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Please enable JavaScript in your browser settings or update your browser.The Third Experiment | Conducting Fascinating Experiments
Probability Theory
course content

Conteúdo do Curso

Probability Theory

Probability Theory

1. Learn Basic Rules
Random VariableBernoulli Trial 1/2Bernoulli Trial 2/2Binomial Probability 1/2Binomial probability 2/2
2. Probabilities of Several Events
Add ProbabilitiesCalculate Connected ProbabilitiesChallengeMultiply probabilitiesDependent probabilitiesLaw of Total ProbabilityBayes Theorem Challenge
3. Conducting Fascinating Experiments
The First ExperimentThe Second ExperimentThe Third ExperimentCombine Your Knowledge
4. Discrete Distributions
Discrete Uniform DistributionCalculate Fascinating ProbabilityBernoulli DistributionBinomial DistributionChallengePoisson DistributionChallenge
5. Normal Distribution
Normal DistributionChallenge 1Сhallenge 2

bookThe Third Experiment

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:


              
1234

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

Explanation:

  1. from scipy.stats import binom importing object from scipy.stats.
  2. 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

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%.

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 3
toggle bottom row

bookThe Third Experiment

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:


              
1234

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

Explanation:

  1. from scipy.stats import binom importing object from scipy.stats.
  2. 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

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%.

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 3
toggle bottom row

bookThe Third Experiment

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:


              
1234

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

Explanation:

  1. from scipy.stats import binom importing object from scipy.stats.
  2. 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

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%.

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

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:


              
1234

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

Explanation:

  1. from scipy.stats import binom importing object from scipy.stats.
  2. 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

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%.

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

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 3. Capítulo 3
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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