Aprenda The Third Experiment | Conducting Fascinating Experiments

Seção 3. Capítulo 3

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It is time to move to the third experiment, which should be useful for we as for data scientist!

General formula:

In this experiment, we will work with the binom.cdf(k, n, p) function. This function helps calculate the probability of receiving k or less successes among n trials with the probability of success for each experiment p.

Real-life example:

Imagine that we are working for the bank, and last month the bank gained 200 customers; we know that the probability for clients to continue working with the bank is 60%. Calculate the probability that 70 or fewer customers will stay with we.

Code:


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from scipy.stats import binom
# Calculate the probability
experiment = binom.cdf(k = 70, n = 200, p = 0.60)
print(experiment)

Explanation:

from scipy.stats import binom importing object from scipy.stats.
binom.cdf(k = 70, n = 200, p=0.60) the probability of getting 70 or less successes amoung 200 trials with the probability of success 60 %

By the way, this function is one of the most commonly used. Indeed it is hard to get zero here because we need 70 or less(in this case), so 1 is a relevant result too! In comparison to the previous functions(experiments) where we would receive at least or exactly defined number of successes.

Tarefa

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Imagine that we work with real research.

Our task here is to calculate the probability that 10 or fewer residents in a specific town with a population of 500 will answer yes to our question, "Do you have your housing?". The probability that the answer will be positive is 40%.

Import binom object from scipy.stats.
Calculate the probability that 10 or fewer people among 500 interviewees will answer "yes", the probability of receiving positive answer is 40%.

Solução

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Seção 3. Capítulo 3

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