Practicing Statistics Interview Questions in R
Zuzanna Chmielewska
Actuary
prefix | purpose | example |
---|---|---|
d | density | dnorm() |
prefix | purpose | example |
---|---|---|
d | density | dnorm() |
p | distribution function | pnorm() |
prefix | purpose | example |
---|---|---|
d | density | dnorm() |
p | distribution function | pnorm() |
q | quantile function | qnorm() |
prefix | purpose | example |
---|---|---|
d | density | dnorm() |
p | distribution function | pnorm() |
q | quantile function | qnorm() |
r | random variates | rnorm() |
sample(1:6, size = 1)
[1] 1
purrr::rdunif(n = 1, b = 6, a = 1)
[1] 2
Probability mass function: $$ P(X = 1) = p $$
$$ P(X = 0) = 1-p $$
$$ p = 0.5 $$
$$ P(X=k)=\binom{n}{k}p^k(1-p)^{n-k} $$
$$ P(X=k)=\binom{n}{k}p^k(1-p)^{n-k} $$
$$ P(X=k)=\binom{n}{k}p^k(1-p)^{n-k} $$
$$ P(X=k)=\binom{n}{k}p^k(1-p)^{n-k} $$
Binomial distribution:
rbinom(n, size = k, prob = p)
Bernoulli distribution:
rbinom(n, size = 1, prob = p)
random_numbers <- sample(1:10, size = 1000, replace = TRUE)
table(random_numbers)
random_numbers
1 2 3 4 5 6 7 8 9 10
101 106 82 102 91 104 105 106 109 94
random_numbers <- sample(1:10, size = 1000, replace = TRUE)
barplot(table(random_numbers))
set.seed(123)
sample(1:10, size = 3) sample(1:10, size = 3)
3 10 2
2 6 3
set.seed(123)
sample(1:10, size = 3)
set.seed(123)
sample(1:10, size = 3)
3 10 2
3 10 2
Practicing Statistics Interview Questions in R