Course Content
Probability Theory
Probability Theory
3. Conducting Fascinating Experiments
5. Normal Distribution
The 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:
Explanation:
from scipy.stats import binom
importing object fromscipy.stats
.binom.cdf(k = 70, n = 200, p=0.60)
the probability of getting70
or less successes amoung200
trials with the probability of success60 %
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.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Everything was clear?
The 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:
Explanation:
from scipy.stats import binom
importing object fromscipy.stats
.binom.cdf(k = 70, n = 200, p=0.60)
the probability of getting70
or less successes amoung200
trials with the probability of success60 %
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.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Everything was clear?
The 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:
Explanation:
from scipy.stats import binom
importing object fromscipy.stats
.binom.cdf(k = 70, n = 200, p=0.60)
the probability of getting70
or less successes amoung200
trials with the probability of success60 %
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.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.
Everything was clear?
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:
Explanation:
from scipy.stats import binom
importing object fromscipy.stats
.binom.cdf(k = 70, n = 200, p=0.60)
the probability of getting70
or less successes amoung200
trials with the probability of success60 %
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.
Task
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 fromscipy.stats
. - Calculate the probability that
10
or fewer people among500
interviewees will answer "yes", the probability of receiving positive answer is40%
.