Normal Probability Distribution
The normal probability distribution is the
most important distribution for describing a
continuous random variable.
It is widely used in statistical inference.
It has been used in a wide variety of
applications
including:
• Heights of people • Test scores
• Rainfall amounts • Scientific measurements
Abraham de Moivre, a French mathematician,
published The Doctrine of Chances in 1733.
He derived the normal distribution.
, Normal Probability Distribution
Normal Probability Density Function
( x )2
1
2 2
f ( x) e , x
2
where:
= mean
= standard deviation
= 3.14159
e = 2.71828
, Normal Probability Distribution
Characteristics
The distribution is symmetric; its skewness
measure is zero.
x
The normal probability distribution is the
most important distribution for describing a
continuous random variable.
It is widely used in statistical inference.
It has been used in a wide variety of
applications
including:
• Heights of people • Test scores
• Rainfall amounts • Scientific measurements
Abraham de Moivre, a French mathematician,
published The Doctrine of Chances in 1733.
He derived the normal distribution.
, Normal Probability Distribution
Normal Probability Density Function
( x )2
1
2 2
f ( x) e , x
2
where:
= mean
= standard deviation
= 3.14159
e = 2.71828
, Normal Probability Distribution
Characteristics
The distribution is symmetric; its skewness
measure is zero.
x
