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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5140 | 2 | null | 5115 | 27 | null | [Leo Breiman](http://en.wikipedia.org/wiki/Leo_Breiman) for CART, bagging, and random forests.
| null | CC BY-SA 3.0 | null | 2010-12-04T15:52:26.967 | 2011-09-22T23:12:26.383 | 2011-09-22T23:12:26.383 | 74 | null | null |
5144 | 1 | 5146 | null | 2 | 206 | I have an experiment with any possible (reasonable) number of parameters (independent variables). I run the experiment several times for each possible combination of my variables. The data I get will be generally numeric.
However I know nothing (and any assumptions are difficult) about the distribution of my data.
Wha... | Appropriate test for multivariate experiment result with unknown distributions | CC BY-SA 2.5 | null | 2010-12-04T18:47:22.257 | 2010-12-06T03:00:55.350 | 2010-12-06T03:00:55.350 | 2261 | 2261 | [
"experiment-design"
] |
5145 | 2 | null | 5115 | 48 | null | [William Sealy Gosset](http://en.wikipedia.org/wiki/William_Sealy_Gosset) for Student's t-distribution and the statistically-driven improvement of beer.
| null | CC BY-SA 2.5 | null | 2010-12-04T20:42:26.150 | 2010-12-04T20:42:26.150 | null | null | 2077 | null |
5146 | 2 | null | 5144 | 6 | null | Well, following your update, it seems you are dealing with a factorial experiment (factorial means that every factors are crossed, or, in other words, each unit is subjected to every possible combination of your factors), with five replicates. Let assume that these are not the same statistical units whose temperature i... | null | CC BY-SA 2.5 | null | 2010-12-04T21:13:59.517 | 2010-12-04T21:23:14.870 | 2010-12-04T21:23:14.870 | 930 | 930 | null |
5147 | 1 | 5148 | null | 2 | 1032 | When trying to find the mode of a nonnegative function $f$ (i.e. maximize the function), one way to do it is to sampling the function viewed as an unnormalized density of some distribution via MCMC.
Suppose we have had a sufficiently long sequence of samples via this method, I was wondering how to determine the mode fr... | Finding the mode of a function by MCMC sampling | CC BY-SA 2.5 | null | 2010-12-04T23:02:34.320 | 2010-12-05T00:01:21.373 | 2010-12-05T00:01:21.373 | null | 1005 | [
"markov-chain-montecarlo",
"optimization",
"monte-carlo"
] |
5148 | 2 | null | 5147 | 4 | null | The mode is indeed the maximum of f(x), so the value of x encountered during the simulation that gives the highest value of f(x) ought to be the best approximation of the mode. AFAICS there is no good reason that the last sample should be the mode, unless you are performing simulated annealing and the temperature has ... | null | CC BY-SA 2.5 | null | 2010-12-04T23:24:09.753 | 2010-12-04T23:24:09.753 | null | null | 887 | null |
5149 | 1 | 5153 | null | 7 | 4141 | I have some time series data and want to test for the existence of and estimate the parameters of a linear trend in a dependent variable w.r.t. time, i.e. time is my independent variable. The time points cannot be considered IID under the null of no trend. Specifically, the error terms for points sampled near each ot... | Determining trend significance in a time series | CC BY-SA 2.5 | null | 2010-12-05T02:33:01.707 | 2010-12-06T03:21:10.097 | 2010-12-05T12:49:08.100 | null | 1347 | [
"time-series",
"regression",
"correlation"
] |
5150 | 1 | null | null | 6 | 2255 | I have a biometric authentication system that is using a person's gait to authenticate them. I extract features from gait, run it through a comparison versus a template and produce a similarity score (where if this similarity score is below a certain threshold, then the user is authenticated). So, I have 72 trials tota... | Choosing the right threshold for a biometric trait authentication system | CC BY-SA 2.5 | null | 2010-12-05T03:24:56.760 | 2010-12-05T13:32:24.757 | null | null | 1224 | [
"matlab",
"mathematical-statistics",
"roc"
] |
5151 | 2 | null | 125 | 3 | null | This book suggests it is aimed at entry level undergraduate level
Biostatistics: A Bayesian Introduction. By George G Woodsworth.
Published by John Wiley & Sons
| null | CC BY-SA 2.5 | null | 2010-12-05T03:47:19.163 | 2010-12-05T03:47:19.163 | null | null | 2030 | null |
5152 | 1 | null | null | 1 | 535 | I am conducting a study on a cohort of people with a follow-up period of 7 years. I wish to use Cox Proportional Hazard model to estimate HR between an exposure and the length of time of an event. One missing information is the date of birth for the all subjects, but month and year are available.This prevents the calcu... | Missing values in survival analysis | CC BY-SA 2.5 | null | 2010-12-05T04:46:48.430 | 2010-12-07T02:59:36.707 | null | null | null | [
"survival",
"missing-data"
] |
5153 | 2 | null | 5149 | 3 | null | What you are describing is commonly referred to as [auto correlated errors](http://en.wikipedia.org/wiki/Autocorrelation). I would suggest you look up resources on ARIMA modelling. ARIMA modelling will allow you to model the correlation in your error term, and hence allow you to assess your trend variable independent o... | null | CC BY-SA 2.5 | null | 2010-12-05T05:13:40.530 | 2010-12-05T05:13:40.530 | null | null | 1036 | null |
5155 | 2 | null | 5115 | 57 | null | [John Tukey](http://en.wikipedia.org/wiki/John_Tukey) for Fast Fourier Transforms, exploratory data analysis (EDA), box plots, projection pursuit, jackknife (along with Quenouille). Coined the words "software" and "bit".
| null | CC BY-SA 3.0 | null | 2010-12-05T05:18:37.237 | 2012-08-02T03:11:29.257 | 2012-08-02T03:11:29.257 | 74 | 74 | null |
5156 | 2 | null | 5092 | 2 | null | I think that power analysis is too elaborate for what you're trying to do, and might let your down.
With a sample size north of 9 million, I think your estimate for `p = Pr(X > 3) = 0.000015` is pretty accurate. So you can use that in a simple binomial(n, p) model to estimate a sample size.
Let's say your goal is ... | null | CC BY-SA 2.5 | null | 2010-12-05T05:29:13.617 | 2010-12-05T05:29:13.617 | null | null | 5792 | null |
5157 | 2 | null | 5149 | 1 | null | Along the lines of a previous answer, if all assumptions for OLS are met except for the fact that errors are correlated, maybe something as simple as a [Cochrane-Orcutt](http://en.wikipedia.org/wiki/Cochrane%E2%80%93Orcutt_estimation) correction would be enough to solve your problem.
| null | CC BY-SA 2.5 | null | 2010-12-05T08:02:27.610 | 2010-12-05T08:02:27.610 | null | null | 892 | null |
5158 | 1 | 5164 | null | 40 | 33748 | I understand that when sampling from a finite population and our sample size is more than 5% of the population, we need to make a correction on the sample's mean and standard error using this formula:
$\hspace{10mm} FPC=\sqrt{\frac{N-n}{N-1}}$
Where $N$ is the population size and $n$ is the sample size.
I have 3 questi... | Explanation of finite population correction factor? | CC BY-SA 4.0 | null | 2010-12-05T09:40:51.387 | 2022-10-06T12:46:35.533 | 2021-05-13T14:42:54.947 | 11887 | 1636 | [
"sampling",
"finite-population"
] |
5159 | 1 | 5169 | null | 3 | 2006 | The height for 1000 students is approximately normal with a mean 174.5cm and a standard deviation of 6.9cm. If 200 random samples of size 25 are chosen from this population and the values of the mean are recorded to the nearest integer, determine the probability that the mean height for the students is more than 176cm.... | Correction due to rounding error | CC BY-SA 2.5 | null | 2010-12-05T11:13:52.800 | 2010-12-07T19:18:04.460 | 2010-12-07T19:18:04.460 | 1636 | 1636 | [
"self-study"
] |
5160 | 1 | null | null | 17 | 5586 | I am looking for a good tutorial on clustering data in `R` using hierarchical dirichlet process (HDP) (one of the recent and popular nonparametric Bayesian methods).
There is `DPpackage` (IMHO, the most comprehensive of all the available ones) in `R` for nonparametric Bayesian analysis. But I am unable to understand t... | Nonparametric Bayesian analysis in R | CC BY-SA 2.5 | null | 2010-12-05T11:14:12.273 | 2012-01-18T15:58:29.560 | 2010-12-06T08:52:21.543 | null | 1307 | [
"r",
"bayesian",
"clustering",
"nonparametric"
] |
5161 | 2 | null | 5159 | 3 | null | I understand the question as one where we know the theoretical distribution of students height with some precision (i.e., with one decimal place). In the present case, this is a gaussian distribution with parameters $\mathcal{N}(174.5;6.9^2)$.
Now, we have empirical measurements of students height on small samples ($n... | null | CC BY-SA 2.5 | null | 2010-12-05T11:26:49.977 | 2010-12-05T11:51:26.913 | 2010-12-05T11:51:26.913 | 930 | 930 | null |
5162 | 2 | null | 5150 | 4 | null | Generally, the cut-off value is chosen such as to maximize the compromise between sensitivity (Se) and specificity (Sp). You can generate a regular sequence of thresholds and plot the resulting ROC curve, as shown below, based on the [DiagnosisMed](http://cran.r-project.org/web/packages/DiagnosisMed/index.html) R packa... | null | CC BY-SA 2.5 | null | 2010-12-05T11:46:24.613 | 2010-12-05T13:32:24.757 | 2010-12-05T13:32:24.757 | 930 | 930 | null |
5163 | 2 | null | 5136 | 4 | null | The relevant section of the classical typology distinguishes between (observed) variables, latent variables, and parameters.
Regular variables are observed and have a distribution. Latent variables are not observed and have a distribution. Parameters are not observed and do not have a distribution.
Parameters vs lat... | null | CC BY-SA 2.5 | null | 2010-12-05T12:21:37.620 | 2010-12-05T12:21:37.620 | null | null | 1739 | null |
5164 | 2 | null | 5158 | 33 | null | The threshold is chosen such that it ensures convergence of the [hypergeometric distribution](http://en.wikipedia.org/wiki/Hypergeometric_distribution) ($\sqrt{\frac{N-n}{N-1}}$ is its SD), instead of a binomial distribution (for sampling with replacement), to a normal distribution (this is the Central Limit Theorem, s... | null | CC BY-SA 2.5 | null | 2010-12-05T12:32:46.047 | 2010-12-05T13:19:33.057 | 2010-12-05T13:19:33.057 | 930 | 930 | null |
5165 | 2 | null | 5149 | 4 | null | Generalised least squares (GLS) is one potential option here. The OLS estimates of the parameters are given by:
$$\hat{\beta} = (X^{T}\Sigma^{-1}X)^{-1}X^{T}\Sigma^{-1}y$$
Normally we leave out $\Sigma$ as in OLS it is defined as $\sigma^2 \mathbf{I}$, i.e. an identity matrix multiplied by the estimated residual stand... | null | CC BY-SA 2.5 | null | 2010-12-05T13:22:35.273 | 2010-12-05T13:22:35.273 | null | null | 1390 | null |
5167 | 1 | null | null | 1 | 1368 | I am sampling covariance matrix from a Inverse Wishart distribution. In one dimensional case, after doing sufficient iterations I am taking the mode value for variance (after removing the burn-in values). How to do the same in a multivariate case?
| Sampling covariance matrix using Gibbs sampling | CC BY-SA 2.5 | null | 2010-12-05T20:54:03.357 | 2010-12-07T10:38:37.483 | 2010-12-07T10:38:37.483 | null | 2157 | [
"markov-chain-montecarlo",
"gibbs"
] |
5168 | 2 | null | 5160 | 13 | null | Here are some online ressources I found interesting without going into detail (and I'm not a specialist of this topic):
- Hierarchical Dirichlet Processes, by Teh et al. (2005)
- Dirichlet Processes A gentle tutorial, by El-Arini (2008)
- Bayesian Nonparametrics, by Rosasco (2010)
- Non-parametric Bayesian Methods,... | null | CC BY-SA 3.0 | null | 2010-12-05T20:54:42.357 | 2012-01-18T15:58:29.560 | 2012-01-18T15:58:29.560 | 2728 | 930 | null |
5169 | 2 | null | 5159 | 5 | null | I interpret this question as supposing that an experiment is conducted 200 times. In this experiment, 25 people are independently drawn from the population (with replacement) and their average height is rounded to the nearest centimeter. This process yields 200 whole numbers. You seem to be asking, what is the chanc... | null | CC BY-SA 2.5 | null | 2010-12-05T21:40:11.867 | 2010-12-05T21:40:11.867 | null | null | 919 | null |
5170 | 1 | 5176 | null | 3 | 4385 | I am new to forecasting in R and am trying to automatically fit an ARIMA model to what I believe is a univariate dataset.
```
> str(p1.z)
'zoo' series from 2009-04-05 to 2010-10-31
Data: int [1:83] 360 570 540 585 570 690 495 660 510 690 ...
Index: Class 'Date' num [1:83] 14339 14346 14353 14360 14367 ...
> head... | Starting out with forecast package in R | CC BY-SA 2.5 | null | 2010-12-05T23:12:33.357 | 2010-12-06T10:26:02.110 | 2010-12-06T10:26:02.110 | 159 | 569 | [
"r",
"time-series",
"forecasting"
] |
5171 | 1 | 5258 | null | 8 | 2402 | I hope this isn't either far too basic or redundant. I have been looking around for guidance but so far I am still uncertain of how to proceed.
My data consists of counts of a particular structure used in conversations between pairs of interlocutors. The hypothesis I want to test is the following: more frequent use of ... | Testing paired frequencies for independence | CC BY-SA 2.5 | null | 2010-12-05T23:43:10.043 | 2022-12-11T17:43:57.517 | 2011-05-10T20:38:31.137 | 930 | 52 | [
"categorical-data",
"independence"
] |
5172 | 1 | null | null | 4 | 8945 | I have several sets of data, unfortunately the data comes to me in a "summary" form. My job is to consolidate the several data sources into one general summary. I'm currently using the median to summarise the data, but I don't know if this is statistically sound. Here's a description of my problem:
There are $N_P$ samp... | Is taking the median of a set of percentages statistically sound? | CC BY-SA 2.5 | null | 2010-12-06T00:17:18.907 | 2017-07-24T11:43:52.403 | 2017-07-24T11:43:52.403 | 11887 | 2271 | [
"sampling",
"median",
"population",
"percentage"
] |
5173 | 1 | 5175 | null | 10 | 1913 | "Spurious regression" (in the context of time series) and associated terms like unit root tests are something I've heard a lot about, but never understood.
Why/when, intuitively, does it occur? (I believe it's when your two time series are cointegrated, i.e., some linear combination of the two is stationary, but I don'... | Resources for learning about spurious time series regression | CC BY-SA 2.5 | null | 2010-12-06T00:33:15.987 | 2015-07-07T14:41:53.283 | 2010-12-06T09:08:35.157 | 159 | 1106 | [
"time-series",
"regression",
"cointegration"
] |
5174 | 2 | null | 5173 | 12 | null | Let's start with the spurious regression. Take or imagine two series which are both driven by a dominant time trend: for example US population and US consumption of whatever (it doesn't matter what item you think about, be it soda or licorice or gas). Both series will be growing because of the common time trend. Now ... | null | CC BY-SA 2.5 | null | 2010-12-06T00:53:50.953 | 2010-12-06T13:26:36.717 | 2010-12-06T13:26:36.717 | 334 | 334 | null |
5175 | 2 | null | 5173 | 11 | null | These concepts have been created to deal with regressions (for instance correlation) between non stationary series.
Clive Granger is the key author you should read.
Cointegration has been introduced in 2 steps:
1/ Granger, C., and P. Newbold (1974): "Spurious Regression in Econometrics,"
In this article, the authors po... | null | CC BY-SA 2.5 | null | 2010-12-06T01:56:44.033 | 2010-12-06T15:36:20.017 | 2010-12-06T15:36:20.017 | 919 | 1709 | null |
5176 | 2 | null | 5170 | 8 | null | `ets()` and `auto.arima()` are not really set up to handle `zoo` objects. Although `ets()` is not returning an error, it will be ignoring any seasonality. `auto.arima()` is failing because it is confused by the `zoo` object with apparent seasonality. I will try to include better checking in a future version.
When using... | null | CC BY-SA 2.5 | null | 2010-12-06T03:12:22.033 | 2010-12-06T03:12:22.033 | null | null | 159 | null |
5177 | 2 | null | 5149 | 6 | null | To add to the existing answers, if you are using R a simple way to proceed is to allow the ARMA errors to be modelled automatically using `auto.arima()`. If `x` is your time series, then you can proceed as follows.
```
t <- 1:length(x)
auto.arima(x,xreg=t,d=0)
```
This will fit the model $x_t = a + bt + e_t$ where $e_... | null | CC BY-SA 2.5 | null | 2010-12-06T03:21:10.097 | 2010-12-06T03:21:10.097 | null | null | 159 | null |
5178 | 2 | null | 5172 | 2 | null | What you are doing does not makes sense if your goal is to categorize what proportion of the entire population (sample A + sample B + sample C) is in category a, b, and c. Consider the following contingency table:
```
a b c a b c
A 8; 1; 1 A .8; .1; .1
B 7; 2; 1 B .7; .2; ... | null | CC BY-SA 2.5 | null | 2010-12-06T04:15:24.173 | 2010-12-06T04:15:24.173 | null | null | 2144 | null |
5179 | 2 | null | 5171 | 0 | null | I would maybe start with a [rank correlation](http://en.wikipedia.org/wiki/Rank_correlation) analysis.
The issue is that you may have very low correlations as the effects you are trying to capture are small.
Both Kendall and Spearman correlation coefficients are implemented in R in
```
cor(x=A, y=B, method = "spearman"... | null | CC BY-SA 2.5 | null | 2010-12-06T05:01:20.237 | 2010-12-06T05:01:20.237 | null | null | 1709 | null |
5180 | 2 | null | 5167 | 0 | null | If I understand your question correctly:
Covariance matrix for 1-dim case reduces to the variance. Wishart Distribution (or Inv wishart distribution depending on your formulation) is a prior of covariance matrices, which for dimensions $\geq$ 2 correspond to multivariate case.
However, I may have misunderstood you. Ple... | null | CC BY-SA 2.5 | null | 2010-12-06T06:10:49.867 | 2010-12-06T06:10:49.867 | null | null | 1307 | null |
5181 | 1 | 5263 | null | 3 | 1637 | Say I'm doing stats on the height of adults from various countries.
I assume the heights of adults from one country are normally distributed, and ignore sex differences (I also ignore the fact that neighbouring countries tend to have similar populations).
I have a bunch of data by country, but for some countries I have... | Estimating distribution parameters from few data points | CC BY-SA 2.5 | null | 2010-12-06T10:30:50.827 | 2010-12-16T03:26:47.083 | 2017-04-13T12:44:37.583 | -1 | 1737 | [
"bayesian",
"estimation",
"normal-distribution",
"uncertainty"
] |
5182 | 2 | null | 5171 | 2 | null | You seem to have ordered categorical data, therefore I suggest a linear-by-linear test as described by Agresti (2007, p229 ff). Function `lbl_test()` of package `coin` implements it in R.
Agresti, A. (2007). Introduction to Categorical Data Analysis. 2nd Ed. Hoboken, New Jersey: John Wiley & Sons. Hoboken, NJ: Wiley.
| null | CC BY-SA 2.5 | null | 2010-12-06T11:00:31.630 | 2010-12-06T11:00:31.630 | null | null | 1909 | null |
5183 | 2 | null | 5107 | 2 | null | If you have very little data, it is not that the distance estimate is wrong, but that your estimate is uncertain. A Bayesian approach would seek to determine the posterior distribution of the distance between the arbitrary point and the multi-variate distribution, rather than a single point estimate, and then marginal... | null | CC BY-SA 2.5 | null | 2010-12-06T11:43:57.957 | 2010-12-06T11:43:57.957 | null | null | 887 | null |
5184 | 1 | null | null | 15 | 5073 | In a recent paper, I fitted a three-way fixed effects model. Since one of the factors wasn't significant (p > 0.1), I removed it and refitted the model with two fixed effects and an interaction.
I've just had referees comments back, to quote:
>
That time was not a significant factor
in the 3-way ANOVA is not of itself... | Removing factors from a 3-way ANOVA table | CC BY-SA 4.0 | null | 2010-12-06T14:04:10.083 | 2022-12-24T07:23:20.347 | 2021-09-15T22:00:52.557 | 919 | 8 | [
"anova",
"fixed-effects-model"
] |
5185 | 2 | null | 5184 | 17 | null | I'm guessing the Underwood in question is [Experiments in Ecology (Cambridge Press 1991)](https://www.cambridge.org/core/books/experiments-in-ecology/DCF3663D5E7C9923D19B5ECE88167780). Its a more-or-less standard reference in the ecological sciences, perhaps third behind [Zar](https://www.pearson.com/en-gb/subject-cata... | null | CC BY-SA 4.0 | null | 2010-12-06T14:53:21.063 | 2022-12-24T07:23:20.347 | 2022-12-24T07:23:20.347 | 362671 | 1475 | null |
5186 | 2 | null | 5184 | 11 | null | I loathe these sort of cut-off-based rules. I think it depends on design and what your a priori hypotheses and expectations were. If you expecting the outcome to vary with time then I'd say you should keep time in, as you would for any other 'blocking' factor. On the other hand, if you were replicating the same experim... | null | CC BY-SA 2.5 | null | 2010-12-06T14:57:16.090 | 2010-12-06T14:57:16.090 | null | null | 449 | null |
5187 | 1 | 5188 | null | 5 | 3746 | I am new to Gibbs Sampling, and have been using WinBUGS, but I find that it is not well-suited towards storing/presenting results, so I have been calling it from R using the R2WinBUGS package. The data is apparently stored as a "bug" class.
I converted it to coda to run diagnostics, and it displays each of the chain... | Using R2WinBUGS, how to extract information from each chain? | CC BY-SA 2.5 | null | 2010-12-06T17:23:20.837 | 2012-01-15T18:56:56.413 | 2010-12-07T10:56:02.763 | null | null | [
"r",
"markov-chain-montecarlo",
"bugs"
] |
5188 | 2 | null | 5187 | 5 | null | The object returned by `read.bugs` is an object of S3 class `mcmc.list`.
You can use the double brackets `[[` to access the separate chains, i.e. the different `mcmc`-objects that make up the larger `mcmc.list` object, which really is simply a list of `mcmc`-objects that inherits some information about thinning and ch... | null | CC BY-SA 2.5 | null | 2010-12-06T18:03:04.027 | 2010-12-06T18:10:12.523 | 2010-12-06T18:10:12.523 | 1979 | 1979 | null |
5189 | 1 | 5194 | null | 7 | 3518 | With some great help from this forum, I have been able to get up and running with some basic time series analysis in R. Right now, my needs are mostly univariate time series.
Here is my question:
I can read in daily data from database into a data frame. I have two columns, date which is understood by R as POSIXct a... | Getting started with time series in R | CC BY-SA 4.0 | null | 2010-12-06T18:40:58.743 | 2018-05-29T12:42:11.920 | 2018-05-29T12:42:11.920 | 128677 | 569 | [
"r",
"time-series"
] |
5190 | 2 | null | 5181 | 4 | null | What you seem to be referring to is called ["shrinkage"](http://en.wikipedia.org/wiki/Shrinkage_%28statistics%29). This allows you to share strength across groups and is frequently used in hierarchical Bayesian models. The (very) basic idea is to impose a prior distribution over the entire population and place more wei... | null | CC BY-SA 2.5 | null | 2010-12-06T20:27:33.010 | 2010-12-06T20:27:33.010 | null | null | 1913 | null |
5191 | 1 | 5193 | null | 2 | 104 | This question is related to my previous question [Bias for kernel density estimator (periodic case)](https://stats.stackexchange.com/questions/5011/bias-for-kernel-density-estimator-periodic-case)
A kernel $K(x)$ is of the order $p$ if
$$\int_{-\infty}^{\infty}K(x)x^{j}=\delta_{0,j}\ j=0,...p-1$$
$$\int_{-\infty}^{\i... | Order of the kernel for periodic case | CC BY-SA 3.0 | null | 2010-12-06T21:08:37.437 | 2015-04-23T05:54:16.560 | 2017-04-13T12:44:41.607 | -1 | 2189 | [
"kernel-smoothing"
] |
5192 | 2 | null | 5115 | 32 | null | [George Box](http://en.wikipedia.org/wiki/George_E._P._Box) for his work on time series, designed experiments and elucidating the iterative nature of scientific discovery (proposing and testing models).
| null | CC BY-SA 3.0 | null | 2010-12-06T21:24:56.347 | 2011-12-14T06:46:31.820 | 2011-12-14T06:46:31.820 | 183 | null | null |
5193 | 2 | null | 5191 | 2 | null | I think the correct analog of this definition in the periodic case is that coefficients $1$ through $p-1$ of the Fourier Series for $K$ all vanish.
The purpose of the definition of order is to obtain estimates of the bias of the kernel estimator. When $K$ "kills" powers $1$ through $p-1$ of $x$, then the bias will be ... | null | CC BY-SA 2.5 | null | 2010-12-07T00:03:46.107 | 2010-12-07T00:03:46.107 | null | null | 919 | null |
5194 | 2 | null | 5189 | 3 | null | It seems like you need the package xts.
Create your time serie using
```
install.packages('xts')
library(xts)
X = xts(coredata(DF[,2]), order.by=DF[,1])
```
Then you will be able to manipulate your data easily.
```
to.weekly(X)
to.monthly(X)
```
Please note that you will then manipulate xts objects and not ts. But... | null | CC BY-SA 2.5 | null | 2010-12-07T01:14:28.133 | 2010-12-07T02:22:22.400 | 2010-12-07T02:22:22.400 | 1709 | 1709 | null |
5195 | 1 | 5209 | null | 13 | 20036 | As title, I need to draw something like this:
Can ggplot, or other packages if ggplot is not capable, be used to draw something like this?
| How to draw funnel plot using ggplot2 in R? | CC BY-SA 2.5 | null | 2010-12-07T01:29:37.223 | 2016-02-12T23:37:36.123 | 2010-12-07T10:53:58.223 | null | 588 | [
"r",
"data-visualization",
"ggplot2",
"funnel-plot"
] |
5196 | 1 | 5201 | null | 19 | 17299 | In order to calibrate a confidence level to a probability in supervised learning (say to map the confidence from an SVM or a decision tree using oversampled data) one method is to use Platt's Scaling (e.g., [Obtaining Calibrated Probabilities from Boosting](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.60.51... | Why use Platt's scaling? | CC BY-SA 3.0 | null | 2010-12-07T01:31:14.380 | 2013-09-27T21:59:39.513 | 2013-09-27T21:59:39.513 | 7290 | 2040 | [
"logistic",
"cross-validation",
"calibration"
] |
5197 | 1 | null | null | 2 | 219 | I have database of 78706 resident incidents in aged care facilities (5 years of data).
I want to to learn and implement a tool allowing analyzing these data using following attributes:
- Resident
- Date/Time
- Location
- Result
- Injury
I want to be able to get following assumptions from my system which will be ... | What type of statistical analysis solves this problem? | CC BY-SA 2.5 | null | 2010-12-07T02:08:09.843 | 2010-12-07T03:07:27.183 | null | null | null | [
"regression",
"clustering"
] |
5198 | 2 | null | 5152 | 3 | null | It is hard to imagine a situation when the effect of age with a precision of a month is not sufficient - even for babies after the first few months of life nobody uses weeks. For adults, even rounding to years should be just fine.
| null | CC BY-SA 2.5 | null | 2010-12-07T02:59:36.707 | 2010-12-07T02:59:36.707 | null | null | 279 | null |
5199 | 2 | null | 5197 | 1 | null | You could consider [association analysis](http://en.wikipedia.org/wiki/Association_rule_learning). If your time is discretized appropriately and the data support your 2nd example (Falls occur in North Wing between 2am and 5am), one possible learned rule that comes out of the analysis might be {North Wing, 2AM-5AM} => ... | null | CC BY-SA 2.5 | null | 2010-12-07T03:07:27.183 | 2010-12-07T03:07:27.183 | null | null | 1815 | null |
5200 | 1 | 5203 | null | 3 | 540 | In Frank Schorfheide's class notes on [likelihood functions of DSGE models](https://web.archive.org/web/20100702005639/http://www.webpages.ttu.edu/pesummer/ECO%205328/notes/4%20likelihood%20dsge.pdf), he expresses the value of the likelihood function for a given vector of parameters $\theta$, and time series $Y^T$ as:
... | Likelihood function of DSGE model using Kalman filter | CC BY-SA 4.0 | null | 2010-12-07T03:47:50.017 | 2023-02-05T17:46:42.410 | 2023-02-05T17:46:42.410 | 362671 | 2251 | [
"kalman-filter",
"likelihood"
] |
5201 | 2 | null | 5196 | 15 | null | I suggest to check out the [wikipedia page of logistic regression](http://en.wikipedia.org/wiki/Logistic_regression). It states that in case of a binary dependent variable logistic regression maps the predictors to the probability of occurrence of the dependent variable. Without any transformation, the probability used... | null | CC BY-SA 2.5 | null | 2010-12-07T07:43:17.147 | 2010-12-07T07:43:17.147 | null | null | 264 | null |
5202 | 2 | null | 5115 | 3 | null | [Teuvo Kohonen](http://en.wikipedia.org/wiki/Teuvo_Kohonen) for invention of the [Self-Organizing-Map](http://en.wikipedia.org/wiki/Self-Organizing_Map) (SOM).
| null | CC BY-SA 2.5 | null | 2010-12-07T07:46:56.347 | 2010-12-07T07:46:56.347 | null | null | 264 | null |
5203 | 2 | null | 5200 | 3 | null | $n$ is the dimension of the observation vector, as you mention in your question.
$F$ is the covariance matrix of innovations; I think you are missing an exponent of -1 in the last term of the likelihood. It should read $v_t' F_{t|t-1}^{-1} v_t$ (using the convention that $v_t$ is a column vector; your notation
seems to... | null | CC BY-SA 2.5 | null | 2010-12-07T08:25:52.680 | 2010-12-07T08:25:52.680 | null | null | 892 | null |
5204 | 2 | null | 5196 | 2 | null | Another method of avoiding over-fitting that I have found useful is to fit the univariate logistic regression model to the leave-out-out cross-validation output of the SVM, which can be approximated efficiently using the [Span bound](http://dx.doi.org/10.1023/A:1012450327387).
However, if you want a classifier that pro... | null | CC BY-SA 2.5 | null | 2010-12-07T08:57:03.053 | 2010-12-07T08:57:03.053 | null | null | 887 | null |
5205 | 2 | null | 5167 | 2 | null | You shouldn't need a Gibbs sampler: the mode of an [inverse-Wishart](http://en.wikipedia.org/wiki/Inverse-Wishart_distribution) has a closed form.
Also, independent random samples from the Cholesky factor of a Wishart can be obtained from the [Bartlett decomposition](http://en.wikipedia.org/wiki/Wishart_distribution#B... | null | CC BY-SA 2.5 | null | 2010-12-07T09:59:50.810 | 2010-12-07T09:59:50.810 | null | null | 495 | null |
5206 | 1 | 5208 | null | 8 | 6576 | This is the confidence interval estimated by prop.test
```
n <- 600; x <- 276; p <- 0.40
prop.test(x, n, p, alternative="two.sided", conf.level=0.95, correct=T)
95 percent confidence interval:
0.4196787 0.5008409
```
I tried to reproduce it, reading the code under prop.test. Here is a simplified way to get those two... | Yates' continuity correction in confidence interval returned by prop.test | CC BY-SA 2.5 | null | 2010-12-07T10:10:47.810 | 2010-12-07T14:08:24.490 | 2010-12-07T10:37:21.260 | null | 339 | [
"r",
"confidence-interval",
"yates-correction"
] |
5207 | 1 | 10734 | null | 9 | 1571 | I tried to simulate from a bivariate density $p(x,y)$ using Metropolis algorithms in R and had no luck. The density can be expressed as $p(y|x)p(x)$, where $p(x)$ is Singh-Maddala distribution
$p(x)=\dfrac{aq x^{a-1}}{b^a (1 + (\frac{x}{b})^a)^{1+q}}$
with parameters $a$, $q$, $b$, and $p(y|x)$ is log-normal with lo... | Sampling from bivariate distribution with known density using MCMC | CC BY-SA 2.5 | null | 2010-12-07T10:35:08.577 | 2018-02-24T13:37:18.813 | 2010-12-07T13:03:37.737 | 2116 | 2116 | [
"sampling",
"monte-carlo",
"metropolis-hastings"
] |
5208 | 2 | null | 5206 | 3 | null | The help page indicates that "Continuity correction is used only if it does not exceed the difference between sample and null proportions in absolute value." This is what line 5 is checking: `x/n` is the empirical proportion, `p` is the null proportion. (Actually, I find the "if" slightly misleading since it's more of ... | null | CC BY-SA 2.5 | null | 2010-12-07T10:55:54.983 | 2010-12-07T10:55:54.983 | null | null | 1909 | null |
5209 | 2 | null | 5195 | 12 | null | Although there's room for improvement, here is a small attempt with simulated (heteroscedastic) data:
```
library(ggplot2)
set.seed(101)
x <- runif(100, min=1, max=10)
y <- rnorm(length(x), mean=5, sd=0.1*x)
df <- data.frame(x=x*70, y=y)
m <- lm(y ~ x, data=df)
fit95 <- predict(m, interval="conf", level=.95)
fit99 <- ... | null | CC BY-SA 2.5 | null | 2010-12-07T11:38:07.737 | 2010-12-07T11:38:07.737 | null | null | 930 | null |
5210 | 2 | null | 5195 | 21 | null | If you are looking for this (meta-analysis) type of [funnel plot](http://en.wikipedia.org/wiki/Funnel_plot), then the following might be a starting point:
```
library(ggplot2)
set.seed(1)
p <- runif(100)
number <- sample(1:1000, 100, replace = TRUE)
p.se <- sqrt((p*(1-p)) / (number))
df <- data.frame(p, number, p.se)
... | null | CC BY-SA 3.0 | null | 2010-12-07T13:19:27.420 | 2013-03-19T05:36:01.310 | 2013-03-19T05:36:01.310 | 307 | 307 | null |
5212 | 2 | null | 5206 | 7 | null | On the second question of where you can find more info on this continuity correction (attributed to Yates in the help for `prop.test` but not in the refs below, I think [as Yates orginally proposed a continuity correction only to the chi-squared test for contingency tables](http://en.wikipedia.org/wiki/Yates%27_correct... | null | CC BY-SA 2.5 | null | 2010-12-07T14:08:24.490 | 2010-12-07T14:08:24.490 | null | null | 449 | null |
5213 | 1 | 5221 | null | 6 | 295 | Suppose I observe a sample $(y_i,x_i)$, $i=1,...,n$. Suppose that I know the following:
$y_i=\alpha_0+\alpha_1x_i+\varepsilon_i$, $i \in J\subset\{1,...,n\}$
$y_i=\beta_0+\beta_1x_i+\varepsilon_i$, $i \in J^c$
where $\varepsilon_i$ are i. i. d. and $J$ is not known in advance. Is it possible to estimate $\alpha_0,\al... | Discerning between two different linear regression models in one sample | CC BY-SA 2.5 | null | 2010-12-07T14:30:19.860 | 2019-09-09T08:17:06.213 | 2019-09-09T08:17:06.213 | 11887 | 2116 | [
"regression",
"classification",
"data-mining",
"mixture-distribution"
] |
5214 | 1 | 5216 | null | 2 | 930 | I have left censored data where the distribution is known (it's near enough lognormal, at least in theory).
I'd like to calculate some simple summary stats: geometric mean and standard deviation in this case.
I've previously used R's `NADA` package for this but it is no longer on CRAN. Is there an alternative availabl... | How do you calculate simple statistics for left censored data in R? | CC BY-SA 2.5 | null | 2010-12-07T15:32:41.797 | 2010-12-07T18:28:04.907 | 2010-12-07T18:28:04.907 | 478 | 478 | [
"r",
"censoring"
] |
5216 | 2 | null | 5214 | 4 | null | I will let other suggest better alternatives to [NADA](http://www.practicalstats.com/nada/nada/downloads_files/NADAforR_Examples.pdf), but it seems the package is still available on CRAN, in the [Archive](http://cran.r-project.org/src/contrib/Archive) section. The last version is from May, 2009.
Installation went fine ... | null | CC BY-SA 2.5 | null | 2010-12-07T15:54:24.510 | 2010-12-07T15:54:24.510 | null | null | 930 | null |
5217 | 2 | null | 5187 | 2 | null | The contents of your chains are stored in three different formats. Take a look at
```
bugs.sim$sims.array
bugs.sim$sims.list
bugs.sim$sims.matrix
```
and read the Value section of `?bugs`.
| null | CC BY-SA 2.5 | null | 2010-12-07T15:57:53.917 | 2010-12-07T15:57:53.917 | null | null | 478 | null |
5218 | 2 | null | 4830 | 5 | null | In logistic regression, highly skewed distributions of outcome variables (where there are far more non-events to events or vis versa), the cut point or probability trigger does need to be adjusted, but it will not have much of an effect on overall classification efficieny. This will always remain roughly the same, but... | null | CC BY-SA 2.5 | null | 2010-12-07T16:08:40.270 | 2010-12-07T16:08:40.270 | null | null | null | null |
5220 | 1 | null | null | 2 | 6993 | I have done a meta analysis and heterogeneity is too high. I am working with (even/Total) for experimental and control groups to calculate the Odds Ratio. I have done fixed-effect and random-effect modeling. I now need to use meta-regression via SPSS.
Can anyone direct me to a good set of materials to learn how t... | How to do meta-regression analysis with SPSS? | CC BY-SA 2.5 | null | 2010-12-07T16:37:20.697 | 2012-05-22T20:15:20.493 | 2010-12-07T21:11:28.573 | 307 | null | [
"regression",
"spss",
"meta-analysis"
] |
5221 | 2 | null | 5213 | 5 | null | You need to model the observations as a mixture model. Define:
$p$ as the probability that a sample belongs to the first data generating process.
Thus, the density function of $y_i$ is given by:
$f(y_i|-) \sim p f_1(y_i|-) + (1-p) f_2(y_i|-)$
where
$f_1(.)$ is the density that arises because of the first data generati... | null | CC BY-SA 2.5 | null | 2010-12-07T17:01:09.163 | 2010-12-07T17:01:09.163 | null | null | null | null |
5222 | 2 | null | 5220 | 7 | null | You could start with David B Wilson's website on "[meta-analysis stuff](http://mason.gmu.edu/~dwilsonb/ma.html)". He offers spss, stata, and sas macros for performing meta-analytic analyses (including meta-regression; metareg.sps) + PPT slides (analysis.ppt, interpretation.ppt).
Another presentation I really like(d) wa... | null | CC BY-SA 3.0 | null | 2010-12-07T17:57:35.953 | 2011-10-17T20:28:11.763 | 2011-10-17T20:28:11.763 | 919 | 307 | null |
5223 | 2 | null | 5077 | 10 | null | I didn't look at the paper you supplied, but let me have a go anyway:
If you have a $p$-dimensional parameter space you can generate a random direction $d$ uniformly distributed on the surface of the unit sphere with
```
x <- rnorm(p)
d <- x/sqrt(sum(x^2))
```
(c.f. [Wiki](http://en.wikipedia.org/wiki/Hypersphere#Gene... | null | CC BY-SA 3.0 | null | 2010-12-07T18:19:10.860 | 2017-04-24T07:45:30.210 | 2017-04-24T07:45:30.210 | 123119 | 1979 | null |
5225 | 2 | null | 5213 | 4 | null | The first hit on [Rseek](http://Rseek.org) with keywords "mixture regression" brings up the `flexmix` package, which does what you want. I seem to recall that there were other packages for this as well.
| null | CC BY-SA 2.5 | null | 2010-12-07T21:03:29.853 | 2010-12-07T21:03:29.853 | null | null | 279 | null |
5226 | 1 | 5231 | null | 5 | 654 | If $X=[x_1,x_2,...,x_n]^T$ is an $n$-dimensional random variable and we have
$E\left\{X\right\} = M = \left[m_1,m_2,...,m_n\right]^T$
$Cov\left\{X\right\} = \Sigma = diag\left(\lambda_1,\lambda_2,...,\lambda_n\right)$
how can I express the following expectation in terms of $M$, $\Sigma$, and $n$ (and maybe raw $m_i$... | Expectation of $\left(X-M\right)^T\left(X-M\right)\left(X-M\right)^T\left(X-M\right)$ | CC BY-SA 2.5 | null | 2010-12-08T00:13:18.207 | 2010-12-08T14:35:35.940 | 2010-12-08T14:35:35.940 | 2148 | 2148 | [
"expected-value"
] |
5227 | 2 | null | 5226 | 2 | null | I believe this depends on the kurtosis of $X$. If I am reading this correctly, and assuming the $X_i$ are independent, you are trying to find the expectation of $\sum_i (X_i - m_i)^4$. Because $X_i^4$ appears, you cannot find this expectation in terms of $M$ and $\Sigma$ without making further assumptions. (Even withou... | null | CC BY-SA 2.5 | null | 2010-12-08T01:02:29.333 | 2010-12-08T03:03:57.627 | 2010-12-08T03:03:57.627 | 795 | 795 | null |
5228 | 1 | 5232 | null | 9 | 1792 | I am looking at the sample kurtosis of a fairly skewed random variable, and the results seem inconsistent. To simply illustrate the problem, I looked at the sample kurtosis of a log-normal RV. In R (which I am slowly learning):
```
library(moments);
samp_size = 2048;
n_trial = 4096;
kvals <- rep(NA,1,n_trial); #pre... | Is sample kurtosis hopelessly biased? | CC BY-SA 2.5 | null | 2010-12-08T01:21:07.327 | 2010-12-08T08:42:18.133 | 2010-12-08T08:42:18.133 | 1390 | 795 | [
"r",
"unbiased-estimator",
"kurtosis"
] |
5229 | 2 | null | 3466 | 20 | null | Daniel B. Wright discusses this in section 5 of his article [Making Friends with your Data](http://www2.fiu.edu/~dwright/pdf/makefriends.pdf).
He suggests (p.130):
>
The only procedure that
is always correct in this situation is
a scatterplot comparing the scores at
time 2 with those at time 1 for the
differen... | null | CC BY-SA 2.5 | null | 2010-12-08T01:31:49.233 | 2010-12-25T06:55:23.157 | 2010-12-25T06:55:23.157 | 183 | 183 | null |
5231 | 2 | null | 5226 | 5 | null | Because $\left(X-M\right)^T\left(X-M\right) = \sum_i{(X_i - m_i)^2}$,
$$\left(X-M\right)^T\left(X-M\right)\left(X-M\right)^T\left(X-M\right) = \sum_{i,j}{(X_i - m_i)^2(X_j - m_j)^2} \text{.}$$
There are two kinds of expectations to obtain here. Assuming the $X_i$ are independent and $i \ne j$,
$$\eqalign{
E \left[ ... | null | CC BY-SA 2.5 | null | 2010-12-08T04:20:55.597 | 2010-12-08T04:20:55.597 | null | null | 919 | null |
5232 | 2 | null | 5228 | 8 | null | There's a [bias correction](http://www.mathworks.com/help/toolbox/stats/kurtosis.html). It's not huge. I believe the sampling variance of the kurtosis is proportional to the eighth (!) central moment, which can be enormous for a lognormal distribution. You would need millions of trials (or far more) in a simulation ... | null | CC BY-SA 2.5 | null | 2010-12-08T04:51:09.113 | 2010-12-08T04:51:09.113 | null | null | 919 | null |
5233 | 1 | 5236 | null | 5 | 312 | Say I have a database of around a million words, and I want to get an intuitive idea about exactly how a particular, quite infrequent, word is distributed throughout this data. My goal is to be able to see clearly whether this word tends to cluster together, or whether it is relatively evenly spaced. What would be some... | Visualizing the distribution of something within a very large body of data | CC BY-SA 2.5 | null | 2010-12-08T05:22:59.760 | 2010-12-08T19:43:07.323 | 2010-12-08T05:33:36.847 | 919 | 52 | [
"r",
"distributions",
"data-visualization"
] |
5234 | 2 | null | 5233 | 3 | null | With a 1200 dpi printer using the thinnest possible line (one pixel) for each word, your plot of a million words would still be almost 20 meters long!
Maybe a [density plot](http://www.statmethods.net/graphs/density.html) would be more helpful.
| null | CC BY-SA 2.5 | null | 2010-12-08T05:32:42.603 | 2010-12-08T05:32:42.603 | null | null | 919 | null |
5235 | 1 | 5252 | null | 31 | 99643 | In multiple linear regression, I can understand the correlations between residual and predictors are zero, but what is the expected correlation between residual and the criterion variable? Should it expected to be zero or highly correlated? What's the meaning of that?
| What is the expected correlation between residual and the dependent variable? | CC BY-SA 3.0 | null | 2010-12-08T05:50:51.757 | 2017-09-07T17:55:53.393 | 2013-04-02T21:45:29.987 | 7290 | 400 | [
"regression",
"residuals"
] |
5236 | 2 | null | 5233 | 2 | null | While Whuber is correct in principle you still might be able to see something because your word is very infrequent and you only want plots of the one word. Something quite uncommon might only appear 30 times, probably not more than 500.
Let's say you convert your words into a single vector of words that's a million lo... | null | CC BY-SA 2.5 | null | 2010-12-08T05:51:16.343 | 2010-12-08T16:37:08.893 | 2010-12-08T16:37:08.893 | 601 | 601 | null |
5237 | 2 | null | 5233 | 2 | null | I don't know if this may be useful in your case, but in bioinformatics I often feel the need to visualize the distribution of gene counts in a give data set. This is definitely not as large as your data set, but I think the strategy can be followed for most of the large data sets.
A typical strategy would be to find a ... | null | CC BY-SA 2.5 | null | 2010-12-08T06:10:45.690 | 2010-12-08T06:10:45.690 | null | null | 1307 | null |
5238 | 1 | 5241 | null | 18 | 12334 | When I work on data analysis projects I often store data in comma or tab-delimited (CSV, TSV) data files. While data often belongs in a dedicated database management system.
For many of my applications, this would be overdoing things.
I can edit CSV and TSV files in Excel (or presumably another Spreadsheet program).
T... | Strategy for editing comma separated value (CSV) files | CC BY-SA 3.0 | null | 2010-12-08T07:14:29.000 | 2017-05-22T18:46:48.997 | 2017-05-20T16:29:55.990 | 101426 | 183 | [
"project-management"
] |
5239 | 2 | null | 5238 | 5 | null | Update: [Having been going through a large backlog of email from R-Help] I am reminded of the thread on "[The behaviour of read.csv()](http://thread.gmane.org/gmane.comp.lang.r.general/213174/focus=213179)". In this, Duncan Murdoch mentions that he prefers to use [Data Interchange Format (DIF)](http://en.wikipedia.org/... | null | CC BY-SA 2.5 | null | 2010-12-08T07:31:04.013 | 2010-12-08T10:35:34.760 | 2010-12-08T10:35:34.760 | 1390 | 1390 | null |
5240 | 2 | null | 4991 | 4 | null | Have a look at the [dlm](http://cran.r-project.org/web/packages/dlm/index.html) package and its [vignette](http://cran.r-project.org/web/packages/dlm/vignettes/dlm.pdf). I think you might find what you are looking for from the vignette. The package authors have also written a book [Dynamic Linear Models with R](http://... | null | CC BY-SA 2.5 | null | 2010-12-08T07:40:03.023 | 2010-12-14T21:58:57.510 | 2010-12-14T21:58:57.510 | 919 | 214 | null |
5241 | 2 | null | 5238 | 14 | null |
- If you are comfortable with R, you can create your basic data.frame and then use the fix() function on it to input data. Along the same line as #5, once you set up the data.frame you can use a series of readLines(n=1) (or whatever) to get your data in, validate it, and the provide the opportunity to add the next ro... | null | CC BY-SA 3.0 | null | 2010-12-08T07:51:55.477 | 2013-01-21T15:24:42.620 | 2013-01-21T15:24:42.620 | 196 | 196 | null |
5242 | 2 | null | 5228 | 5 | null | [Just on the R Style - @whuber has answered the Kurtsosis Q]
This was a bit too complicated to stick into a comment. For such simple loops like the one you use, we can combine @whuber's suggestion of encapsulating the simulation in a function with the `replicate()` function. `replicate()` takes care of allocation and r... | null | CC BY-SA 2.5 | null | 2010-12-08T08:41:48.057 | 2010-12-08T08:41:48.057 | null | null | 1390 | null |
5243 | 2 | null | 5238 | 1 | null | I like Gnumeric because it does not try to be so much idiot-resistant as others (it doesn't shout about lost functionality) and works with large data... yet I think it is Linux-only.
| null | CC BY-SA 2.5 | null | 2010-12-08T09:09:34.283 | 2010-12-08T09:09:34.283 | null | null | null | null |
5244 | 2 | null | 5238 | 2 | null | After I asked this question, I started having a look at [CSVed](http://csved.sjfrancke.nl/index.html).
From the website:
>
CSVed is an easy and powerful CSV file
editor, you can manipulate any CSV
file, separated with any separator.
I'm not sure if anyone has experience with it.
| null | CC BY-SA 2.5 | null | 2010-12-08T09:09:51.760 | 2010-12-08T09:09:51.760 | null | null | 183 | null |
5245 | 2 | null | 5235 | 4 | null | So, the residuals are your unexplained variance, the difference between your model's predictions and the actual outcome you're modeling. In practice, few models produced through linear regression will have all residuals close to zero unless linear regression is being used to analyze a mechanical or fixed process.
Ideal... | null | CC BY-SA 2.5 | null | 2010-12-08T09:27:14.297 | 2010-12-08T09:35:02.833 | 2010-12-08T09:35:02.833 | 2166 | 2166 | null |
5246 | 2 | null | 5238 | 2 | null | Excel is not very CSV friendly. For example, if you were to enter "1,300" into Excel, and save that as a comma separated value, it would let you! This can be a big problem (I encounter it on a regular basis when receiving files from others).
I personally use OpenOffice.org Calc, I also use many of the solutions listed ... | null | CC BY-SA 2.5 | null | 2010-12-08T09:42:47.957 | 2010-12-08T09:42:47.957 | null | null | 2166 | null |
5247 | 1 | 5261 | null | 4 | 2597 | I am going to host a training session to teach healthcare staff how to use control chart (c-chart to be specific), and I need to tell that why these rules ([Western Electric Rules](http://en.wikipedia.org/wiki/Western_Electric_rules#Zone_rules)) are so called rules that when pattern is matched, outbreak can be consider... | How to calculate the probability for pattern that violates "control chart rules" to occur? | CC BY-SA 2.5 | null | 2010-12-08T09:59:17.943 | 2015-03-10T20:43:36.770 | 2010-12-08T15:43:49.840 | 919 | 588 | [
"r",
"poisson-distribution",
"control-chart"
] |
5248 | 2 | null | 4989 | 2 | null | Looking at the [Heckman article](http://www.jstor.org/stable/full/1912352?seq=1) I see no reason, why two selection rules cannot be applied. In the article Heckman postulates the model as:
$Y_{1i}=X_{1i}\beta_1+U_{1i}$
$Y_{2i}=X_{2i}\beta_1+U_{2i}$
The goal is to estimate the first equation, but some data is missing.... | null | CC BY-SA 2.5 | null | 2010-12-08T10:22:15.833 | 2010-12-08T10:22:15.833 | null | null | 2116 | null |
5249 | 1 | null | null | 17 | 742 | In a previous question I asked about tools for editing [CSV files](https://stats.stackexchange.com/questions/5238/strategy-for-editing-comma-separated-value-csv-files).
[Gavin](https://stats.stackexchange.com/questions/5238/strategy-for-editing-comma-separated-value-csv-files/5239#5239)
linked to a comment on R Help by... | Simple, reliable, open, and interoperable plain text format for storing data | CC BY-SA 2.5 | null | 2010-12-08T10:43:56.587 | 2017-05-20T16:31:28.313 | 2017-05-20T16:31:28.313 | 101426 | 183 | [
"project-management"
] |
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