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  • best way to statistically detect anomalies in data

    - by reinier
    Hi, our webapp collects huge amount of data about user actions, network business, database load, etc etc etc All data is stored in warehouses and we have quite a lot of interesting views on this data. if something odd happens chances are, it shows up somewhere in the data. However, to manually detect if something out of the ordinary is going on, one has to continually look through this data, and look for oddities. My question: what is the best way to detect changes in dynamic data which can be seen as 'out of the ordinary'. Are bayesan filters (I've seen these mentioned when reading about spam detection) the way to go? Any pointers would be great! EDIT: To clarify the data for example shows a daily curve of database load. This curve typically looks similar to the curve from yesterday In time this curve might change slowly. It would be nice that if the curve from day to day changes say within some perimeters, a warning could go off. R

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  • Where can I find simple beta cdf implementation.

    - by Gacek
    I need to use beta distribution and inverse beta distribution in my project. There is quite good but complicated implementation in GSL, but I don't want to use such a big library only to get one function. I would like to either, implement it on my own or link some simple library. Do you know any sources that could help me? I'm looking for any books/articles about numerical approximation of beta PDF, libraries where it could be implemented. Any other suggestions would be also appreciated. Any programming language, but C++/C# preffered.

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  • Smoothing Small Data Set With Second Order Quadratic Curve

    - by Rev316
    I'm doing some specific signal analysis, and I am in need of a method that would smooth out a given bell-shaped distribution curve. A running average approach isn't producing the results I desire. I want to keep the min/max, and general shape of my fitted curve intact, but resolve the inconsistencies in sampling. In short: if given a set of data that models a simple quadratic curve, what statistical smoothing method would you recommend? If possible, please reference an implementation, library, or framework. Thanks SO!

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  • Using recode in R

    - by celenius
    I'm trying to use 'recode' in R (from the 'cars' package) and it is not working. I read in data from a .csv file into a data frame called 'results'. Then, I replace the values in the column 'Built_year', according to the following logic. recode(results$Built_year, "2 ='1950s';3='1960s';4='1970s';5='1980s';6='1990s';7='2000 or later'") When I check results$Built_year after doing this step, it appears to have worked. However, it does not store this value, and returns to its previous value. I don't understand why. Thanks. (at the moment something is going wrong and I can't see any of the icons for formatting)

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  • Generating lognormally distributed random number from mean, coeff of variation

    - by Richie Cotton
    Most functions for generating lognormally distributed random numbers take the mean and standard deviation of the associated normal distribution as parameters. My problem is that I only know the mean and the coefficient of variation of the lognormal distribution. It is reasonably straight forward to derive the parameters I need for the standard functions from what I have: If mu and sigma are the mean and standard deviation of the associated normal distribution, we know that coeffOfVar^2 = variance / mean^2 = (exp(sigma^2) - 1) * exp(2*mu + sigma^2) / exp(mu + sigma^2/2)^2 = exp(sigma^2) - 1 We can rearrange this to sigma = sqrt(log(coeffOfVar^2 + 1)) We also know that mean = exp(mu + sigma^2/2) This rearranges to mu = log(mean) - sigma^2/2 Here's my R implementation rlnorm0 <- function(mean, coeffOfVar, n = 1e6) { sigma <- sqrt(log(coeffOfVar^2 + 1)) mu <- log(mean) - sigma^2 / 2 rlnorm(n, mu, sigma) } It works okay for small coefficients of variation r1 <- rlnorm0(2, 0.5) mean(r1) # 2.000095 sd(r1) / mean(r1) # 0.4998437 But not for larger values r2 <- rlnorm0(2, 50) mean(r2) # 2.048509 sd(r2) / mean(r2) # 68.55871 To check that it wasn't an R-specific issue, I reimplemented it in MATLAB. (Uses stats toolbox.) function y = lognrnd0(mean, coeffOfVar, sizeOut) if nargin < 3 || isempty(sizeOut) sizeOut = [1e6 1]; end sigma = sqrt(log(coeffOfVar.^2 + 1)); mu = log(mean) - sigma.^2 ./ 2; y = lognrnd(mu, sigma, sizeOut); end r1 = lognrnd0(2, 0.5); mean(r1) % 2.0013 std(r1) ./ mean(r1) % 0.5008 r2 = lognrnd0(2, 50); mean(r2) % 1.9611 std(r2) ./ mean(r2) % 22.61 Same problem. The question is, why is this happening? Is it just that the standard deviation is not robust when the variation is that wide? Or have a screwed up somewhere?

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  • Mathematica, PDF Curves and Shading

    - by Venerable Garbage Collector
    I need to plot a normal distribution and then shade some specific region of it. Right now I'm doing this by creating a plot of the distribution and overlaying it with a RegionPlot. This is pretty convoluted and I'm certain there must be a more elegant way of doing it. I Googled, looked at the docs, found nothing. Help me SO! I guess Mathematica counts as programming? :D

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  • need more complex index !

    - by silversky
    I'm sorry if it's not an appropriate question for this site, and if it's necesary I'll close this question. But maybe someone could give me an ideea: I'm trying to find a more complex index to make an hierarchy. For example: 5 votes from 6 = 83% AND 500 votes from 600 = 83%; 10 votes from 600 = 1.66% If I make a hierarchy with the %, first two will be on the same place, but I think that 83% from 600 it's more valuable than the first one. I could compare 5, 10, 500, but again it's not fair because the third case (10 votes) will be in front of the first case (5 votes), wich it's not fair beacuse the third case has only 1.66% Maybe someone could give me an ideea how to give more weight for the second case but in the same time let the let the new entries have a fair chance.

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  • Exporting Stata results

    - by Max M.
    I'm sure this is an issue anyone who uses Stata for publications or reports has run into: how do you conveniently export your output to something that can be parsed by a scripting language or Excel? There are a few ADO files that to this for specific commands (try findit tabout or findit outreg2). But what about exporting the output of the table command? Or the results of an anova? I'd love to hear about how Stata users address this problem for either specific commands or in general.

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  • mean and variance of image in single pass

    - by ajith
    hi everyone,am trying to calculate mean and variance using 3X3 window over image(hXw) in opencv...here is my code...is there any accuracy issues with this??or is there any other efficient method to do it in one pass.? int pi,a,b; for(i=1;i

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  • Is it possible to do A/B testing by page rather than by individual?

    - by mojones
    Lets say I have a simple ecommerce site that sells 100 different t-shirt designs. I want to do some a/b testing to optimise my sales. Let's say I want to test two different "buy" buttons. Normally, I would use AB testing to randomly assign each visitor to see button A or button B (and try to ensure that that the user experience is consistent by storing that assignment in session, cookies etc). Would it be possible to take a different approach and instead, randomly assign each of my 100 designs to use button A or B, and measure the conversion rate as (number of sales of design n) / (pageviews of design n) This approach would seem to have some advantages; I would not have to worry about keeping the user experience consistent - a given page (e.g. www.example.com/viewdesign?id=6) would always return the same html. If I were to test different prices, it would be far less distressing to the user to see different prices for different designs than different prices for the same design on different computers. I also wonder whether it might be better for SEO - my suspicion is that Google would "prefer" that it always sees the same html when crawling a page. Obviously this approach would only be suitable for a limited number of sites; I was just wondering if anyone has tried it?

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  • Naive Bayes matlab, row classification

    - by Jungle Boogie
    How do you classify a row of seperate cells in matlab? Atm I can classify single coloums like so: training = [1;0;-1;-2;4;0;1]; % this is the sample data. target_class = ['posi';'zero';'negi';'negi';'posi';'zero';'posi']; % target_class are the different target classes for the training data; here 'positive' and 'negetive' are the two classes for the given training data % Training and Testing the classifier (between positive and negative) test = 10*randn(25, 1); % this is for testing. I am generating random numbers. class = classify(test,training, target_class, 'diaglinear') % This command classifies the test data depening on the given training data using a Naive Bayes classifier Unlike the above im looking at wanting to classify: A B C Row A | 1 | 1 | 1 = a house Row B | 1 | 2 | 1 = a garden Can anyone help? Here is a code example from matlabs site: nb = NaiveBayes.fit(training, class) nb = NaiveBayes.fit(..., 'param1',val1, 'param2',val2, ...) I dont understand what param1 is or what val1 etc should be?

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  • Linear regression confidence intervals in SQL

    - by Matt Howells
    I'm using some fairly straight-forward SQL code to calculate the coefficients of regression (intercept and slope) of some (x,y) data points, using least-squares. This gives me a nice best-fit line through the data. However we would like to be able to see the 95% and 5% confidence intervals for the line of best-fit (the curves below). What these mean is that the true line has 95% probability of being below the upper curve and 95% probability of being above the lower curve. How can I calculate these curves? I have already read wikipedia etc. and done some googling but I haven't found understandable mathematical equations to be able to calculate this. Edit: here is the essence of what I have right now. --sample data create table #lr (x real not null, y real not null) insert into #lr values (0,1) insert into #lr values (4,9) insert into #lr values (2,5) insert into #lr values (3,7) declare @slope real declare @intercept real --calculate slope and intercept select @slope = ((count(*) * sum(x*y)) - (sum(x)*sum(y)))/ ((count(*) * sum(Power(x,2)))-Power(Sum(x),2)), @intercept = avg(y) - ((count(*) * sum(x*y)) - (sum(x)*sum(y)))/ ((count(*) * sum(Power(x,2)))-Power(Sum(x),2)) * avg(x) from #lr Thank you in advance.

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  • statistical cosinor analysis,

    - by Jared
    Hey i am trying to calculate a cosinor analysis in statistica but am at a loss as to how to do so. I need to calculate the MESOR, AMPLITUDE, and ACROPHASE of ciracadian rhythm data. http://www.wepapers.com/Papers/73565/Cosinor_analysis_of_accident_risk_using__SPSS%27s_regression_procedures.ppt there is a link that shows how to do it, the formulas and such, but it has not given me much help. Does anyone know the code for it, either in statistica or SPSS?? I really need to get this done because it is for my thesis paper at UC Berkeley, if anyone can offer any help it would be so awesome.

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  • What is the ratio of Java programmers to C#.net programmers?

    - by Vaccano
    How many Java Programmers are there to every C# programmer? I have a coworker that says it was 3:1 (3 Java to 1 C#) but it is now more like 2:1 (2 java to 1 C#) Is this valid? Is there somewhere I could go for this info? Edit: This question needs to be a bit more limited in scope. I am referring to US programmers and those who would consider their career to be more focused in one side than the other. (If you are evenly balanced then you would cancel out.)

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  • How can I structure and recode messy categorical data in R?

    - by briandk
    I'm struggling with how to best structure categorical data that's messy, and comes from a dataset I'll need to clean. The Coding Scheme I'm analyzing data from a university science course exam. We're looking at patterns in student responses, and we developed a coding scheme to represent the kinds of things students are doing in their answers. A subset of the coding scheme is shown below. Note that within each major code (1, 2, 3) are nested non-unique sub-codes (a, b, ...). What the Raw Data Looks Like I've created an anonymized, raw subset of my actual data which you can view here. Part of my problem is that those who coded the data noticed that some students displayed multiple patterns. The coders' solution was to create enough columns (reason1, reason2, ...) to hold students with multiple patterns. That becomes important because the order (reason1, reason2) is arbitrary--two students (like student 41 and student 42 in my dataset) who correctly applied "dependency" should both register in an analysis, regardless of whether 3a appears in the reason column or the reason2 column. How Can I Best Structure Student Data? Part of my problem is that in the raw data, not all students display the same patterns, or the same number of them, in the same order. Some students may do just one thing, others may do several. So, an abstracted representation of example students might look like this: Note in the example above that student002 and student003 both are coded as "1b", although I've deliberately shown the order as different to reflect the reality of my data. My (Practical) Questions Should I concatenate reason1, reason2, ... into one column? How can I (re)code the reasons in R to reflect the multiplicity for some students? Thanks I realize this question is as much about good data conceptualization as it is about specific features of R, but I thought it would be appropriate to ask it here. If you feel it's inappropriate for me to ask the question, please let me know in the comments, and stackoverflow will automatically flood my inbox with sadface emoticons. If I haven't been specific enough, please let me know and I'll do my best to be clearer.

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  • Optimal two variable linear regression calculation

    - by Dave Jarvis
    Problem Am looking to apply the y = mx + b equation (where m is SLOPE, b is INTERCEPT) to a data set, which is retrieved as shown in the SQL code. The values from the (MySQL) query are: SLOPE = 0.0276653965651912 INTERCEPT = -57.2338357550468 SQL Code SELECT ((sum(t.YEAR) * sum(t.AMOUNT)) - (count(1) * sum(t.YEAR * t.AMOUNT))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as SLOPE, ((sum( t.YEAR ) * sum( t.YEAR * t.AMOUNT )) - (sum( t.AMOUNT ) * sum(power(t.YEAR, 2)))) / (power(sum(t.YEAR), 2) - count(1) * sum(power(t.YEAR, 2))) as INTERCEPT, FROM (SELECT D.AMOUNT, Y.YEAR FROM CITY C, STATION S, YEAR_REF Y, MONTH_REF M, DAILY D WHERE -- For a specific city ... -- C.ID = 8590 AND -- Find all the stations within a 15 unit radius ... -- SQRT( POW( C.LATITUDE - S.LATITUDE, 2 ) + POW( C.LONGITUDE - S.LONGITUDE, 2 ) ) < 15 AND -- Gather all known years for that station ... -- S.STATION_DISTRICT_ID = Y.STATION_DISTRICT_ID AND -- The data before 1900 is shaky; insufficient after 2009. -- Y.YEAR BETWEEN 1900 AND 2009 AND -- Filtered by all known months ... -- M.YEAR_REF_ID = Y.ID AND -- Whittled down by category ... -- M.CATEGORY_ID = '001' AND -- Into the valid daily climate data. -- M.ID = D.MONTH_REF_ID AND D.DAILY_FLAG_ID <> 'M' GROUP BY Y.YEAR ORDER BY Y.YEAR ) t Data The data is visualized here: Question The following results (to calculate the start and end points of the line) appear incorrect. Why are the results off by ~10 degrees (e.g., outliers skewing the data)? (1900 * 0.0276653965651912) + (-57.2338357550468) = -4.66958228 (2009 * 0.0276653965651912) + (-57.2338357550468) = -1.65405406 I would have expected the 1900 result to be around 10 (not -4.67) and the 2009 result to be around 11.50 (not -1.65). Related Sites Least absolute deviations Robust regression Thank you!

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  • Randomized experiments in R

    - by gd047
    Here is a simple randomized experiment. In the following code I calculate the p-value under the null hypothesis that two different fertilizers applied to tomato plants have no effect in plants yields. The first random sample (x) comes from plants where a standard fertilizer has been used, while an "improved" one has been used in the plants where the second sample (y) comes from. x <- c(11.4,25.3,29.9,16.5,21.1) y <- c(23.7,26.6,28.5,14.2,17.9,24.3) total <- c(x,y) first <- combn(total,length(x)) second <- apply(first,2,function(x) total[!total %in% x]) dif.treat <- apply(second,2,mean) - apply(first,2,mean) # the first element of dif.treat is the one that I'm interested in (p.value <- length(dif.treat[dif.treat >= dif.treat[1]]) / length(dif.treat)) Do you know of any R function that performs tests like this one?

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  • mysql/algorithm: Weighting an average to accentuate differences from the mean

    - by Sai Emrys
    This is for a new feature on http://cssfingerprint.com (see /about for general info). The feature looks up the sites you've visited in a database of site demographics, and tries to guess what your demographic stats are based on that. All my demgraphics are in 0..1 probability format, not ratios or absolute numbers or the like. Essentially, you have a large number of data points that each tend you towards their own demographics. However, just taking the average is poor, because it means that by adding in a lot of generic data, the number goes down. For example, suppose you've visited sites S0..S50. All except S0 are 48% female; S0 is 100% male. If I'm guessing your gender, I want to have a value close to 100%, not just the 49% that a straight average would give. Also, consider that most demographics (i.e. everything other than gender) does not have the average at 50%. For example, the average probability of having kids 0-17 is ~37%. The more a given site's demographics are different from this average (e.g. maybe it's a site for parents, or for child-free people), the more it should count in my guess of your status. What's the best way to calculate this? For extra credit: what's the best way to calculate this, that is also cheap & easy to do in mysql?

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  • What is the best Java numerical method package?

    - by Bob Cross
    I am looking for a Java-based numerical method package that provides functionality including: Solving systems of equations using different numerical analysis algorithms. Matrix methods (e.g., inversion). Spline approximations. Probability distributions and statistical methods. In this case, "best" is defined as a package with a mature and usable API, solid performance and numerical accuracy. Edit: derick van brought up a good point in that cost is a factor. I am heavily biased in favor of free packages but others may have a different emphasis.

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  • How to use boost normal distribution classes?

    - by David Alfonso
    Hi all, I'm trying to use boost::normal_distribution in order to generate a normal distribution with mean 0 and sigma 1. The following code uses boost normal classes. Am I using them correctly? #include <boost/random.hpp> #include <boost/random/normal_distribution.hpp> int main() { boost::mt19937 rng; // I don't seed it on purpouse (it's not relevant) boost::normal_distribution<> nd(0.0, 1.0); boost::variate_generator<boost::mt19937&, boost::normal_distribution<> > var_nor(rng, nd); int i = 0; for (; i < 10; ++i) { double d = var_nor(); std::cout << d << std::endl; } } The result on my machine is: 0.213436 -0.49558 1.57538 -1.0592 1.83927 1.88577 0.604675 -0.365983 -0.578264 -0.634376 As you can see all values are not between -1 and 1. Thank you all in advance!

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  • How to notice unusual news activity

    - by ??iu
    Suppose you were able keep track of the news mentions of different entities, like say "Steve Jobs" and "Steve Ballmer". What are ways that could you tell whether the amount of mentions per entity per a given time period was unusual relative to their normal degree of frequency of appearance? I imagine that for a more popular person like Steve Jobs an increase of like 50% might be unusual (an increase of 1000 to 1500), while for a relatively unknown CEO an increase of 1000% for a given day could be possible (an increase of 2 to 200). If you didn't have a way of scaling that your unusualness index could be dominated by unheard-ofs getting their 15 minutes of fame.

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  • R Question. Numeric variable vs. Non-numeric and "names" function

    - by Michael
    > scores=cbind(UNCA.score, A.score, B.score, U.m.A, U.m.B) > names(scores)=c('UNCA.scores', 'A.scores', 'B.scores','UNCA.minus.A', 'UNCA.minus.B') > names(scores) [1] "UNCA.scores" "A.scores" "B.scores" "UNCA.minus.A" "UNCA.minus.B" > summary(UNCA.scores) X6.69230769230769 Min. : 4.154 1st Qu.: 7.333 Median : 8.308 Mean : 8.451 3rd Qu.: 9.538 Max. :12.000 > is.numeric(UNCA.scores) [1] FALSE > is.numeric(scores[,1]) [1] TRUE My question is, what is the difference between UNCA.scores and scores[,1]? UNCA.scores is the first column in the data.frame 'scores', but they are not the same thing, since one is numeric and the other isn't. If UNCA.scores is just a label here how can I make it be equivalent to 'scores[,1]? Thanks!

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