [
"data-transformation",
"tables",
"presentation"
] |
11310 | 1 | 16875 | null | 6 | 10629 | I've found critical values for the Anderson Darling test for a Normal Distribution at 1%, 2.5%, 5%, 10% and 15% significance levels from various sources, including wikipedia:
[http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test](http://en.wikipedia.org/wiki/Anderson%E2%80%93Darling_test)
I'd really like a critic... | Critical values for Anderson-Darling test | CC BY-SA 3.0 | null | 2011-05-27T13:30:12.590 | 2017-01-10T06:02:58.947 | 2011-05-27T14:02:45.477 | null | 4780 | [
"distributions",
"hypothesis-testing",
"normal-distribution"
] |
11311 | 2 | null | 11310 | 5 | null | You can use simulation (this is not a new idea, it is how Gosset/Student derived the original t table (but we have faster tools than he did)).
Generate a psuedo random sample from a normal distribution (or at least as close as the computer can come) of the sample size of interest and compute the Anderson Darling Statis... | null | CC BY-SA 3.0 | null | 2011-05-27T15:04:37.847 | 2011-05-27T15:04:37.847 | null | null | 4505 | null |
11312 | 2 | null | 11309 | 2 | null | Mapping a set of observed value onto the observable values expected for a given variable?
That is, a variable is characterized by all hypothetical values that can be observed when using it, but observed values may not reflect the full range of possible values. For example, when collecting n=100 discrete scores on a 0-... | null | CC BY-SA 3.0 | null | 2011-05-27T15:27:04.487 | 2011-05-27T15:27:04.487 | null | null | 930 | null |
11313 | 2 | null | 726 | 8 | null | >
"He who loves practice without theory
is like the sailor who boards ship
without a rudder and compass and never
knows where he may be cast."
- Leonardo da Vinci, 1452-1519
Found [here](http://socserv.mcmaster.ca/jfox/).
| null | CC BY-SA 3.0 | null | 2011-05-27T15:30:01.647 | 2011-05-27T15:30:01.647 | null | null | 253 | null |
11314 | 2 | null | 11290 | 9 | null | You may know that weighting generally aims at ensuring that a given sample is representative of its target population. If in your sample some attributes (e.g., gender, SES, type of medication) are less well represented than in the population from which the sample comes from, then we may adjust the weights of the incrim... | null | CC BY-SA 3.0 | null | 2011-05-27T16:14:16.013 | 2016-07-10T21:14:22.153 | 2016-07-10T21:14:22.153 | 43080 | 930 | null |
11315 | 1 | 11323 | null | 15 | 11474 | So when I assume that the error terms are normally distributed in a linear regression, what does it mean for the response variable, $y$?
| How does the distribution of the error term affect the distribution of the response? | CC BY-SA 3.0 | null | 2011-05-27T16:14:56.817 | 2011-05-28T23:34:21.947 | 2011-05-27T18:37:16.203 | 930 | 4496 | [
"regression",
"distributions"
] |
11316 | 2 | null | 11315 | 19 | null | The short answer is that you cannot conclude anything about the distribution of $y$, because it depends on the distribution of the $x$'s and the strength and shape of the relationship. More formally, $y$ will have a "mixture of normals" distribution, which in practice can be pretty much anything.
Here are two extreme e... | null | CC BY-SA 3.0 | null | 2011-05-27T16:36:35.590 | 2011-05-28T02:52:27.773 | 2011-05-28T02:52:27.773 | 279 | 279 | null |
11318 | 2 | null | 11315 | 8 | null | We invent the error term by imposing a fictitious model on real data; the distribution of the error term does not affect the distribution of the response.
We often assume that the error is distributed normally and thus try to construct the model such that our estimated residuals are normally distributed. This can be di... | null | CC BY-SA 3.0 | null | 2011-05-27T16:54:10.607 | 2011-05-28T19:05:10.253 | 2011-05-28T19:05:10.253 | 3874 | 3874 | null |
11319 | 1 | 11332 | null | 4 | 256 | I have an experiment where people click on different ads online. My measure is click counts.
I end up finding that I should use models for count data such as Poisson, Quasi-Poisson, or Negative Binomial regression.
Is there a standard in marketing regarding what model should be used for click counts?
Thanks
| Is there a standard procedure or regression model in marketing for explaining click rates on ads? | CC BY-SA 3.0 | null | 2011-05-27T16:57:08.000 | 2011-05-28T17:17:05.713 | 2011-05-27T20:18:44.890 | 930 | 4679 | [
"poisson-distribution",
"count-data"
] |
11320 | 2 | null | 726 | 17 | null | >
The Earth is round. p < .05
Jacob Cohen
| null | CC BY-SA 3.0 | null | 2011-05-27T18:19:55.340 | 2011-05-27T18:19:55.340 | null | null | 686 | null |
11321 | 2 | null | 726 | 17 | null | >
When I see articles with lots of
significance tests, I say that the
statisticians are p-ing on the
research.
Herman Friedmann (by recollection, he said this in class)
| null | CC BY-SA 3.0 | null | 2011-05-27T18:21:27.587 | 2011-05-27T18:21:27.587 | null | null | 686 | null |
11322 | 1 | 28503 | null | 8 | 935 | Here is a recent Google correlate query:
[http://www.google.com/trends/correlate/search?e=internet+usage&t=weekly#](http://www.google.com/trends/correlate/search?e=internet+usage&t=weekly#)
As you can see in the search box at that link, I entered "internet usage" and Google did the rest. It shows a value of 0.9298 as t... | What method is used in Google's correlate? | CC BY-SA 3.0 | null | 2011-05-27T20:07:41.690 | 2012-05-26T07:08:01.567 | 2012-05-26T07:08:01.567 | 5505 | 2775 | [
"time-series",
"correlation"
] |
11323 | 2 | null | 11315 | 8 | null | Maybe I'm off but I think we ought to be wondering about $f(y|\beta, X)$, which is how I read the OP. In the very simplest case of linear regression if your model is $y=X\beta + \epsilon$ then the only stochastic component in your model is the error term. As such it determines the sampling distribution of $y$. If $\eps... | null | CC BY-SA 3.0 | null | 2011-05-27T23:07:30.837 | 2011-05-27T23:07:30.837 | null | null | 26 | null |
11324 | 2 | null | 11277 | 3 | null | About the simplest thing you can do is interpolate normalized counts over time and (almost) the simplest form of interpolation is linear.
Specifically, suppose $y_i$ is the state population at time $i$ and $x_i$ is some other count (by age, tract, or whatever). Define $\xi_i = x_i/y_i$. Suppose $i$ is a year for whic... | null | CC BY-SA 3.0 | null | 2011-05-27T23:41:14.597 | 2011-05-27T23:41:14.597 | null | null | 919 | null |
11325 | 2 | null | 726 | 6 | null | >
Statistics' real contribution to society is primarily moral, not technical.
Steve Vardeman and Max Morris
| null | CC BY-SA 3.0 | null | 2011-05-28T13:02:46.950 | 2012-06-20T18:18:51.487 | 2012-06-20T18:18:51.487 | 1381 | 2669 | null |
11326 | 2 | null | 11315 | 2 | null | If you write out the response as
$$\bf{y}=m+e$$
Where $\bf{m}$ is the "model" (the prediction for $\bf{y}$) and $\bf{e}$ is the "errors", then this can be re-arranged to indicate $\bf{y}-m=e$. So assigning a distribution for the errors is the same thing as indicating the ways your model is incomplete. To put it anoth... | null | CC BY-SA 3.0 | null | 2011-05-28T13:14:20.353 | 2011-05-28T23:34:21.947 | 2011-05-28T23:34:21.947 | 2392 | 2392 | null |
11327 | 1 | 11330 | null | 5 | 2337 | We've created a survey asking students, among other things, their GPA (=weighted average of grades) and their marks in some specific courses (which count towards GPA).
We wanted to see which regressors influence the GPA using a simple OLS model. Is it sensible to use a formula like this?
```
GPA ~ grade_maths + grade_s... | Dependent variable is a function of independent variables; can I sensibly include them in a regression? | CC BY-SA 3.0 | null | 2011-05-28T13:37:12.280 | 2011-05-30T08:16:18.460 | 2011-05-30T07:44:27.810 | 2116 | 4788 | [
"regression",
"least-squares"
] |
11328 | 1 | null | null | 0 | 85 | >
Possible Duplicate:
Wrong results using ANOVA with repeated measures
Hello everybody,
I did an experiment and I need to understand how to detect, by means of an ANOVA (repeated measures), the differences between males and females evaluations at stimulus level.
In the experiment, participants had to evaluate 7 sti... | Detecting significant differences for each stimulus using ANOVA repeated measures | CC BY-SA 3.0 | null | 2011-05-28T13:41:53.917 | 2011-05-28T13:41:53.917 | 2017-04-13T12:44:39.283 | -1 | 4701 | [
"anova",
"repeated-measures",
"t-test"
] |
11329 | 1 | 14862 | null | 7 | 387 | Suppose we have a set $S$ consisting of $p$ features, and a subset $S_+$ of the features are positive. If $Q$ is any subset of $S$, define the false positive rate as the proportion of features in $Q$ which are not positive:
$$FPR[Q] = 1 - \frac{|Q \cap S_+|}{|Q|}$$
where $|\cdot|$ denotes cardinality. If $Q$ is a fun... | Inverse of false discovery rate (FDR) | CC BY-SA 3.0 | null | 2011-05-28T13:56:54.953 | 2011-12-06T01:06:12.233 | 2011-05-29T01:03:15.980 | 3567 | 3567 | [
"error",
"multiple-comparisons"
] |
11330 | 2 | null | 11327 | 2 | null | I see no problem with fitting the regression. We do regressions because we believe that the predictors may be related to the response, you just have more knowledge to begin with.
But what questions are you actually trying to answer? The fact that certain coefficients are significant is not surprising, so those were n... | null | CC BY-SA 3.0 | null | 2011-05-28T15:38:07.043 | 2011-05-28T15:38:07.043 | null | null | 4505 | null |
11331 | 1 | null | null | 3 | 1837 | I asked this on the mathematics site, but now I think this is a better place. Sorry for the cross-post.
Given any line graph, is there a reliable way to identify any sort of regular oscillation?
Let's assume I'm charting the prevalence of different species of animals in a single location, over the span of several year... | Identifying oscillation in a time series | CC BY-SA 3.0 | null | 2011-05-28T16:04:40.950 | 2022-08-30T19:38:44.387 | 2011-05-28T22:28:15.937 | 4792 | 4792 | [
"time-series",
"data-visualization"
] |
11332 | 2 | null | 11319 | 4 | null | You can use Poisson regression and in more general form, Poisson process when data is following a Poisson distribution. In terms of Bayesian inference, you can make your own likelihood model, and then by conjugating your Poisson prior by likelihood, derive your posterior distribution.
Here I borrow an example from glm ... | null | CC BY-SA 3.0 | null | 2011-05-28T17:17:05.713 | 2011-05-28T17:17:05.713 | null | null | 4581 | null |
11333 | 2 | null | 11248 | 1 | null | I don't know if I am understanding correctly your question. But I guess you may use the posterior density to assess the uncertainty around point estimates like the mean. You may plot a histogram, calculate standard deviations. This is easy to do, if you have the MCMC output. Just take the values sampled (after a burnin... | null | CC BY-SA 3.0 | null | 2011-05-28T18:12:47.033 | 2011-05-31T22:49:06.153 | 2011-05-31T22:49:06.153 | 3058 | 3058 | null |
11334 | 2 | null | 11248 | 3 | null | Don't use the mean of the sampled coefficients for making predictions, instead compute the predictions for logistic regression models with all of the sampled coefficient vectors and take the mean of those predictions (or better still treat the predictions for all sampled coefficient vectors as the posterior distributio... | null | CC BY-SA 3.0 | null | 2011-05-28T18:57:08.913 | 2011-05-28T18:57:08.913 | null | null | 887 | null |
11335 | 2 | null | 11327 | 4 | null | Another point to consider: what enables a student to do well in one course is related to what enables him/her to do well in another. There are overarching factors (cognitive, personality, circumstances) that play some role in determining each of the individual course grades. So to use regression--to see how X1 relat... | null | CC BY-SA 3.0 | null | 2011-05-28T20:45:28.527 | 2011-05-29T12:07:32.897 | 2011-05-29T12:07:32.897 | 2669 | 2669 | null |
11336 | 1 | 11345 | null | 6 | 2023 | I have a multinomial model estimated with the zelig package in R. Whenever I try to use the setx() command, I get an error message saying there is more than one mode. So in stead of using Zelig, I thought I would do it the hard way. I used the instructions [here](http://www.ats.ucla.edu/stat/r/dae/mlogit.htm), but I am... | Predicted probabilities from a multinomial regression model using zelig and R | CC BY-SA 3.0 | null | 2011-05-28T23:03:15.690 | 2011-05-30T02:45:08.173 | 2011-05-30T02:45:08.173 | 183 | 2704 | [
"r",
"probability",
"multinomial-distribution"
] |
11337 | 1 | 11340 | null | 7 | 7941 | A six-sided die is rolled 100 times. Using the normal approximation, find the probability that the face showing six turns up between 15 and 20 times. Find the probability that the sum of the face values of the 100 trials is less than 300.
For the first part of the question, I did the following:
$P(15 \le X \le 20) = \s... | Probability of a certain sum of values from a set of dice rolls | CC BY-SA 3.0 | null | 2011-05-28T23:35:40.667 | 2011-05-31T05:07:31.587 | 2011-05-31T05:07:31.587 | 183 | 4401 | [
"self-study",
"binomial-distribution",
"dice"
] |
11338 | 2 | null | 11337 | 4 | null | Due to the CLT, a sum of i.i.d. random variables is distributed:
$$
\sum_{i=1}^nX_i \sim N\left(\mu =n\cdot\mu_{X_i},\sigma^2 = n\cdot\sigma^2_{X_i}\right)
$$
The mean of a single dice roll ($X_i$) is 3.5 and the variance is 35/12.
That should help you find the answer.
| null | CC BY-SA 3.0 | null | 2011-05-29T00:02:10.430 | 2011-05-30T07:10:59.470 | 2011-05-30T07:10:59.470 | 2116 | 2310 | null |
11339 | 2 | null | 11331 | 6 | null | You may want to look at spectral analysis techniques. Look at,
[Shumway & Stoffer](http://rads.stackoverflow.com/amzn/click/144197864X) (among many other books which treat the subject) or look "Spectral analysis" in the Wikipedia for some pointers.
| null | CC BY-SA 3.0 | null | 2011-05-29T07:17:39.377 | 2011-05-29T07:17:39.377 | null | null | 892 | null |
11340 | 2 | null | 11337 | 4 | null | In the comments to Glen's answer you seem to have used a normal approximation `pnorm(300, 350, sqrt(3500/12))` to get 0.001707396. This is not a bad answer, though you can do better.
If you used the continuity correction the continuity correction `pnorm(299.5, 350, sqrt(3500/12))` you would get `0.001553355`. I suspe... | null | CC BY-SA 3.0 | null | 2011-05-29T10:52:08.007 | 2011-05-29T10:52:08.007 | null | null | 2958 | null |
11341 | 1 | null | null | 4 | 1881 | Is there a way to give (in R or Minitab or Statgraphics) a fractional factorial design like that and inspect the generators and the complete defining relation ($2^4 - 1$ relations)?
```
A B C D E F G H
-1 1 1 1 -1 -1 1 -1
-1 -1 -1 1 1 1 1 -1
-1 1 1 -1 1 1 -1 -1
-1 -1 ... | Inspect generators and defining relations of a fractional factorial design | CC BY-SA 3.0 | null | 2011-05-29T12:00:36.773 | 2017-07-31T14:53:42.900 | 2017-07-31T14:53:42.900 | 11887 | 339 | [
"r",
"experiment-design"
] |
11342 | 2 | null | 11341 | 3 | null | Disclaimer: Not really a positive answer...
Take a look at the [FrF2](http://cran.r-project.org/web/packages/FrF2/index.html) package, for example:
```
des.24 <- FrF2(16,8)
design.info(des.24)$aliased # look at the alias structure
```
create a randomized fractional design with 8 factors, 16 runs. To print al... | null | CC BY-SA 3.0 | null | 2011-05-29T12:25:29.513 | 2011-05-30T08:39:55.627 | 2011-05-30T08:39:55.627 | 930 | 930 | null |
11343 | 2 | null | 11248 | 3 | null | I wouldn't use the means at all for the classifier. You don't need to apply "corrections" or to "smooth out" a Bayesian solution, it is the optimal one for the prior information and data that you have actually used. But the means can be useful for giving you a feel for which combinations of regressor variables are li... | null | CC BY-SA 3.0 | null | 2011-05-29T12:42:17.257 | 2011-05-29T12:42:17.257 | null | null | 2392 | null |
11344 | 2 | null | 11296 | 11 | null | You need to have the raw data, so that the response variable is 0/1 (not smoke, smoke). Then you can use binary logistic regression. It is not correct to group BMI into intervals. The cutpoints are not correct, probably don't exist, and you are not officially testing whether BMI is associated with smoking. You are ... | null | CC BY-SA 3.0 | null | 2011-05-29T13:18:28.267 | 2011-05-29T13:18:28.267 | null | null | 4253 | null |
11345 | 2 | null | 11336 | 3 | null | The first thing to do is to construct the "linear predictors" or "logits" for each category for each prediction. So you have your model equation:
$$\eta_{ir}=\sum_{j=1}^{p}X_{ij}\hat{\beta}_{jr}\;\; (i=1,\dots,m\;\; r=1,\dots,R)$$
Where for notational convenience, the above is to be understood to have $\hat{\beta}_{jR... | null | CC BY-SA 3.0 | null | 2011-05-29T15:32:53.610 | 2011-05-29T15:32:53.610 | null | null | 2392 | null |
11346 | 1 | 11350 | null | 4 | 1064 | This is probably a pretty simple question, but I have been having some trouble interpreting the documentation for the `predict` function.
I am generating a simple linear model from a data frame containing (X, Y) pairs, which I would then like to use to predict Y given new X. My code looks something like this:
```
my_lm... | Obtaining predictions from linear model | CC BY-SA 3.0 | null | 2011-05-29T17:20:28.353 | 2011-05-29T18:13:04.837 | null | null | 3031 | [
"r",
"linear-model"
] |
11347 | 1 | 11349 | null | 0 | 1471 | I want to jointly estimate a very simple MV-Normal two-dimensional AR[1] process,
$[x_t,y_t]=[x_{t-1},y_{t-1}]+\text{[Bivariate Gaussian error]}$, in BUGS. But the syntax has been impossible to figure out. Here's the problem part of the code:
```
## transition model (aka random walk prior)
for(i in 2:NPERIODS1){ ... | Multivariate random walks in BUGS | CC BY-SA 3.0 | null | 2011-05-29T17:35:12.357 | 2012-07-19T06:52:44.510 | 2011-05-29T18:57:29.510 | 919 | 996 | [
"time-series",
"multivariate-analysis",
"markov-chain-montecarlo",
"bugs"
] |
11348 | 2 | null | 11242 | 1 | null | First: Why can't you get the raw data from the GSS? It's easily available. Fail that, you can work with ANES or with the US sample of the World Value Survey. Or raw exit poll data. If you need academic access to get the files, contact me.
Second: The poly-sci way to do this is to run the Ideal or OC to construct a d-d... | null | CC BY-SA 3.0 | null | 2011-05-29T18:01:20.593 | 2011-05-29T18:01:20.593 | null | null | 996 | null |
11349 | 2 | null | 11347 | 1 | null | Have you tried replacing omega[,] with omega[1:2,1:2]? I haven't got BUGS here but IIRC that's what it expects inside dmnorm.
| null | CC BY-SA 3.0 | null | 2011-05-29T18:11:00.007 | 2011-05-29T18:11:00.007 | null | null | 26 | null |
11350 | 2 | null | 11346 | 5 | null | The `type` argument specifies if you want predictions of the response (the $Y$ variable) or if you want predictions for the individual terms in the model. In combination with the `terms` argument you can get predictions for some or all (default) of the terms.
In your example, there is just one term in addition to the ... | null | CC BY-SA 3.0 | null | 2011-05-29T18:13:04.837 | 2011-05-29T18:13:04.837 | null | null | 4376 | null |
11351 | 1 | 11352 | null | 12 | 6653 | This is pretty hard for me to describe, but I'll try to make my problem understandable. So first you have to know that I've done a very simple linear regression so far. Before I estimated the coefficient, I watched the distribution of my $y$. It is heavy left skewed. After I estimated the model, I was quite sure to obs... | Left skewed vs. symmetric distribution observed | CC BY-SA 3.0 | null | 2011-05-29T20:14:17.173 | 2014-03-09T16:22:20.847 | 2014-03-09T16:22:20.847 | 36515 | 4496 | [
"regression",
"residuals",
"skewness"
] |
11352 | 2 | null | 11351 | 25 | null | To answer your question, let's take a very simple example. The simple regression model is given by $y_i = \beta_0 + \beta_1 x_i + \epsilon_i$, where $\epsilon_i \sim N(0,\sigma^2)$. Now suppose that $x_i$ is dichotomous. If $\beta_1$ is not equal to zero, then the distribution of $y_i$ will not be normal, but actually ... | null | CC BY-SA 3.0 | null | 2011-05-29T21:06:10.660 | 2011-05-29T21:06:10.660 | null | null | 1934 | null |
11353 | 1 | null | null | 3 | 520 | I have hundreds of explanatory variables and under 100 observations (saturated data set). I'd like to create a linear model in which I have two or so composite variables made up of a dozen of the explanatory variables each. How do I find the best variables to use for the composites without going through every combin... | How do I find the best model with a saturated dataset? | CC BY-SA 3.0 | null | 2011-05-29T23:16:54.367 | 2011-05-31T13:35:40.367 | 2011-05-30T06:07:01.963 | 2116 | 4798 | [
"regression",
"modeling"
] |
11355 | 2 | null | 11353 | 2 | null | How about factor analysis over your variables ? That will give you the list of variables which behave similarly to give you a latent variable. Apart from this you should also run multicollinearity diagonistics to detect collinearity in your present list of 100 variables. Chances are high that many of your variables wou... | null | CC BY-SA 3.0 | null | 2011-05-30T07:03:04.490 | 2011-05-30T07:03:04.490 | null | null | 1763 | null |
11356 | 2 | null | 11353 | 2 | null | The standard answer when determining the "best" linear combinations of variables is [principal component analysis](http://en.wikipedia.org/wiki/Principal_component_analysis). Its regression counterparts are [principal components regression](http://en.wikipedia.org/wiki/Principal_component_regression) and [partial least... | null | CC BY-SA 3.0 | null | 2011-05-30T07:08:04.773 | 2011-05-30T07:08:04.773 | null | null | 2116 | null |
11358 | 2 | null | 11327 | 3 | null | Strong exogeneity is a term related to dynamic models, i.e. when there is time-series data involved. Since you are doing one-time survey, this term does not apply. What might be the problem with the regression though is [omitted variable bias](http://en.wikipedia.org/wiki/Omitted-variable_bias). Since GPA is a weighted... | null | CC BY-SA 3.0 | null | 2011-05-30T08:16:18.460 | 2011-05-30T08:16:18.460 | null | null | 2116 | null |
11359 | 1 | 11373 | null | 24 | 57959 | What is the primary reason that someone would apply the square root transformation to their data? I always observe that doing this always increases the $R^2$. However, this is probably just due to centering the data. Any thoughts are appreciated!
| What could be the reason for using square root transformation on data? | CC BY-SA 4.0 | null | 2011-05-30T08:47:46.340 | 2020-04-25T13:16:17.543 | 2020-04-25T13:16:17.543 | 273266 | 4496 | [
"regression",
"data-transformation",
"variance-stabilizing"
] |
11361 | 2 | null | 7249 | 7 | null | Try [Orange Canvas](http://orange.biolab.si/), it will give you option to build interactive decision tree.
| null | CC BY-SA 3.0 | null | 2011-05-30T09:13:13.920 | 2011-05-31T06:39:19.290 | 2011-05-31T06:39:19.290 | 2116 | 4802 | null |
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