1 econ 240a power four 1 1. 2 last time probability
TRANSCRIPT
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Econ 240A
Power Four
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Last Time
• Probability
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Problem 6.61
• A survey of middle aged men reveals that 28% of them are balding at the crown of their head. Moreover, it is known that such men have an 18% probability of suffering a heart attack in the next ten years. Men who are not balding in this way have an 11% probability of a heart attack. Find the probability that a middle aged man will suffer a heart attack in the next ten years.
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Middle Aged men
Bald
P (Bald and MA) = 0.28
Not Bald
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Middle Aged men
Bald
P (Bald and MA) = 0.28
Not Bald
P(HA/Bald and MA) = 0.18
P(HA/Not Bald and MA)= 0.11
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Probability of a heart attack in the next ten
years• P(HA) = P(HA and Bald and MA) +
P(HA and Not Bald and MA)• P(HA) = P(HA/Bald and
MA)*P(BALD and MA) + P(HA/Not BALD and MA)* P(Not Bald and MA)
• P(HA) = 0.18*0.28 + 0.11*0.72 = 0.054 + .0792 = 0.1296
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Random Variables
• There is a natural transition or easy segue from our discussion of probability and Bernoulli trials last time to random variables
• Define k to be the random variable # of heads in 1 flip, 2 flips or n flips of a coin
• We can find the probability that k=0, or k=n by brute force using probability trees. We can find the histogram for k, its central tendency and its dispersion
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Outline
• Random Variables & Bernoulli Trials• example: one flip of a coin
– expected value of the number of heads– variance in the number of heads
• example: two flips of a coin• a fair coin: frequency distribution of the
number of heads– one flip– two flips
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Outline (Cont.)
• Three flips of a fair coin, the number of combinations of the number of heads
• The binomial distribution• frequency distributions for the binomial• The expected value of a discrete
random variable• the variance of a discrete random
variable
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Concept
• Bernoulli Trial– two outcomes, e.g. success or failure– successive independent trials– probability of success is the same in
each trial
• Example: flipping a coin multiple times
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Flipping a Coin Once
Heads, k=1
Tails, k=0
Prob. = p
Prob. = 1-p
The random variable k is the number of headsit is variable because k can equal one or zeroit is random because the value of k depends on probabilities of occurrence, p and 1-p
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Flipping a coin once
• Expected value of the number of heads is the value of k weighted by the probability that value of k occurs– E(k) = 1*p + 0*(1-p) = p
• variance of k is the value of k minus its expected value, squared, weighted by the probability that value of k occurs– VAR(k) = (1-p)2 *p +(0-p)2 *(1-p) =
VAR(k) = (1-p)*p[(1-p)+p] =(1-p)*p
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Flipping a coin twice: 4 elementary outcomes
heads
tails
heads
tails
heads
tails
h, h
h, t
t, h
t, t
h, h; k=2
h, t; k=1
t, h; k=1
t, t; k=0
Prob =p
Prob =p
Prob =1-p
Prob =1-p
Prob=p
Prob=1-p
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Flipping a Coin Twice
• Expected number of heads– E(k)=2*p2 +1*p*(1-p) +1*(1-p)*p + 0*(1-
p)2 E(k) = 2*p2 + p - p2 + p - p2 =2p– so we might expect the expected value of
k in n independent flips is n*p
• Variance in k– VAR(k) = (2-2p)2 *p2 + 2*(1-2p)2 *p(1-p) +
(0-2p)2 (1-p)2
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Continuing with the variance in k
– VAR(k) = (2-2p)2 *p2 + 2*(1-2p)2 *p(1-p) + (0-2p)2 (1-p)2
– VAR(k) = 4(1-p)2 *p2 +2*(1 - 4p +4p2)*p*(1-p) + 4p2 *(1-p)2
– adding the first and last terms, 8p2 *(1-p)2 + 2*(1 - 4p +4p2)*p*(1-p)
– and expanding this last term, 2p(1-p) -8p2 *(1-p) + 8p3 *(1-p)
– VAR(k) = 8p2 *(1-p)2 + 2p(1-p) -8p2 *(1-p)(1-p)– so VAR(k) = 2p(1-p) , or twice VAR(k) for 1 flip
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• So we might expect the variance in n flips to be np(1-p)
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Frequency Distribution for the Number of Heads
• A fair coin
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O heads 1 head
1/2
probability
# of heads
One Flip of the Coin
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0 1 2 # of heads
probability
1/4
1/2
Two Flips of a Fair Coin
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Three Flips of a Fair Coin
• It is not so hard to see what the value of the number of heads, k, might be for three flips of a coin: zero, one ,two, three
• But one head can occur two ways, as can two heads
• Hence we need to consider the number of ways k can occur, I.e. the combinations of branching probabilities where order does not count
H
T
H
T
p
H
T
1-p
p
1 - p
p
H
T
H
T
H
T
H
T
p
1-p
p
1-p
Three flips of a coin; 8 elementary outcomes
3 heads
2 heads
2 heads1 head
2 heads1 head
1 head
0 heads
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Three Flips of a Coin
• There is only one way of getting three heads or of getting zero heads
• But there are three ways of getting two heads or getting one head
• One way of calculating the number of combinations is Cn(k) = n!/k!*(n-k)!
• Another way of calculating the number of combinations is Pascal’s triangle
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0 1 2
1/8
2/8
3/8
Probability
3 # of heads
Three Flips of a Coin
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The Probability of Getting k Heads
• The probability of getting k heads (along a given branch) in n trials is: pk *(1-p)n-k
• The number of branches with k heads in n trials is given by Cn(k)
• So the probability of k heads in n trials is Prob(k) = Cn(k) pk *(1-p)n-k
• This is the discrete binomial distribution where k can only take on discrete values of 0, 1, …k
Expected Value of a discrete random variable
• E(x) =
• the expected value of a discrete random variable is the weighted average of the observations where the weight is the frequency of that observation
n
i
ixpix0
)]([*)(
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Expected Value of the sum of random variables
• E(x + y) = E(x) + E(y)
Expected Number of Heads After Two Flips
• Flip One: kiI heads
• Flip Two: kjII heads
• Because of independence p(kiI and kj
II) = p(ki
I)*p(kjII)
• Expected number of heads after two flips: E(ki
I + kjII) = (ki
I + kjII)
p(kiI)*p(kj
II)
• E(kiI + kj
II) = kiI p(ki
I)* p(kjII) +
1
0
1
0 ji
1
0i
1
0j
Cont.
• E(kiI + kj
II) = kiI p(ki
I)* p(kjII)
+ kjII *p(kj
II) p(kiI)
• E(kiI + kj
II) = E(kiI) + E(kj
II) = p*1 + p*1 =2p
• So the mean after n flips is n*p
1
0i
1
0j
1
0j
1
0i
Variance of a discrete random variable
• VAR(xi) =
• the variance of a discrete random variable is the weighted sum of each observation minus its expected value, squared,where the weight is the frequency of that observation
)]([})]([)({[ 2
0
ixpixEixn
i
Cont.
• VAR(xi) =
• VAR(xi) =
• VAR(xi) =
• So the variance equals the second moment minus the first moment squared
)]([*})]([)({ 2
0
ixpixEixn
i
})([*})]([)(*)]([2)]({[0
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n
i
ixpiExixixEix
2
0
2 )]([)]}([)]([{ ixEixpixn
i
The variance of the sum of discrete random variables
• VAR[xi + yj] = E[xi + yj - E(xi + yj)]2
• VAR[xi + yj] = E[(xi - Exi) + (yj - Eyj)]2
• VAR[xi + yj] = E[(xi - Exi)2 + 2(xi - Exi) (yj - Eyj) + (yj - Eyj)2]
• VAR[xi + yj] = VAR[xi] + 2 COV[xi*yj] + VAR[yj]
The variance of the sum if x and y are independent
• COV [xi*yj] = E(xi - Exi) (yj - Eyj)
• COV [xi*yj]= (xi - Exi) (yj - Eyj)
• COV [xi*yj]= (xi - Exi) p[x(i)]* (yj - Eyj)* p[y(j)]
• COV [xi*yj] = 0
)]()([ jyixp
n
j
m
i 00
m
i 0
n
j 0
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Variance of the number of heads after two flips
• Since we know the variance of the number of heads on the first flip is p*(1-p)
• and ditto for the variance in the number of heads for the second flip
• then the variance in the number of heads after two flips is the sum, 2p(1-p)
• and the variance after n flips is np(1-p)
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The Field Poll
• In a sample of 731 people, 33% indicate they will vote for Bill Jones as Senator. The others are for Boxer (50%) or otherwise inclined (17% undecided or for others)
• If the poll is an accurate reflection or subset of the population of voters on Nov. 2, what is the expected proportion that will vote for Jones?
• How much uncertainty is in that expectation?
Field Poll
• The estimated proportion, from the sample, that will vote for Jones is:
• where is 0.33 or 33%• k is the number of “successes”, the
number of people sampled who are for Jones, approximately 241
• n is the size of the sample, 731
nkp /ˆ
p̂
Field Poll
• What is the expected proportion of voters Nov. 2 that will vote for Jones?
• = E(k)/n = np/n = p, where from the binomial distribution, E(k) = np
• So if the sample is representative of voters and their preferences, 33% should vote for Jones next November
)ˆ( pE
Field Poll
• How much dispersion is in this estimate, i.e. as reported in newspapers, what is the margin of sampling error?
• The margin of sampling error is calculated as the standard deviation or square root of the variance in
• = VAR(k)/n2 = np(1-p)/n2 =p(1-p)/n
• and using 0.33 as an estimate of p,• = 0.33*0.67/731 =0.00030
p̂
)ˆ( pVAR
)ˆ( pVAR
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Field Poll
• So the sampling error should be 0.017 or 1.7%, i.e. the square root of 0.00030
• The Field Poll reports a 95% confidence interval or about two standard errors , I.e 2*1.7%
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Field Poll
• Is it possible that Bill Jones could win? This estimate of 0.33 plus or minus twice the sampling error of 0.017, creates an interval of 0.31 to 035.
• Based on a normal approximation to the binomial, the true proportion voting for Jones should fall in this interval with probability of about 95%, unless sentiments change.
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Boxer has been slipping, But Jones hasn’t gained much