# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= n = 50 sample_unif = runif(n, 0, 1) sample_norm = rnorm(n, 0.5, sqrt(1/12)) print(sample_norm) print(sample_unif) ks.test(sample_norm, sample_unif) sample_joined = c(sample_norm, sample_unif) ecdf(sample_joined) hist(sample_unif, breaks = seq(0, 1, 0.2), col = 'coral1') par(new = T) hist(sample_norm, breaks = seq(min(sample_norm), max(sample_norm), diff(range(sample_norm))/5), col = 'deepskyblue') # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= me = 3.55 samp = c(4.8, 4.0, 3.8, 4.3, 3.9, 4.6, 3.1, 3.7) tt = abs(length(samp[samp < 3.55]) - 4) print(tt) dbinom(8, 8, 1/2) dbinom(0, 8, 1/2) rank(c(1, 5, 2, 3, 9, 10)) # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= q_score = 28 samp = c(51, 53, 43, 36, 55, 55, 39, 43, 45, 27, 21, 26, 22, 43) hist(samp, breaks = seq(min(samp), max(samp), diff(range(samp))/5)) wilcox.test(samp) Tt = sum(rank(abs(samp - q_score))) n = length(samp) m = n*(n + 1)/4 d = n*(n + 1)*(2*n + 1)/24 x = (Tt - m)/sqrt(d) 1 - pnorm(x) # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= sampA = c(10854, 9106, 10325, 11627, 10051, 10001, 10000, 13720, 11632, 11222) sampB = c(11000, 11072, 8851, 10245, 11000, 10030, 11197, 10959, 9157, 11513, 9540, 10856) par(mfrow = 1:2) hist(sampA, col = 'coral1') hist(sampB, col = 'deepskyblue') wilcox.test(sampA, sampB, paired = FALSE) # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= mat = matrix(nrow = 2, ncol = 2, data = c(168, 73, 42, 26), byrow = T) mat = as.table(mat) dimnames(mat) = list(vk = c("da", "ne"), proizvodnja = c("da", "ne")) print(mat) chisq.test(mat) # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=