MathDotNet should have the functions you are looking for, although it may be a bit of overkill depending on how much functionality you need. It offers: Bernoulli Beta Binomial Categorical Cauchy Chi Chi Square Continuous Uniform Conway Maxwell Poisson Dirichlet Discrete Uniform Erlang Exponential etc…. (about 3x more on the list) For a complete list … Read more
Cross-tabs or pivot tables is the answer. From there you can SELECT FROM … INSERT INTO … or create a VIEW from the single SELECT. Something like: SELECT country, MAX( IF( key=’President’, value, NULL ) ) AS President, MAX( IF( key=’Currency’, value, NULL ) ) AS Currency, … FROM table GROUP BY country;
Reddit uses the Wilson score interval for comment ranking, an explanation and python implementation can be found here #Rewritten code from /r2/r2/lib/db/_sorts.pyx from math import sqrt def confidence(ups, downs): n = ups + downs if n == 0: return 0 z = 1.0 #1.44 = 85%, 1.96 = 95% phat = float(ups) / n return … Read more
Have you tried scipy? You will need to installl the scipy library…more about installing it here: http://www.scipy.org/install.html Once installed, you can replicate the Excel functionality like such: from scipy import stats #Studnt, n=999, p<0.05, 2-tail #equivalent to Excel TINV(0.05,999) print stats.t.ppf(1-0.025, 999) #Studnt, n=999, p<0.05%, Single tail #equivalent to Excel TINV(2*0.05,999) print stats.t.ppf(1-0.05, 999) You … Read more
cramers V seems pretty over optimistic in a few tests that I did. Wikipedia recommends a corrected version. import scipy.stats as ss def cramers_corrected_stat(confusion_matrix): “”” calculate Cramers V statistic for categorial-categorial association. uses correction from Bergsma and Wicher, Journal of the Korean Statistical Society 42 (2013): 323-328 “”” chi2 = ss.chi2_contingency(confusion_matrix)[0] n = confusion_matrix.sum() phi2 … Read more