statistics 300: elementary statistics section 6-2

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Statistics 300: Elementary Statistics Section 6-2

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Page 1: Statistics 300: Elementary Statistics Section 6-2

Statistics 300:Elementary Statistics

Section 6-2

Page 2: Statistics 300: Elementary Statistics Section 6-2

Continuous Probability Distributions

• Quantitative data

• Continuous values– Physical measurements are common

examples

• No “gaps” in the measurement scale

• Probability = “Area under the Curve”

Page 3: Statistics 300: Elementary Statistics Section 6-2

The Uniform DistributionX ~ U[a,b]

a b

The totalarea = 1

Lik

elih

ood

Page 4: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[a,b] thenP(c < x < d) = ?

a bdc

Lik

elih

ood

Page 5: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[a,b] then

a bdc

Lik

elih

ood

a-b

c-dd)xP(c

Page 6: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(120 < x < 170) =

100 120 170 500

Page 7: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(300 < x < 400) =

100 300 400 500

Page 8: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(x = 300) = (300-300)/(500-100)

• = 0

100 300 400 500

Page 9: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(x < 220) =

100 220 500

Page 10: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(120 < x < 170 or 380 < x) =

100 120 170 380 500

Page 11: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(20 < x < 220) =

20 100 120 220 500

Page 12: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(300 < x < 400 or 380 < x) =

100 300 380 400 500

Page 13: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(300 < x < 400 or 380 < x) =

100 300 380 400 500

Page 14: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(300 < x < 400 or 380 < x) =

100 300 380 400 500

Page 15: Statistics 300: Elementary Statistics Section 6-2

If X ~ U[100,500] then

• P(300 < x < 400 or 380 < x) =

100 300 380 400 500

Page 16: Statistics 300: Elementary Statistics Section 6-2

The Normal DistributionX ~ N(,)

Normal Distribution

0 .0 0

0 .0 5

0 .1 0

0 .1 5

0 .2 0

0 .2 5

0 5 10 15 20

Value of Observation

Re

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ve

lik

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d

Page 17: Statistics 300: Elementary Statistics Section 6-2

Normal Distributions

• Family of “bell-shaped” distributions

• Each unique normal distribution is determined by the mean () and the standard deviation ()

• The mean tells where the center of the distribution is located

• The Standard Deviation tells how spread out (wide) the distribution is

Page 18: Statistics 300: Elementary Statistics Section 6-2

Normal Distributions

• If I say “the rectangle I am thinking of has base = 10 centimeters (cm) and height = 4 cm.” You know exactly what shape I am describing.

• Similarly, each normal distribution is determined uniquely by the mean () and the standard deviation ()

Page 19: Statistics 300: Elementary Statistics Section 6-2

The Standard Normal Distribution

• The “standard normal distribution” has = 0 and = 1.

• Table A.2 in the textbook contains information about the standard normal distribution

• Section 6-2 shows how to work with the standard normal distribution using Table A.2

Page 20: Statistics 300: Elementary Statistics Section 6-2

The Standard Normal Distribution

• The “standard normal distribution” has = 0 and = 1.

• Sometimes called the “Z distribution” because ...

• If x ~ N( = 0, = 1), then every value of “x” is its own z-score.

xxx

z

1

)0()(

Page 21: Statistics 300: Elementary Statistics Section 6-2

Standard Normal Distribution :

Two types of problems

• Given a value for “z”, answer probability questions relating to “z”

• Given a specified probability, find the corresponding value(s) of “z”

Page 22: Statistics 300: Elementary Statistics Section 6-2

Standard Normal Distribution :

Given “z”, find probability

• Given: x ~ N( = 0 and = 1)

• For specified constant values (a,b,c, …)

• Examples: P(0 < x < 1.68) P(x < 1.68) [not the same as above] P(x < - 1.68) [not same as either of above]

Page 23: Statistics 300: Elementary Statistics Section 6-2

Normal Distribution

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

0 . 2 0

0 . 2 5

0 . 3 0

0 . 3 5

0 . 4 0

0 . 4 5

-3 -2 -1 0 1 2 3

Value of Observation

Re

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lik

elih

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d

P(0 < x < 1.68)

Page 24: Statistics 300: Elementary Statistics Section 6-2

Normal Distribution

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

0 . 2 0

0 . 2 5

0 . 3 0

0 . 3 5

0 . 4 0

0 . 4 5

-3 -2 -1 0 1 2 3

Value of Observation

Re

lati

ve

lik

elih

oo

d

P(0 < x < 1.68)

Page 25: Statistics 300: Elementary Statistics Section 6-2

Normal Distribution

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

0 . 2 0

0 . 2 5

0 . 3 0

0 . 3 5

0 . 4 0

0 . 4 5

-3 -2 -1 0 1 2 3

Value of Observation

Re

lati

ve

lik

elih

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d

P(x < - 1.68)

Page 26: Statistics 300: Elementary Statistics Section 6-2

Standard Normal Distribution :

Given probability, find “z”• Given: x ~ N( = 0 and = 1)• For specified probability, find “z”• Example: For N( = 0 and = 1), what value

of “z” is the 79th percentile, P79?

• Example: For N( = 0 and = 1), what value of “z” is the 19th percentile, P19?

Page 27: Statistics 300: Elementary Statistics Section 6-2

Normal Distribution

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

0 . 2 0

0 . 2 5

0 . 3 0

0 . 3 5

0 . 4 0

0 . 4 5

-3 -2 -1 0 1 2 3

Value of Observation

Re

lati

ve

lik

elih

oo

d

What is P79 for N(0,1)

Page 28: Statistics 300: Elementary Statistics Section 6-2

Normal Distribution

0 . 0 0

0 . 0 5

0 . 1 0

0 . 1 5

0 . 2 0

0 . 2 5

0 . 3 0

0 . 3 5

0 . 4 0

0 . 4 5

-3 -2 -1 0 1 2 3

Value of Observation

Re

lati

ve

lik

elih

oo

d

What is P19 for N(0,1)