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Learn Cumulative Distribution Function (CDF) 1/2 | Probability Functions
Probability Theory Update
course content

Course Content

Probability Theory Update

Probability Theory Update

1. Probability Basics
2. Statistical Dependence
3. Learn Crucial Terms
4. Probability Functions
5. Distributions

book
Cumulative Distribution Function (CDF) 1/2

What is it?

This function calculates the probability that the random variable X will take the value less than or equal to x. Example:

Calculate the probability that we will have success with the fair coin (the chance of getting head or tail is 50%) in at least in 4 out of 15 trials. We assume that success means getting a head.

Python realization:

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# Import required library import scipy.stats as stats # The desired number of success trial x = 4 # The number of attempts n = 15 # The probability of getting a success p = 0.5 # The resulting probability probability = stats.binom.cdf(x, n, p) print("The probability is", probability * 100, "%")
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Section 4. Chapter 4
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book
Cumulative Distribution Function (CDF) 1/2

What is it?

This function calculates the probability that the random variable X will take the value less than or equal to x. Example:

Calculate the probability that we will have success with the fair coin (the chance of getting head or tail is 50%) in at least in 4 out of 15 trials. We assume that success means getting a head.

Python realization:

1234567891011121314
# Import required library import scipy.stats as stats # The desired number of success trial x = 4 # The number of attempts n = 15 # The probability of getting a success p = 0.5 # The resulting probability probability = stats.binom.cdf(x, n, p) print("The probability is", probability * 100, "%")
copy

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 4
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