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Lecture 6

# STAT 2507 Lecture Notes - Lecture 6: Exponential Distribution, Probability Distribution, Random Variable

Department
Statistics
Course Code
STAT 2507
Professor
Masoud Nasari
Lecture
6

This preview shows half of the first page. to view the full 3 pages of the document. Chapter 6
The Normal Probability Distribution
Probability Distributions For Continuous Random Variables Examples: Heights Weights,
Length of life of a particular product Distance travelled on a trip Probability Density Function
A probability density function (f(x)) is a model for the probability distribution of a continuous
random variable X.
Probability Distributions For Continuous Random Variables Examples: Heights Weights,
Length of life of a particular product Distance travelled on a trip Probability Density Function
A probability density function (f(x)) is a model for the probability distribution of a continuous
random variable X.
Probability Density Function The probability density function has the following properties:
fx 0. The area under the probability density function equals 1. P(X=a)=0. In other words,
the probability at a specific point is equal to zero. PX  a=PX > a and PX  a=PX < a) as a
result from the previous point. (Remember that this is not true for discrete random
variables!!!!! Note: Pa < X < b = Pa  X < b = Pa < X  b = Pa  X  b .
Exercise 1
Uniform Distribution
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