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Tài liệu thống kê - LAMNGHIEPVN.INFO (Hưng Bùi) ď tài liệu, giáo án, bài giảng , luận văn, luận án, đồ án, bài tập lớn v...

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Examples of continuous probability

distributions:

The normal and standard normal

Sometimes data can be

modeled using certain

equations For example,

the normal distribution

Normal distribution

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The Normal Distribution

X

f(X)

Changing μ shifts the

distribution left or right

Changing σ increases or decreases the spread

The Normal Distribution:

as mathematical function (pdf)

2 ) (

2 1

2

1 )

x

e x

f

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The Normal PDF

1 2

2

1



dx e

x

It’s a probability function, so no matter what the values of 

and , must integrate to 1!

Normal distribution is defined by its mean

and standard dev

E(X)=  =

Var(X)=  2 =

dx e

x

x



 2( )2 1

2

1 

2 )

( 2

1 2

) 2

1 (

2



dx e

x

x

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**The beauty of the normal curve:

No matter what  and  are, the area between - and

+ is about 68%; the area between -2 and +2 is

about 95%; and the area between -3 and +3 is

about 99.7% Almost all values fall within 3 standard

deviations

68-95-99.7 Rule

68% of the data

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Example of normally distributed data

 IQ scores are normal with a mean 100, standard deviation

of 15, so

What if we want to know the

probability of having an IQ below

110? (P(X<110) one of the first

things we should do in these types

of problems is draw a picture

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many standard deviations 110

is away from the mean

This is called the z-score

We have a table that gives us

probability of being less than a certain

z- score

So we look this number up in the table

So the probability of having an IQ below 110 is 0.7486

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Other distribution: (FYI)

The number of squared standard normal

added together is the degrees of freedom

This is used to test for independence

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Used in linear regression, and to test

where or not 3 or more groups can have

the same mean

It goes to a standard normal as

used in small samples

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