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9811 | 2 | null | 5396 | 1 | null | for your first question:
this should be possible with `Table`/ `Create Table`
(I'm using the german version of `JMP7`, so I'm not sure how the menu commands are called in the english version)
| null | CC BY-SA 3.0 | null | 2011-04-20T23:57:53.813 | 2011-04-20T23:57:53.813 | null | null | 3760 | null |
9813 | 1 | null | null | 5 | 114 | Consider data where each observation was generated as follows.
- We draw $Z_1,...,Z_m$ from some distribution. (Possibly they're independent or related in some other simple way.)
- Next, based on the $Z_1,...,Z_m$, we choose a sequence $0=I_0 < I_1 < ... < I_N=m$ so that, for each $k$, (i) $I_k-I_{k-1}$ is not too sm... | Averages of random subsets of variables | CC BY-SA 3.0 | null | 2011-04-21T04:00:49.700 | 2011-04-22T09:29:57.620 | 2011-04-21T13:57:43.063 | 3891 | 3891 | [
"modeling",
"censoring"
] |
9814 | 1 | null | null | 1 | 681 | I got some nice data on multi-environment trials (MET) data for genotype evaluation and would like to use some new developed techniques as discussed in [Smith et al. 2005](http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9411&context=infopapers). I'm specifically interested in Factor Analytic (FA) structure. Authors me... | Multiplicative mixed models for analyzing variety-by-environment data | CC BY-SA 3.0 | null | 2011-04-21T05:33:27.247 | 2016-01-04T04:59:54.717 | 2016-01-04T04:59:54.717 | 21599 | 3903 | [
"r",
"mixed-model",
"factor-analysis",
"experiment-design",
"sas"
] |
9815 | 2 | null | 9801 | 13 | null | You can also use formulas from [Matrix cookbook](http://web.archive.org/web/20110430075537/http://matrixcookbook.com/). We have
$$(y-X\beta)'(y-X\beta)=y'y-\beta'X'y-y'X\beta+\beta'X'X\beta$$
Now take derivatives of each term. You might want to notice that $\beta'X'y=y'X\beta$. The derivative of term $y'y$ with respect... | null | CC BY-SA 4.0 | null | 2011-04-21T06:26:04.143 | 2021-11-22T04:52:33.890 | 2021-11-22T04:52:33.890 | 311814 | 2116 | null |
9816 | 2 | null | 9794 | 6 | null | The cannonical way is probably [MIDAS](http://en.wikipedia.org/wiki/Mixed_data_sampling) regression. There is a Matlab toolbox for estimating, available upon request from the [author Eric Ghysels](http://www.unc.edu/~eghysels/). You might look into [user guide](http://www.unc.edu/~eghysels/papers/MIDAS_Usersguide_Versi... | null | CC BY-SA 3.0 | null | 2011-04-21T06:50:58.667 | 2013-11-27T14:46:12.393 | 2013-11-27T14:46:12.393 | 2116 | 2116 | null |
9817 | 1 | 9830 | null | 4 | 4974 | I am trying to use Maximum Likelihood Estimation to learn the structure of a DAG, G.
How is the number of free parameters of G calculated to compare the complexity of different graphical models?
Is it based on one of the following, or something else?
- Number of edges in the graph
- Maximum number of possible edges i... | What is the number of free parameters for a directed acyclic graph? | CC BY-SA 3.0 | null | 2011-04-21T08:56:58.870 | 2011-04-21T15:07:33.127 | null | null | 3595 | [
"bayesian",
"graphical-model"
] |
9818 | 2 | null | 9801 | 7 | null | One way which may help you understand is to not use matrix algebra, and differentiate with each respect to each component, and then "store" the results in a column vector. So we have:
$$\frac{\partial}{\partial \beta_{k}}\sum_{i=1}^{N}\left(Y_{i}-\sum_{j=1}^{p}X_{ij}\beta_{j}\right)^{2}=0$$
Now you have $p$ of these e... | null | CC BY-SA 3.0 | null | 2011-04-21T09:35:00.280 | 2011-04-21T09:35:00.280 | null | null | 2392 | null |
9819 | 2 | null | 9779 | 7 | null | No Model
Wayne points out that you do not say anything about the process generating the data. In particular, you don't say whether the quantity you are tracking, the 'state' is fixed or moving. If it's fixed - the case Wayne considers - then you may as well keep a running average of all the observations and hope for t... | null | CC BY-SA 3.0 | null | 2011-04-21T09:39:02.513 | 2011-04-21T09:39:02.513 | null | null | 1739 | null |
9820 | 1 | 9823 | null | 2 | 501 | Assume I have a trading system that I'm evaluating over a three-year period. The returns are 25%, -40% and 25%. Empirically, I can see that this system loses because at the end of three years, I have less than when I started.
Wikipedia defines `expected return` as follows:
```
E(R)= Sum: probability (in scenario i) * ... | Calculating expected return | CC BY-SA 3.0 | null | 2011-04-21T12:42:03.650 | 2011-04-21T13:53:43.563 | 2011-04-21T13:53:43.563 | 3306 | 3306 | [
"expected-value"
] |
9821 | 2 | null | 9813 | 2 | null | You could apply linear regression with regularization ([Lasso](http://www-stat.stanford.edu/~tibs/lasso.html)) to solve this problem. The idea would be to fit the data with piece-wise constant functions adding a penalty for every jump that occurs. The objective you have to minimize is
$x^* = \arg\min_{x\in\mathbb{R}^m}... | null | CC BY-SA 3.0 | null | 2011-04-21T12:43:00.117 | 2011-04-21T12:43:00.117 | null | null | 4272 | null |
9822 | 2 | null | 9820 | 2 | null | Wikipedia is right. So are your calculations: this system should win.
However: it also assumes that your investments in each part are equal in size (because you give equal probability 1/3 to each). If this is not true, that may explain the difference with your empirical observations (perhaps you should share the number... | null | CC BY-SA 3.0 | null | 2011-04-21T13:25:33.207 | 2011-04-21T13:25:33.207 | null | null | 4257 | null |
9823 | 2 | null | 9820 | 2 | null | The difference between the two ways to look at the return is whether you are reinvesting your gains. If you start with 100 dollars and reinvest all of the money the next year, your balances will be: 125, 75, 93.75 dollars - you lost money. However if you invest 100 dollars every year, then you get +25 - 40 +25 = +10 do... | null | CC BY-SA 3.0 | null | 2011-04-21T13:39:55.973 | 2011-04-21T13:39:55.973 | null | null | 279 | null |
9824 | 2 | null | 9801 | 9 | null | Here is a technique for minimizing the sum of squares in regression that actually has applications to more general settings and which I find useful.
Let's try to avoid vector-matrix calculus altogether.
Suppose we are interested in minimizing
$$
\newcommand{\err}{\mathcal{E}}\newcommand{\my}{\mathbf{y}}\newcommand{\... | null | CC BY-SA 3.0 | null | 2011-04-21T14:10:55.193 | 2011-04-21T14:32:57.543 | 2011-04-21T14:32:57.543 | 2970 | 2970 | null |
9825 | 1 | null | null | 21 | 8224 | I have some data that is highly correlated. If I run a linear regression I get a regression line with a slope close to one (= 0.93). What I'd like to do is test if this slope is significantly different from 1.0. My expectation is that it is not. In other words, I'd like to change the null hypothesis of the linear regre... | Changing null hypothesis in linear regression | CC BY-SA 3.0 | null | 2011-04-21T14:12:41.697 | 2021-06-18T22:39:00.810 | 2011-04-21T14:16:00.223 | null | 4274 | [
"regression",
"correlation",
"hypothesis-testing"
] |
9826 | 2 | null | 7505 | 1 | null | When you move from testing a difference in the conversion rate to testing the difference in volume, the characteristics of your underlying data is changing.
The conversion rates you were testing were proportions based on the number of "successes" out of the number of "trials" which meant their intervals were [0,1]. Tha... | null | CC BY-SA 3.0 | null | 2011-04-21T14:16:03.350 | 2011-04-21T15:05:07.710 | 2011-04-21T15:05:07.710 | 4260 | 4260 | null |
9827 | 2 | null | 9825 | 11 | null | ```
set.seed(20); y = rnorm(20); x = y + rnorm(20, 0, 0.2) # generate correlated data
summary(lm(y ~ x)) # original model
summary(lm(y ~ x, offset= 1.00*x)) # testing against slope=1
summary(lm(y-x ~ x)) # testing against slope=1
```
Outputs:
```
Estimate Std. Error t value... | null | CC BY-SA 3.0 | null | 2011-04-21T14:25:14.817 | 2011-04-21T14:38:38.310 | 2011-04-21T14:38:38.310 | 3911 | 3911 | null |
9828 | 2 | null | 9825 | 7 | null | Your hypothesis can be expressed as $R\beta=r$ where $\beta$ is your regression coefficients and $R$ is restriction matrix with $r$ the restrictions. If our model is
$$y=\beta_0+\beta_1x+u$$
then for hypothesis $\beta_1=0$, $R=[0,1]$ and $r=1$.
For these type of hypotheses you can use `linearHypothesis` function from ... | null | CC BY-SA 3.0 | null | 2011-04-21T14:43:27.883 | 2011-04-21T14:43:27.883 | null | null | 2116 | null |
9830 | 2 | null | 9817 | 2 | null | The answer depends on your likelihood for the data $X$
1) Joint Gaussian
You can fit the model by sequentially fitting the conditional distribution of each node $v$ given its parents $\mathrm{pa}(v)$, in this case:
$X_v | X_{\mathrm{pa}(v)} \sim N\big(\mu_v + \beta_v^\top [X_{\mathrm{pa}(v)} - \mu_{\mathrm{pa}(v)}], \s... | null | CC BY-SA 3.0 | null | 2011-04-21T15:07:33.127 | 2011-04-21T15:07:33.127 | null | null | 495 | null |
9831 | 2 | null | 9825 | 3 | null | The point of testing is that you want to reject your null hypothesis, not confirm it. The fact that there is no significant difference, is in no way a proof of the absence of a significant difference. For that, you'll have to define what effect size you deem reasonable to reject the null.
Testing whether your slope is ... | null | CC BY-SA 3.0 | null | 2011-04-21T15:08:24.687 | 2011-04-21T15:08:24.687 | null | null | 1124 | null |
9832 | 2 | null | 9825 | 6 | null | It seems you're still trying to reject a null hypothesis. There are loads of problems with that, not the least of which is that it's possible that you don't have enough power to see that you're different from 1. It sounds like you don't care that the slope is 0.07 different from 1. But what if you can't really tell?... | null | CC BY-SA 3.0 | null | 2011-04-21T15:08:36.183 | 2011-04-21T15:08:36.183 | null | null | 601 | null |
9833 | 1 | null | null | 27 | 15003 | In my elementary statistics course, I learnt how to construct 95% confidence interval such as population mean, $\mu$, based on asymptotic normality for "large" sample sizes. Apart from resampling methods (such as bootstrap), there is another approach based on "profile likelihood". Could someone elucidate this approach?... | Constructing confidence intervals based on profile likelihood | CC BY-SA 3.0 | null | 2011-04-21T15:11:29.673 | 2016-09-02T13:49:32.110 | 2016-09-02T13:49:32.110 | 28666 | null | [
"confidence-interval",
"profile-likelihood"
] |
9835 | 1 | 9847 | null | 4 | 226 | Do there exist any systems for symbolically solving expectations?
This is sort of a follow-up to my question [List of Tricks for Solving Messy Expectations?](https://stats.stackexchange.com/q/9809/3577) Basically, I'm looking for ways to solve a messy expectation after I've exhausted all obvious routes.
EDIT: BACKGRO... | Systems for symbolically solving expectations? | CC BY-SA 3.0 | null | 2011-04-21T16:31:04.693 | 2012-09-07T23:52:43.753 | 2017-04-13T12:44:46.680 | -1 | 3577 | [
"expected-value"
] |
9836 | 1 | null | null | 8 | 12819 | Can somebody give me references (book/online resource) on using R for Marketing Mix Modelling?
| Market mix modelling with R | CC BY-SA 3.0 | null | 2011-04-21T16:44:01.063 | 2022-08-30T14:59:54.507 | 2022-08-30T14:59:54.507 | 11887 | 4278 | [
"r",
"references",
"marketing"
] |
9837 | 2 | null | 9833 | 27 | null | In general, the confidence interval based on the standard error strongly depends on the assumption of normality for the estimator. The "profile likelihood confidence interval" provides an alternative.
I am pretty sure you can find documentation for this. For instance, [here](http://people.upei.ca/hstryhn/stryhn208.pdf)... | null | CC BY-SA 3.0 | null | 2011-04-21T17:12:45.203 | 2011-04-21T17:34:22.940 | 2011-04-21T17:34:22.940 | 3019 | 3019 | null |
9838 | 2 | null | 8784 | 3 | null | Why not? The models are estimating how much 1 unit of change in any model predictor will influence the probability of "1" for the outcome variable. I'll assume the models are the same-- that they have the same predictors in them. The most informative way to compare the relative magnitudes of any given predictor in the ... | null | CC BY-SA 3.0 | null | 2011-04-21T17:48:27.180 | 2011-04-22T23:30:14.527 | 2011-04-22T23:30:14.527 | 11954 | 11954 | null |
9839 | 1 | null | null | 5 | 2612 | I am trying to write a prediction algorithm for a set of temperature data. I settled on Holt-Winters since it seemed to be a simple time series prediction algorithm and I can easily code it up in python to understand what is going on with it.
When I am plotting the smoothing function as it learns this is how it looks.... | Predicting temperature time series with Holt-Winters | CC BY-SA 3.0 | null | 2011-04-21T17:49:53.597 | 2017-12-11T23:35:11.010 | 2017-12-11T23:35:11.010 | 128677 | null | [
"time-series",
"exponential-smoothing"
] |
9840 | 2 | null | 9835 | 2 | null | My favourite and free symbolic algebra system is [Sage](http://www.sagemath.org/), currently available as a Linux installer and via a [web interface](http://www.sagenb.org/). It is powerful, but I haven't tested how good it is in solving expectations.
| null | CC BY-SA 3.0 | null | 2011-04-21T18:26:17.807 | 2011-04-21T22:08:27.317 | 2011-04-21T22:08:27.317 | 3911 | 3911 | null |
9841 | 1 | 9902 | null | 5 | 218 | We have data on the day in which a butterfly pupates (forms a cocoon) in the summer/fall of 2 different pairs of years (2005-2006 vs 2009-2010). At the time that the pupa forms it can either be in diapause (suspended animation) or not.
Individual butterfly caterpillars were randomly selected from a wild population a... | What is the appropriate way to test for a shift in probability using multiple logistic regression? | CC BY-SA 3.0 | null | 2011-04-21T18:42:44.193 | 2011-04-23T11:22:22.667 | 2011-04-21T20:11:02.477 | 4048 | 4048 | [
"logistic"
] |
9842 | 1 | 9869 | null | 16 | 8409 | I need some resources to get started on using neural networks for time series forecasting. I am wary of implementing some paper and then finding out that they have greatly over stated the potential of their methods. So if you have experience with the methods you are suggesting it will be even more awesome.
| Getting started with neural networks for forecasting | CC BY-SA 3.0 | null | 2011-04-21T19:36:55.910 | 2019-05-29T09:24:16.947 | 2019-05-29T09:24:16.947 | 53690 | null | [
"time-series",
"neural-networks",
"forecasting",
"references"
] |
9843 | 2 | null | 9839 | 3 | null | Jason,
Holt-Winters is a particular model form, normally additive or multiplicative and apparently may not be applicable to your particular time series. In general a Transfer Function incorporating both stochastic and deterministic structure has been found to a powerful way of handling problems like this. The problem y... | null | CC BY-SA 3.0 | null | 2011-04-21T19:40:45.750 | 2011-04-21T19:40:45.750 | null | null | 3382 | null |
9844 | 1 | 19132 | null | 7 | 593 | While studying machine learning, I've read the following statement:
>
The kernel $K(x,y)=(x\cdot y+1)^d$ , for $x, y \in \mathbb{R}^p$, has $M={p+d \choose d}$ eigenfunctions that span the space of polynomials in $\mathbb{R}^p$ of total degree $d$.
I do not understand how does the $M={p+d \choose d}$ come from? What... | Number of eigenfunctions for kernel | CC BY-SA 3.0 | null | 2011-04-21T20:05:29.060 | 2015-04-19T20:51:00.977 | 2015-04-19T20:51:00.977 | 9964 | 3026 | [
"machine-learning",
"svm",
"kernel-trick"
] |
9845 | 1 | null | null | -1 | 935 | I have 1000 data for two continuous variables (pressure and temperature). I'd like to calculate Bayesian probability between two variables.
In other words, I would like to determine probability that temperature will increase/decrease if pressure is changed?
| How to calculate Bayesian probability between two variables? | CC BY-SA 3.0 | null | 2011-04-21T21:22:26.710 | 2012-09-02T02:55:25.620 | 2012-09-02T02:55:25.620 | 3826 | 4282 | [
"probability",
"correlation",
"bayesian"
] |
9846 | 2 | null | 9845 | 2 | null | Relationships between physical quantities (like pressure and temperature) often may be described using functions or equations, and sometimes the measurement error is small – this might be the case here as well. If so you could derive the type of relationship and the specific function from physics knowledge you could us... | null | CC BY-SA 3.0 | null | 2011-04-21T21:54:04.843 | 2011-04-21T21:54:04.843 | null | null | 3911 | null |
9847 | 2 | null | 9835 | 4 | null | Mathematica will do integrals (and simplify the results) like no tomorrow. You have to be a little careful specifying your assumptions - that is, you should specify all of them - but it works quite well. If you're a student then your university may well have a site license but if you're just using it for a couple of pr... | null | CC BY-SA 3.0 | null | 2011-04-21T23:13:24.443 | 2011-04-21T23:13:24.443 | null | null | 26 | null |
9848 | 2 | null | 9845 | 3 | null | I think there is a relationship between pressure, temperature and volume (if my memory of high school chemistry serves me correctly - confirmed by [wikipedia](http://en.wikipedia.org/wiki/Gas_laws)):
$$T=\frac{PV}{kN}$$
$$\begin{array} \\
P=\text{Pressure} \\
V=\text{Volume} \\
k=\text{Boltzman's constant}\\
N=\tex... | null | CC BY-SA 3.0 | null | 2011-04-22T01:20:12.637 | 2011-04-23T14:37:22.967 | 2011-04-23T14:37:22.967 | 2392 | 2392 | null |
9850 | 1 | null | null | 16 | 47783 | I tried clustering a set of data (a set of marks) and got 2 clusters. I would like to graphically represent it. Bit confused about the representation, since I don't have the (x,y) coordinates.
Also looking for MATLAB/Python function for doing so.
EDIT
I think posting data make the question clearer. I have two clusters ... | How to plot data output of clustering? | CC BY-SA 3.0 | null | 2011-04-22T02:22:15.400 | 2017-08-11T09:10:39.017 | 2014-06-19T03:16:03.887 | 7290 | 2721 | [
"clustering",
"data-visualization",
"python"
] |
9851 | 1 | null | null | 5 | 432 | Where can I obtain more weather data?
NOAA has some:
- http://weather.noaa.gov/pub/SL.us008001/DF.an/DC.sflnd/DS.synop/
- http://weather.noaa.gov/pub/SL.us008001/DF.an/DC.sflnd/DS.metar/
- http://weather.noaa.gov/pub/SL.us008001/DF.an/DC.sfmar/DS.dbuoy/
- http://weather.noaa.gov/pub/SL.us008001/DF.an/DC.sfmar/... | Where can I obtain more weather data? | CC BY-SA 3.0 | null | 2011-04-22T02:35:47.893 | 2013-09-09T01:17:23.823 | 2013-09-09T01:17:23.823 | 7290 | null | [
"data-mining",
"dataset"
] |
9852 | 1 | 9953 | null | 9 | 5994 | Let's say I have two regression models, one with three variables and one with four. Each spits out an adjusted r^2, which I can compare directly.
Obviously, the model with the higher adjusted r^2 is the better fit, but is there way to test the difference between the two adjusted r^2 and get a p-value?
I know you can d... | Testing difference between two (adjusted) r^2 | CC BY-SA 3.0 | null | 2011-04-22T02:41:34.453 | 2017-05-11T14:55:18.430 | 2011-04-22T03:40:03.617 | 1977 | 1977 | [
"regression"
] |
9853 | 2 | null | 9852 | 0 | null | Take a look at Mallow's Cp:
[Mallow's Cp](http://en.wikipedia.org/wiki/Mallows%27_Cp)
Here's a related question:
[Is there a way to optimize regression according to a specific criterion?](https://stats.stackexchange.com/questions/8918/is-there-a-way-to-optimize-regression-according-to-a-specific-criterion)
| null | CC BY-SA 3.0 | null | 2011-04-22T02:51:35.427 | 2011-04-22T02:51:35.427 | 2017-04-13T12:44:48.343 | -1 | 2775 | null |
9854 | 1 | null | null | 1 | 5300 | I tried `CorrelationFunction[Transpose[{data,data}]][[All,1,2]]` but it doesn't work! I mean the results are identical with those if I run `CorrelationFunction[data]`.
| How can I calculate the autocorrelation of a signal in Mathematica environment? | CC BY-SA 3.0 | null | 2011-04-22T03:26:07.057 | 2012-09-13T19:02:10.430 | 2011-04-22T06:17:24.753 | 2116 | 4286 | [
"autocorrelation",
"mathematica"
] |
9855 | 5 | null | null | 0 | null | Mathematica is a software package for symbolic and numerical computation. It has a graphical user interface with high quality graphics output. It is used for numerical, mathematical, statistical and other calculations.
| null | CC BY-SA 3.0 | null | 2011-04-22T06:52:58.393 | 2013-06-27T09:54:27.757 | 2013-06-27T09:54:27.757 | 805 | 805 | null |
9856 | 4 | null | null | 0 | null | Mathematica is a software package for symbolic mathematical computations. | null | CC BY-SA 3.0 | null | 2011-04-22T06:52:58.393 | 2012-04-23T01:38:57.783 | 2012-04-23T01:38:57.783 | 919 | 2116 | null |
9857 | 3 | null | null | 0 | null | null | CC BY-SA 3.0 | null | 2011-04-22T06:59:10.240 | 2011-04-22T06:59:10.240 | 2011-04-22T06:59:10.240 | -1 | -1 | null | |
9858 | 3 | null | null | 0 | null | Linear regression is a type of regression when regression function is linear. It is most widely used regression type. | null | CC BY-SA 3.0 | null | 2011-04-22T06:59:10.240 | 2011-04-22T09:24:38.193 | 2011-04-22T09:24:38.193 | 2116 | -1 | null |
9859 | 1 | 14654 | null | 22 | 3189 | Consider a vector of parameters $(\theta_1, \theta_2)$, with $\theta_1$ the parameter of interest, and $\theta_2$ a nuisance parameter.
If $L(\theta_1, \theta_2 ; x)$ is the likelihood constructed from the data $x$, the profile likelihood for $\theta_1$ is defined as $L_P(\theta_1 ; x) = L(\theta_1, \hat{\theta}_2(\the... | What are the disadvantages of the profile likelihood? | CC BY-SA 3.0 | null | 2011-04-22T07:01:16.563 | 2021-03-15T07:10:51.920 | 2011-04-22T07:24:18.283 | 3019 | 3019 | [
"maximum-likelihood",
"likelihood",
"profile-likelihood"
] |
9860 | 5 | null | null | 0 | null | null | CC BY-SA 3.0 | null | 2011-04-22T07:09:42.677 | 2011-04-22T07:09:42.677 | 2011-04-22T07:09:42.677 | -1 | -1 | null | |
9861 | 4 | null | null | 0 | null | Dynamic regression is a type of regression, where one of the independent variables is a lagged dependent variable. | null | CC BY-SA 3.0 | null | 2011-04-22T07:09:42.677 | 2011-04-22T07:24:23.333 | 2011-04-22T07:24:23.333 | 2116 | 2116 | null |
9862 | 2 | null | 9854 | 6 | null | It's been a long since I didn't play with Mathematica, and I just had a quick look on Google, but can't you just use (here with some fake data)
```
x = Table[Sin[x] + 0.2 RandomReal[], {x, -4, 4, .1}];
ListPlot[x, DataRange -> {-4, 4}]
```
the functi... | null | CC BY-SA 3.0 | null | 2011-04-22T07:15:33.557 | 2011-04-22T07:23:44.970 | 2011-04-22T07:23:44.970 | 930 | 930 | null |
9863 | 2 | null | 9807 | 8 | null |
### 1. How do I input them in SPSS?
You can open an Excel file in SPSS.
Use the standard file open option, and select `file type = *xls`.
Try to ensure that the first row has the variable names.
### 2. How do I work out the frequency of replies for each recipient?
- Do you mean the frequency of responses for eac... | null | CC BY-SA 3.0 | null | 2011-04-22T07:55:35.147 | 2011-04-22T08:19:58.057 | 2011-04-22T08:19:58.057 | 183 | 183 | null |
9864 | 1 | 9873 | null | 6 | 827 | I'm trying to use the EM cluster algorithm, provided by the software Weka, to classify my data and it only finds one cluster.
- Could I interpret this as there are no ways to distinguish the instances in my sample?
This is a result that is coherent with others analysis that I'm doing to the data, but I don't know... | Interpretation of a one cluster solution using the EM cluster algorithm | CC BY-SA 3.0 | 0 | 2011-04-22T08:34:21.153 | 2011-04-24T18:02:58.863 | 2011-04-24T18:02:58.863 | 4203 | 4203 | [
"machine-learning",
"clustering",
"weka"
] |
9865 | 2 | null | 9813 | 1 | null | Your problem would fall under the category of "missing data." Ultimately, one way or another you are going to have to infer the hidden variables $Z$. This can be done using the [Expectation-Maximization Algorithm](http://en.wikipedia.org/wiki/Expectation-maximization_algorithm).
| null | CC BY-SA 3.0 | null | 2011-04-22T09:29:57.620 | 2011-04-22T09:29:57.620 | null | null | 3567 | null |
9866 | 2 | null | 9735 | 2 | null | I will take this as an opportunity to explain some fundamental issues regarding the difference between frequentist and Bayesian statistics, by interpreting frequentist practices from a Bayesian standpoint.
In this example, we have observed data $D_1$ for the original and data $D_2$ for the combination case. One assume... | null | CC BY-SA 3.0 | null | 2011-04-22T09:59:08.940 | 2011-04-22T10:05:54.733 | 2011-04-22T10:05:54.733 | 3567 | 3567 | null |
9867 | 1 | null | null | 7 | 3010 | I have performed a Cochran's Q test for a within-subjects experimental design with 3 conditions and 36 participants with a dichotomous dependent variable.
I found a (just) statistically significant effect ($\chi^2$ = 6.00, df = 2, p = 0.04979) and would like to also report the effect size, but haven't been able to find... | Effect size of Cochran's Q | CC BY-SA 3.0 | null | 2011-04-22T11:44:46.420 | 2018-04-04T16:19:40.230 | 2014-07-25T15:04:43.617 | 44269 | 4258 | [
"categorical-data",
"repeated-measures",
"effect-size",
"cochran-q"
] |
9868 | 1 | null | null | 0 | 5141 | Hey, im a beginner to R and trying to run pvclust so as to test a cluster solution.
I've managed to load data and run the heirachical cluster, however the code i find online for running pvclust is constantly producing errors - just wondering if someone can point out where I'm going wrong...
here is my code (data alread... | Problem with pvclust in R | CC BY-SA 3.0 | null | 2011-04-22T12:00:12.613 | 2014-09-29T11:21:29.533 | 2011-04-22T16:05:37.807 | null | null | [
"r",
"clustering"
] |
9869 | 2 | null | 9842 | 11 | null | Here's a good quick introduction:
[intro to neural networks.](http://arxiv.org/pdf/cs/0308031)
Note that R has neural-network functionality, so no need to spend any time implementing NN yourself until you've given it a spin and decided it looks promising for your application.
Neural networks are not obsolete, but they ... | null | CC BY-SA 3.0 | null | 2011-04-22T12:58:02.280 | 2011-04-22T12:58:02.280 | 2017-04-13T12:44:46.083 | -1 | 2917 | null |
9870 | 2 | null | 9842 | 6 | null | While it is focussed on statistical pattern recognition, rather than time series forecasting, I would strongly recommend Chris Bishop's book [Neural Networks for Pattern Recognition](http://www.oup.com/us/catalog/general/subject/Medicine/Neuroscience/?view=usa&ci=9780198538646) becuase it is the best introduction to ne... | null | CC BY-SA 3.0 | null | 2011-04-22T13:18:17.590 | 2011-04-22T13:18:17.590 | null | null | 887 | null |
9871 | 1 | 9875 | null | 14 | 3818 | I have a "basic statistics" concept question. As a student I would like to know if I'm thinking about this totally wrong and why, if so:
Let's say I am hypothetically trying to look at the relationship between "anger management issues" and say divorce (yes/no) in a logistic regression and I have the option of usin... | Is a predictor with greater variance "better"? | CC BY-SA 3.0 | null | 2011-04-22T13:28:43.467 | 2011-04-23T15:48:17.837 | 2011-04-22T16:11:54.387 | null | 4054 | [
"regression",
"logistic"
] |
9872 | 1 | null | null | 6 | 5018 | I have a set of variables for building credit scorecards with logistic-regression. I need to bin some variables, for e.g. years of credit history. What is the method to determine how many bins and what is the interval for each bin?
| Binning raw data prior to building a logistic regression model | CC BY-SA 4.0 | null | 2011-04-22T13:42:38.583 | 2020-03-18T01:04:57.843 | 2020-03-18T01:04:57.843 | 11887 | null | [
"regression",
"logistic",
"binning"
] |
9873 | 2 | null | 9864 | 1 | null | Two assumptions here: 1) Weka's finding the number of clusters (k) without issues, and 2) I believe EM uses mixtures of Gaussians which means the clusters need to be round/elliptical.
So, given that Weka's algorithm is finding the best k, the answer would be that using round/elliptical clusters, the most likely cluster... | null | CC BY-SA 3.0 | null | 2011-04-22T13:58:04.903 | 2011-04-22T13:58:04.903 | null | null | 1764 | null |
9874 | 2 | null | 9871 | 1 | null | Always check the assumptions for the statistical test you're using!
One of the assumptions of logistic regression is independence of errors which means that cases of data should not be related. Eg. you can't measure the same people at different points in time which I fear you may have done with your anger management su... | null | CC BY-SA 3.0 | null | 2011-04-22T14:05:57.473 | 2011-04-22T14:05:57.473 | null | null | 3597 | null |
9875 | 2 | null | 9871 | 11 | null | A few quick points:
- Variance can be arbitrarily increased or decreased by adopting a different scale for your variable. Multiplying a scale by a constant greater than one would increase the variance, but not change the predictive power of the variable.
- You may be confusing variance with reliability. All else bei... | null | CC BY-SA 3.0 | null | 2011-04-22T14:11:05.953 | 2011-04-23T15:48:17.837 | 2011-04-23T15:48:17.837 | 2669 | 183 | null |
9876 | 1 | 9914 | null | 4 | 1703 | Suppose that I am conducting a questionnaire study that is trying to measure level of awareness of subjects about a programming language and find the relation of those level of awareness to working conditions and methods etc.
To improve my precision I decided to go with stratified sampling. If I have 1 criterion for st... | Stratified sampling question | CC BY-SA 3.0 | null | 2011-04-22T14:31:04.797 | 2017-08-10T10:34:13.537 | 2017-08-10T10:34:13.537 | 173120 | 4043 | [
"stratification"
] |
9877 | 2 | null | 9872 | 6 | null | Binning will result in a more complex model, i.e., you will need more terms in the model to predict the outcome as well as a model that treats the predictors as continuous. Bins also bring a degree of arbitrariness into the model. Take a look at regression splines as an alternative. Notes about this may be found at ... | null | CC BY-SA 3.0 | null | 2011-04-22T15:32:13.207 | 2011-04-22T15:32:13.207 | null | null | 4253 | null |
9878 | 2 | null | 9868 | 2 | null | It's difficult to answer without seeing the data itself, but my best guess is that you have some non numerical entries in the matrix/dataframe (which is what is expected by `pvclust`). For example,
```
> as.numeric(c(1,2,"NA"))
[1] 1 2 NA
```
or
```
> dist(c(1,2,"NA"))
1 2
2 1
3 NA NA
```
will produce the... | null | CC BY-SA 3.0 | null | 2011-04-22T15:58:34.223 | 2011-04-22T15:58:34.223 | null | null | 930 | null |
9879 | 1 | 9882 | null | 11 | 24728 | Given two multivariate gaussian (say in 2D with mean $\mu$ as a 2D point and convariance marix $\Sigma$ as $2$x$2$ Matrix) $N_1(\mu_1,\Sigma_1)$ and $N_2(\mu_2,\Sigma_1)$, I would like to derive the pdf of $N_1+N_2$.
Can any one point me to the reference where i can find the pdf derivation of $N_1 + N_2$.
Thanks in ad... | Addition of multivariate gaussians | CC BY-SA 3.0 | null | 2011-04-22T16:49:50.407 | 2022-02-25T12:53:49.053 | 2011-04-23T08:07:08.500 | null | 4290 | [
"multivariate-analysis",
"normal-distribution"
] |
9880 | 1 | 9958 | null | 3 | 336 | I'd like to model a set of processes. The processes in question are related to human-decision making, so the model will need a measure of input, processing, and then, finally, output.
Ideally, the model would be implemented in something like R (which I know quite well) or Python (which I know less well).
### Question... | What models and software are suited to modelling human decision making? | CC BY-SA 3.0 | null | 2011-04-22T17:40:49.883 | 2011-04-25T16:55:53.633 | 2011-04-23T07:09:31.090 | 183 | 4204 | [
"r",
"modeling"
] |
9881 | 2 | null | 9868 | 0 | null | Pvclust is a bit odd in that it expects your data to be organized with the observations in the rows, rather than the columns. This is what the documentation in `?pvclust` tries to explain, just not very well.
try transposing your original data matrix in the call to `pvclust()` and see if it runs.
i.e.,
```
fit <- pvcl... | null | CC BY-SA 3.0 | null | 2011-04-22T17:42:06.957 | 2011-04-22T17:42:06.957 | null | null | 1475 | null |
9882 | 2 | null | 9879 | 19 | null |
## Method 1: characteristic functions
Referring to (say) the Wikipedia article on the [multivariate normal distribution](http://en.wikipedia.org/wiki/Multivariate_normal_distribution) and using the 1D technique to compute sums in the article on [sums of normal distributions](http://en.wikipedia.org/wiki/Sum_of_norma... | null | CC BY-SA 4.0 | null | 2011-04-22T19:46:13.887 | 2022-02-25T12:53:49.053 | 2022-02-25T12:53:49.053 | 919 | 919 | null |
9883 | 2 | null | 9871 | 9 | null | A simple example helps us identify what is essential.
Let
$$Y = C + \gamma X_1 + \varepsilon$$
where $C$ and $\gamma$ are parameters, $X_1$ is the score on the first instrument (or independent variable), and $\varepsilon$ represents unbiased iid error. Let the score on the second instrument be related to the first on... | null | CC BY-SA 3.0 | null | 2011-04-22T20:15:55.110 | 2011-04-22T20:15:55.110 | 2017-04-13T12:44:24.947 | -1 | 919 | null |
9884 | 1 | null | null | 3 | 1830 |
### Question on pairs()
I'd like to use `pairs()` to choose a functional form for modeling a set of data. I know that several of my independent and dependent variables are probably lognormally distributed, so I'd like to produce `pairs()` plots where some of my variables are plotted on log axes and some are not.
It... | Selectively tweaking pairs() axes? | CC BY-SA 3.0 | null | 2011-04-22T21:52:28.570 | 2011-04-23T06:57:50.600 | 2011-04-23T06:57:50.600 | 183 | 4237 | [
"r",
"data-visualization",
"scatterplot"
] |
9885 | 1 | 9915 | null | 39 | 1089 | I have a Machine Learning course this semester and the professor asked us to find a real-world problem and solve it by one of machine learning methods introduced in the class, as:
- Decision Trees
- Artificial Neural Networks
- Support Vector Machines
- Instance-based Learning (kNN, LWL)
- Bayesian Networks
- Rei... | Application of machine learning methods in StackExchange websites | CC BY-SA 3.0 | null | 2011-04-22T22:27:24.467 | 2011-11-10T17:36:47.890 | 2011-04-23T22:30:19.920 | 2970 | 2148 | [
"machine-learning"
] |
9886 | 1 | null | null | 6 | 2841 | I have a set of data that looks at the number of "hits" a specific program makes over the course of time. The data goes back to September 2010, and includes data up to March 2011, so the data points are monthly. What I want to see if the most recent data (March 2011) shows a statistically significant decrease in the ... | Statistical test for a series of data over time | CC BY-SA 3.0 | null | 2011-04-22T23:01:54.647 | 2013-02-25T20:26:55.607 | null | null | 4292 | [
"time-series",
"statistical-significance"
] |
9887 | 2 | null | 9885 | 9 | null | I was thinking about tag prediction, too, I like the idea. I have the feeling that it is possible, but you may need to overcome many issues before you arrive to your final dataset. So I speculate the tag prediction may need a lot of time. In addition to incorrect tags the limit of max 5 tags may play a role. Also that ... | null | CC BY-SA 3.0 | null | 2011-04-22T23:16:59.540 | 2011-04-22T23:16:59.540 | null | null | 3911 | null |
9890 | 2 | null | 9809 | 2 | null | If you are just looking to simplify an expression involving expectations, Economists’ Mathematical Manual has a nice concise list of identities. You can find a copy online [here](http://www.tbparis.com/Econ230/References%20from%20Web/Sydsaeter%20etal%20-%20Economists%27%20Mathematical%20Manual,%204th%20Edition,%20Sprin... | null | CC BY-SA 3.0 | null | 2011-04-23T02:12:41.907 | 2011-04-23T02:12:41.907 | null | null | 4281 | null |
9892 | 1 | null | null | 9 | 10359 | I am trying to perform logistic regression with lasso. For the logistic regression part I am using `PROC LOGISTIC` but I am not sure how to do lasso with `PROC LOGISTIC`. I searched online and found that `PROC GLMSELECT` allows us to do lasso. But I am not sure how to do logistic regression with lasso using `PROC GLMS... | How to perform logistic regression with lasso using GLMSELECT? | CC BY-SA 3.0 | null | 2011-04-23T02:46:51.203 | 2011-06-21T01:31:29.493 | 2011-04-23T07:56:14.857 | null | 3897 | [
"logistic",
"sas",
"lasso"
] |
9893 | 1 | 9896 | null | 2 | 4885 | Context: To recommend a minimum sample size when performing multivariate testing of a web page. The sample size would vary based on the number of factors being tested (e.g. A heading, and an image) and the number of variations of a factor (e.g. Two different headings, and three different images). The goal could be to... | Formula for recommended sample size for multivariate testing | CC BY-SA 3.0 | null | 2011-04-23T00:55:25.810 | 2011-04-23T06:48:32.757 | 2011-04-23T06:48:32.757 | 930 | 4346 | [
"multivariate-analysis",
"sample-size"
] |
9895 | 1 | 55221 | null | 2 | 6693 | I've tough luck with the use of nls() in R for the following model
$$N_e = N_o\{1-exp[\frac{(d+bN_o)(T_h N_e - T)}{(1+c N_o)}]\}$$
where $b>0$, $c\geq 0$, $T_h>0$, and $T=72$.
This code
```
T <- 72
NLS.Fit3 <- nls(Ne~No*(1-exp((d+b*No)*(Th*Ne-T)/(1+c*No))), data = Data,
start = list(d = 0.01, b = 0.01, Th... | Having trouble with nls function in R | CC BY-SA 3.0 | null | 2011-04-23T03:51:41.710 | 2013-04-05T08:06:00.117 | 2011-04-23T09:16:25.693 | 3903 | 3903 | [
"r",
"nonlinear-regression"
] |
9896 | 2 | null | 9893 | 2 | null | Commonly, the different values that a factor can attain in an experiment are called "levels". So let's say there are $k$ factors, and factor $j$ has $n_j$ levels.
There are $n_{f1}\cdot n_{f2}\cdot \dots \cdot n_{fk}$ possible factor combinations, i.e. possible versions of web pages that could be viewed. To answer the ... | null | CC BY-SA 3.0 | null | 2011-04-23T04:20:25.040 | 2011-04-23T04:20:25.040 | null | null | 4062 | null |
9897 | 2 | null | 9884 | 2 | null | With pairs, you can set the upper, lower and diagonal panels to display differently by passing functions which setup the layout of the plot (so, use `text()` or `points()` or `lines()` rather than `plot()` which makes a new plot).
So something like this:
```
set.seed(123)
#fake some data
exp1 <- rnorm(1000,5,1)
exp2 <... | null | CC BY-SA 3.0 | null | 2011-04-23T05:27:54.883 | 2011-04-23T05:27:54.883 | null | null | 3732 | null |
9898 | 1 | 9900 | null | 20 | 33089 | Could someone come up with R code to plot an ellipse from the eigenvalues and the eigenvectors of the following matrix
$$
\mathbf{A} = \left(
\begin{array} {cc}
2.2 & 0.4\\
0.4 & 2.8
\end{array} \right)
$$
| How to plot an ellipse from eigenvalues and eigenvectors in R? | CC BY-SA 4.0 | null | 2011-04-23T06:48:25.657 | 2020-03-20T13:03:06.523 | 2020-03-20T02:39:36.017 | 11887 | 3903 | [
"r",
"multivariate-analysis",
"matrix",
"matrix-decomposition",
"geometry"
] |
9899 | 2 | null | 9898 | 9 | null | I think this is the R code that you want. I borrowed the R-code from this [thread](https://stat.ethz.ch/pipermail/r-help/2006-October/114652.html) on the r-mailing list. The idea basically is: the major and minor half-diameters are the two eigen values and you rotate the ellipse by the amount of angle between the first... | null | CC BY-SA 3.0 | null | 2011-04-23T09:18:26.517 | 2011-04-23T18:20:42.837 | 2011-04-23T18:20:42.837 | 1307 | 1307 | null |
9900 | 2 | null | 9898 | 19 | null | You could extract the eigenvectors and -values via `eigen(A)`. However, it's simpler to use the Cholesky decomposition. Note that when plotting confidence ellipses for data, the ellipse-axes are usually scaled to have length = square-root of the corresponding eigenvalues, and this is what the Cholesky decomposition giv... | null | CC BY-SA 3.0 | null | 2011-04-23T09:25:59.283 | 2011-04-23T11:04:09.037 | 2011-04-23T11:04:09.037 | 1909 | 1909 | null |
9901 | 2 | null | 9895 | 2 | null | I now think you have a problem with data. Why?
First of all, let's get rid of $e^x$, solving it out and taking $\log$ of both sides
$$\log\left(\frac{N_0-N_e}{N_0}\right)=\frac{(d+bN_0)(T_h N_e -T)}{1+cN_0}.$$
Now, this log should be a linear function of $N_e$ for fixed $N_0$. As I understand, $T-T_h N_e $ is a time av... | null | CC BY-SA 3.0 | null | 2011-04-23T10:40:49.003 | 2011-04-24T18:04:48.237 | 2011-04-24T18:04:48.237 | null | null | null |
9902 | 2 | null | 9841 | 2 | null | Your initial logistic regression approach seems reasonable, assuming good diagnostics. However, if I understand the scientific question right, you'll need an interaction between year and day to see differences between years in the tendency to be in diapause as day increases. The main effect of year only says that one... | null | CC BY-SA 3.0 | null | 2011-04-23T11:22:22.667 | 2011-04-23T11:22:22.667 | null | null | 1739 | null |
9903 | 2 | null | 9886 | 1 | null | As GaBorgulya pointed out one needs to have a model to detect the potential anomaly. This model needs to generate a "white noise" error series or be sufficient to separate signal and noise. With this model in hand based upon older data one could then compare the new value with the prediction interval. This is the class... | null | CC BY-SA 3.0 | null | 2011-04-23T12:39:47.857 | 2011-04-23T13:08:48.037 | 2011-04-23T13:08:48.037 | 3382 | 3382 | null |
9904 | 1 | 9906 | null | 6 | 4415 | The location and scale of a normally distributed data can be estimated by sampling the data then taking the mean of the sample means and standard deviations, respectively. For non-normal (heavy-tailed) data, is it correct to take the median of the sample medians and IQR/MAD, instead? That is, is it correct to use the m... | Median of medians as robust mean of means? | CC BY-SA 3.0 | null | 2011-04-23T13:28:08.003 | 2019-07-08T19:16:50.617 | 2012-06-11T12:42:35.523 | 4856 | 3074 | [
"robust",
"mad"
] |
9905 | 2 | null | 9880 | 2 | null | Have you thought about agent based modeling/social simulation? It's not that clear from your question what exactly you're trying to achieve but ABM may be suitable for your purposes as it does lend itself to modelling human decision making as you can programme agents with different characteristics. It's also very good ... | null | CC BY-SA 3.0 | null | 2011-04-23T13:36:38.763 | 2011-04-23T13:36:38.763 | null | null | 3597 | null |
9906 | 2 | null | 9904 | 3 | null | If all the samples come from the same distribution, then yes the median of the sample medians is a fairly robust estimate of the median of the underlying distribution (though this need not be the same as the mean), since the median of a sample from a continuous distribution has probability 0.5 of being below (or above)... | null | CC BY-SA 4.0 | null | 2011-04-23T13:42:53.630 | 2019-07-08T19:16:50.617 | 2019-07-08T19:16:50.617 | 2958 | 2958 | null |
9907 | 1 | null | null | 2 | 477 | This questions consists of two parts that are quite similar and concern conditional probability.
Firstly, I would like to confirm the calculation of the conditional probability when we know the value of a random variable. Let's assume that we have a conditional matrix which gives us the values for $P(D|A\&B)$ and that ... | Conditional probability and instantiation | CC BY-SA 3.0 | 0 | 2011-04-23T17:03:22.900 | 2014-05-03T18:27:51.233 | 2011-04-23T18:04:16.397 | null | 4297 | [
"conditional-probability"
] |
9911 | 1 | null | null | 2 | 237 | I'm new in stats, and maybe this is a duplicated question, but I could not find a similar one.
I'm trying to reduce a dimension of my dataset. Maybe reduce is not a good word. I need to sample some of my dimensions.
Setup
For example:
- I have $M$ events (let's say $M \tilde{} 60$). They are ALL labeled.
- I h... | Sampling dataset, choosing among N dimensions | CC BY-SA 3.0 | null | 2011-04-23T20:27:33.923 | 2011-04-25T18:15:06.373 | 2011-04-25T08:24:48.713 | 2148 | 1313 | [
"pca",
"sampling",
"large-data"
] |
9913 | 1 | null | null | 5 | 20816 | In the log-log regression case,
$$\log(Y) = B_0 + B_1 \log(X) + U \>,$$
can you show $B_1$ is the elasticity of $Y$ with respect to $X$, i.e., that $E_{yx} = \frac{dY}{dX}(\frac{Y}{X})$?
| Elasticity of log-log regression | CC BY-SA 3.0 | null | 2011-04-23T18:08:25.280 | 2020-04-04T22:33:35.513 | 2011-04-24T23:06:07.013 | 2970 | null | [
"regression",
"self-study"
] |
9914 | 2 | null | 9876 | 7 | null | There isn't going to be one best answer for this kind of sampling. It depends on the observable covariates in your sampling frame, the variables you expect to be important determinants of survey response, and the analysis you want to run once the survey is complete.
With that said, there are a couple of general princi... | null | CC BY-SA 3.0 | null | 2011-04-23T21:47:19.023 | 2011-04-25T18:05:38.407 | 2011-04-25T18:05:38.407 | 4110 | 4110 | null |
9915 | 2 | null | 9885 | 28 | null | Yes, I think tag prediction is an interesting one and one for which you have a good shot at "success".
Below are some thoughts intended to potentially aid in brainstorming and further exploration of this topic. I think there are many potentially interesting directions that such a project could take. I would guess that... | null | CC BY-SA 3.0 | null | 2011-04-23T22:26:56.633 | 2011-04-25T23:08:59.523 | 2011-04-25T23:08:59.523 | 2970 | 2970 | null |
9916 | 2 | null | 9876 | 4 | null | You basically run a pilot study so you can guess which strata are more variable and then oversample on those strata.
Acquire a book on sampling. (Maybe check out the QA 278 section of a university library. I have Sampling of Populations by Levy and Lemeshow, but I'm sure others are fine.) Look at sample size estimation... | null | CC BY-SA 3.0 | null | 2011-04-23T23:48:58.730 | 2011-04-24T00:05:45.970 | 2011-04-24T00:05:45.970 | 3874 | 3874 | null |
9917 | 1 | 9924 | null | 4 | 268 | I was actually looking at this [problem](http://math.mit.edu/~jsoto/localpapers/spams2010.pdf) on slide 12. I will write it here briefly:
Problem:
Unknown number of people arriving in a fixed time period and my goal is to maximize my probability of picking the best candidate
Assumptions:
- Assume people arrive in in... | Meaning of this expectation equation? | CC BY-SA 3.0 | null | 2011-04-24T03:17:39.473 | 2011-05-24T13:49:46.970 | 2011-04-24T03:25:03.007 | 2164 | 2164 | [
"probability",
"algorithms",
"stochastic-processes",
"expected-value"
] |
9918 | 1 | 9944 | null | 14 | 34484 | I have a matrix of 1000 observations and 50 variables each measured on a 5-point scale. These variables are organized into groups, but there aren't an equal number of variables in each group.
I'd like to calculate two types of correlations:
- Correlation within groups of variables (among characteristics): some measur... | How to compute correlation between/within groups of variables? | CC BY-SA 3.0 | null | 2011-04-24T03:41:44.080 | 2013-01-22T18:52:18.873 | 2011-04-25T08:26:32.587 | 930 | 4301 | [
"correlation",
"psychometrics",
"scales"
] |
9919 | 2 | null | 4609 | 0 | null | Double-click the output to obtain the test statistics box.
| null | CC BY-SA 3.0 | null | 2011-04-24T04:00:58.850 | 2011-04-24T04:00:58.850 | null | null | 2025 | null |
9920 | 1 | null | null | 3 | 1624 | My question concerns the assumption of additivity for intraclass correlation. I shall first explain what I have done and then end with my questions.
I want to calculate inter-rater reliability using intra-class correlation so I can report an overall coefficient (as done in previous similar research), and perhaps replac... | Assumption of additivity for intra-class correlation | CC BY-SA 3.0 | null | 2011-04-24T05:03:58.663 | 2020-11-16T23:00:43.997 | null | null | 2025 | [
"reliability",
"agreement-statistics",
"intraclass-correlation"
] |
9922 | 2 | null | 856 | 2 | null | One quick thing about sample weights - they are usually a way to incorporate some information about the population that one is sampling from - but usually they are based on "big sample" type scenarios (typically constrained BLUP or BLUE prediction in disguise). So I would imagine that sample weights will probably do n... | null | CC BY-SA 3.0 | null | 2011-04-24T08:04:05.637 | 2011-04-24T08:04:05.637 | null | null | 2392 | null |
9923 | 2 | null | 9233 | 2 | null | The required number of observations to Identify a model depends on the ratio of signal to noise in the data and the form of the model. If I am given the numbers ,1,2,3,4,5 , I will predict 6,7,8,.... Box-Jenkins model identification is an approach to determine the underlying General Term much like the test for "numeric... | null | CC BY-SA 3.0 | null | 2011-04-24T11:34:08.887 | 2011-04-24T11:34:08.887 | null | null | 3382 | null |
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