Normal and Binomial Distributions: Linking the Two, Study notes of Introduction to Macroeconomics

A portion of lecture notes from a mathematical statistics course for food and resource economics. It covers the univariate normal distribution, including its definition, properties, and relationship to the binomial distribution. The document also includes examples and integrals to help understand the concepts.

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Pre 2010

Uploaded on 03/18/2009

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Normal Random Variables
Lecture XII
I. Univariate Normal Distribution.
A. Definition 5.2.1. The normal density is given by
2
2
11
exp , 0
2
2
x
f x x



 

When
X
has the above density, we write symbolically
2
~,XN
.
B. Theorem 5.2.1. Let
X
be
2
,N
. The
EX
and
2
VX
.
2
2
2
11
exp 2
2
x
E X x dx





 

1. Using the change in variables technique, we create a new random variable
z
such that
Substituting into the original integral yields:
2
22
2
11
exp 2
2
11
exp 2
2
11
exp 2
2
E X z z dz
z z dz
z dz

















Taking the integral of the first term first, we have:
2 2 2
2
1 1 1
exp exp
22
2
1
exp 0
2
z z dz C z z dz
Cz


 





pf3
pf4
pf5

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Normal Random Variables

Lecture XII

I. Univariate Normal Distribution.

A. Definition 5.2.1. The normal density is given by

2

2

exp , 0

2 2

x f x x

When X has the above density, we write symbolically  

2

X ~ N  ,.

B. Theorem 5.2.1. Let X be  

2

N   ,. The E X    and  

2

V X  .

2

2 2

exp 2 2

x E X x dx



1. Using the change in variables technique, we create a new random variable z

such that

x z x z

dx dz

   

Substituting into the original integral yields:

2

2 2

2

exp 2 2

exp 2 2

exp

2 2

E X z z dz

z z dz

z dz







Taking the integral of the first term first, we have:

2 2 2

2

exp exp

2 2 2

exp 0 2

z z dz C z z dz

C z

 

 



 ^ 

Professor Charles B. Moss Fall 2007

2. The value of the second integral becomes by polar integration (see Lecture V

notes). The variance of the normal is similarly defined except that the initial

integral now becomes:

2 2

2

(^2 )

2 2 2

exp 2 2

exp 2 2

exp 2 2

x V X x dx

z z dz

z z dz







3. This formulation is then completed using integration by parts:

2

2

2 2 2

exp 2

1 exp 2

exp exp exp 2 2 2

u z dv z z

du v z

z z z dz z z z dz

  

  

The first term of the integration by parts is clearly zero while the second is

defined by polar integral. Thus,

0 exp

2 2

V X  z dz 



C. Theorem 5.2.2. Let X be  

2

N   , and let Y     X. Then we have

2 2

Y ~ N     ,.

1. This theorem can be demonstrated using Theorem 3.6.1 (the theorem on changes

in variables):

1 1 d g y f y dy

      

In this case

1

1 1

y x x y

d y

dy

Professor Charles B. Moss Fall 2007

2. 2. Going to 100 draws yields:

0 5 10 15 20 25

Binomial Normal

  • Professor Charles B. Moss Fall