Course Content
Probability Theory
Probability Theory
3. Conducting Fascinating Experiments
5. Normal Distribution
Bernoulli Trial 2/2
It is time to connect your math knowledge with your programming skills. Look to this example:
You can treat this function as an actual experiment; whenever you click the run
button, the output is different.
Explanation of the code above:
- You need to import
bernoulli
object fromscipy.stats
. With this object, we will conduct a probability experiment on a computer. bernoulli.rvs(p = 0.5, size = 5)
means that the probability of getting head is 50 %,p = 0.5
, the sample size in experiment is 5,size = 5
.- The output shows an array with five results for each coin 1 means success and 0 means failure.
[1 1 1 1 0]
we had a successful result for 4 coins and failed for the last one.
Note
In this chapter and many other chapters, we will use the
np.random.seed()
function, do not be petrified it should be written to make your and my outputs equal. Do not change it.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Everything was clear?
Bernoulli Trial 2/2
It is time to connect your math knowledge with your programming skills. Look to this example:
You can treat this function as an actual experiment; whenever you click the run
button, the output is different.
Explanation of the code above:
- You need to import
bernoulli
object fromscipy.stats
. With this object, we will conduct a probability experiment on a computer. bernoulli.rvs(p = 0.5, size = 5)
means that the probability of getting head is 50 %,p = 0.5
, the sample size in experiment is 5,size = 5
.- The output shows an array with five results for each coin 1 means success and 0 means failure.
[1 1 1 1 0]
we had a successful result for 4 coins and failed for the last one.
Note
In this chapter and many other chapters, we will use the
np.random.seed()
function, do not be petrified it should be written to make your and my outputs equal. Do not change it.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Everything was clear?
Bernoulli Trial 2/2
It is time to connect your math knowledge with your programming skills. Look to this example:
You can treat this function as an actual experiment; whenever you click the run
button, the output is different.
Explanation of the code above:
- You need to import
bernoulli
object fromscipy.stats
. With this object, we will conduct a probability experiment on a computer. bernoulli.rvs(p = 0.5, size = 5)
means that the probability of getting head is 50 %,p = 0.5
, the sample size in experiment is 5,size = 5
.- The output shows an array with five results for each coin 1 means success and 0 means failure.
[1 1 1 1 0]
we had a successful result for 4 coins and failed for the last one.
Note
In this chapter and many other chapters, we will use the
np.random.seed()
function, do not be petrified it should be written to make your and my outputs equal. Do not change it.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.
Everything was clear?
It is time to connect your math knowledge with your programming skills. Look to this example:
You can treat this function as an actual experiment; whenever you click the run
button, the output is different.
Explanation of the code above:
- You need to import
bernoulli
object fromscipy.stats
. With this object, we will conduct a probability experiment on a computer. bernoulli.rvs(p = 0.5, size = 5)
means that the probability of getting head is 50 %,p = 0.5
, the sample size in experiment is 5,size = 5
.- The output shows an array with five results for each coin 1 means success and 0 means failure.
[1 1 1 1 0]
we had a successful result for 4 coins and failed for the last one.
Note
In this chapter and many other chapters, we will use the
np.random.seed()
function, do not be petrified it should be written to make your and my outputs equal. Do not change it.
Task
Your task is to play a little bit with the function. Imagine that you have an unbelievable successful coin and in 90% of cases tossing a coin you receive a head. Follow the algorithm to experiment:
- Import the
bernoulli
object fromscipy.stats
. - Conduct the experiment with
bernoulli
object using.rvs()
method.- Set
p
parameter equal to0.9
. - Set
size
parameter equal to1
.
- Set
By the way, you can comment on the line where np.random.seed()
was defined and "play with the coin" to receive various outputs.