4.3 NORMAL PROBABILITY DISTRIBUTIONS

The Most Important Probability Distribution in Statistics

A random variable X with mean and standard deviation is normally distributed if its probability density function is given by

...71828.2eand...14159.3where

xe2

1)x(f

2x

)2/1(

...71828.2eand...14159.3where

xe2

1)x(f

2x

)2/1(

Normal Distribution

Normal Probability Distributions

The expected value (also called the mean) can be any number

The standard deviation can be any nonnegative number

There are infinitely many normal distributions

7: Normal Probability Distributions

4

Parameters μ and σ Normal pdfs have two parameters

μ - expected value (mean “mu”) σ - standard deviation (sigma)

σ controls spreadμ controls location

The effects of The effects of and and

How does the standard deviation affect the shape of f(x)?

= 2

=3 =4

= 10 = 11 = 12How does the expected value affect the location of f(x)?

X83 6 9 120

A family of bell-shaped curves that differ only in their means and standard deviations.

µ = the mean of the distribution

= the standard deviation

µ = 3 and = 1

X3 6 9 120

X3 6 9 120

µ = 3 and = 1

µ = 6 and = 1

X83 6 9 120

X83 6 9 120

µ = 6 and = 2

µ = 6 and = 1

EXAMPLE A Normal Random Variable

The following data represent the heights (in inches) of a random sample of 50 two-year old males.

(a) Create a relative frequency distribution with the lower class limit of the first class equal to 31.5 and a class width of 1.

(b) Draw a histogram of the data.

(c ) Do you think that the variable “height of 2-year old males” is normally distributed?

36.0 36.2 34.8 36.0 34.6 38.4 35.4 36.834.7 33.4 37.4 38.2 31.5 37.7 36.9 34.034.4 35.7 37.9 39.3 34.0 36.9 35.1 37.033.2 36.1 35.2 35.6 33.0 36.8 33.5 35.035.1 35.2 34.4 36.7 36.0 36.0 35.7 35.738.3 33.6 39.8 37.0 37.2 34.8 35.7 38.937.2 39.3

7: Normal Probability Distributions

14

68-95-99.7 Rule forNormal Distributions

68% of the AUC within ±1σ of μ 95% of the AUC within ±2σ of μ 99.7% of the AUC within ±3σ of μ

7: Normal Probability Distributions

16

Example: 68-95-99.7 Rule Wechsler adult intelligence scores: Normally distributed with μ = 100 and σ = 15; X ~ N(100, 15)

68% of scores within μ ± σ = 100 ± 15 = 85 to 115

95% of scores within μ ± 2σ = 100 ± (2)(15) = 70 to 130

99.7% of scores in μ ± 3σ = 100 ± (3)(15) = 55 to 145

Property and Notation

Property: normal density curves are symmetric around the population mean , so the population mean = population median = population mode =

Notation: X ~ N( is written to denote that the random variable X has a normal distribution with mean and standard deviation .

Standardizing

Suppose X~N( Form a new random variable by

subtracting the mean from X and dividing by the standard deviation :

(X This process is called standardizing the

random variable X.

Standardizing (cont.)

(X is also a normal random variable; we will denote it by Z:

Z = (X has mean 0 and standard deviation

1:E(Z) = = 0; SD(Z) =

The probability distribution of Z is called

the standard normal distribution.

Standardizing (cont.) If X has mean and stand. dev. , standardizing

a particular value of x tells how many standard deviations x is above or below the mean .

Exam 1: =80, =10; exam 1 score: 92

Exam 2: =80, =8; exam 2 score: 90

Which score is better?

1 exam on 92 than better is 2 exam on 90

1.258

10

8

8090z

1.210

12

10

8092z

2

1

X83 6 9 120

µ = 6 and = 2

Z0 1 2 3-1-2-3

.5.5

µ = 0 and = 1

(X-6)/2

Pdf of a standard normal rv

A normal random variable x has the following pdf:

zez

pdf

forandforsubstituteNZ

xexf

z

x

,2

1)(

becomes rv normal standard for the

10)1,0(~

,)(

2

2

2)(21

2

1

21

Z = standard normal random variable

= 0 and = 1

Z0 1 2 3-1-2-3

.5.5

Standard Normal Distribution

.5.5

Important Properties of Z

#1. The standard normal curve is symmetric around the mean 0

#2. The total area under the curve is 1;

so (from #1) the area to the left of 0 is 1/2, and the area to the right of 0 is 1/2

Finding Normal Percentiles by Hand (cont.)

Table Z is the standard Normal table. We have to convert our data to z-scores before using the table.

The figure shows us how to find the area to the left when we have a z-score of 1.80:

Areas Under the Z Curve: Using the Table

P(0 < Z < 1) = .8413 - .5 = .3413

0 1Z

.1587.3413

.50

Standard normal probabilities have been calculated and are provided in table Z.

The tabulated probabilities correspondto the area between Z= - and some z0

Z = z0

P(- <Z<z0)

Example – continued X~N(60, 8)

In this example z0 = 1.25

0.89440.8944

0.89440.89440.89440.8944= 0.8944

60 70 60( 70)

8 8

( 1.25)

XP X P

P z

P(z < 1.25)

Examples

P(0 z 1.27) =

1.270 z

Area=.3980

.8980-.5=.3980

P(Z .55) = A1

= 1 - A2

= 1 - .7088 = .2912

0 .55

A2

Examples

P(-2.24 z 0) =

Area=.4875

.5 - .0125 = .4875

z-2.24 0Area=.0125

P(z -1.85) = .0322

Examples (cont.)

A1 A2

0 2.73z

-1.18

P(-1.18 z 2.73) = A - A1

= .9968 - .1190

= .8778

.1190

.9968

A1

A

P(-1 ≤ Z ≤ 1) = .8413 - .1587 =.6826

vi) P(-1≤ Z ≤ 1)

.8413.1587

.6826

Is k positive or negative?

Direction of inequality; magnitude of probability

Look up .2514 in body of table; corresponding entry is -.67

6. P(z < k) = .25146. P(z < k) = .2514

.5 .5.2514

-.67

Examples (cont.) viii)

250 275( 250) ( )

4325

( ) ( .58) 1 .2810 .719043

P X P Z

P Z P Z

.2810

.7190

Examples (cont.) ix)

225 275 275 375 27543 43 43

) (225 375)

( 1.16 2.33) .9901 .1230 .8671

x

ix P x

P

P z

.9901.1230

.8671

X~N(275, 43) find k so that P(x<k)=.9846

88.367275)43(16.2

table)normal standard from(16.243275

43275

43275

43275)(9846.

k

k

kzP

kxPkxP

P( Z < 2.16) = .9846

0 2.16 Z.1587

.4846

Area=.5

.9846

Example

Regulate blue dye for mixing paint; machine can be set to discharge an average of ml./can of paint.

Amount discharged: N(, .4 ml). If more than 6 ml. discharged into paint can, shade of blue is unacceptable.

Determine the setting so that only 1% of the cans of paint will be unacceptable

Solution

XX

P X

P x P P zx

= amount of dye discharged into can~ N( , .4); determine so that

(from standard normal table)

= 6 - 2.33(.4) = 5.068

( ) .

. ( )

.

. . .

.

6 01

01 6

2 33

46

46

46

4