tttjjj/elt_bench huggingface dataset:
I've used the term "model" purposefully. Not all models are good ones. On a question like this we are trying to figure out what a professor wants. For example, another possibility is to model car sales as Poisson with daily expected demand of 1.24, which would give $P[Y \\geq 1] = 0.7106$ Is it a better model? I would say it is lacking since it won't meet the known variance over the 50-day period.
@gmacfarlane: I can do that. Most of the questions I have are not concerned with the implementation, but the methodical approach. I'll paste a minimal version of my R script if it helps.
Aha! I see that @whuber has already added sequential-analysis as a tag.  :)  That's the right path.
1) No, it doesn't break formal assumptions for point-biserial _r_. 2) Yes it does limit statistical power.
Thank you very much StackExchange and IrishStat. I can never forget the guidance and help from you. This paper will help the Governments of developing countries in making health policy decisions and save millions of children.
@PeterRabbit Might I suggest you edit the question to make it a little less confusing? Currently, $\\beta$ seems to be referred to as both a parameter and a random variable. Which is it? Second, you state in some places that $\\beta$ is constrained to be either 0 or 1, yet in other places that it is constrained to lie between 0 or 1 (the two are obviously VERY different).
While this is definitely a very similar question to the suggested duplicate, it's not exactly the same. Given the level of the question, I don't think it's reasonable to assume that the questioner could infer the answer to this question from the (excellent) answer to the other.
Interesting question, I believe Whuber's answer explains it perfectly well. My first response to the question was: 'increased variance does not entail higher class-discriminatory information'.
Start with a simpler model (fewer predictors), simpler correlation structure, and  build up from there. You might also have problems if $P(Y=1)$ is close to 0 or 1.
"Because the difference you are seeing is fairly large an overfit is possible here"- is there any "standard difference" by which you can say that the possibility of overfitting is there?
Baayen's textbook, *Practical Data Analysis for the Language Sciences with R*, may still be found at http://bit.ly/aQ1KGb
I think you might be interested in existing (and validated) instruments; e.g., the [PROQOLID](http://www.proqolid.org/) database which surveys a lot of questionnaires targeting health-related quality of life and patient reported outcomes. If no questionnaire really suits your needs, you can still get an idea of what domains to assess and how to devise specific items, depending on your very own interests. As for coding practice, there are a lot of possibilities, ranging from yes/no or Likert-type items, visual analog scale (VAS), preference scaling, etc.
@Stéphane Laurent: The paywall is letting you through but not me.
A lot has been written about why stepwise regression is a poor approach. If you believe that (and you should), then lasso would be "better" simply because it is less bad.
Could you elaborate on how to convert partial correlation data into correlation data? What I am looking for is to _make_ a time series if I have pre-defined partial correlation data.
One way of understanding Placidia's answer is by recognizing that $\\sigma^{2}_{f(x)} \\ne f(\\sigma^{2}_{x})$. Because the test statistics by which $p$-values/significance is determined are usually based on division by something looking very much like an estimate of the variance, transformation will change the $p$-values and significance.
Why not leave it at that?  Do you really need a numerical score?  Sometimes the ranking is good enough for decision-making, whereas providing a number can mislead whoever doesn't read the fine print into using the value quantitatively, a use that is not supported by the theory.
I am also confused on choosing the "right" estimator. With the lagged DV you have severe autocorrelation and with different countries you should have heteroskedastic error terms. As i get rather bad results with OLS (I only care about the model coefficients and not on standard errors per se), using HAC standard errors would return the same coefficients as regular OLS.
You need to find a possibility to adjust the breaks for the histogram in the Excel plugin.
You might want to explore zero-inflated Poisson and zero-inflated negative binomial models. A canonical example of when to use is in predicting how many fish people catch: in order to catch any fish, you have to go fishing. So these models estimate first whether you are going fishing, and then if you are what your catch probabilities are.
A very interesting twist on my idea and, what is more, your idea will be much easier computationally - it won't require permutation of the whole data set and nor will it require an algorithm to identify the new highs and lows of these randomly permuted "alternative futures."
Hi, thanks for your answer. In Jaccard & Turrisi's book Interaction Effects in Multiple Regression, they state that in interaction models (using a 2-term + interaction model as an example), "The coefficient for X estimates the effect of X on Y when Z is at a specific value, namely, when Z = 0. The coefficient for Z estimates the effect of Z on Y when X is at a specific value, namely, when X = 0." So then wouldn't β1 be the coefficient for gender when treatment = traditional, and β2 the coef for treatment when gender = female? Or is that not what they mean?
The strict answer to your question is "no" for the simple reason that the distribution of elements of sequence could change after a certain point and if you only observe the first $n$, then you will miss the change in distribution. Now, if you specify what you mean by error tolerance, then there is room for maneuver.
+1 Just to clarify: I was not necessarily recommending an appropriate transformation as an alternative. My suggestion was that one indication of a multiplicative structure would be that a log transformation appears to stabilize the spread-vs-mean relationship.
You're right that they're not independent. One approach would be to condition on the total prey taken at each point (since you're interested in *preference*, not how hungry they were) and look at it as a binomial response; you'd presumably do a logistic regression. That is, your model would be for the *proportion* of those taken that were species A. You mention GLMM, so why wouldn't you do something like a binomial logit model?
@rolando2:  If I guess 30%, I can only be off (about) a factor of 3 either way.  And yes, I meant $\\pm 30-50\\%$ as that you would have an uncertainty of about that much.  I think better accuracy is hard to come by, and probably not worth the effort as there will be larger uncertainties to come.
Ah, I see. Well I don't think thats a very efficient or proper way to do a mediation analysis, but it might get the job done. You will not be able to interpret specific paths in your model (e.g., through which mediator the effect is occurring), but it will tell you if variance between your IV and DV is shared by other variables. If this is for class, then try your best to do what the instructor wants. If it is not for class, then take a look at this wonderful macro which works in SPSS: http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html
Under Linux Window emulation is not necessary, the shared library can be directly used. Only the GUI is not working this way, but that shouldn't be a problem for DanB as they want to invoke it from R.
GRNN is Generalized Regression Neural Network. For the thetaf() function I use R software.
And next question is why do you consider $Var(X)$ and not the error variance $Var(X-\\hat X)$? Thanks. http://en.wikipedia.org/wiki/Minimum_mean_square_error#Linear_MMSE_estimator_for_linear_observation_process
Thanks a lot for this reference.
There is actually quite a large literature on this subject (see the link in my earlier comment) including literature on how the spacing of different word choices is perceived.
I don't understand your reluctance to characterize each store (in part) by its mean on the continuous variables.  What else would you try to do with those variables in a cluster analysis?  As far as the categorical vars. go, it's possible that, even without much variation in modes, the variation that does exist would help form clusters of some distinctness.  But if you don't believe this is true, it would seem you are arguing against the utility of cluster analysis in your situation, in which case who are we to talk you out of that position?
@Elvis: The definition of $P_\\lambda$, for $\\lambda\\in [0,1]$, is, for each $x\\in X$: $$P_\\lambda(x) = \\frac{P^\\lambda_1(x)P^{1-\\lambda}(x)}Z$$ where $Z$ is a normalization factor. Chernoff information $C(P_1,P_2)$ is $D(P_{\\lambda^*}||P_1)$, for the (unique) $\\lambda^*\\in [0,1]$ s.t. $$D(P_{\\lambda^*}||P_1)=D(P_{\\lambda^*}||P_2)$$ (cf. p. 378--380 of Cover-Thomas, 2/e).
Note that "deaths" and "injuries" are things that you count; why would this not be discrete?
Usually such tables will have a footnote that represents significance levels.  Failing that, you might be able to look at the raw differences between variables and either assume equal errors or fine elsewhere in the paper what error terms were used.  From there you could try to derive the relative magnitudes of A, B, and C.  Failing that, you could contact the authors and ask them.
My understanding is that the EM algorithm is useful for modeling the likelihood of a positive match because it takes into account the unknown (or "latent") probabilities of incorrectly linking two different records or incorrectly not linking two matching records. Estimates of these probabilities are refined during each step of the algorithm in order to maximize the likelihood function.
Great answer from @whuber.  But I don't understand why you don't produce the graphics in R too?
What would be the "hazard" in this example? The outcome of interest stated by the OP is "weight gain", and hence a statistical technique to evaluate the time to an event does not make any sense.
Very cool illustrations, @MånsT. I de-italicized the words 'Beta' and 'Binomial' in your answer - I hope you don't mind.
I find it important to make the following observation: What @arthur suggests are the so-called response residuals. There are many others. Because of the link function, there is no single definition of what a model residual is in GLM. There are the deviance, working, partial and pearson residuals too.
(a) Yes, what I called the error term can be considered AWGN. So wether you call it error term, eta/epsilon or noise - important is that it is a model assumption and can not be determined or verified.
As [illustrated here](http://stats.stackexchange.com/a/28493/919) and [here](http://stats.stackexchange.com/a/46508/919) (with full `R` code), you can create scatterplots of the *residuals* of variables with respect to the covariates.  The correlation coefficients of these residual scatterplots are the partial correlation coefficients.
Each individual was presented with a number of prey, I calculated how many of those were captured at first attempt (strike) = prey capturing success
In general there can be no such relations. Consider the following two examples. **Example 1**: Let $X = \\pm n$ with probability 1/2 each. What is the variance? (*It will vary with* $n$.) What is the skewness? (*It will be constant.*) **Example 2**: Let $X = -n$ with probability $p = 1 / 3n^2$, $X = 2n$ with probability $p/2$ and $X = 0$ otherwise. What is the variance? (*It will be constant.*) What is the skewness? (*It will vary with* $n$.) Conclusion?
You have a contradiction - is a higher score better or lower?
What about just using quantiles?!
thanks. Of course you're right.
$\\text{E}(T_1T_2) = \\text{Cov}(T_1,T_2) + \\text{E}(T_1)\\text{E}(T_2)= \\rho \\sigma_1 \\sigma_2 + \\text{E}(T_1)\\text{E}(T_2)$
+1 for the examples
One of the assumptions is of continuous distributions. I don't think that one holds.
okay I will do that.  I do think it is an answer to the question though. It is just not spelled out in detail.  But I agree that spelling this out improves the anwer.  Thanks for the comment.
@Paul it should never increase (except for numerical precision issues). If it does, you have a bug in your code. However, depending on the implementation, it may take an extra iteration to compute the final assignments (assuming you use "cluster assignments no longer change" as termination criterion, which is the proper).
The answer is blatantly wrong on three counts. (1) Even under simplistic assumptions about the targeting errors (namely, that they are approximately Normal and do not favor any particular direction over any other), the degrees of freedom will not be $6$! They will be either $14$ or $21$ depending on whether errors are computed in two or three dimensions. (2) Even with $6$ df the rejection region would not extend all the way to $164$--it only goes to $12.6$. (3) Worse, this is the wrong rejection region! "Strong evidence" would be a significantly *small* (not large) value. Throw this book away!
Yes, it is Latex. See this meta thread: http://meta.stats.stackexchange.com/questions/218/tex-processing-for-stats
@Gottfried, I'd rather disagree with the way you put it. All FA methods require the number of factors _m_ be known: you fit the model for _m_ you specify. You can use various criterions which may help to decide on _m_, but all these are not part of factor extraction methods themselves. With the exception of that chi-square, computed along with GLS and ML methods. Also, with PA method, if you know true communalities in advance (which is very seldom), you can make _them_ to guide you towards the best _m_. But in any case, its you, not an extraction algorithm, decides on _m_.
You want the simulated data to have the exact same mean(s) and var(s) as stated in the problem.  One way to do this (there are many others) is to use the `mvrnorm` function in the `MASS` package (you need to set the empirical argument to TRUE).
As @Penguin_Knight hints at, I think your main issue is understanding statistics, after which you can decide which R output  is useful.  Narrow your question to receive specific help.
The marginal distribution of a multivariate normal is a univariate normal with corresponding $\\mu_i$ and $\\Sigma_{ii}$
+1. May I just add, for the visually oriented, that we often plot the sample path $(n, \\frac{1}{n}S_n)$ and expect that the graph will level of around the expectation for large $n$. This is *precisely* what the SLLN says will happen. The WLLN says something that is more vague, and it does not guarantee that the graph will level of eventually. Since the SLLN holds, it will, but there are other counterexamples that show that we can have convergence in probability and yet with probability one the sample path will not level of.
Welcome to the site. Short answers like this are usually comments rather than answers.
Ah, I just realised why I have them backwards now. It is because I followed one of the example from a website shows how to calculate recall and precision for classifier evaluation. BUT when I did my confusion matrix, I did it backward. I flipped the label and predicted axis..
Not that this makes the question any less important or interesting, but from reports I've found on the web, it appears that Owen claimed they had consensual sex and somebody else murdered her - so I suppose that the DNA evidence was not an important factor in his conviction (except that his connection to the case would not have been discovered without it)?
The logspline package is for if you have data (not just the summaries) and want to fit a distribution to the data and generate new observations from that distribution.  I suggested it in case the summaries that you posted were from some actual data that you have.  If you have only the summaries then, while possible, this will be much harder using logspline and there are not simple tools for this approach.
First of all you made an incorrect statement which is the basis of your argument. It is **NOT TRUE** that independence of events implies P(UA$_i$)=∑P(A$_i$). That holds if the events A$_i$ are dsijoint. So I think you need to reformulate your question.
Gallamine, it seems possible to me that when you say PDF you really mean likelihood function... Conventionally the confidence interval is for the true (fixed) probability of success for each trial. The notion of a PDF in that circumstance fit well with the idea that you had information about the distribution of possible values for that true probability of success... Oh well, back to the Wilson's interval I guess.
@Michael Your answer is not closed; it is only locked to prevent misuse of the comments. I agree that the dissertation did not define its terms. As my first comment to your answer states, I do not object to your answer *per se,* but I think that the context established by the dissertation is a different one than assumed by that answer. Finally, your question intimates that something in my answer is not correct. This used to be right, but Cardinal kindly (and silently) fixed an (embarrassing) typographical error with an edit. Is there something else that should be improved?
@jerad The proposal distribution is adaptive, not the chain. It is possible to have an adaptive proposal and a Markov chain, as shown in some of the references here:

http://stats.stackexchange.com/questions/7286/can-i-change-the-proposal-distribution-in-random-walk-mh-mcmc-without-affecting
Haha, I kind of just assumed that this was it because of all the similarities (number of variables, test/train split, binary output). 

Not sure this stuff is going to be helpful at all then, most of the responses made some inference about how the data was generated. If the problem is real, not sure if any of these will be helpful. However, if this data was artificially generated for an artificial problem, these methods may be useful.
Feel free to click the check mark if you feel this is an acceptable answer.
+1 @John I agree with Dunlap et al's arguments. I don't quite follow your claim that it has been over-generalised. In g-power 3 for example, if you want to calculate statistical power you supply the correlation between the two conditions and a between subjects measure of d. And even when a between subjects design is not possible, I would still find the between subjects measure of effect size a more useful standardisation metric for describing the amount of change that has been observed.
I accept this answer, because this is the precise, and exhaustive, answer to my question. It is my fault that my question was less then perfect and that I actually meant something else; what I really meant were the Sankey diagrams :-)
Or, ESS could be "error SS" and RSS could be "regression SS"
Of course, this has problems. It depends on the weight you give to a production company that produces lots of fairly good films relative to a company that makes one or two films that rank very highly.
I guess I don't get the point of the question. $E[e_iu_i]\\gt0$ means different things depending on whether the error is additive or subtractive. In the former case, you are stipulating that a positive error in $X$ tends to be associated with a positive error in $Y$. In the latter case, you are stipulating that a positive error in $X$ tends to be associated with a negative error in $Y$. Another way to put it is that you might as well assume a "positive" measurement error; having done that, **you** need to decide whether to assume $E[e_iu_i]$ is positive or negative.
Welcome to the site, @Neo182. Would you mind giving an overview of the paper so that users can decide it it's what they're looking for? It might also be nice to have an official citation in case of linkrot. Since you're new here, you should read our [FAQ](http://stats.stackexchange.com/faq), which contains info about the site like this.
Sequence A and B are indeed independent from each other. With regard to the normality assumption: The games that make up sequences A and B have been sampled in some way, but after the sampling I take the games as a given. The way I see it, both sequences are ‘Poisson binomial distributions’(see: http://en.wikipedia.org/wiki/Poisson_binomial_distribution) because the p’s are not all the same. I have assumed that these probability distributions can be can be approximated with a normal distribution. I hope this assumptions is correct.
Sorting is detected by the sequence of signs of the changes in values.  The distribution of such sequences is well known (at least when the "known distribution" is continuous) and independent of your distribution; it is the basis of a [runs test](http://en.wikipedia.org/wiki/Wald%E2%80%93Wolfowitz_runs_test).
In terms of labels, the only label that might be useful is "in control" when the data is inside the control limits, but even that might be considered overkill.   I've looked at a lot of control charts and I've never seen the "outside of control limit" region or "in control" region labelled.  But, I have seen them shown in different colors (red for outside of the control limits).
+1 Nicely illustrated and explained, Josh.
I agree with @mark999. It's clear that there is a difference - you don't need a statistical test to see that. What are your scientific goals? To see if there is a difference, to describe the difference or something else?
What do you mean by "validate"? One meaning could be "check assumptions"; another "check that it fits other data". Possible more meanings that that, too.
+1 for coming back, I'm waiting for your update. If you're working with different classifiers, you might have a look at [ROCR](http://rocr.bioinf.mpi-sb.mpg.de/).
(+1) Nice points, especially the latest.
In a frequentist setting, one would *not* "assert that you have only 75% confidence that your interval contains the true value" in reference to a CI, in the first place. Herein, lies the crux of the issue. :)
Special C4.5. I want to know a formula or way to estimate the leaves and nodes needed.
2&3. My data is a set of recorded information on a bunch of seminars that cost money to attend. I'm trying to figure out what factors influence revenue and build a predictive model as well. The factors include things like day of week, topic, etc. The complicated one is the name of the seminar speaker...we draw from a pool of 154 different speakers. Usually just one will host the event, but sometimes we have two, three, or (very rarely) even four. Please let me know if you need any other information.
But wouldn't the fact that there is only one DV measurement used for clusters of students artificially reduce the standard error?
@Dail The B-P test takes the *squared* residuals for the regression. I updated my answer to provide more details and to really show what is happening. Hopefully, this will clarify things for you.
They are not entirely based on theory. These predictors have not been tested before and the dependent variabele is new as well. The predictors are variables I suggest that will have an effect. I chose one predictor that came from literature. The sample size for the particular question of the dependent variable is 68 (entire sample size is 259). The correlations between variables exists but it not higher than ,500. Is it ok to use the backward-method in this case?
There is no way to tell unless either 1) There is theory to support a model or 2) You run the models.
@linker cannot agree on the startup thing. I have a master in CS (wrote master thesis in ML though) and my first position was a startup. Startups should not care about titles since they are regularly looking for broad thinkers who are able to get the job done (and play nicely with the other in the company) meanwhile I suspect bigger companies to follow policies carved into stone to avoid too much responsibility for single HR drones (no bitterness, just humor here ;))
Thank you very much, Peter ! I think I will include some precise numbers in the text.
@miura Appologies I thought you were the person that asked a similar Q about R & related things on StackOverflow. I'll delete my comment as I am obviously mistaken. Should have had coffee before writing...