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6014 | 2 | null | 5995 | 5 | null | Make a table:
```
No renewal Renewal Total
---------- ------- ------
Attribute No 79800 200 80000
Yes 19700 300 20000
---------------------------------------
Total 99500 500 100000
```
The computations are:
- Number of non-renewers = 100,000... | null | CC BY-SA 2.5 | null | 2011-01-05T19:37:25.097 | 2011-01-05T19:37:25.097 | null | null | 919 | null |
6015 | 2 | null | 6013 | 13 | null | Based on the way you phrase the question
>
are outliers not necessarily the best
way to attack the problem of finding
'badness'?
It is not clear that you are looking for outliers. For example, it seems that you are interested in machines performing above/below some threshold.
As an example, if all of your serve... | null | CC BY-SA 3.0 | null | 2011-01-05T20:04:41.887 | 2011-09-29T13:24:27.140 | 2011-09-29T13:24:27.140 | 2817 | 1381 | null |
6016 | 2 | null | 6013 | 4 | null | A simple way to find anomalous servers would be to assume they are identically distributed, estimate the population parameters, and sort them according to their likelihoods, ascending. Column likelihoods would be combined either with their product or their minimum (or some other T-norm). This works pretty well as long ... | null | CC BY-SA 2.5 | null | 2011-01-05T20:59:42.467 | 2011-01-05T20:59:42.467 | null | null | 2456 | null |
6017 | 2 | null | 2691 | 20 | null | From someone who has used PCA a lot (and tried to explain it to a few people as well) here's an example from my own field of neuroscience.
When we're recording from a person's scalp we do it with 64 electrodes. So, in effect we have 64 numbers in a list that represent the voltage given off by the scalp. Now since we re... | null | CC BY-SA 3.0 | null | 2011-01-05T21:11:38.220 | 2018-02-20T19:13:32.433 | 2018-02-20T19:13:32.433 | 2660 | 2660 | null |
6019 | 2 | null | 6013 | 2 | null | Identifying a given data point as an outlier implies that there is some data generating process or model from which the data are expected to come from. It sounds like you are not sure what those models are for the given metrics and clusters you are concerned about. So, here is what I would consider exploring: [statisti... | null | CC BY-SA 4.0 | null | 2011-01-06T04:54:35.017 | 2023-01-04T16:07:59.133 | 2023-01-04T16:07:59.133 | 362671 | 1080 | null |
6020 | 1 | 6031 | null | 3 | 11112 | I'm a programmer with little statistical background, and I'm trying to create something similar to what facebook did recently (with other data):
[http://www.facebook.com/notes/facebook-data-team/whats-on-your-mind/477517358858](http://www.facebook.com/notes/facebook-data-team/whats-on-your-mind/477517358858)
That is to... | Doing correlation on one variable vs many | CC BY-SA 2.5 | null | 2011-01-06T05:27:21.180 | 2011-01-06T12:50:15.300 | null | null | 2664 | [
"r",
"correlation"
] |
6021 | 1 | 6030 | null | 10 | 8112 | THis is a data visualization question. I have a database that contains some data that is constantly revised (online update).
What is the best way in R to update a graph every let say 5 or 10 seconds. (without plotting again the all thing is possible)?
fRed
| R: update a graph dynamically | CC BY-SA 3.0 | null | 2011-01-06T06:44:19.350 | 2011-05-09T04:55:43.877 | 2011-05-09T04:55:43.877 | 1709 | 1709 | [
"r",
"data-visualization"
] |
6022 | 1 | 6038 | null | 30 | 29230 | What methods can I use to infer a distribution if I know only three percentiles?
For example, I know that in a certain data set, the fifth percentile is 8,135, the 50th percentile is 11,259, and the 95th percentile is 23,611. I want to be able to go from any other number to its percentile.
It's not my data, and those... | Estimating a distribution based on three percentiles | CC BY-SA 2.5 | null | 2011-01-06T08:11:22.483 | 2021-09-28T13:36:41.830 | 2011-01-07T13:49:30.270 | 2116 | 2665 | [
"r",
"regression",
"quantiles"
] |
6023 | 2 | null | 6021 | 8 | null | For offline visualization, you can generate PNG files and convert them to an animated GIF using [ImageMagick](http://www.imagemagick.org/). I used it for demonstration (this redraw all data, though):
```
source(url("http://aliquote.org/pub/spin_plot.R"))
dd <- replicate(3, rnorm(100))
spin.plot(dd)
```
This generates ... | null | CC BY-SA 2.5 | null | 2011-01-06T08:14:27.433 | 2011-01-06T08:14:27.433 | null | null | 930 | null |
6024 | 1 | null | null | 5 | 201 | Given observations of $\{y, x_1, x_2, \cdots, x_n\}$, we can always do a linear regression and get all the coefficients $\{c_i\}$ for the model
$$y = c_0 + c_1 x_1 + \cdots + c_n x_n.$$
However, this may not be the best answer. Let me explain it.
When we are doing a regression, we have estimates $\{d_i\}$ for the stand... | Effectively fitting this kind of model: $y = c_1 (x_3 - x_4) + c_2 (x_1 - x_9)$ | CC BY-SA 2.5 | null | 2011-01-06T08:51:17.610 | 2011-01-07T05:26:54.567 | 2011-01-06T16:02:04.530 | 919 | null | [
"regression"
] |
6025 | 2 | null | 6022 | 6 | null | For a lognormal the ratio of the 95th percentile to the median is the same as the ratio of the median to the 5th percentile. That's not even nearly true here so lognormal wouldn't be a good fit.
You have enough information to fit a distribution with three parameters, and you clearly need a skew distribution. For analyt... | null | CC BY-SA 2.5 | null | 2011-01-06T08:56:43.687 | 2011-01-06T08:56:43.687 | null | null | 449 | null |
6026 | 1 | 6028 | null | 20 | 84467 | I'm a medical student trying to understand statistics(!) - so please be gentle! ;)
I'm writing an essay containing a fair amount of statistical analysis including survival analysis (Kaplan-Meier, Log-Rank and Cox regression).
I ran a Cox regression on my data trying to find out if I can find a significant difference be... | How do I interpret Exp(B) in Cox regression? | CC BY-SA 3.0 | null | 2011-01-06T09:12:48.257 | 2019-11-13T10:42:47.700 | 2011-09-07T08:51:16.793 | null | 2652 | [
"regression",
"survival",
"hazard"
] |
6027 | 2 | null | 6022 | 2 | null | About the only things you can infer from the data is that the distribution is nonsymmetric. You can't even tell whether those quantiles came from a fitted distribution or just the ecdf.
If they came from a fitted distribution, you could try all the distributions you can think of and see if any match. If not, there's no... | null | CC BY-SA 2.5 | null | 2011-01-06T10:12:31.150 | 2011-01-06T10:12:31.150 | null | null | 2456 | null |
6028 | 2 | null | 6026 | 24 | null | Generally speaking, $\exp(\hat\beta_1)$ is the ratio of the hazards between two individuals whose values of $x_1$ differ by one unit when all other covariates are held constant. The parallel with other linear models is that in Cox regression the hazard function is modeled as $h(t)=h_0(t)\exp(\beta'x)$, where $h_0(t)$ i... | null | CC BY-SA 4.0 | null | 2011-01-06T10:49:53.407 | 2019-11-13T10:42:47.700 | 2019-11-13T10:42:47.700 | 230 | 930 | null |
6029 | 2 | null | 6020 | 2 | null | Correlation is a rather vague word meaning the fact that one variable is dependent on the other; in many cases this is just a synonym for [Pearson correlation coefficient](http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient), which assumes linear dependence (i.e. $y=A \cdot x+B$), so things like... | null | CC BY-SA 2.5 | null | 2011-01-06T12:00:24.480 | 2011-01-06T12:00:24.480 | null | null | null | null |
6030 | 2 | null | 6021 | 7 | null | Assuming you want to update R `windows()` or `x11()` graph, you can use functions like `points()` and `lines()` to add new points or extend lines on a graph without redraw; yet note that this won't change the axes range to accommodate points that may go out of view. In general it is usually a good idea to make the plot... | null | CC BY-SA 2.5 | null | 2011-01-06T12:10:37.987 | 2011-01-06T12:10:37.987 | null | null | null | null |
6031 | 2 | null | 6020 | 6 | null | To help you get started with the visualization, here is a snippet of R code with simulated data (a matrix with age and counts for 20 words, arranged in columns, for 100 subjects). The computation are done as proposed my @mbq (correlation).
```
n <- 100 # No. subjects
k <- 20 # No. words
words <- paste("word", 1:k, s... | null | CC BY-SA 2.5 | null | 2011-01-06T12:21:07.763 | 2011-01-06T12:50:15.300 | 2011-01-06T12:50:15.300 | 930 | 930 | null |
6032 | 2 | null | 6022 | 2 | null | The use of quantiles to estimate parameters of a priori distributions is discussed in the literature on human response time measurement as "quantile maximum probability estimation" (QMPE, though originally erroneously dubbed "quantile maximum likelihood estimation", QMLE), discussed at length by [Heathcote and colleagu... | null | CC BY-SA 2.5 | null | 2011-01-06T13:10:16.490 | 2011-01-06T13:10:16.490 | null | null | 364 | null |
6033 | 1 | null | null | 13 | 10113 | I came across a picture of an application prototype that finds significant changes ("trends" - not spikes/outliers) in traffic data:
I want to write a program (Java, optionally R) that is able to do the same - but because my statistic skills are a little rusty, I need to... | Detect changes in time series | CC BY-SA 2.5 | null | 2011-01-06T13:53:16.580 | 2022-10-17T21:00:56.683 | 2011-02-28T12:37:08.800 | 2116 | 2667 | [
"time-series",
"change-point"
] |
6036 | 2 | null | 726 | 15 | null | >
Anyone who considers arithmetical
methods of producing random digits is,
of course, in a state of sin.
-- Von Neumann
| null | CC BY-SA 2.5 | null | 2011-01-06T14:22:03.547 | 2011-01-06T14:22:03.547 | null | null | 930 | null |
6037 | 2 | null | 4551 | 17 | null | While I can relate to much of what Michael Lew says, abandoning p-values in favor of likelihood ratios still misses a more general problem--that of overemphasizing probabilistic results over effect sizes, which are required to give a result substantive meaning. This type of error comes in all shapes and sizes and I fi... | null | CC BY-SA 4.0 | null | 2011-01-06T15:28:05.167 | 2022-12-07T12:54:23.950 | 2022-12-07T12:54:23.950 | 362671 | 2669 | null |
6038 | 2 | null | 6022 | 19 | null | Using a purely statistical method to do this work will provide absolutely no additional information about the distribution of school spending: the result will merely reflect an arbitrary choice of algorithm.
You need more data.
This is easy to come by: use data from previous years, from comparable districts, whatever. ... | null | CC BY-SA 2.5 | null | 2011-01-06T16:29:31.403 | 2011-01-06T19:32:30.900 | 2011-01-06T19:32:30.900 | 919 | 919 | null |
6039 | 1 | null | null | 1 | 1939 | I am kinda new to stats and understand random sampling, however I am just learning PCA and wondering if it is just a more sophisticated form of sampling? In other words if I have a large data set. and take a random sample would I then apply PCA to it? Or just apply PCA to the data set.
Thanks for any help..
Mike
| Random Sampling and PCA | CC BY-SA 2.5 | null | 2011-01-06T17:11:11.887 | 2011-01-07T01:34:31.820 | null | null | null | [
"pca"
] |
6040 | 2 | null | 6024 | 4 | null | You can not use the 36-coefficient model, and not because it's going to be slow. Speed is the least of your worries here.
The real trouble is that you've taken an already under-determined problem (because of the correlations), and converted it into a problem which is severely under-determined for any data, because of ... | null | CC BY-SA 2.5 | null | 2011-01-06T17:24:19.430 | 2011-01-06T17:24:19.430 | null | null | 2658 | null |
6041 | 2 | null | 6039 | 2 | null | Well, if you were interested in cross validation (i.e. how much your model will predict on a different sample), then you could use PCA on a random subset and then fit that model to the rest of your data.
That being said, PCA is a tool for summarising a covariance matrix in a smaller matrix, so it may not be the best th... | null | CC BY-SA 2.5 | null | 2011-01-06T17:38:35.500 | 2011-01-06T17:38:35.500 | null | null | 656 | null |
6042 | 1 | 6043 | null | 3 | 766 | (I'm asking this question for a friend, honest...)
>
Is there an easy way to convert from
an SPSS file to a SAS file, which
preserves the formats AND labels?
Saving as a POR file gets me the
labels (I think) but not the POR file.
I tried to save to a SAS7dat file but
it didn't work. Thanks,
| Converting an SPSS file to a SAS file? | CC BY-SA 2.5 | null | 2011-01-06T17:52:31.127 | 2011-01-07T11:01:23.480 | 2011-01-07T11:01:23.480 | null | 253 | [
"spss",
"sas"
] |
6043 | 2 | null | 6042 | 2 | null | I would just suggest they make the syntax to relabel and reformat the variables. You can use the command, `display dictionary.` in PASW (aka SPSS) to output the dictionary in a table that you can copy and paste the variable names and labels. Looking at this [example](http://www.ats.ucla.edu/stat/sas/modules/labels.htm)... | null | CC BY-SA 2.5 | null | 2011-01-06T18:47:42.000 | 2011-01-06T18:59:07.933 | 2011-01-06T18:59:07.933 | 1036 | 1036 | null |
6044 | 1 | 6062 | null | 1 | 4109 | As the title says, I'd like to calculate the percentage difference for two sets of points. For example, suppose I have $S_{1}=\{(1,x_{1}),(2,x_{2}),(3,x_{3})\}$ and $S_{2}=\{(1,y_{1}),(2,y_{2}),(3,y_{3})\}$. How can I know the difference in percentage between both sets of data. What is the correct way to do that? Is th... | Calculate percentage difference for two sets of points | CC BY-SA 2.5 | null | 2011-01-06T19:35:49.930 | 2011-01-07T13:45:52.677 | 2011-01-07T01:03:12.160 | 2676 | 2676 | [
"quantiles"
] |
6045 | 2 | null | 5922 | 7 | null | See
Levina, E. and Bickel, P. (2004) “Maximum Likelihood Estimation of Intrinsic Dimension.” Advances in Neural Information Processing Systems 17
[http://books.nips.cc/papers/files/nips17/NIPS2004_0094.pdf](http://books.nips.cc/papers/files/nips17/NIPS2004_0094.pdf)
Their idea is that if the data are sampled from a sm... | null | CC BY-SA 2.5 | null | 2011-01-06T19:40:32.547 | 2011-01-08T20:17:36.033 | 2011-01-08T20:17:36.033 | 1670 | 1670 | null |
6046 | 1 | null | null | 3 | 3519 | I have animals, that could be virgin or mated (reproductive state is the fixed factor), which I've stimulated sequentially with 4 different doses of an odour (doses are the repeated measures, the same animal was blown with 4 increasing doses of the same odorant). Then, I measure the neuronal response (variable: number ... | How to solve a case of unbalanced repeated measures? | CC BY-SA 2.5 | null | 2011-01-06T19:49:26.740 | 2011-01-10T14:53:32.060 | 2011-01-07T09:36:30.217 | 159 | null | [
"repeated-measures"
] |
6047 | 1 | 40978 | null | 18 | 1149 | Obviously events A and B are independent iff Pr$(A\cap B)$ = Pr$(A)$Pr$(B)$. Let's define a related quantity Q:
$Q\equiv\frac{\mathrm{Pr}(A\cap B)}{\mathrm{Pr}(A)\mathrm{Pr}(B)}$
So A and B are independent iff Q = 1 (assuming the denominator is nonzero). Does Q actually have a name though? I feel like it refers to s... | Does this quantity related to independence have a name? | CC BY-SA 2.5 | null | 2011-01-06T19:50:41.317 | 2018-03-21T18:12:03.933 | null | null | 2485 | [
"probability",
"terminology",
"independence"
] |
6048 | 2 | null | 6046 | 2 | null | Mixed effects analysis (available in [R](http://www.r-project.org) via the [lme4](http://cran.r-project.org/web/packages/lme4/index.html) package, free as always) can handle missing data like this. My understanding is (possibly erroneous? Mixed effects modelling experts please feel free to provide correction) of how th... | null | CC BY-SA 2.5 | null | 2011-01-06T21:06:18.200 | 2011-01-10T14:53:32.060 | 2011-01-10T14:53:32.060 | 364 | 364 | null |
6049 | 2 | null | 6044 | 1 | null | It seems to me like you need to formulate a question you want your data to answer. Let me suggest a few (perhaps you can edit your post to reflect what questions make sense for your data):
- As the DSMC value increases, does the theoretical result also increase?
- If I know the value of the theoretical result, how ac... | null | CC BY-SA 2.5 | null | 2011-01-06T22:25:22.827 | 2011-01-06T22:25:22.827 | null | null | 1540 | null |
6050 | 1 | 6052 | null | 13 | 15896 | I'm doing a simple AIC-based backward elimination model where some variables are categorical variables with multiple levels. These variables are modeled as a set of dummy variables. When doing backward elimination, should I be removing all the levels of a variable together? Or should I treat each dummy variable separat... | How should I handle categorical variables with multiple levels when doing backward elimination? | CC BY-SA 4.0 | null | 2011-01-07T00:15:28.330 | 2020-01-24T00:55:08.693 | 2020-01-24T00:55:08.693 | 11887 | 2308 | [
"model-selection",
"stepwise-regression"
] |
6051 | 2 | null | 4600 | 1 | null | In an anova context, the partial eta squared will tell what % of the Y variance is explained by a given X when controlling for all other X's. In a regression context, you could refer to the squared partial correlation of the X of interest.
| null | CC BY-SA 2.5 | null | 2011-01-07T00:54:44.693 | 2011-01-07T00:54:44.693 | null | null | 2669 | null |
6052 | 2 | null | 6050 | 8 | null | I think you'd have to remove the entire categorical variable. Imagine a logistic regression in which you're trying to predict if a person has a disease or not. Country of birth might have a major impact on that, so you include it in your model. If the specific USAmerican origin didn't have any impact on AIC and you ... | null | CC BY-SA 2.5 | null | 2011-01-07T01:23:04.920 | 2011-01-07T01:23:04.920 | 2017-04-13T12:44:29.923 | -1 | 71 | null |
6053 | 2 | null | 6039 | 1 | null | Principal Components Analysis is a way of distilling a large set of variables into a few topics or themes or fundamentals. It's dimension reduction. The only resemblance I see to sampling is that sampling also involves a kind of reduction.
| null | CC BY-SA 2.5 | null | 2011-01-07T01:34:31.820 | 2011-01-07T01:34:31.820 | null | null | 2669 | null |
6054 | 2 | null | 868 | 1 | null | One picayune thing that could matter down the road is, in your equation
P = 1 / [1 + e^(-35 + (4*4 + 4*4 + 3*0 + 1*1)]
you've misplaced the "-": it needs to go outside the parenthesis. So it'd be
P = 1 / [1 + e^-(a + B1*X1 + B2*X2 + B3*X3...+ Bn*Xn)].
| null | CC BY-SA 2.5 | null | 2011-01-07T01:55:24.827 | 2011-01-07T01:55:24.827 | null | null | 2669 | null |
6055 | 2 | null | 6042 | 0 | null | SPSS writes SAS7dat format files normally with no problems. When you it did not work, what actually happened?
| null | CC BY-SA 2.5 | null | 2011-01-07T02:17:31.810 | 2011-01-07T02:17:31.810 | null | null | null | null |
6056 | 1 | null | null | 8 | 5008 | I try to install rpy2 in my system,
(I compile R with --enable-R-shlib and with --enable-BLAS-shlib flags)
but when I try in python console
```
import rpy2
import rpy2.robjects
```
I got:
```
Traceback (most recent call last):
File "<stdin>", line 1, in<module>
File "/usr/lib/python2.6/dist-packages/rpy2/robje... | Problems with libRblas.so on ubuntu with rpy2 | CC BY-SA 3.0 | null | 2011-01-07T03:23:36.777 | 2017-01-12T19:25:09.840 | 2016-06-29T17:17:36.103 | 119149 | 2680 | [
"r",
"python"
] |
6057 | 2 | null | 6047 | 0 | null | Maybe you are asking how this quantity is related to the Odds Ratio,
as a quantity for measuring independence.
I think you are searching for "Relation to statistical independence". See [http://en.wikipedia.org/wiki/Odds_ratio](http://en.wikipedia.org/wiki/Odds_ratio)
| null | CC BY-SA 2.5 | null | 2011-01-07T03:33:37.813 | 2011-01-07T09:38:47.577 | 2011-01-07T09:38:47.577 | 159 | 2680 | null |
6058 | 2 | null | 6056 | 5 | null | It looks like you tried to do things locally but didn't quite get there. I happen to maintain the Debian packages of R (which get rebuilt for Ubuntu and are accessible at [CRAN](http://cran.r-project.org/bin/linux/ubuntu/). These builds use external BLAS. rpy2 then builds just fine as well.
I would recommend that you ... | null | CC BY-SA 2.5 | null | 2011-01-07T03:52:44.340 | 2011-01-07T03:52:44.340 | null | null | 334 | null |
6059 | 2 | null | 6024 | 5 | null | (This response picks up where @AVB, who has provided useful comments, left off by suggesting we need to figure out which differences $X_i - X_j$ ought to be included among the independent variables.)
The big question here is what is an effective method to identify the model. Later we can worry about faster methods. (... | null | CC BY-SA 2.5 | null | 2011-01-07T05:07:13.817 | 2011-01-07T05:26:54.567 | 2011-01-07T05:26:54.567 | 919 | 919 | null |
6060 | 2 | null | 6047 | 11 | null | I think that you are looking for `Lift` (or improvement). Lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. It is used to interpret the importance of a rule in [association rule mining](http://en.wikipedia.org/wiki/Association_rule_learning... | null | CC BY-SA 2.5 | null | 2011-01-07T08:54:25.257 | 2011-01-07T08:54:25.257 | null | null | 339 | null |
6062 | 2 | null | 6044 | 1 | null | First, let's compare two lists of numbers — are they from the same distribution ?
For example, how close are the lists of 20 numbers, "|" marks,
```
||||||.||.||...||.....||.|................|.....................................
|||.|...|..|...|.......||...|...|...|.....|..|.................|.....|..........
```
? To... | null | CC BY-SA 2.5 | null | 2011-01-07T11:26:21.673 | 2011-01-07T13:45:52.677 | 2017-04-13T12:44:35.347 | -1 | 557 | null |
6063 | 1 | null | null | 4 | 6888 | I have been running 3-level multilevel models with [HLM](http://www.ssicentral.com/hlm/), and my main interest is in some cross-level interaction effects that I am finding. My concern is that the effect sizes of these interactions appear to be small – I am wondering whether they are really meaningful.
I am turning to y... | Evaluating effect sizes of interactions in multiple regression | CC BY-SA 4.0 | null | 2011-01-07T11:46:10.653 | 2021-01-21T18:20:04.660 | 2021-01-21T18:20:04.660 | 11887 | null | [
"regression",
"interaction",
"multilevel-analysis",
"effect-size"
] |
6065 | 2 | null | 6022 | 25 | null | As @whuber pointed out, statistical methods do not exactly work here. You need to infer the distribution from other sources. When you know the distribution you have a non-linear equation solving exercise. Denote by $f$ the quantile function of your chosen probability distribution with parameter vector $\theta$. What yo... | null | CC BY-SA 4.0 | null | 2011-01-07T13:49:08.650 | 2021-09-28T13:36:41.830 | 2021-09-28T13:36:41.830 | 46761 | 2116 | null |
6066 | 1 | null | null | 5 | 1037 | I have carried out a stepwise logistic regression in JMP. Then (using the proper button in the program window), I have chosen to build a nominal logistic regression model using (only) the variables identified by the stepwise procedure.
Anyhow, comparing the summary tables of the stepwise regression and the nominal one,... | Discrepancy between stepwise and nominal logistic regression results in JMP | CC BY-SA 3.0 | null | 2011-01-07T14:49:08.103 | 2021-01-09T13:01:05.337 | 2012-09-30T21:44:14.593 | 686 | 1219 | [
"logistic",
"stepwise-regression",
"jmp"
] |
6067 | 1 | 6086 | null | 120 | 127752 | Okay, so I think I have a decent enough sample, taking into account the 20:1 rule of thumb: a fairly large sample (N=374) for a total of 7 candidate predictor variables.
My problem is the following: whatever set of predictor variables I use, the classifications never get better than a specificity of 100% and a sensiti... | Does an unbalanced sample matter when doing logistic regression? | CC BY-SA 2.5 | null | 2011-01-07T16:48:03.487 | 2022-07-21T17:24:22.547 | 2022-07-21T17:24:22.547 | 1352 | 2690 | [
"regression",
"logistic",
"sample-size",
"unbalanced-classes",
"faq"
] |
6070 | 2 | null | 6063 | 1 | null | Pursuant to my discussion on the conceptual overlap between effects size and likelihood ratios [here](https://stats.stackexchange.com/questions/4551/what-are-common-statistical-sins/6037#6037), I wonder if the likelihood ratio for each effect against its respective null might serve as a useful metric to achieve the aim... | null | CC BY-SA 2.5 | null | 2011-01-07T17:27:20.800 | 2011-01-07T17:27:20.800 | 2017-04-13T12:44:29.013 | -1 | 364 | null |
6071 | 1 | 11496 | null | 5 | 1796 | I'm not sure if this is precisely a measurement error model or not. I'm working on performing meta analysis, and the model I'm starting with is fairly basic.
\begin{aligned}
X_i &= \mu_i + e_i \\
Y_i &= \beta \mu_i + g_i + \delta_i
\end{aligned}
The random components are $e_i$, $g_i$, and $\delta_i$,and the variance i... | Classical measurement error model in R | CC BY-SA 3.0 | null | 2011-01-07T18:02:14.250 | 2018-01-31T16:36:20.850 | 2018-01-31T16:36:20.850 | 101426 | 1364 | [
"r",
"meta-analysis"
] |
6072 | 2 | null | 6067 | 53 | null | The problem is not that the classes are imbalanced per se, it is that there may not be sufficient patterns belonging to the minority class to adequately represent its distribution. This means that the problem can arise for any classifier (even if you have a synthetic problem and you know you have the true model), not ... | null | CC BY-SA 4.0 | null | 2011-01-07T18:29:10.353 | 2021-07-29T18:27:50.260 | 2021-07-29T18:27:50.260 | 887 | 887 | null |
6073 | 2 | null | 5873 | 2 | null | Thank you, whuber, for making me aware of Wald's Sequential Probability Ratio Test (SPRT). At your recommendation, I will relist this [Quantitative Skills site](http://quantitativeskills.com/sisa/statistics/sprt.htm). They will give you an out-of-the-box table to determine whether to continue or stop testing.
I also to... | null | CC BY-SA 2.5 | null | 2011-01-07T20:05:42.093 | 2011-01-07T20:05:42.093 | null | null | 2591 | null |
6074 | 1 | null | null | 12 | 10498 | Context: I am a programmer with some (half-forgotten) experience in statistics from uni courses. Recently I stumbled upon [http://akinator.com](http://akinator.com) and spent some time trying to make it fail. And who wasn't? :)
I've decided to find out how it could work. After googling and reading related blog posts an... | Akinator.com and Naive Bayes classifier | CC BY-SA 2.5 | null | 2011-01-07T22:08:40.717 | 2012-04-02T07:40:39.303 | 2011-01-07T23:40:20.670 | null | 2696 | [
"machine-learning",
"naive-bayes"
] |
6075 | 2 | null | 3779 | 15 | null | It is very hard to draw a rack that does not contain any valid word in
Scrabble and its variants. Below is an R program I wrote to estimate the
probability that the initial 7-tile rack does not contain a valid word. It
uses a monte carlo approach and the [Words With Friends](http://newtoyinc.com/) lexicon (I
couldn’t f... | null | CC BY-SA 2.5 | null | 2011-01-07T23:19:55.983 | 2011-01-08T20:45:41.183 | 2011-01-08T20:45:41.183 | 1670 | 1670 | null |
6076 | 1 | 6085 | null | 2 | 765 | I'm a statistics newbie (medical student) trying my luck with a Cox regression for a survival analysis on the outcome of a specific type of operation. And I'm trying to determine which variables to control for. And how to handle the age of the patient at operation-time (measure-start)...
Currently I've set the followin... | How to handle age at measure-start in Cox regression? | CC BY-SA 2.5 | null | 2011-01-07T23:22:09.210 | 2011-01-08T15:28:00.433 | 2011-01-07T23:43:23.037 | null | 2652 | [
"regression",
"spss"
] |
6077 | 1 | 6084 | null | 2 | 1761 | Another newbie question here (probably piece of cake for you guys).
When I run a Cox Regression and one of my covariates come out as significant:
```
95,0% CI for Exp(B)
B SE Wald df Sig. Exp(B) Lower Upper
risk 2,224 1,107 4,036 ... | Interpretation of log likelihood and covariate significance in Cox regression | CC BY-SA 2.5 | null | 2011-01-07T23:48:05.703 | 2011-01-08T15:20:27.973 | 2011-01-07T23:57:54.437 | null | 2652 | [
"regression"
] |
6078 | 1 | 6088 | null | 3 | 471 | I want to do a chi-squared test on data that looks like this:
```
A B
0 0
1 0
0 1
1 1
8 0
3 4
...
```
You can think of each pair as one trial with two participants. In each trial, there are a different number of observations for each participant.
I have binned each data pair like so: I counted how many pairs have 0 f... | Expected values for chi-squared test on binned paired counts | CC BY-SA 2.5 | null | 2011-01-08T02:52:10.047 | 2011-01-08T19:36:42.637 | 2011-01-08T05:20:53.410 | 52 | 52 | [
"chi-squared-test",
"expected-value"
] |
6079 | 1 | 6090 | null | 5 | 5381 | I did a linear regression in R and got the following result:
```
Estimate Std. Error t value Pr(>|t|)
(Intercept) 192116.40 6437.27 29.844 < 2e-16 ***
cdd 272.74 26.94 10.123 1.56e-09 ***
pmax(hdd - 450, 0) 61.73 22.54 2.738 0.0123 *
---
Sig... | R-squared result in linear regression and "unexplained variance" | CC BY-SA 2.5 | null | 2011-01-08T03:03:48.000 | 2011-01-10T18:19:19.323 | 2011-01-10T18:19:19.323 | 1764 | 1764 | [
"regression"
] |
6080 | 1 | null | null | 4 | 787 | I'm trying to fit the GAMLSS library's Sichel distribution to some zero-truncated data, but the only way to get the function to work is to include the zero-class anyway but give it a frequency of 0, which doesn't take into account the zero-truncated nature of my data. Can anyone suggest a way to properly "redistribute"... | Zero-truncated Sichel distribution in R | CC BY-SA 3.0 | null | 2011-01-08T06:24:18.810 | 2017-03-01T12:49:00.607 | 2017-03-01T12:49:00.607 | 11887 | null | [
"r",
"distributions",
"count-data",
"gamlss"
] |
6081 | 1 | 6083 | null | 13 | 9775 | So assuming that there is a point in testing the normality assumption for anova (see [1](https://stats.stackexchange.com/questions/2492/normality-testing-essentially-useless) and [2](https://stats.stackexchange.com/questions/2824/checking-anova-assumptions))
How can it be tested in R?
I would expect to do something lik... | Testing the normality assumption for repeated measures anova? (in R) | CC BY-SA 2.5 | null | 2011-01-08T11:26:07.737 | 2015-10-26T04:39:09.957 | 2017-04-13T12:44:41.967 | -1 | 253 | [
"r",
"anova",
"normality-assumption",
"repeated-measures"
] |
6082 | 2 | null | 6079 | 4 | null | R^2 is the percent of variance in the DV accounted for by the whole model. That is, your intercept and your IVS combined account for that much of the variance, using the linear regression model.
In your case, you got an R^2 of 0.85, indicating that intercept, plus cdd plus pmax combined account for 85% of the variance... | null | CC BY-SA 2.5 | null | 2011-01-08T11:28:51.557 | 2011-01-08T13:07:34.513 | 2011-01-08T13:07:34.513 | 449 | 686 | null |
6083 | 2 | null | 6081 | 5 | null | You may not get a simple response to `residuals(npk.aovE)` but that does not mean there are no residuals in that object. Do `str` and see that within the levels there are still residuals. I would imagine you were most interested in the "Within" level
```
> residuals(npk.aovE$Within)
7 8 9 ... | null | CC BY-SA 2.5 | null | 2011-01-08T14:26:28.970 | 2011-01-08T14:26:28.970 | null | null | 2129 | null |
6084 | 2 | null | 6077 | 3 | null | Credibility depends on context and you've not offered any. If the other 5 degrees of freedom are in the model for good reasons but you aren't particularly interested in estimating the effects, only interested in the impact of "risk", then you may have something interesting but the really wide confidence interval preven... | null | CC BY-SA 2.5 | null | 2011-01-08T15:20:27.973 | 2011-01-08T15:20:27.973 | null | null | 2129 | null |
6085 | 2 | null | 6076 | 3 | null | You are right that you don't need to use a time-dependent model for this situation. If, however, age would be expected to have a dominant effect (as might be expected), you may be safer using an interval-censored model. With R, that is setup with three arguments to the Surv object, but I don't have any experience with... | null | CC BY-SA 2.5 | null | 2011-01-08T15:28:00.433 | 2011-01-08T15:28:00.433 | null | null | 2129 | null |
6086 | 2 | null | 6067 | 98 | null | Balance in the Training Set
For logistic regression models unbalanced training data affects only the estimate of the model intercept (although this of course skews all the predicted probabilities, which in turn compromises your predictions). Fortunately the intercept correction is straightforward: Provided you know, ... | null | CC BY-SA 4.0 | null | 2011-01-08T16:01:51.453 | 2018-11-06T21:25:56.047 | 2018-11-06T21:25:56.047 | 28666 | 1739 | null |
6087 | 2 | null | 6081 | 1 | null | I think that the normality assumption can be assessed for each of the repeated measures, before performing the analysis. I would reshape the dataframe so that each column corresponds to a repeated measure, and then perform a shapiro.test to each one of those columns.
```
apply(cast(melt(npk,measure.vars="yield"), ...~... | null | CC BY-SA 2.5 | null | 2011-01-08T19:33:45.350 | 2011-01-08T19:33:45.350 | null | null | 339 | null |
6088 | 2 | null | 6078 | 3 | null | First of all, if your counts come from a different number of trials, then you cannot just ignore that. Apparently your data is better represent as follows:
```
Pair Subject NTrials Hits
1 A 200 0
1 B 150 0
2 A 100 1
2 B 215 0
etc
```
Second, there does not seem t... | null | CC BY-SA 2.5 | null | 2011-01-08T19:36:42.637 | 2011-01-08T19:36:42.637 | null | null | 279 | null |
6089 | 2 | null | 6081 | 2 | null | Another option would be to use the `lme` function of the `nlme` package (and then pass the obtained model to `anova`). You can use `residuals` on its output.
| null | CC BY-SA 2.5 | null | 2011-01-08T19:46:46.810 | 2011-01-08T19:46:46.810 | null | null | 582 | null |
6090 | 2 | null | 6079 | 11 | null | $R^2$ is the squared correlation of the OLS prediction $\hat{Y}$ and the DV $Y$. In a multiple regression with three predictors $X_{1}, X_{2}, X_{3}$:
```
# generate some data
> N <- 100
> X1 <- rnorm(N, 175, 7) # predictor 1
> X2 <- rnorm(N, 30, 8) # pr... | null | CC BY-SA 2.5 | null | 2011-01-08T20:05:44.290 | 2011-01-08T22:10:36.727 | 2011-01-08T22:10:36.727 | 1909 | 1909 | null |
6091 | 1 | 6094 | null | 1 | 165 | I have a bunch of data like this:
```
P1 [1, 2, 3, 4, 5, 6, 7, 8, 10]
P2 [5, 8, 10, 12, 20]
P3 [10, 201, 440]
P4 [1, 2, 10]
P5 [1, 2]
```
Right now it's ranked by the size of its data set (as shown above), so values that are heavily represented on the chart are on top e.g., P1 has the most)
I'm trying to rank this dat... | Good way to rank/sort data that falls in the lower range | CC BY-SA 2.5 | null | 2011-01-08T21:00:42.343 | 2011-01-08T23:36:27.173 | null | null | 2664 | [
"r",
"distributions",
"mean"
] |
6093 | 1 | null | null | 0 | 2204 | >
Possible Duplicate:
What book would you recommend for non-statisticians?
Hello all,
Which is a good book for self-learning of statistics? I tried the "Head first statistics" which starts very well, but when talking on t-distribution and some more complex issues, its quality degrades (just my opinion). Also, smth... | Good book on statistics | CC BY-SA 2.5 | null | 2011-01-08T23:09:08.913 | 2011-01-09T01:02:26.773 | 2017-04-13T12:44:52.277 | -1 | 976 | [
"references"
] |
6094 | 2 | null | 6091 | 3 | null | If you take the average of each row, divide by its length, and order the rows by that value, it produces the results you wanted:
```
> ranks <- unlist(lapply(p,mean))/unlist(lapply(p,length))
> ranks
P1 P2 P3 P4 P5
0.5679012 2.2000000 72.3333333 1.4444444 0.7500000
> p[orde... | null | CC BY-SA 2.5 | null | 2011-01-08T23:36:27.173 | 2011-01-08T23:36:27.173 | null | null | 697 | null |
6095 | 2 | null | 6093 | 1 | null | If you liked Feller, then you definitely need to think about a real stats book. My suggestion as a self-learner at perhaps slightly less sophisticated level than you would be to look for Cox and Hinkley's "Theoretical Statistics". Very readable for a stats text. Not much, if any, in the way of problems sets, though.
| null | CC BY-SA 2.5 | null | 2011-01-09T01:02:26.773 | 2011-01-09T01:02:26.773 | null | null | 2129 | null |
6096 | 1 | null | null | 6 | 322 | I'm working on benchmarking the speed of various JavaScript methods.
Part of the benchmark process requires repeating a test for a minimum time (to reduce the percentage uncertainty to at or less than 1%). There is some overhead on each test (the cost of the loop, incrementing a counter variable and so on). I currently... | Correct way to calibrate means | CC BY-SA 2.5 | null | 2011-01-09T08:20:48.563 | 2023-03-03T10:38:46.273 | 2011-01-09T17:29:55.527 | 2634 | 2634 | [
"calibration"
] |
6097 | 1 | null | null | 6 | 150 | Say I have a process that gives me 3 outputs: $O^1$, $O^2$ and $O^3$.
The outputs are generated from a semi-deterministic process, i.e. there is a deterministic component in the outputs, along with a random component.
In particular, having $n$ measurements over time, the outputs $O_j \quad j=1,2,...,n$ are -at least in... | Predicting a semi-deterministic process | CC BY-SA 2.5 | null | 2011-01-09T11:03:31.153 | 2011-01-09T23:19:23.807 | 2011-01-09T23:19:23.807 | 159 | 582 | [
"time-series",
"probability",
"bayesian",
"forecasting",
"predictive-models"
] |
6098 | 2 | null | 6046 | 4 | null | Repeated Measures
Personally I would pursue a hierarchical model where the basic observations are, for each animal, the 4 (or fewer) levels of odour and the corresponding neuronal responses. And the predictor for the per animal intercept and slope on this relationship is the animal's reproductive status. (Here I'm as... | null | CC BY-SA 2.5 | null | 2011-01-09T12:27:42.207 | 2011-01-09T12:27:42.207 | null | null | 1739 | null |
6100 | 2 | null | 6096 | 5 | null | There is something called "small error propagation", and it says that the error of a function $f$ of variables $x_1,x_2,\cdots,x_n$ with errors $\Delta x_1,\Delta x_2,\cdots,\Delta x_n$ equals
$$\Delta f=\sqrt{\sum_i\left(\frac{\partial f}{\partial x_i}\Delta x_i\right)^2},$$
so for $f(a,b):=a-b$ the error is $\Delta f... | null | CC BY-SA 4.0 | null | 2011-01-09T13:24:09.993 | 2023-03-03T10:38:46.273 | 2023-03-03T10:38:46.273 | 362671 | null | null |
6101 | 1 | null | null | 2 | 562 | I have a real-life situation that can be solved using Queueing Theory.
This should be easy for someone in the field. Any pointers would be appreciated.
Scenario:
There is a single Queue and N Servers.
When a server becomes free, the Task at the front of the queue gets serviced.
The mean service time is T seconds.
The ... | Queueing Theory: How to estimate steady-state queue length for single queue, N servers? | CC BY-SA 2.5 | null | 2011-01-09T16:51:41.767 | 2011-04-13T10:33:54.043 | 2011-04-13T10:33:54.043 | 449 | 2711 | [
"poisson-distribution",
"networks",
"simulation",
"queueing"
] |
6102 | 1 | 6105 | null | 3 | 2538 | I'm currently applying the Roy Zelner test of poolability as shown
in the excellent [article of Andrea Vaona](http://doc.rero.ch/lm.php?url=1000,42,6,20080417092244-DQ/wp0804.pdf),
in fact I'm working with panel N=17 T=5, and my model looks like
this :
$$Y_{it}= a_0+B_1X_1+B_2X_2+B_3X_3+B_4X_4+e_{it}$$
My question ... | Why are my constraints getting dropped? | CC BY-SA 2.5 | null | 2011-01-09T18:51:29.387 | 2011-01-13T20:54:35.390 | 2011-01-13T20:54:35.390 | 8 | 1251 | [
"regression",
"panel-data"
] |
6103 | 2 | null | 5026 | 27 | null |
- Statistics is concerned with probabilistic models, specifically inference on these models using data.
- Machine Learning is concerned with predicting a particular outcome given some data. Almost any reasonable machine learning method can be formulated as a formal probabilistic model, so in this sense machine learni... | null | CC BY-SA 2.5 | null | 2011-01-09T19:59:14.237 | 2011-01-09T19:59:14.237 | null | null | null | null |
6104 | 1 | 6121 | null | 8 | 13019 | I start with three independent random variables, $X_1, X_2, X_3$. They are each normally distributed with:
$$X_i \sim N(\mu_i, \sigma^2), i = 1, 2, 3.$$
I then have three transformations,
$$\eqalign{
Y_1 &= -X_1/\sqrt{2} + X_2/\sqrt{2} \cr
Y_2 &= -X_1/\sqrt{3} - X_2/\sqrt{3} + X_3/\sqrt{3} \cr
Y_3 &= X_1/\sqrt{... | Proving that the squares of normal rv's is Chi-square distributed | CC BY-SA 2.5 | null | 2011-01-09T20:08:12.767 | 2015-11-16T21:52:34.593 | 2011-01-10T02:02:53.867 | 919 | 1118 | [
"distributions",
"probability",
"self-study",
"chi-squared-test",
"mathematical-statistics"
] |
6105 | 2 | null | 6102 | 5 | null | You have a panel data regression
$$y_{it}=x_{it}'\beta+u_{it},$$
where $x_{it}$ in your case is $(1,X_1,X_2,X_3,X_4)$. Poolability tests test whether alternative model is actually correct:
$$y_{it}=x_{it}'\beta_i+u_{it}.$$
So the null hypothesis is that $\beta_i=\beta$. To test this hypothesis we need to estimate $\ha... | null | CC BY-SA 2.5 | null | 2011-01-09T20:15:40.220 | 2011-01-09T20:15:40.220 | null | null | 2116 | null |
6106 | 2 | null | 6097 | 4 | null | If you want to forecast time-series data, first you need to check whether it is [stationary](http://en.wikipedia.org/wiki/Stationary_process). Basically this means checking whether data has trends. If for example some time trend is present, you can concern yourself only with its forecast, because time-trends usually do... | null | CC BY-SA 2.5 | null | 2011-01-09T20:48:21.907 | 2011-01-09T20:48:21.907 | null | null | 2116 | null |
6107 | 2 | null | 6104 | 1 | null | Have you tried simply multiplying out the squared Y^2's in terms of the X[1:3] terms. I suspect that when you are all done that you will see that you simply have: (1/2 +1/3 +1/6)* X1^2 + (1/2 +1/3 +1/6)*X2^2 + (1/2 +1/3 +1/6)*X3^2 . This, of course, assumes that X1X3=X3X1, i.e. that your random variable algebra is comm... | null | CC BY-SA 2.5 | null | 2011-01-09T21:09:26.587 | 2011-01-09T21:09:26.587 | null | null | 2129 | null |
6108 | 1 | 6116 | null | 2 | 803 | Suppose I have a biased coin (heads with probability p), and I keep flipping it until I get t tails. So I had to flip n times in total to get t tails. How do I find a (frequentist) confidence interval for the probability of heads p?
| Confidence interval for success probability in negative binomial experiment | CC BY-SA 2.5 | null | 2011-01-09T21:57:48.190 | 2011-01-10T01:31:24.363 | null | null | 1106 | [
"confidence-interval",
"negative-binomial-distribution"
] |
6109 | 1 | 6117 | null | 3 | 1899 | This is a softmax probability distribution:
$$P(i| w_1, w_2, \ldots, w_n) = \frac{exp(w_i)}{\sum_{i=1}^n exp(w_i)}.$$
It known also as Boltzmann distribution. It is used in generalized Bradley-Terry model and in multinomial logistic regression. There are efficient minorization-maximization algorithms for infering $\vec... | Is there a SoftMax distribution with confidence parameters? | CC BY-SA 2.5 | null | 2011-01-09T22:08:34.650 | 2022-06-18T22:02:36.787 | 2011-01-10T02:14:39.147 | 919 | 217 | [
"distributions",
"maximum-likelihood",
"bradley-terry-model"
] |
6110 | 2 | null | 6109 | 2 | null | The Maxwell distribution is the classical limit under conditions of high temperature and non-interacting wave functions of both Fermi-Dirac statistics and Bose-Einstein statistics. I would expect that you would want to look at the F-D statistics if you are interested in higher variance, since Bose-Einstein statistics l... | null | CC BY-SA 4.0 | null | 2011-01-09T22:28:29.373 | 2022-06-18T22:02:36.787 | 2022-06-18T22:02:36.787 | 361019 | 2129 | null |
6111 | 1 | null | null | 2 | 1260 | Here is the problem: A survey contains 7 binary questions (Yes/No responses). If two people are answering the survey, what is the probability for their answers on 4 or more of the questions to match? In other words, if we have four or more matching answers, we can consider the overall survey response to be similar for ... | The probability for two people to provide identical answers on survey questions | CC BY-SA 2.5 | null | 2011-01-09T22:42:13.717 | 2017-02-18T19:33:24.767 | 2017-02-18T19:33:24.767 | 28666 | null | [
"combinatorics"
] |
6112 | 2 | null | 6111 | 0 | null | If for each question the probability of selecting the same answer is equal to 0.5, the answer is the following:
$$\sum_{i=4}^7{\binom{7}{i}p^i(1-p)^{7-i}}$$
where $p=0.5.$
In this case it is a [binomial distribution](http://en.wikipedia.org/wiki/Binomial_distribution).
| null | CC BY-SA 2.5 | null | 2011-01-09T23:22:43.173 | 2011-01-10T01:54:31.270 | 2011-01-10T01:54:31.270 | 919 | 1540 | null |
6113 | 2 | null | 6063 | 4 | null | The effect sizes of interactions in a multivariate regression can be assessed in same way as the effect sizes of any other predictor. The common thing is to look at the incremental contribution to R^2 (semi-partial R^2), but there are other possibilities, including Cohen's f^2 for nested models (this is a likelihood ra... | null | CC BY-SA 2.5 | null | 2011-01-10T00:19:12.147 | 2011-01-10T00:19:12.147 | null | null | 11954 | null |
6114 | 2 | null | 6111 | 2 | null | I assume that the survey will be answered independently by the participants. First, you need estimates for the baseline probabilities $p_{i}$ that an answer $i$ will be answered "yes". The probability of two persons answering "yes" for question $i$ is then $p_{i}^{2}$. Likewise, the probability of two persons answering... | null | CC BY-SA 2.5 | null | 2011-01-10T01:07:09.440 | 2011-01-10T19:04:34.480 | 2011-01-10T19:04:34.480 | 1909 | 1909 | null |
6115 | 2 | null | 6109 | 2 | null | I am not sure, but I think your probability model is a special case of [Multinomial logit](http://en.wikipedia.org/wiki/Multinomial_logit) model with no covariates and only the intercept terms ($w_i$ will be the intercepts).
This is model is a special case of GLM and hence there exits an iteratively weighted least squa... | null | CC BY-SA 2.5 | null | 2011-01-10T01:26:19.127 | 2011-01-10T02:51:03.933 | 2011-01-10T02:51:03.933 | 1307 | 1307 | null |
6116 | 2 | null | 6108 | 3 | null | If you are looking for ML estimation (which may or may not be what you want) please check the `fitdistr` function in the `MASS`library in `R`. This function can estimate the unknown parameters using ML estimation. But please be careful about the parametrization of the negative binomial distribution of `MASS`. Apart fro... | null | CC BY-SA 2.5 | null | 2011-01-10T01:31:24.363 | 2011-01-10T01:31:24.363 | null | null | 1307 | null |
6117 | 2 | null | 6109 | 8 | null | This question appears to confuse two distinct things. Any additional parameter in the model would (by definition) describe the distribution of $i$, not the distributions of any of the $w_i$. Unless you adopt a Bayesian prior for $\vec{w}$ (which does not seem to be part of this question), the parameters do not have a... | null | CC BY-SA 2.5 | null | 2011-01-10T02:14:16.637 | 2011-01-10T02:14:16.637 | null | null | 919 | null |
6119 | 1 | 197272 | null | 7 | 5341 | I'm analyzing people based on their twitter stream. We are using a 'word bag' model of users, which basically amounts to counting how often each word appears in a persons twitter stream (and then using that as a proxy for a more normalized 'probability they will use a given word' in a particular length of text).
Due t... | Is principal components analysis valid if the distribution(s) are Zipf like? What would be similar to PCA but suited to non gaussian data? | CC BY-SA 2.5 | null | 2011-01-10T05:50:59.077 | 2023-01-23T16:08:31.280 | 2011-03-15T19:05:03.893 | 8 | 2717 | [
"pca",
"normal-distribution",
"zipf"
] |
6121 | 2 | null | 6104 | 4 | null | We have $X_1\sim N(\mu_1,\sigma^2)$ and $X_2\sim N(\mu_2,\sigma^2)$, hence
$$EY_1=E(-X_1/\sqrt{2}+X_2/\sqrt{2})=-1/\sqrt{2}EX_1+1/\sqrt{2}EX_2=0$$
\begin{align*}
EY_1^2&=E(-X_1/\sqrt{2}+X_2/\sqrt{2})^2\\\\
&=E(X_1/\sqrt{2})^2-2E(X_1X_2/2)+E(X_2/\sqrt{2})^2\\\\
&=1/2\sigma^2+1/2\sigma^2=\sigma^2
\end{align*}
Hence $Y_1... | null | CC BY-SA 3.0 | null | 2011-01-10T08:10:03.080 | 2014-04-17T19:27:03.137 | 2014-04-17T19:27:03.137 | 37240 | 2116 | null |
6122 | 1 | 6124 | null | 2 | 1062 | I have almost two questions. I need a single covariate logistic regression (LR) for each of my variables. Should I do it manually in SPSS, selecting each variable and do logistic regression? Is there a "for each" cycle to do it? I should switch to R language to have what I want.
In the multivariables (multi covariates... | Simple and multiple logistic regression | CC BY-SA 2.5 | null | 2011-01-10T09:54:02.767 | 2011-01-10T15:04:25.203 | 2011-01-10T10:37:10.423 | 930 | 2719 | [
"r",
"logistic",
"spss"
] |
6123 | 1 | 6140 | null | 5 | 515 | I have a sample survey of a population whose distributions of certain characteristics are not identical to the distributions of the overall population. For example, the age of my respondents may be biased downward, or the incomes in my sample may be too high compared to the population (or my theoretical population dis... | Is it possible to weight survey observations to more than one distribution? | CC BY-SA 2.5 | null | 2011-01-10T10:06:20.720 | 2011-01-10T20:17:54.023 | null | null | 1195 | [
"distributions",
"survey"
] |
6124 | 2 | null | 6122 | 4 | null | If I understand you correctly, you want to fit two successive simple logistic regression model. I don't know if there's a specific instruction in SPSS that allows to switch the covariate of interest or cycle through them, but I guess you can run the two models in succession. In R, if your data are organized in a matrix... | null | CC BY-SA 2.5 | null | 2011-01-10T10:33:39.490 | 2011-01-10T10:33:39.490 | null | null | 930 | null |
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