Sample from normal distribution

The normal distribution is a frequent topic during interviews due to the vast applications of this distribution.

A random sample is a set of observed items from the whole population. You can make inferences about the population based on a random sample taken from the population. For example, you can calculate the sample probability which is an estimate of the population's true probability.

To compute the sample probability, calculate the proportion of the observations in a sample that meet the given criteria.

To compute the true probability, use probability functions.

Recall that:

the standard normal distribution has \(\mu = 0\) and \(\sigma^2 = 1\) (referred to as \(N(0, 1)\)),
pnorm(q = k) returns \(P(X \le k)\) for \(X \sim N(0, 1)\).

Latihan ini adalah bagian dari kursus

Practicing Statistics Interview Questions in R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

set.seed(123)

# Generate data points
data_points <- rnorm(___ = ___)

# Inspect the distribution
___(data_points)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Practicing Statistics Interview Questions in R

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Want to increase your odds of acing your job interview? If so, brush up on your knowledge of probability theory. In this chapter, we'll roll dice and shoot baskets to explain probabilities using real-life examples.

Exercise 1: Discrete distributions Exercise 2: Probability functions Exercise 3: Bernoulli trials Exercise 4: Binomial distribution Exercise 5: Continuous distributions Exercise 6: Uniform distribution Exercise 7: Shape of normal distribution Exercise 8: Sample from normal distribution

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