18 décembre 2024
https://ben18785.shinyapps.io/distribution-zoo/
rbinom(n = 10, size = 30, prob = 0.5)
[1] 15 13 15 11 16 21 19 14 15 12
rbinom(n = 10, size = 1, prob = 0.7)
[1] 1 1 1 1 1 1 1 0 1 1
rnorm(n = 10, mean = 2600, sd = 1000)
[1] 2352.854 3789.456 2844.240 2103.881 3758.489 3057.468 4906.261 3556.442 [9] 3885.535 3807.580
rpois(n = 10, lambda = 3)
[1] 0 8 4 3 5 3 6 2 5 4
rbeta(n = 10, shape1 = 7, shape2 = 3)
[1] 0.5551196 0.4152760 0.5146809 0.8318127 0.6721343 0.8759235 0.7662018 [8] 0.4818727 0.7812606 0.5406850
population = data.frame(revenu = rnorm(n = 100000, mean = 1300, sd = 600), celibataire = rbinom(n = 100000, size = 1, prob = 0.35))
tibble::as_tibble(population)
# A tibble: 100,000 × 2 revenu celibataire <dbl> <int> 1 865. 0 2 102. 0 3 2077. 1 4 252. 0 5 411. 0 6 1248. 0 7 1789. 0 8 669. 1 9 1078. 0 10 1180. 0 # ℹ 99,990 more rows
echantillon = dplyr::slice_sample(population, n = 31) mean_revenu = mean(echantillon$revenu) proportion_celibataires = sum(echantillon$celibataire == 1) / 31 echantillon |> as_tibble() mean_revenu proportion_celibataires
# A tibble: 31 × 2 revenu celibataire <dbl> <int> 1 1454. 0 2 1647. 1 3 747. 0 4 2049. 1 5 1362. 1 6 1786. 0 7 1124. 0 8 652. 0 9 2182. 0 10 1380. 0 # ℹ 21 more rows
[1] 1384.903
[1] 0.2258065
echantillons = replicate(n = 1000, dplyr::slice_sample(population, n = 31), simplify = FALSE) params_echantillons = purrr::map(echantillons, ~summarise(.x, mean_revenu = mean(revenu), proportion_celibataires = sum(celibataire == 1) / 31)) |> list_rbind()
ggplot(params_echantillons) + geom_density(aes(x = mean_revenu)) + geom_rug(aes(x = mean_revenu))
ggplot(params_echantillons) + geom_density(aes(x = proportion_celibataires))
