Create post about MC Simulation for passing a test


library(dplyr)
library(magrittr)
library(purrr)
library(ggplot2)

250
58

practice_results <- c(rep(1, 250), rep(0, 58))

# sum(sample(practice_results, 33, replace = FALSE))

simulation_results <- replicate(
  10000, 
  sum(sample(practice_results, 33, replace = FALSE))
  )

hist(simulation_results, breaks = 20, col = "skyblue", border = "black")

sum(practice_results < 17)/10000

?lower.tri
(m2 <- matrix(1:20, 4, 5))
lower.tri(m2)
m2[lower.tri(m2)] <- NA

results_mat <- matrix(0, 308, 308) 
results_mat[lower.tri(results_mat)] <- 1
results_mat <- t(results_mat)

simulation_all_levels <- results_mat %>%
  as.data.frame() %>%
  tibble::tibble() %>%
  purrr::map(
    ~replicate(
      10000, 
      sum(sample(.x, 33, replace = FALSE))
    )
  )

simulation_all_levels %>%
  map_dbl(
    ~sum(.x >= 17)/10000
  ) %>%
  tibble::tibble(passig_probability = .) %>%
  mutate(number_correct_practice = row_number()) %>%
  ggplot(aes(number_correct_practice, passig_probability)) +
  geom_point() +
  geom_line()

NeuroShepherd / neuroshepherd.github.io

Create post about MC Simulation for passing a test #2