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Lesson 3 of 20

Cumulative Distribution Function

We can also construct a cumulative distribution function for a random variable. A cumulative distribution function gives the probability that the random variable X is less than or equal to x, for every value x. In case of discrete random variables, the cumulative distribution function is the sum of the probabilities of all outcomes unto and including the specific outcome x.

The cumulative distribution function is expressed as  .

We will build upon our earlier probability distribution example.

xiP(xi)F(xi)
10.20.2
20.30.5
30.40.9
40.11

Probability that X =1 is 0.2

Probability that X = 1 or 2 = 0.2 + 0.3 = 0.5

Probability that X = 1 or 2 or 3 = 0.2 + 0.3 +0.4 = 0.9

Probability that X = 1 or 2 or 3 or 4= 0.2 + 0.3 +0.4 +0.1= 1.0

The histogram for cumulative distribution will look as follows:

cdp
cdp

The above cumulative distribution was for a discrete random variable. Even a continuous random variable will have a cumulative distribution function.

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Discrete Vs. Continuous Random Variable

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Discrete Uniform Random Variable

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Common Probability Distributions

20 lessons

Lessons

1
What is a Probability Distribution
2
Discrete Vs. Continuous Random Variable
3
Cumulative Distribution Function
4
Discrete Uniform Random Variable
5
Bernoulli and Binomial Distribution
6
Stock Price Movement Using a Binomial Tree
7
Tracking Error and Tracking Risk
8
Continuous Uniform Distribution
9
Normal Distribution
10
Univariate Vs. Multivariate Distribution
11
Confidence Intervals for a Normal Distribution
12
Standard Normal Distribution
13
Calculating Probabilities Using Standard Normal Distribution
14
Shortfall Risk
15
Safety-first Ratio
16
Lognormal Distribution and Stock Prices
17
Discretely Compounded Rate of Return
18
Continuously Compounded Rate of Return
19
Option Pricing Using Monte Carlo Simulation
20
Historical Simulation Vs Monte Carlo Simulation

Quizzes

Common Probablity Distributions
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