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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
11149 | 1 | null | null | 8 | 18390 | Is the method of mean substitution for replacing missing data out of date? Are there more sophisticated models that should be used? If so, what are they?
| Is the method of mean substitution for replacing missing data out of date? | CC BY-SA 3.0 | null | 2011-05-23T11:33:59.683 | 2011-05-24T10:59:39.857 | 2011-05-23T11:54:25.253 | 183 | 4716 | [
"missing-data"
] |
11150 | 2 | null | 11149 | 14 | null | Barring the fact that it's not necessary to shoot mosquitoes with a cannon (i.e. if you have one missing value in a million data points, just drop it), using the mean could be suboptimal to say the least: the result can be biased, and you should at least correct the result for the uncertainty.
There are some other opti... | null | CC BY-SA 3.0 | null | 2011-05-23T11:53:59.070 | 2011-05-23T12:21:03.900 | 2011-05-23T12:21:03.900 | 2116 | 4257 | null |
11151 | 2 | null | 11149 | 11 | null | You did not tell us very much about the nature of your missing data. Did you check for MCAR ([Missing Completely at Random](http://en.wikipedia.org/wiki/Missing_completely_at_random))? Given that you cannot assume MCAR, mean substitution can lead to biased estimators.
As a non-mathematical starting point, I can recomm... | null | CC BY-SA 3.0 | null | 2011-05-23T11:54:42.270 | 2011-05-23T11:54:42.270 | null | null | 307 | null |
11152 | 1 | 11154 | null | 3 | 123 | I am going to be hosting a number (~10) of [potluck meals](http://en.wikipedia.org/wiki/Potluck) over the course of the summer, my pool of people to invite is about 40 people with about 10-15 coming to each meal. So I figure this would be a good opportunity to record data over time about the meals/people. The issue I a... | Statistics of events and invitations | CC BY-SA 3.0 | null | 2011-05-23T13:12:51.430 | 2011-05-23T22:49:21.180 | 2011-05-23T22:49:21.180 | 307 | 4717 | [
"dataset",
"multilevel-analysis",
"trend"
] |
11153 | 2 | null | 11149 | 2 | null | If your missing values are randomly distributed, or your sample size is small, you might be better off just using the mean. I would first split the data into two parts: 1 with the missing values and the other without and then test for the difference in means of some key variables between the two samples. If there is ... | null | CC BY-SA 3.0 | null | 2011-05-23T15:03:30.203 | 2011-05-23T15:03:30.203 | null | null | 3489 | null |
11154 | 2 | null | 11152 | 3 | null | Yoel, great question! I will address your question of what can be an "efficient and concise way of recording the data". Given your small data set, the following thoughts are more of theoretical nature than of practical use.
You have (what social scientists call) a multilevel data set, e.g. students (level 1) are nested... | null | CC BY-SA 3.0 | null | 2011-05-23T15:06:28.850 | 2011-05-23T22:44:00.780 | 2011-05-23T22:44:00.780 | 307 | 307 | null |
11155 | 2 | null | 11101 | 1 | null | With a sample size of 104, any factor analysis is going to be shaky at best. The best approach is probably to collect more data (not really that useful an answer, but its true). [This page](http://www.technion.ac.il/docs/sas/stat/chap26/sect21.htm) gives some useful advice.
[Fabrigar et al (1999)](http://www.statpower.... | null | CC BY-SA 3.0 | null | 2011-05-23T15:54:10.807 | 2011-05-23T15:54:10.807 | null | null | 656 | null |
11156 | 2 | null | 11088 | 4 | null | The best (fastest to run, not fastest to code;) free solution I have found in Matlab was to wrap R's MATHLIB_STANDALONE c library with a mex function. This gives you access to R's t-distribution PRNG. One advantage of this approach is that you also can use the same trick to get variates from a non-central t distributio... | null | CC BY-SA 3.0 | null | 2011-05-23T17:29:06.840 | 2011-06-15T04:05:39.333 | 2011-06-15T04:05:39.333 | 795 | 795 | null |
11157 | 1 | 11162 | null | 4 | 1045 | I have time series data that represent dates/times of trades taken in a financial market.
I would like to assign a score to this data that represents whether the trades are `mostly clustered` around particular time values or if they are `mostly spread out` evenly. I am going to have about 1000+ results per dataset.
E... | What statistical test can I use to detect clumping? | CC BY-SA 3.0 | null | 2011-05-23T18:01:24.313 | 2011-05-24T21:34:01.523 | null | null | 4544 | [
"time-series",
"statistical-significance",
"standard-error"
] |
11158 | 2 | null | 11021 | 1 | null | I contacted Sean at RezScore and he clarified some things for me. In a nutshell, inserting buzzwords into a hidden text box seems to be a good idea if you don't want to put them in your actual resume. However, you should be selective about which words you include because many of the algorithms penalize verbosity.
Maybe... | null | CC BY-SA 3.0 | null | 2011-05-23T18:12:47.270 | 2011-05-23T18:12:47.270 | null | null | 4685 | null |
11159 | 2 | null | 11138 | 1 | null | I'm answering about another approach that doesn't use hard cuts on the dendrogram. I would suggest you to use something like linear discriminant analysis (LDA) or any other technique that allows you to predict the class of the unlabeled points. (There are many techniques that can do the job, but I find LDA the easiest)... | null | CC BY-SA 3.0 | null | 2011-05-23T18:21:27.423 | 2011-05-23T18:21:27.423 | null | null | 2902 | null |
11160 | 2 | null | 11138 | 0 | null | Just my two cents, but I would look at decision trees or using your initial cluster analysis to determine a suitable number of clusters, and then use kmeans to refine. From there, you can get the cluster centers and reclassify new cases based on those centers.
HTH.
| null | CC BY-SA 3.0 | null | 2011-05-23T18:45:34.503 | 2011-05-23T18:45:34.503 | null | null | 569 | null |
11161 | 1 | null | null | 1 | 137 | I have a sequence of integers that represent total sales of my product for each day. From time to time, we have large press or marketing events that increase sales on the day of the event and for a few days after that but then eventually taper down to the long-run average. Here's some made-up numbers showing what I mea... | How can I isolate the effect of an event on a sequence of sales numbers? | CC BY-SA 3.0 | null | 2011-05-23T19:24:32.360 | 2011-05-24T01:52:15.880 | null | null | 4718 | [
"mean"
] |
11162 | 2 | null | 11157 | 1 | null | I would simply calculate a rolling window of the number of trades (or dollar volume) per hour, day, week, or whatever time frame that makes sense. For example, you might use 1 day as the rolling window. If 1 trade per day is a low degree of clustering then 10 trades per day might be a high degree of clustering. If... | null | CC BY-SA 3.0 | null | 2011-05-23T19:35:59.183 | 2011-05-24T02:24:52.420 | 2011-05-24T02:24:52.420 | 2775 | 2775 | null |
11163 | 1 | null | null | 7 | 549 | I would like to test that two difference/distance/dissimilarity matrices are not the same. i.e. the rows and columns between the two matrices represent the same features, but the distances are obtained from 2 populations and I'm interested in whether the difference matrices "look different" between the populations.
I'm... | How to test whether two distance/difference matrices are different? | CC BY-SA 3.0 | null | 2011-05-23T19:43:20.070 | 2011-06-23T01:02:47.060 | 2011-05-23T23:19:53.857 | null | 4720 | [
"clustering"
] |
11164 | 1 | 11966 | null | 5 | 118 | Suppose we have 500 students nested in 20 classes (different classrooms), 25 students per class
```
student<-factor(1:500)
class<-rep(LETTERS[1:20],each=25)
```
They all take a test.
```
score<-rnorm(500,mean=80,sd=5)
```
The model below would tell you about the average scores and variability among students and class... | Testing for the effect of an intervention when it is applied on a group of which each individual is measured | CC BY-SA 3.0 | null | 2011-05-23T19:50:26.923 | 2012-08-30T23:40:21.070 | 2012-08-30T23:40:21.070 | 5739 | 3874 | [
"r",
"mixed-model",
"multilevel-analysis",
"blocking"
] |
11165 | 1 | 11173 | null | 7 | 1888 | Take the task of fitting an a priori distribution like the ex-Gaussian
to a collection of observed human response times (RT). One method is to compute the sum log likelihood of each observed RT given a set of candidate ex-Gaussian parameters, then try to find the set of parameters that maximizes this sum log likelihood... | Is this a reasonable approach to fitting distributions? | CC BY-SA 3.0 | null | 2011-05-23T20:08:50.817 | 2013-07-04T10:37:48.477 | 2013-07-04T10:37:48.477 | 17230 | 364 | [
"distributions",
"fitting"
] |
11166 | 2 | null | 11165 | 0 | null | Take a look at the QQ-Plot (under my answer) in the following link:
[Need help identifying a distribution by its histogram](https://stats.stackexchange.com/questions/8662/need-help-identifying-a-distribution-by-its-histogram/8674#8674)
| null | CC BY-SA 3.0 | null | 2011-05-23T20:19:23.160 | 2011-05-23T20:19:23.160 | 2017-04-13T12:44:39.283 | -1 | 2775 | null |
11167 | 2 | null | 11165 | 6 | null | What you are proposing is called quantile matching, though the way you propose to do it will be exhausting. The ex-Gaussian distribution can be found in the package `gamlss.dist` with quantiles as `qexGAUS` etc.; it uses `nu` where you use `tau`.
A similar quantile matching method can be used in the function `fitdist... | null | CC BY-SA 3.0 | null | 2011-05-23T22:21:40.967 | 2011-05-23T22:21:40.967 | null | null | 2958 | null |
11168 | 1 | null | null | 10 | 48626 | Should the n for sample size be capitalized? Is there a difference between n and N?
| Capitalization of n for sample size | CC BY-SA 3.0 | null | 2011-05-23T23:59:06.663 | 2021-06-09T20:54:57.477 | 2011-05-24T01:13:30.937 | 2902 | 4722 | [
"notation"
] |
11169 | 2 | null | 11168 | 11 | null | There is actually a difference in some textbooks: $N$ generally means population size and $n$ sample size.
However, this is not always the case. You should check in your textbook.
:)
| null | CC BY-SA 3.0 | null | 2011-05-24T00:03:19.040 | 2011-05-24T00:03:19.040 | null | null | 2902 | null |
11170 | 2 | null | 11163 | 2 | null | I am not sure I understand what you mean by difference/distance/dissimilarity matrix. Assuming that $D_{i,j}^2 = (v_i - v_j)^{\top}(v_i - v_j)$ for some vectors $v_i, v_j$, if you can accept a transformation to the crossproduct matrix $G_{i,j} = -2 v_i^{\top}v_j$ (say for example the vectors are normalized so $v_i^{\to... | null | CC BY-SA 3.0 | null | 2011-05-24T00:53:14.377 | 2011-05-24T00:53:14.377 | null | null | 795 | null |
11171 | 2 | null | 11161 | 1 | null | To answer your question , one would be advised to build a single equation model which captured day-of-the-week effects (6 dummy indicators) and an indicator for the "event". Software exists to capture any lead, contemporaneous and.or lag effects around known event. In the absence of such software you might try and "rol... | null | CC BY-SA 3.0 | null | 2011-05-24T01:19:16.530 | 2011-05-24T01:52:15.880 | 2011-05-24T01:52:15.880 | 3382 | 3382 | null |
11172 | 1 | null | null | 4 | 2057 | I'm trying to figure out how to calculate the standard error of a mean correlation coefficient.
I have 6 bilateral correlation coefficients for 4 countries. I have transformed them using the Fisher z transformation in order to calculate their mean correlation coefficient. I'm trying to figure out what the standard erro... | How to calculate standard error of the mean of a set of correlation coefficients | CC BY-SA 3.0 | null | 2011-05-24T01:49:43.733 | 2011-05-24T10:38:39.853 | 2011-05-24T04:41:24.997 | 183 | 4724 | [
"correlation",
"confidence-interval"
] |
11173 | 2 | null | 11165 | 6 | null | One problematic feature is that there may be a continuum of optimal solutions. In most settings the quantiles are continuous functions of the parameters. When the distributions are continuous, almost surely there will be positive intervals between the data values. Suppose your objective function is optimized by a pa... | null | CC BY-SA 3.0 | null | 2011-05-24T02:13:03.453 | 2011-05-24T02:13:03.453 | null | null | 919 | null |
11174 | 2 | null | 11168 | 6 | null | In terms of ANOVA small n (usually subscripted) could mean the sample size of a particular group while capital N might mean the total sample size. It depends on context.
| null | CC BY-SA 3.0 | null | 2011-05-24T03:03:16.627 | 2011-05-24T03:03:16.627 | null | null | 2310 | null |
11175 | 1 | null | null | 11 | 16951 | It is mentioned [here](http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set#The_Elbow_Method) that one of the methods to determine the optimal number of clusters in a data-set is the "elbow method". Here the percentage of variance is calculated as the ratio of the between-group variance to the ... | Elbow criteria to determine number of cluster | CC BY-SA 4.0 | null | 2011-05-24T04:43:59.053 | 2018-05-07T11:01:06.643 | 2018-05-07T11:01:06.643 | 207324 | 4290 | [
"clustering",
"k-means"
] |
11176 | 1 | 442966 | null | 6 | 2407 | [Heathcote, Brown & Mewhort](https://doi.org/10.3758%2FBF03196299?from=SL) (2002, [PDF](https://doi.org/10.3758%2FBF03196299?from=SL)) present Quantile Maximum Probability Estimation (originally termed Quantile Maximum Likelihood Estimation but later corrected) as a method of fitting distributional data, and find that ... | Can anyone explain quantile maximum probability estimation (QMPE)? | CC BY-SA 4.0 | null | 2011-05-24T05:22:53.910 | 2022-03-26T13:11:49.697 | 2022-03-26T13:11:49.697 | 11887 | 364 | [
"distributions",
"quantiles",
"fitting"
] |
11177 | 2 | null | 11176 | 1 | null | Just a small suggestion:
Have you checked out the [Newcastle Cognition Lab's page on QMPE](http://www.newcl.org/?q=node/10)?
It has source code, a getting started guide, and a few other resources.
| null | CC BY-SA 3.0 | null | 2011-05-24T06:22:09.193 | 2011-05-24T06:22:09.193 | null | null | 183 | null |
11178 | 1 | null | null | 0 | 12437 | >
Possible Duplicate:
Logistic Regression in R (Odds Ratio)
I need to do a logistic regression in R. My response variable is `surv=0`; `surv=1` and I have about 18 predictor variables.
After reading my model, I got the table of Coefficients below and I need to go through some steps, which I am not familiar with, un... | How to interpret table of logistic regression coefficients using glm function in R | CC BY-SA 3.0 | null | 2011-05-24T07:17:41.610 | 2011-05-24T07:58:08.240 | 2017-04-13T12:44:52.660 | -1 | 4263 | [
"r",
"logistic"
] |
11179 | 2 | null | 11175 | 13 | null | The idea underlying the k-means algorithm is to try to find clusters that minimize the within-cluster variance (or up to a constant the corresponding sum of squares or SS), which amounts to maximize the between-cluster SS because the total variance is fixed. As mentioned on the wiki, you can directly use the within SS ... | null | CC BY-SA 3.0 | null | 2011-05-24T07:53:03.603 | 2013-10-18T12:54:47.860 | 2013-10-18T12:54:47.860 | 264 | 930 | null |
11180 | 2 | null | 11178 | 4 | null | Call
```
exp(your.model$coefficients)
```
where `your.model` is your R object with `glm` class. Similar question was ask previously; detailed answer is [here](https://stats.stackexchange.com/questions/8661/logistic-regression-in-r-odds-ratio).
| null | CC BY-SA 3.0 | null | 2011-05-24T07:58:08.240 | 2011-05-24T07:58:08.240 | 2017-04-13T12:44:20.840 | -1 | 609 | null |
11182 | 1 | 11183 | null | 95 | 98946 | Does anybody know why offset in a Poisson regression is used? What do you achieve by this?
| When to use an offset in a Poisson regression? | CC BY-SA 3.0 | null | 2011-05-24T08:12:01.783 | 2020-03-10T07:48:42.057 | 2013-10-04T02:25:03.373 | 7290 | 4496 | [
"poisson-regression",
"offset"
] |
11183 | 2 | null | 11182 | 135 | null | Here is an example of application.
Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individuals are not followed the same amount of time. For example, six cases over 1 year should not amount to the same as six ca... | null | CC BY-SA 3.0 | null | 2011-05-24T09:03:34.040 | 2011-05-24T09:03:34.040 | null | null | 3019 | null |
11184 | 2 | null | 11172 | 4 | null | Just a few thoughts:
- n is the sample size for each bivariate correlation, i.e. $n \neq 6$.
- I am not sure if this makes sense but you could run a small meta-analysis (based on the Fisher's transformed correlations). This would give you a pooled standard error (see page 4).
- Whatever you do, your effect sizes ... | null | CC BY-SA 3.0 | null | 2011-05-24T10:38:39.853 | 2011-05-24T10:38:39.853 | null | null | 307 | null |
11185 | 2 | null | 11149 | 0 | null | Missing data is one big issue everywhere. I wish you'd answer the following question first. 1) what %age of the data is missing ? -- if its more than 10% of the data you'd not risk imputing it with mean. Because imputing such missing with mean is equivalent to telling the LR box that look ..this variable has mean most ... | null | CC BY-SA 3.0 | null | 2011-05-24T10:59:39.857 | 2011-05-24T10:59:39.857 | null | null | 1763 | null |
11186 | 1 | null | null | 3 | 573 | Suppose we have time-series $ X_t $ and it has the following decomposition
$$X_t=\mu + \varepsilon_t,$$
where $\mu$ is a mean and $\varepsilon_t$ - the error term.
The model complexity will increase if we divide this time-series in to some segments,say $k$, and repeat above process. As the model complexity increases ... | Number of segments to divide a time-series | CC BY-SA 3.0 | null | 2011-05-24T11:08:06.330 | 2011-05-24T13:36:02.940 | 2011-05-24T13:36:02.940 | 3722 | 3722 | [
"time-series",
"regularization",
"change-point"
] |
11187 | 2 | null | 11186 | 2 | null | Seems that you have a [change point problem](http://en.wikipedia.org/wiki/Structural_break). Also look at [change-point tag](https://stats.stackexchange.com/questions/tagged/change-point) for related questions in this site. For fitting these type of models R for example has the packages segmented and strucchange. The r... | null | CC BY-SA 3.0 | null | 2011-05-24T11:45:53.530 | 2011-05-24T11:45:53.530 | 2017-04-13T12:44:52.660 | -1 | 2116 | null |
11189 | 1 | null | null | 4 | 5154 | I received the following question by email:
>
I was wondering should I use tick the
option for pairwise exclusion of
missing data when I carry out
regression analyses (or any analyses
for that matter) rather than using [some other missing values replacement strategy].
Julie Pallant recommends pairwise
excl... | When, if ever, to use pairwise deletion in multiple regression? | CC BY-SA 3.0 | null | 2011-05-24T14:02:48.443 | 2011-05-24T20:41:41.547 | null | null | 183 | [
"regression",
"missing-data"
] |
11190 | 1 | null | null | 2 | 113 | I am working on 4 different species of tomatoes. From the data I had, I looked at the occurrence of a particular "event" in certain intervals of their genome (this interval is identical in all 4 plants) and I have a file for each of the species with their probability of occurrence. The file looks something like this:
>... | R: statistical test | CC BY-SA 3.0 | null | 2011-05-24T14:08:15.747 | 2018-10-01T22:52:30.817 | 2018-10-01T22:52:30.817 | 11887 | 4731 | [
"r",
"hypothesis-testing",
"genetics"
] |
11191 | 1 | 11197 | null | 7 | 3662 | This question came up in a consulting context, and I was interested in your thoughts.
### Context
One strategy for dealing with occasional missing data when calculating scale means looks like this in the language of SPSS:
```
COMPUTE depmean = mean.4(dep1, dep2, dep3, dep4, dep5, dep6).
EXECUTE.
```
I.e., calculat... | Appropriateness of calculating scale means based on available non-missing responses (i.e., person-mean imputation) | CC BY-SA 3.0 | 0 | 2011-05-24T14:12:45.140 | 2011-05-25T13:31:15.813 | 2011-05-25T06:43:02.630 | 183 | 183 | [
"scales",
"missing-data"
] |
11193 | 1 | 11224 | null | 18 | 54704 | I have a data frame that contains some duplicate ids. I want to remove records with duplicate ids, keeping only the row with the maximum value.
So for structured like this (other variables not shown):
```
id var_1
1 2
1 4
2 1
2 3
3 5
4 2
```
I want to generate this:
```
id var_1
1 4
2 3
3 5
4 2
```
I know about uniq... | How do I remove all but one specific duplicate record in an R data frame? | CC BY-SA 3.0 | null | 2011-05-24T14:23:45.017 | 2015-06-21T16:16:11.437 | null | null | 4110 | [
"r"
] |
11194 | 2 | null | 11193 | 7 | null | You actualy want to select the maximum element from the elements with the same id. For that you can use `ddply` from package plyr:
```
> dt<-data.frame(id=c(1,1,2,2,3,4),var=c(2,4,1,3,4,2))
> ddply(dt,.(id),summarise,var_1=max(var))
id var_1
1 1 4
2 2 3
3 3 4
4 4 2
```
`unique` and `duplicated` is for r... | null | CC BY-SA 3.0 | null | 2011-05-24T14:33:45.407 | 2011-05-24T19:43:38.453 | 2011-05-24T19:43:38.453 | 2116 | 2116 | null |
11195 | 2 | null | 10111 | 2 | null | The eta-square ($\eta^2$) value you are describing is intended to be used as a measure of effect size in the observed data (i.e., your sample), as it amounts to quantify how much of the total variance can be explained by the factor considered in the analysis (that is what you wrote in fact, BSS/TSS). With more than one... | null | CC BY-SA 3.0 | null | 2011-05-24T14:34:40.337 | 2011-05-24T14:34:40.337 | null | null | 930 | null |
11196 | 2 | null | 9671 | 5 | null | One generally consider that a "good partitioning" must satisfy one or more of the following criteria: (a) compactness (small within-cluster variation), connectedness (neighbouring data belong to the same cluster), and spatial separation (must be combined with other criteria like compactness or balance of cluster sizes)... | null | CC BY-SA 3.0 | null | 2011-05-24T15:05:40.330 | 2011-05-24T15:05:40.330 | null | null | 930 | null |
11197 | 2 | null | 11191 | 6 | null | Some years ago, I thought it might be a good idea to apply person-mean imputation (person-mean substitution or case-mean imputation) in case of item non-response. Nowadays, however, it seems obvious to me that this approach assumes that all scale items share similar characteristics (similar variance, standard deviation... | null | CC BY-SA 3.0 | null | 2011-05-24T15:05:55.980 | 2011-05-24T15:20:22.497 | 2011-05-24T15:20:22.497 | 307 | 307 | null |
11198 | 2 | null | 11189 | 1 | null | I think it depends on the situation at hand. If you're missing a couple values out of several hundred or thousand observations, sure, delete them.
If one of your important variables is 10% missing, you may need to think up a strategy for dealing with this.
| null | CC BY-SA 3.0 | null | 2011-05-24T15:25:33.190 | 2011-05-24T15:25:33.190 | null | null | 2817 | null |
11199 | 2 | null | 11193 | 6 | null | The base-R solution would involve `split`, like this:
```
z<-data.frame(id=c(1,1,2,2,3,4),var=c(2,4,1,3,4,2))
do.call(rbind,lapply(split(z,z$id),function(chunk) chunk[which.max(chunk$var),]))
```
`split` splits the data frame into a list of chunks, on which we perform cutting to the single row with max value and then ... | null | CC BY-SA 3.0 | null | 2011-05-24T15:35:27.920 | 2011-05-24T15:35:27.920 | null | null | null | null |
11200 | 1 | 11201 | null | 6 | 1778 | Today I opened two STATA windows and ran the following command in both:
```
set obs 100
gen x = rnormal()
sort x
```
(the difference is that on the second window I generated a variable called y). Summing up: I asked STATA to give me 100 pseudo-random numbers taken from a standard normal distribution, then I sorted it... | Generating sorted pseudo-random numbers in Stata | CC BY-SA 3.0 | null | 2011-05-24T15:53:05.780 | 2011-05-24T16:07:17.587 | 2011-05-24T16:07:17.587 | 919 | 2929 | [
"stata",
"random-generation"
] |
11201 | 2 | null | 11200 | 8 | null | The help for `set_seed` states
>
The sequences these functions produce are determined by the seed, which is just a number and which is set to 123456789 every time Stata is launched.
Stata's philosophy emphasizes reproducibility, so this consistency is not surprising. Of course you can set the seed yourself. See t... | null | CC BY-SA 3.0 | null | 2011-05-24T16:05:08.697 | 2011-05-24T16:05:08.697 | null | null | 919 | null |
11202 | 1 | 11205 | null | 1 | 6167 | Hello
I am trying to forecast using different exponential smoothing methods(Linear and Winter's). For the optimal parameters, I am getting negative values of the forecasats.
I am assuming it means that the values will be zero, since it is a sales forecast.
I wanted to know if negative values denote something wrong wi... | Can the forecasts using exponential smoothing be negative in value? | CC BY-SA 3.0 | null | 2011-05-24T16:39:54.080 | 2011-05-24T17:15:30.827 | null | null | 4445 | [
"forecasting"
] |
11203 | 1 | 11228 | null | 3 | 549 | Hi
I am using Linear and exponential forecasting models to do sales forecasting. In the model itself, we use the forecasts of period t to get next forecast and so on.
While analyzing the accuracy of the forecast using Mean Absolute Percentage Error, I get good results. But when I compare the intermediate forecast value... | Should we compare the individual monthly forecasts with actual values? | CC BY-SA 3.0 | null | 2011-05-24T16:46:48.120 | 2016-04-17T12:00:00.623 | 2016-04-17T12:00:00.623 | 1352 | 4445 | [
"time-series",
"forecasting",
"mape"
] |
11205 | 2 | null | 11202 | 7 | null | Holt's or Winter-Holt's exponential smoothing methods can give negative values for purely non-negative input values because of the trend factor which acts as a kind of inertia, which can drive the time series below zero. Normal exponential smoothing doesn't have this problem, it's always smoothing inwards, it never ove... | null | CC BY-SA 3.0 | null | 2011-05-24T17:14:00.257 | 2011-05-24T17:14:00.257 | null | null | 4360 | null |
11206 | 2 | null | 11202 | 0 | null | You can get negative values for certain kinds of models. You might want to explore more complicated models than simple exponential smoothing.
| null | CC BY-SA 3.0 | null | 2011-05-24T17:15:30.827 | 2011-05-24T17:15:30.827 | null | null | 2817 | null |
11207 | 2 | null | 11203 | 6 | null | Yes, you should absolutely compare your predicted values with actual values. This is good practice with any kind of statistical modeling, not just time series analysis.
If certain months are consistently off, you should use a seasonal model.
| null | CC BY-SA 3.0 | null | 2011-05-24T17:20:16.913 | 2011-05-24T17:20:16.913 | null | null | 2817 | null |
11208 | 1 | null | null | 8 | 661 | Problem is that government wants to close electronic roulette and they claim that roulette failed at statistical test.
Sorry for my language but this is translated from Slovenian law as good as possible
Official (by law) requirements are:
- frequency of each event should not differ from expected frequency by more than... | Statistics for gambling machine validation | CC BY-SA 3.0 | null | 2011-05-24T18:19:51.373 | 2012-05-04T02:20:36.217 | 2011-05-25T06:18:33.933 | 4738 | 4738 | [
"correlation",
"statistical-significance",
"chi-squared-test"
] |
11209 | 1 | 11230 | null | 8 | 2935 | I have written a 3-way ANOVA in C++. I have 3 factors, lets say A, B and C and my aim is to check the strength of all possible interactions and main effects. The result of my code is the same as in MATLAB when I use type-I sum of squares.
But when I change the data so that the number of replicates/samples is high in ... | The effect of the number of samples in different cells on the results of ANOVA | CC BY-SA 3.0 | null | 2011-05-24T19:12:20.207 | 2016-04-29T23:42:42.427 | 2016-04-29T23:42:42.427 | 28666 | 2885 | [
"anova",
"matlab",
"sums-of-squares"
] |
11210 | 1 | 11217 | null | 72 | 25352 | I appreciate the usefulness of the bootstrap in obtaining uncertainty estimates, but one thing that's always bothered me about it is that the distribution corresponding to those estimates is the distribution defined by the sample. In general, it seems like a bad idea to believe that our sample frequencies look exactly ... | Assumptions regarding bootstrap estimates of uncertainty | CC BY-SA 3.0 | null | 2011-05-24T19:53:26.753 | 2011-05-24T23:06:16.357 | 2011-05-24T22:34:07.543 | null | 4733 | [
"bootstrap",
"uncertainty"
] |
11211 | 2 | null | 9867 | 2 | null | I found this paper with Google but I cannot access it, so I don't really know what it is about really:
>
Berry KJ, Johnston JE, Mielke PW Jr.
An alternative measure of effect size
for Cochran's Q test for related
proportions. Percept Mot Skills.
2007 Jun;104(3 Pt 2):1236-42.
I initially thought that using pa... | null | CC BY-SA 3.0 | null | 2011-05-24T20:07:50.003 | 2011-05-24T20:07:50.003 | null | null | 930 | null |
11212 | 2 | null | 11189 | 3 | null | Pairwise is a dangerous method in this case, IMO. If you delete pairwise then you'll end up with different numbers of observations contributing to different parts of your model, which can make interpretation difficult.
That being said, casewise deletion tends to discard lots and lots of information, so I suppose it dep... | null | CC BY-SA 3.0 | null | 2011-05-24T20:41:41.547 | 2011-05-24T20:41:41.547 | null | null | 656 | null |
11213 | 2 | null | 11210 | 10 | null | The main trick (and sting) of bootstrapping is that it is an asymptotic theory: if you have an infinite sample to start with, the empirical distribution is going to be so close to the actual distribution that the difference is negligible.
Unfortunately, bootstrapping is often applied in small sample sizes. The common f... | null | CC BY-SA 3.0 | null | 2011-05-24T21:01:51.687 | 2011-05-24T21:01:51.687 | null | null | 4257 | null |
11214 | 2 | null | 11157 | 0 | null | Maybe use an adaptation of [J-Charts](http://www.investopedia.com/articles/technical/04/060204.asp) and/or [Market Profile charts](http://daytrading.about.com/od/indicators/a/MarketProfile.htm), but instead of plotting price (y-axis) vs volume (x-axis) you could plot time of trade (y-axis) vs no. of trades (x-axis) and... | null | CC BY-SA 3.0 | null | 2011-05-24T21:34:01.523 | 2011-05-24T21:34:01.523 | null | null | 226 | null |
11215 | 2 | null | 11210 | 5 | null | I would argue not from the perspective of "asymptotically, the empirical distribution will be close to the actual distribution" (which, of course, is very true), but from a "long run perspective". In other words, in any particular case, the empirical distribution derived by bootstrapping will be off (sometimes shifted ... | null | CC BY-SA 3.0 | null | 2011-05-24T21:55:49.077 | 2011-05-24T22:30:27.953 | 2011-05-24T22:30:27.953 | 1934 | 1934 | null |
11216 | 2 | null | 11193 | 5 | null | I prefer using `ave`
```
dt<-data.frame(id=c(1,1,2,2,3,4),var=c(2,4,3,3,4,2))
## use unique if you want to exclude duplicate maxima
unique(subset(dt, var==ave(var, id, FUN=max)))
```
| null | CC BY-SA 3.0 | null | 2011-05-24T22:39:14.203 | 2011-05-24T22:39:14.203 | null | null | 375 | null |
11217 | 2 | null | 11210 | 64 | null | There are several ways that one can conceivably apply the bootstrap. The two most basic approaches are what are deemed the "nonparametric" and "parametric" bootstrap. The second one assumes that the model you're using is (essentially) correct.
Let's focus on the first one. We'll assume that you have a random sample $X_... | null | CC BY-SA 3.0 | null | 2011-05-24T22:48:41.360 | 2011-05-24T23:06:16.357 | 2011-05-24T23:06:16.357 | 2970 | 2970 | null |
11218 | 2 | null | 11210 | 12 | null | Here is a different approach to thinking about it:
Start with the theory where we know the true distribution, we can discover properties of sample statistics by simulating from the true distribution. This is how Gosset developed the t-distribution and t-test, by sampling from known normals and computing the statistic.... | null | CC BY-SA 3.0 | null | 2011-05-24T23:00:19.693 | 2011-05-24T23:00:19.693 | null | null | 4505 | null |
11219 | 1 | null | null | 8 | 39091 | I have been reading about appropriate measures of central tendency for ordinal level data.
So far I have learned that the median and mode can be used but that the latter can only be used in some cases. Some sources state that the median can only be used with Likert questions when there is an odd number of scores. It i... | Median value on ordinal scales | CC BY-SA 3.0 | null | 2011-05-25T00:29:48.363 | 2012-09-04T18:44:50.493 | 2011-05-25T03:22:21.527 | 4498 | 4498 | [
"median"
] |
11220 | 1 | null | null | 8 | 2031 | I have data from a load test of a web site with several thousand data points spread out over roughly 30 minutes (the values are the response time of the site in milliseconds). The values are spread out among the 30 minute range, but not at a constant rate (i.e. there may be a few milliseconds between some points, other... | Preferred methods for graphing time-series data to present "averages"? | CC BY-SA 3.0 | null | 2011-05-25T00:56:40.383 | 2011-05-25T16:02:58.067 | 2011-05-25T13:24:19.233 | 4739 | 4739 | [
"time-series",
"data-visualization"
] |
11221 | 2 | null | 11145 | 2 | null | As far as your statistical test, it might be a choice between 1) ancova with pretest weight as the covariate and 2) anova with change scores as the outcome. You'd use ancova if you believed posttest weight would naturally be different from pretest weight even without the treatment, and that posttest weight would be a ... | null | CC BY-SA 3.0 | null | 2011-05-25T01:35:52.533 | 2011-05-25T01:35:52.533 | null | null | 2669 | null |
11222 | 2 | null | 11191 | 4 | null | Person-mean imputation with an minimum-item threshold is a simple strategy for retaining scale scores where participants miss the occasional response.
### Some general principles
- If missing data is minimal (e.g., less than 5% of participants are missing 1 item on a 10 item scale), the method of dealing with missi... | null | CC BY-SA 3.0 | null | 2011-05-25T01:49:14.433 | 2011-05-25T02:07:08.603 | 2020-06-11T14:32:37.003 | -1 | 183 | null |
11223 | 2 | null | 11219 | 3 | null | No, the median is the value where half the data is less than or equal to that value and half the data is greater than or equal to that value.
So if your ordinal scale had 100 respondents then find the value that has at least 50 less or equal and 50 greater than or equal. It would only be 3 if half the responses were... | null | CC BY-SA 3.0 | null | 2011-05-25T01:57:05.730 | 2011-05-25T01:57:05.730 | null | null | 4505 | null |
11224 | 2 | null | 11193 | 25 | null | One way is to reverse-sort the data and use `duplicated` to drop all the duplicates.
For me, this method is conceptually simpler than those that use apply. I think it should be very fast as well.
```
# Some data to start with:
z <- data.frame(id=c(1,1,2,2,3,4),var=c(2,4,1,3,5,2))
# id var
# 1 2
# 1 4
# 2 1
# ... | null | CC BY-SA 3.0 | null | 2011-05-25T02:59:08.417 | 2011-05-25T19:40:10.713 | 2011-05-25T19:40:10.713 | 4740 | 4740 | null |
11225 | 1 | null | null | 3 | 495 | I need to calculate an exponential moving average for a series of data. The intended sampling interval is fixed (say 1s) but the data stream has varying intervals (data intervals vary from 0.01s to 10s or so). The data is somewhat noisy (a random data sample would virtually never be on the average).
My impression is th... | Exponential moving average with sub-interval relevance / varying timeframe | CC BY-SA 3.0 | null | 2011-05-25T04:43:24.693 | 2011-05-25T06:55:33.350 | 2011-05-25T06:55:33.350 | 2116 | 4741 | [
"time-series",
"sampling",
"exponential-smoothing"
] |
11226 | 2 | null | 11219 | 7 | null |
### Definitional issues:
- The median is the middle value of the data; it is not by definition the middle value of the scale.
- When the sample size is even, then the median is the mean of the values either side of middle most point after rank ordering all values (see wikipedia description).
### When to use med... | null | CC BY-SA 3.0 | null | 2011-05-25T05:41:03.343 | 2011-05-25T05:41:03.343 | null | null | 183 | null |
11227 | 1 | null | null | 4 | 206 | Say I have 2 sets, $A$ and $B$ with $n_{A}$ and $n_{B}$ elements respectively, which I assume is known. I would like to estimate $| A \bigcup B |$ using samples of $\tilde{A} \subset A$ and $ \tilde{B} \subset B$.
That is if $\tilde{A}$'s elements are uniformly sampled from $A$, and likewise for $\tilde{B}$, will $ ... | Bias in sampling for set intersections | CC BY-SA 3.0 | null | 2011-05-25T05:52:01.007 | 2011-10-24T13:13:28.450 | 2011-05-25T08:40:23.070 | null | 4742 | [
"sampling",
"unbiased-estimator",
"bias"
] |
11228 | 2 | null | 11203 | 5 | null | MAPE is known to have problems, when the time series have values close to zero. Check whether this is the case, since high MAPEs may be the problem of time series values close to zero, not of model accuracy. For a discussion on accuracy measures I recommend [this article](http://www.buseco.monash.edu.au/ebs/pubs/wpaper... | null | CC BY-SA 3.0 | null | 2011-05-25T06:52:17.017 | 2011-05-25T07:54:30.247 | 2011-05-25T07:54:30.247 | 2116 | 2116 | null |
11229 | 2 | null | 11220 | 6 | null | I suggest adding an example or two of what you are presently doing so we can better see what you are dealing with.
What you are concerned with is an important issue: how do you convey the "overall" pattern in the time series data while also not misleading viewers by showing just average values? One way I have dealt wit... | null | CC BY-SA 3.0 | null | 2011-05-25T07:22:42.653 | 2011-05-25T15:50:44.233 | 2011-05-25T15:50:44.233 | 1080 | 1080 | null |
11230 | 2 | null | 11209 | 12 | null | I don't have Matlab but from what I've read in the on-line help for [N-way analysis of variance](http://www.mathworks.com/help/toolbox/stats/anovan.html) it's not clear to me whether Matlab would automatically adapt the `type` (1--3) depending on your design. My best guess is that yes you got different results because ... | null | CC BY-SA 3.0 | null | 2011-05-25T07:53:11.060 | 2011-05-25T08:56:55.523 | 2011-05-25T08:56:55.523 | 930 | 930 | null |
11231 | 1 | null | null | 4 | 1887 | I am investigating many different kinds of PCA versions, I am trying to find out whether PCR will apply to my analysis thus the question on use of PCR.
| Applications of principal component analysis versus principal component regression? | CC BY-SA 3.0 | null | 2011-05-25T09:46:14.150 | 2019-03-28T11:34:44.353 | 2019-03-28T11:34:44.353 | 128677 | 4747 | [
"regression",
"pca",
"dimensionality-reduction"
] |
11232 | 1 | null | null | 4 | 1164 | I am trying to compare the difference between two means with two pairwise samples. Unfortunately, my data are very far of being normal. What test would you recommend to use in this situation? Should I revert to a nonparametric test?
| Testing difference between two means with pairwise data and absence of normality | CC BY-SA 3.0 | null | 2011-05-25T11:30:44.473 | 2011-05-25T13:44:31.233 | 2011-05-25T11:40:42.647 | 2116 | 6245 | [
"hypothesis-testing"
] |
11233 | 1 | 28627 | null | 6 | 10138 | Assume, I have a data set, which is similar to
```
require(nlme)
?Orthodont
```
and my model is
```
fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
```
How can I use the model fit object `fm2` to generate several datasets, which have sample sizes 300, 400, 500, ... ?
I read this [great answer on... | How to simulate data based on a linear mixed model fit object in R? | CC BY-SA 3.0 | null | 2011-05-25T11:51:00.517 | 2013-12-20T21:23:36.717 | 2012-06-19T12:11:28.537 | 183 | 4559 | [
"r",
"mixed-model",
"simulation"
] |
11234 | 2 | null | 11232 | 3 | null | Sounds like a job for the [paired Wilcoxon test](http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test).
Note that this method compares the medians of the two samples, not their means. In any case, the mean is often not a good estimator when the distributions are not normally distributed, as it is easily biased by ex... | null | CC BY-SA 3.0 | null | 2011-05-25T12:15:55.230 | 2011-05-25T12:15:55.230 | null | null | 656 | null |
11235 | 2 | null | 11232 | 5 | null | A paired t-test assumes that the differences are normal: the original values could have any distribution. More precisely, just like with a t-test, the differences don't even have to be normal, just the sampling distribution of the mean. This usually means that with a large enough sample you can use a t-test even withou... | null | CC BY-SA 3.0 | null | 2011-05-25T13:16:56.460 | 2011-05-25T13:16:56.460 | null | null | 279 | null |
11236 | 1 | 11291 | null | 5 | 764 | I've been using R's `lm` to do some linear regression, but decided to give `MCMCregress` a try to get a feel for how it works. As expected, I got basically the same coefficients, but the extra `sigma2` value puzzles me.
When I do a `qqmath` plot of the coefficients, I get the following graph, and I'm puzzled by the sig... | QQ plot of sigma2 from an MCMC regression? | CC BY-SA 3.0 | null | 2011-05-25T13:29:57.467 | 2014-11-20T09:49:04.697 | 2020-06-11T14:32:37.003 | -1 | 1764 | [
"r",
"regression",
"markov-chain-montecarlo",
"qq-plot"
] |
11237 | 2 | null | 11191 | 1 | null | one more piece of advice: make sure the full 6-item composite scale is reliable & that none of the included items reduces scale reliability. If those conditions aren't satisfied, you shouldn't be averaging them even in cases where data are complete. If these conditions are satisfied, then using a subset of items for ca... | null | CC BY-SA 3.0 | null | 2011-05-25T13:31:15.813 | 2011-05-25T13:31:15.813 | null | null | 11954 | null |
11238 | 2 | null | 11232 | 4 | null | Your description of your design is not too precise as it allows two interpretations.
First, it is possible that you have a 2 (between) x 2 (within) design (i.e., two groups with two pairwise samples).
Second, it is possible that you have a simple design with one group which was measured two times.
Only in the second ca... | null | CC BY-SA 3.0 | null | 2011-05-25T13:44:31.233 | 2011-05-25T13:44:31.233 | 2017-04-13T12:44:39.283 | -1 | 442 | null |
11239 | 2 | null | 11193 | 1 | null | Yet another way to do this with base:
```
dt<-data.frame(id=c(1,1,2,2,3,4),var=c(2,4,1,3,4,2))
data.frame(id=sort(unique(dt$var)),max=tapply(dt$var,dt$id,max))
id max
1 1 4
2 2 3
3 3 4
4 4 2
```
I prefer mpiktas
' plyr solution though.
| null | CC BY-SA 3.0 | null | 2011-05-25T14:34:17.263 | 2011-05-25T14:34:17.263 | null | null | 3094 | null |
11240 | 2 | null | 11220 | 3 | null | Have you considered a scatterplot of the data themselves? [That's an approach I really like](https://stats.stackexchange.com/questions/173/time-series-for-count-data-with-counts-20). It lets the viewer make their own conclusions about the presence and significance of trends, and it doesn't conceal variability or outl... | null | CC BY-SA 3.0 | null | 2011-05-25T16:02:58.067 | 2011-05-25T16:02:58.067 | 2017-04-13T12:44:45.640 | -1 | 71 | null |
11242 | 1 | null | null | 1 | 199 | This is a question strongly related to Cauchy "characters".
I'm constructing a 4 question canvassing questionnaire that will tell the likely voter being contacted which of the presidential candidates most closely matches them. The advantage of this approach for a dark horse presidential candidate is obvious, presuming... | Optimal blind poll construction | CC BY-SA 3.0 | null | 2011-05-25T14:45:39.393 | 2011-05-29T18:01:20.593 | 2011-05-25T19:38:19.693 | null | 4753 | [
"survey",
"experiment-design"
] |
11243 | 1 | null | null | 2 | 161 | Context
I have a regression framework and two sets of data. Using leave-one-out cross-validation, the first set gives very good performance and the second set gives rather poor performance. I need to explain the reason for this difference in performance.
Having looked at the data, it is clear that the first set is a mu... | Explaining regression performance differences | CC BY-SA 3.0 | null | 2011-05-25T17:11:02.417 | 2011-05-25T17:11:02.417 | null | null | 3052 | [
"regression",
"cross-validation",
"ridge-regression"
] |
11246 | 1 | 11265 | null | 4 | 301 | The last line is an example of what I'm looking for:
```
data(airquality)
attach(airquality)
lm1 <- lm(Ozone ~ Solar.R+Wind)
lm2 <- lm(Ozone ~ Solar.R+Wind+Temp)
anova(lm1 , lm2)
require(rpart)
rp1 <- rpart(Ozone ~ Solar.R+Wind)
rp2 <- rpart(Ozone ~ Solar.R+Wind+Temp)
anova(rp1 , rp2) # this doesn't exist - is there ... | Is there an ANOVA table generalization for two nested CART models? | CC BY-SA 3.0 | null | 2011-05-25T18:44:42.107 | 2011-05-26T07:49:23.200 | 2011-05-25T19:42:03.047 | null | 253 | [
"anova",
"cart"
] |
11247 | 2 | null | 11242 | 1 | null | EDIT in response to last comments.
Here is my suggestion for how to run the contest.
- The contest holder should decide on a list of "test questions". The 4-item questionnaires will be scored on how well they allow the guesser to guess the voter's responses to these "test questions". These test questions will be mad... | null | CC BY-SA 3.0 | null | 2011-05-25T18:46:50.477 | 2011-05-25T23:37:05.250 | 2011-05-25T23:37:05.250 | 3567 | 3567 | null |
11248 | 1 | 11343 | null | 6 | 940 | I'm using Gibbs sampling to learn the distributions of coefficients for a multinomial logistic regression model. At the end, I end up using the mean values of distributions of coefficients, and the resulting logistic regression is used as a classifier.
I'm trying to find out advantages of having probability distributi... | How can I use credibility intervals in Bayesian logistic regression? | CC BY-SA 3.0 | null | 2011-05-25T21:05:29.583 | 2011-06-29T12:32:46.913 | 2011-05-29T11:33:36.090 | 3280 | 3280 | [
"logistic",
"bayesian",
"credible-interval"
] |
11249 | 1 | null | null | 5 | 6600 | I have a large data set which is in .dbf format right now and what I would like to do is be able to manipulate it easily in Excel and do something like subtotal and calculate stdev and ratios.
Details of the data set;
This data set contains shopper information. It has 1.2 million rows and 20 columns where the rows are ... | What would be a good way to work with a large data set in Excel? | CC BY-SA 3.0 | null | 2011-05-25T21:32:13.980 | 2014-09-16T15:17:59.700 | 2011-05-26T06:08:23.393 | 2116 | 4755 | [
"excel",
"large-data"
] |
11250 | 2 | null | 11231 | 4 | null | When doing a PCA, you are effectively choosing a new set of 'variables' that you know for all your observations. Their main property is that they maximize the variance-content in one dimension (first PC has the most,...), while being linear combinations of the original covariates. This is the way it works like a dimens... | null | CC BY-SA 3.0 | null | 2011-05-25T21:45:02.643 | 2011-05-25T21:45:02.643 | null | null | 4257 | null |
11251 | 2 | null | 11249 | 15 | null | If you feel you may start more of such very large Excel type projects in the future, then you should consider installing and spending 10 hours learning the basics of R (free), which will let you do what you mention in your question, in a much more efficient manner than Excel.
[R for Beginners PDF](http://cran.r-project... | null | CC BY-SA 3.0 | null | 2011-05-25T22:39:07.793 | 2011-05-26T17:33:25.187 | 2011-05-26T17:33:25.187 | 4329 | 4329 | null |
11252 | 1 | 11503 | null | 4 | 4025 | I received a question today that I wasn't exactly sure how to answer.
I have built a predictive model using a fairly basic logistic regression that works pretty well and fits our business needs. Recently, we purchased a CRM tool that allows us to build "probability" scores, but only allows the end users to give inte... | Weight variables for predictive model | CC BY-SA 3.0 | null | 2011-05-25T23:57:37.597 | 2011-08-02T00:36:09.090 | 2011-05-26T09:21:28.103 | null | 569 | [
"logistic",
"predictive-models",
"validation"
] |
11253 | 1 | 11278 | null | 6 | 170 | If I take a set of measurements and test correlation of variable $A$ vs variable $B$ and get a significant correlation, that makes sense to me. But what if further analysis reveals that of those factors, there is only a significant positive correlation within one group, and that group is over-represented. Is the glob... | Factor dependent correlation | CC BY-SA 3.0 | null | 2011-05-26T02:40:02.887 | 2011-05-27T06:51:02.347 | 2011-05-27T06:51:02.347 | 2116 | 1327 | [
"correlation"
] |
11254 | 2 | null | 11246 | 4 | null | Recursive partitioning does not provide such inferential statistics. It is a highly exploratory method that would require an enormous multiplicity adjustment should you compute regression and error sum of squares from the result. Better would be to do formal but flexible modeling of the two predictors, e.g., using re... | null | CC BY-SA 3.0 | null | 2011-05-26T03:12:00.933 | 2011-05-26T03:12:00.933 | null | null | 4253 | null |
11255 | 1 | null | null | 23 | 1602 | I've noticed this issue coming up a lot in statistical consulting settings and i was keen to get your thoughts.
### Context
I often speak to research students that have conducted a study approximately as follows:
- Observational study
- Sample size might be 100, 200, 300, etc.
- Multiple psychological scales hav... | Whether to use structural equation modelling to analyse observational studies in psychology | CC BY-SA 3.0 | null | 2011-05-26T03:20:04.987 | 2015-12-18T14:02:37.680 | 2011-05-26T08:12:19.967 | 183 | 183 | [
"scales",
"causality",
"structural-equation-modeling",
"observational-study"
] |
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