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Lesson 1 of 20
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What is a Probability Distribution

We know that a random variable is an uncertain quantity or a number. Its value is determined by chance. For example, the outcome of rolling a die is random. We could get any number from 1 to 6. In case of a die, the probability of getting any number is 1/6. Each outcome has the same probability. However, the probability of each outcome could be different.

A probability distribution is a graph or a table that describes the probabilities of each outcome of a random variable.

In a probability distribution, each value or outcome of the random variable is represented as x. The probability of getting x is represented as P(x). So, if X is the random variable, we are saying that the probability of random variable X being equal to x is P(X=x) or P(x). This is called the probability function.

The probability distribution of rolling a die is shown below:

xiP(xi)
11/6
21/6
31/6
41/6
51/6
61/6

Note that the sum of all probabilities should be equal to 1 and the probability of each outcome, P(x) is between 0 and 1.

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Discrete Vs. Continuous 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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