It is time to deal with three experiments; they are very similar, so firstly, let's recall some theory.


|**Function**|**Explanation**|
|---|---|
|`binom.pmf(k, n, p)`|Calculate the probability to archive *exactly* **k** successes among **n** trials with the probability of success **p**|
|`binom.sf(k, n, p)`|Calculate the probability to archive **k** or *more* successes among **n** trials with the probability of success **p**|
|`binom.cdf(k, n, p)`|Calculate the probability to archive **k** or *less* successes among **n** trials with the probability of success **p**|


We meet probability every day but do not even recognize it! This course will introduce you to the basic terms. We will combine math and programming learning with real-life examples.

It's time to dive deeper into the probability theory. Here we will learn basic formulas. The main idea of the section is to help to understand the term probability using real-life examples. 

Here, we will get acquainted with the rules in probability theory. This section will be more complicated than the previous one. It will be attractive because we are going to work with the real challenges. But it should be noted that we learn some formulas.

Doing experiments is always funny, but here we will combine exciting tasks with programming learning. In this section, we will work with the binom object to conduct experiments in Python.

Distribution is a key to probability learning, but don't be petrified by this term. This section provides you with real-life explanations and relevant formulas. 

The normal distribution is the most commonly used one. Here we will work with the distribution with the help of real-life challenges.

Function	Explanation
`binom.pmf(k, n, p)`	Calculate the probability to archive exactly k successes among n trials with the probability of success p
`binom.sf(k, n, p)`	Calculate the probability to archive k or more successes among n trials with the probability of success p
`binom.cdf(k, n, p)`	Calculate the probability to archive k or less successes among n trials with the probability of success p

Function	Explanation
`binom.pmf(k, n, p)`	Calculate the probability to archive exactly k successes among n trials with the probability of success p
`binom.sf(k, n, p)`	Calculate the probability to archive k or more successes among n trials with the probability of success p
`binom.cdf(k, n, p)`	Calculate the probability to archive k or less successes among n trials with the probability of success p

Combine Your Knowledge

Ratkaisu

Combine Your Knowledge

Ratkaisu