The First Experiment
To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!
Real-life example:
Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.
In this course, we will learn such interesting functions programming-wise.
Look at the example and everything will be clear:
General formula:
In this experiment, we will work with the binom.pmf(k, n, p)
function. This function helps calculate the probability of receiving exactly k
successes among n
trials with the probability of success for each experiment p
.
1234from scipy.stats import binom # Calculate the probability experiment = binom.pmf(k = 1000, n = 20000, p=0.20) print(experiment)
Explanation:
- As usual, we are importing objects from
scipy.stats
with this codefrom scipy.stats import binom
. binom.pmf(k = 1000, n = 20000, p=0.20)
the probability of getting1000
successes amoung20 000
trials with the probability of success20%
.
Interesting fact:
The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.
Swipe to start coding
Imagine that our task is to do some calculations for a charity organization just for practice.
Your task here is to calculate the probability that exactly 5
kittens will find a home; there are 12
kittens in the shelter. In this city, kittens are taken from a shelter with a probability of 75%
. Import relevant library to do it.
- Import
binom
object fromscipy.stats
. - Calculate the probability that exactly
5
kittens out of12
will find a home with the probability of success75
%.
The output here is going to be less hopeful.
Løsning
Takk for tilbakemeldingene dine!
single
Spør AI
Spør AI
Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår
Awesome!
Completion rate improved to 3.7
The First Experiment
Sveip for å vise menyen
To be a data analyst, it is crucial to understand that we are going to conduct a lot of experiments. Here, we are going to get acquainted with several gripping functions!
Real-life example:
Imagine that we are working for a real estate agency, and we need to know how many positive answers we will get from all interviewees.
In this course, we will learn such interesting functions programming-wise.
Look at the example and everything will be clear:
General formula:
In this experiment, we will work with the binom.pmf(k, n, p)
function. This function helps calculate the probability of receiving exactly k
successes among n
trials with the probability of success for each experiment p
.
1234from scipy.stats import binom # Calculate the probability experiment = binom.pmf(k = 1000, n = 20000, p=0.20) print(experiment)
Explanation:
- As usual, we are importing objects from
scipy.stats
with this codefrom scipy.stats import binom
. binom.pmf(k = 1000, n = 20000, p=0.20)
the probability of getting1000
successes amoung20 000
trials with the probability of success20%
.
Interesting fact:
The result of our code is zero, but we worked with an enormous sample; in the task, we will receive a more understandable result.
Swipe to start coding
Imagine that our task is to do some calculations for a charity organization just for practice.
Your task here is to calculate the probability that exactly 5
kittens will find a home; there are 12
kittens in the shelter. In this city, kittens are taken from a shelter with a probability of 75%
. Import relevant library to do it.
- Import
binom
object fromscipy.stats
. - Calculate the probability that exactly
5
kittens out of12
will find a home with the probability of success75
%.
The output here is going to be less hopeful.
Løsning
Takk for tilbakemeldingene dine!
Awesome!
Completion rate improved to 3.7single