chi-squared

I want to fit poission distribution on my data points and want to decide based on chisquare test that should I accept or reject this proposed distribution. I only used 10 observations. Here is my code #Fitting function: def Poisson_fit(x,a): return (a*np.exp(-x)) #Code hist, bins= np.histogram(x, bins=10, density=True) print("hist: ",hist) #hist: [5.62657158e-01, 5.14254073e-01, 2.03161280e-01, 5.84898068e-02, 1.35995217e-02,2.67094169e-03,4.39345778e-04,6.59603327e-05,1.01518320e-05, 1.06301906e-06] XX = np.arange(len(hist)) print("XX: ",XX) #XX: [0 1 2 3 4 5 6 7 8 9] plt.scatter(XX, hist,

I have a set of observations obs . If I plot the histogram of the observation I see that they could come from a gamma distribution [counts,x] = hist(obs,[1:max(obs)]); I would like to prove it using chi square goodness of fit. So I first estimate the gamma parameters paramEsts = fitdist(obs,'Gamma'); and the use chi2gof to see if the hypotesis is true (h=0). [h,p] = chi2gof(obs,'CDF',paramEsts) My problem is that I get a p = NaN...how is it possible? Where is my mistake? Thanks to test it please download the obs.mat file https://drive.google.com/file/d/0B3vXKJ_zYaCJbHU2SHhac29MRms/view?usp

I am trying to find the Chi-Square test from my following SQL Server Query on the sample data: SELECT sessionnumber, sessioncount, timespent, expected, dev, dev*dev/expected as chi_square FROM (SELECT clusters.sessionnumber, clusters.sessioncount, clusters.timespent, (dim1.cnt * dim2.cnt * dim3.cnt)/(dimall.cnt*dimall.cnt) as expected, clusters.cnt-(dim1.cnt * dim2.cnt * dim3.cnt)/(dimall.cnt*dimall.cnt) as dev FROM clusters JOIN (SELECT sessionnumber, SUM(cnt) as cnt FROM clusters GROUP BY sessionnumber) dim1 ON clusters.sessionnumber = dim1.sessionnumber JOIN (SELECT sessioncount, SUM(cnt)

I'm trying to conduct a chi square test on several groups within the dplyr frame . The problem is, group_by() %>% summarise() doesn't seem to do trick. Simulated data (same structure as problematic data, but random, so p.values should be high) set.seed(1) data.frame(partido=sample(c("PRI", "PAN"), 100, 0.6), genero=sample(c("H", "M"), 100, 0.7), GM=sample(c("Bajo", "Muy bajo"), 100, 0.8)) -> foo I want to compare several groups defined by GM to see if there are changes in the p.values for the crosstab of partido and genero, conditional to GM. The obvious dplyr way should be: foo %>% group_by

I have two 4*4 matrices in JAVA, where one matrix holds observed counts and the other expected counts. I need an automated way to calculate the p-value from the chi-square statistic between these two matrices; however, JAVA has no such function as far as I am aware. I can calculate the chi-square and its p-value by reading the two matrices into R as .csv file formats, and then using the chisq.test function as follows: obs<-read.csv("obs.csv") exp<-read.csv("exp.csv") chisq.test(obs,exp) where the format of the .csv files would as follows: A, C, G, T A, 197.136, 124.32, 63.492, 59.052 C, 124.32