1 introduction to biostatistics (pubhlth 540) multiple random variables

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Introduction to Biostatistics (PUBHLTH 540)Introduction to Biostatistics (PUBHLTH 540) Multiple Random Multiple Random

VariablesVariables

22

Multiple Random VariablesMultiple Random Variables

Linear Combinations of Linear Combinations of Random VariablesRandom Variables– Expected ValueExpected Value– VarianceVariance

Stochastic ModelsStochastic Models Covariance of two Random Covariance of two Random

VariablesVariables IndependenceIndependence CorrelationCorrelation

33SPH&HS, UMASS AmherstSPH&HS, UMASS Amherst 33

An ExampleAn Example Choose a Simple Random Sample with Replacement of size Choose a Simple Random Sample with Replacement of size

n=2 from a Population of N=3n=2 from a Population of N=3 Observe:Observe:

– 1 Response (i.e. Age) on each Subject in the Sample1 Response (i.e. Age) on each Subject in the Sample Question: Question:

– What is the average age of subjects in the population?What is the average age of subjects in the population?

Use the sample mean to estimate the Population Average Use the sample mean to estimate the Population Average AgeAge

Daisy Lily Rose

Introducing….


PopulationPopulation

55

Population of N=3Population of N=3

Note: Note: Population meanPopulation mean

Variance.Variance.

ID ID (s)(s)

SubjectSubject Response Response (Age)(Age)

11 DaisyDaisy 2525

22 LilyLily 3232

33 RoseRose 3333

22

1

1 3812.67

3

N

ii

xN

66

Pick SRS with Replacement of Pick SRS with Replacement of n=2n=2

a random variable representing a random variable representing the 1the 1stst selection selection

ID (s)ID (s) SubjectSubject ResponsResponsee

11 DaisyDaisy 2525

22 LilyLily 3232

33 RoseRose 3333

1Y

i=1,…,n=2

2Ya random variable representing a random variable representing

the 2nd selectionthe 2nd selection

77

Use as an Estimator: Sample Use as an Estimator: Sample MeanMean

1

1 2

1

1 1 1...

n

ii

n

Y Yn

Y Y Yn n n

A Linear Estimator- a sum of random variables

When n=2,1 2

1

2

1 1

2 2

1 1

2 2

Y Y Y

Y

Y

c Y

11 1

2c

1

2

Y

Y

Y

11

12

c

88

Linear Combination of Random VariablesLinear Combination of Random Variables Example: Sample Mean Example: Sample Mean

1

1 2

1

2

1

1 1 1...

11 1 1

n

ii

n

n

Y Yn

Y Y Yn n n

Y

Y

n

Y

c Y

1nn

c 1

1 2 nY Y Y Y

99

Models for ResponseModels for Response

2 32y

3 3 (=N)(=N)

22

11

ID (s)ID (s)

LilyLily

RoseRose

DaisyDaisy

ResponsResponsee

SubjectSubject

1 25y

3 33y

s sy s

30 5

23

3030

s sy Non-Stochastic model (Deterministic)

i iY E Stochastic model


Finite PopulationFinite Population

i iY E

1i 2i

1Y

2Y

Pick a SRS with replacement of size n=2

1E 2E

Stochastic model



i iY E

1i 2i

1Y

with replacement

1 1y

1E 2Y 2E

Stochastic model



i iY E

1i 2i

with replacement

1 1y

2Y 2E

2 2y

Stochastic model


Sampling- n=2Sampling- n=2

1 1Y E 1i 2i

with replacement

2 2Y E

Random Variables

1

1 n

ii

Y Yn

c Y

Linear Combination of Random Variables

Stochastic model


Sampling- n=2Sampling- n=2

1i 2i

with replacement

1 1Y y

1 1 y Realized Values

2 2Y y

2 2 y


Other Possible SamplesOther Possible Samples

1i 2i

with replacement

1 1 y 2 2 y

1717

Sample (t) Probability

1 1/9 25 25

2 1/9 25 32

3 1/9 25 33

4 1/9 32 25

5 1/9 32 32

6 1/9 32 33

7 1/9 33 25

8 1/9 33 32

All Possible Samples

1 1Y y 2 2Y y

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1 1/9 25 25 2.78 2.78

2 1/9 25 32 2.78 3.56

3 1/9 25 33 2.78 3.67

4 1/9 32 25 3.56 2.78

5 1/9 32 32 3.56 3.56

6 1/9 32 33 3.56 3.67

7 1/9 33 25 3.67 2.78

8 1/9 33 32 3.67 3.56

9 1/9 33 33 3.67 3.67

1 1Y y 2 2Y y 1 1 1P Y y y 2 2 2P Y y y

1 30E Y 2 30E Y

1

T

i i i it

E Y P Y y y

Expected Values

1919


1 1/9 25 -5 25

2 1/9 25 -5 25

3 1/9 25 -5 25

4 1/9 32 2 4

5 1/9 32 2 4

6 1/9 32 2 4

7 1/9 33 3 9

8 1/9 33 3 9

9 1/9 33 3 9

0.00 12.67

1 1Y y

2

1

varT

i i i it

Y P Y y y

iy 2

iy

1var Y

2020

2 2Y y

2

1

varT

i i i it

Y P Y y y

iy 2

iy

2var YSample

(t) Probability

1 1/9 25 -5 25

2 1/9 32 2 4

3 1/9 33 3 9

4 1/9 25 -5 25

5 1/9 32 2 4

6 1/9 33 3 9

7 1/9 25 -5 25

8 1/9 32 2 4

9 1/9 33 3 9

0.00 12.67

2121

1 2 1 1 2 2 1 1 2 21

cov , ;T

t

Y Y P Y y Y y y E Y y E Y

Covariance of Two Random Variables

1

cov , ;T

t

Y Z P Y y Z z y E Y z E Z

2222


1 1/9 25 25 -5 -5 25

2 1/9 25 32 -5 2 -10

3 1/9 25 33 -5 3 -15

4 1/9 32 25 2 -5 -10

5 1/9 32 32 2 2 4

6 1/9 32 33 2 3 6

7 1/9 33 25 3 -5 -15

8 1/9 33 32 3 2 6

9 1/9 33 33 3 3 9

1 2 1 1 2 2 1 1 2 21

cov , ;T

t


1 1Y y 2 2Y y 1y 2y 1 2y y

1 2cov , 0Y Y

Based on simple random sampling with replacement

2323

Variance MatrixVariance Matrix

When n=2, and SRS with replacement:When n=2, and SRS with replacement:

1 1 21

1 2 22

var cov ,var

cov , var

Y Y YY

Y Y YY

21

22

2

0var

0

1 0

0 1

Y

Y

2

1 0

0 1

I

Identity Matrix

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Variance Matrix for n Random Variance Matrix for n Random VariablesVariables

1 1 1 2 1

2 1 2 2 2

1 2

var cov , cov ,

cov , var cov ,var

cov , cov , var

n

n

n n n n

Y Y Y Y Y Y

Y Y Y Y Y Y

Y Y Y Y Y Y

2525

Covariance of Random Variables When SRS Covariance of Random Variables When SRS without Replacment (n=2)without Replacment (n=2)

1 2 1 1 2 2 1 1 2 21

cov , ;T

t


Sample

(t) Probability

1 1/6 25 32 -5 2 -10

2 1/6 25 33 -5 3 -15

3 1/6 32 25 2 -5 -10

4 1/6 32 33 2 3 6

5 1/6 33 25 3 -5 -15

6 1/6 33 32 3 2 6

1 1Y y 2 2Y y 1y 2y 1 2y y

1 2cov , 6.33Y Y

2626

Covariance of two random variables when Covariance of two random variables when sampling without replacementsampling without replacement

2

cov ,1i jY Y

N

1

2 2

1 11

1 11 1

1var 1 1

1 11

1 1

n

N NY

YN N

Y

N N

2727

Estimating the CovarianceEstimating the CovarianceEstimate the variance: Estimate the variance: assuming srsassuming srs

22

1

1 N

ss

yN

22

1

1

1

n

ii

S Y Yn

Estimate the Estimate the covariance: covariance:

assuming srsassuming srs

1

1 N

xy s y s xs

y xN

1

1ˆ

1

n

xy i ii

Y Y X Xn

2828

IndependenceIndependence

Two random variables, Y and Z are Two random variables, Y and Z are independent ifindependent if

P(Y=y|Z=z)=P(Y=y)P(Y=y|Z=z)=P(Y=y)

P(Y=y|Z=z) means the probability that Y P(Y=y|Z=z) means the probability that Y has a value of y, given Z has a value of has a value of y, given Z has a value of zz

(see Text, sections 6.1 and 6.2) (see Text, sections 6.1 and 6.2)

2929

Example: SRS with rep n=2Example: SRS with rep n=2

AreAre 2Yandand independent?independent?

2 2 1 1 2 2|P Y y Y y P Y y DoesDoes ??


11 DaisyDaisy 2525

22 LilyLily 3232

33 RoseRose 3333

1Y


Sampling n=2 (with rep)Sampling n=2 (with rep)

1i 2i

1 1y

1 1Y E

1 1 1/ 3Y yP 1 1 1/ 3P Y y 1 1 1/ 3Y yP

2 2Y E

2 2 1/ 3Y yP 2 2 1/ 3P Y y 2 2 1/ 3Y yP

2 2 1 1|P Y y Y y

AreAre 2Yandand independent?independent?1Y

2 2 1 1| 1 / 3P y yY Y 2 2 1 1| ?P Y y Y y YesYes



1i 2i

1 1y

1 1Y E

1 1 1/ 3Y yP 1 1 1/ 3P Y y 1 1 1/ 3Y yP

2 2Y E

2 2 1/ 3Y yP 2 2 1/ 3P Y y 2 2 1/ 3Y yP

2 2 1 1|P Y y Y y



3333

Example: SRS without rep Example: SRS without rep n=2n=2

AreAre 2Yandand independent?independent?

2 2 1 1 2 2|P Y y Y y P Y y DoesDoes ??


11 DaisyDaisy 2525

22 LilyLily 3232

33 RoseRose 3333

1Y


Sampling n=2 (without replacement)Sampling n=2 (without replacement)

1i 2i

1 1y

1 1Y E

1 1 1/ 3Y yP 1 1 1/ 3P Y y 1 1 1/ 3Y yP

2 2Y E

2 2 1/ 3Y yP 2 2 1/ 3P Y y 2 2 1/ 3Y yP

2 2 1 1|P Y y Y y


2 2 1 1| 0P Y Yy y 2 2 1 1| ?P Y y Y y NoNo


1i 2i

1 1y

1 1Y E

1 1 1/ 3Y yP 1 1 1/ 3P Y y 1 1 1/ 3Y yP

2 2Y E

2 2 1/ 3Y yP 2 2 1/ 3P Y y 2 2 1/ 3Y yP

2 2 1 1|P Y y Y y


2 2 1 1| 1 / 2P y yY Y 2 2 1 1| ?P Y y Y y NoNo


3737

Relationship between Relationship between Independence and CovarianceIndependence and Covariance

If two random variables are If two random variables are independent, then their covariance is independent, then their covariance is 0.0.

If the covariance of two random If the covariance of two random variables is zero, the two may (or variables is zero, the two may (or may not) be independentmay not) be independent

3838

Expected Value of a Linear Combination of Expected Value of a Linear Combination of Random VariablesRandom Variables

Write linear combinations using vector notationWrite linear combinations using vector notation..

1

1

2

1

11 1 1

n

ii

n

Y Yn

Y

Y

n

Y

c Y

1

nn c 1

1 2 nY Y Y Y

Constants

Random variables

3939

E Y E

E

c Y

c Y

1 2 nE E Y E Y E Y Y where

Example: SRS of size n:

1

111 1 1

11 1 1

n

E Y E

E Y

E Y

n

E Y

n

c Y

4040

Example 2: Suppose two independent SRS w/o replacement are selected from populations of boy and girl babies, and the weight recorded. Let us represent the boy weight by Y and the girl weight by X. Suppose sample results are given as follows:

BoysBoys

n=25n=25GirlsGirls

n=40n=40Sample Sample MeanMean

VariancVariancee

Y X

2y 2

x

An estimate is wanted of the average birth weight in Europe, where for every 1000 births, 485 are girls, while 515 are boys.

Write a linear combination that can be used to construct an estimator.

0.485 0.515

0.485 0.515

Z X Y

X

Y

4141

Variance of a Linear Combination of Variance of a Linear Combination of Random VariablesRandom Variables

var var c Y c Y c

2

1

2 c 1 1 2Y Y Y

Constants Random variables

Example: Sample mean, n=2 srs with replacement

1

2

2

2

11 1var 1 1 var

12 2

1011 1

14 0

Y

Y

c Y

4242

Matrix MultiplicationMatrix Multiplication

1 2 1 2 1 2

a bc c c a c d c b c e

d e

2

2

2 2

2

2

101var 1 1

14 0

11

14

12

4

2

c YHence

4343

Practice: Variance of a Linear Combination Practice: Variance of a Linear Combination of Random Variablesof Random Variables

2

1

2 c 1 1 2Y Y Y

ConstantsRandom variables

Example: Sample mean, n=2 srs withOUT replacement

from a population of N

2

2

2

11

11var 1 11 14

11

11 11 1

14 1 1

11

1 2

N

N

N N

N

c Y

1 2

2

11

1var1

11

Y NY

N

4444

Correlation (see 17.1, 17.2 in text)Correlation (see 17.1, 17.2 in text)

The correlation between two random The correlation between two random variables is defined as variables is defined as

cov ,

var var

X Y

X Y

Based on a simple random sample, Based on a simple random sample, we estimate the correlation by we estimate the correlation by

2 2

ˆ xy

x y

rS S

1

1ˆ

1

n

xy i ii

X X Y Yn

22

1

1

1

n

x ii

S X Xn

22

1

1

1

n

y ii

S Y Yn

1 introduction to biostatistics (pubhlth 540) multiple random variables

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