Ismor Fischer, 5/26/2016 4.1-1
4. Classical Probability Distributions
4.1 Discrete Models
FACT:
Experiment 3a: Roll one fair die... Discrete random variable X = “value obtained”
Sample Space: S = {1, 2, 3, 4, 5, 6} #(S) = 6
Because the die is fair, each of the six faces has an equally likely probability of
occurring, i.e., 1/6. The probability distribution for X can be defined by a so-called
probability mass function (pmf) p(x), organized in a probability table, and
displayed via a corresponding probability histogram, as shown.
Comment on notation:
Event
( 4 )P X = 1/6
Translation: “The probability of rolling 4 is 1/6.”
Likewise for the other probabilities P(X = 1), P(X = 2),…, P(X = 6) in this example.
A mathematically succinct way to write such probabilities is by the notation P(X = x),
where x = 1, 2, 3, 4, 5, 6. In general therefore, since this depends on the value of x,
we can also express it as a mathematical function of x (specifically, the pmf; see
above), written p(x). Thus the two notations are synonymous and interchangeable.
The previous example could just as well have been written f(4) = 1/6.
Event Probability
x p(x) = P(X = x)
1 1/6
2 1/6
3 1/6
4 1/6
5 1/6
6 1/6
1
Random variables can be used to define events that involve measurement!
“Uniform Distribution”
1
6
1
6
1
6
1
6
1
6
1
6
X
Ismor Fischer, 5/26/2016 4.1-2
Experiment 3b: Roll two distinct, fair dice. Outcome = (Die 1, Die 2)
Sample Space: S = {(1, 1), …, (6, 6)} #(S) = 62
= 36
Discrete random variable X = “Sum of the two dice (2, 3, 4, …, 12).”
Events: “X = 2” = {(1, 1)} #(X = 2) = 1
“X = 3” = {(1, 2), (2, 1)} #(X = 3) = 2
“X = 4” = {(1, 3), (2, 2), (3, 1)} #(X = 4) = 3
“X = 5” = {(1, 4), (2, 3), (3, 2), (4, 1)} #(X = 5) = 4
“X = 6” = {(1, 5), (2, 4), (3, 3), (4, 2), (5, 1)} #(X = 6) = 5
“X = 7” = {(1, 6), (2, 5), (3, 4), (4, 3), (5, 2), (6, 1)} #(X = 7) = 6
“X = 8” = {(2, 6), (3, 5), (4, 4), (5, 3), (6, 2)} #(X = 8) = 5
“X = 9” = {(3, 6), (4, 5), (5, 4), (6, 3)} #(X = 9) = 4
“X = 10” = {(4, 6), (5, 5), (6, 4)} #(X = 10) = 3
“X = 11” = {(5, 6), (6, 5)} #(X = 11) = 2
“X = 12” = {(6, 6)} #(X = 12) = 1
Recall that, by definition, each event “X = x” (where x = 2, 3, 4,…, 12) corresponds
to a specific subset of outcomes from the sample space (of ordered pairs, in this
case). Because we are still assuming equal likelihood of each die face appearing,
the probabilities of these events can be easily calculated by the “shortcut” formula
#( )
( )
#( )
A
P A
S
. Question for later: What if the dice are “loaded” (i.e., biased)?
(1, 1) (1, 2) (1, 3) (1, 4) (1, 5) (1, 6)
(2, 1) (2, 2) (2, 3) (2, 4) (2, 5) (2, 6)
(3, 1) (3, 2) (3, 3) (3, 4) (3, 5) (3, 6)
(4, 1) (4, 2) (4, 3) (4, 4) (4, 5) (4, 6)
(5, 1) (5, 2) (5, 3) (5, 4) (5, 5) (5, 6)
(6, 1) (6, 2) (6, 3) (6, 4) (6, 5) (6, 6)
Ismor Fischer, 5/26/2016 4.1-3
3 5 7 9 11
1
36
6
36
1
36
2
36
2
36
3
36
3
36
4
36
4
36
5
36
5
36
2 3 4 5 6 7 8 9 10 11 12
Again, the probability distribution for X can be organized in a probability table,
and displayed via a probability histogram, both of which enable calculations to be
done easily:
x p(x) = P(X = x)
2 1/36
3 2/36
4 3/36
5 4/36
6 5/36
7 6/36
8 5/36
9 4/36
10 3/36
11 2/36
12 1/36
1
 P(X = 7 or X = 11) Note that “X = 7” and “X = 11” are disjoint!
= P(X = 7) + P(X = 11) via Formula (3) above
= 6/36 + 2/36 = 8/36
 P(5 X 8)
= P(X = 5 or X = 6 or X = 7 or X = 8)
= P(X = 5) + P(X = 6) + P(X = 7) + P(X = 8)
= 4/36 + 5/36 + 6/36 + 5/36
= 20/36
 P(X < 10) = 1 P(X 10) via Formula (1) above
= 1 [P(X = 10) + P(X = 11) + P(X = 12)]
= 1 [3/36 + 2/36 + 1/36] = 1 6/36 = 30/36
Exercise: How could event E = “Roll doubles” be characterized in terms of a
random variable? (Hint: Let Y = “Difference between the two dice.”)
Ismor Fischer, 5/26/2016 4.1-4
The previous example motivates the important topic of...
Discrete Probability Distributions
In general, suppose that all of the distinct population values of a discrete random
variable X are sorted in increasing order: x1 < x2 < x3 < …, with corresponding
probabilities of occurrence p(x1), p(x2), p(x3), … Formally then, we have the
following.
Definition: p(x) is a probability mass function for the discrete random variable X if,
for all x,
p(x) 0 AND
all
( )
x
p x = 1.
In this case, p(x) = P(X = x), the probability that the value x occurs in the population.
The cumulative distribution function (cdf) is defined as, for all x,
F(x) = P(X x) =
all
( )
i
i
x x
p x = p(x1) + p(x2) + … + p(x).
Therefore, F is piecewise constant, increasing from 0 to 1.
Furthermore, for any two population values a < b, it follows that
P(a X b) = ( )p x
b
a
= F(b) – F(a )
where a is the value just preceding a in the sorted population.
Exercise: Sketch the cdf F(x)
for Experiments 3a and 3b above.
Total Area = 1
…
…
X| | | |
x1 x2 x3 … x …
p(x1)
p(x2)
p(x3)
p(x)
1
X| | | |
x1 x2 x3 … x …
F(x1)
F(x2)
F(x3)
0
Ismor Fischer, 5/26/2016 4.1-5
Population Parameters μ and σ2
(vs. Sample Statistics x and s2
)
population mean = the “expected value” of the random variable X
= the “arithmetic average” of all the population values
Compare this with the relative frequency definition of sample mean given in §2.3.
population variance = the “expected value” of the squared deviation of the
random variable X from its mean (μ)
Compare the first with the definition of sample variance given in §2.3.
(The second is the analogue of the alternate computational formula.) Of course,
the population standard deviation σ is defined as the square root of the variance.
*Exercise: Algebraically expand the expression (X )2
, and use the properties of expectation given above.
If X is a discrete numerical random variable, then…
μ = E[X] = x p(x), where pmf p(x) = P(X = x), the probability of x.
If X is a discrete numerical random variable, then…
σ 2
= E[(X )2
] = (x )2
p(x).
Equivalently,*
σ 2
= E[X 2
] 2
= x2
p(x) 2
,
where pmf p(x) = P(X = x), the probability of x.
Properties of Mathematical Expectation
1. For any constant c, it follows that E[cX] = cE[X].
2. For any two random variables X and Y, it follows that
 E[X + Y] = E[X] + E[Y] and, via Property 1,
 E[X − Y] = E[X] − E[Y].
Any “operator” on variables satisfying 1 and 2 is said to be linear.
Ismor Fischer, 5/26/2016 4.1-6
10%
20%
30%
40%
Experiment 4: Two populations, where the daily number of calories consumed is
designated by X1 and X2, respectively.
Population 1
Probability Table
 Mean(X1) = µ1 = (2300)(0.1) + (2400)(0.2) +
(2500)(0.3) + (2600)(0.4) = 2500 cals
 Var(X1) = σ1
2
= (–200)2
(0.1) + (–100)2
(0.2) +
(0)2
(0.3) + (+100)2
(0.4) = 10000 cals2
Population 2
Probability Table
 Mean(X2) = µ2 = (2200)(0.2) + (2300)(0.3) + (2400)(0.5) = 2330 cals
 Var(X2) = σ2
2
= (–130)2
(0.2) + (–30)2
(0.3) + (70)2
(0.5) = 6100 cals2
x p1(x)
2300 0.1
2400 0.2
2500 0.3
2600 0.4
x p2(x)
2200 0.2
2300 0.3
2400 0.5
20%
30%
50%
2300
2400
2500
2600
2200
2300
2400
0.1
0.2
0.3
0.4
0.2
0.3
0.5
Ismor Fischer, 5/26/2016 4.1-7
Summary (Also refer back to 2.4 - Summary)
POPULATION
Discrete random variable X
Probability Table Probability Histogram
x p(x) = P(X = x)
x1 f(x1)
x2 f(x2)
.
.
.
.
.
.
1
= E[X] = x p(x)
E[(X )2
] = (x )2
p(x)
2
= or
E[X2
] 2
= x2
p(x) 2
XParameters
SAMPLE, size n
Relative Frequency Table Density Histogram
x p(x) =
freq(x)
n
x1 p(x1)
x2 p(x2)
.
.
.
.
.
.
xk p(xk)
1
x = x p(x)
n
n 1
(x x )2
p(x)
s 2
= or
n
n 1
[ x2
p(x) x 2
]
X
Statistics
X and 2
S
can be shown
to be
unbiased
estimators of
and 2
,
respectively.
That is,
E X ,
and
2 2
E S .
(In fact, they
are MVUE.)
Ismor Fischer, 5/26/2016 4.1-8
~ Some Advanced Notes on General Parameter Estimation ~
Suppose that is a fixed population parameter (e.g., ), and
ˆ is a sample-based estimator (e.g., X ). Consider all the
random samples of a given size n, and the resulting “sampling
distribution” of ˆ values. Formally define the following:
 Mean (of ˆ) = ˆ[ ]E , the expected value of ˆ.
 Bias = ˆ[ ]E , the difference between the expected
value of ˆ, and the “target” parameter .
 Variance (of ˆ) =
2
ˆ ˆ[ ]E E , the expected value
of the squared deviation of ˆ from its mean ˆ[ ]E ,
or equivalently,*
= 2 2ˆ ˆ[ ]E E .
 Mean Squared Error (MSE) = 2ˆ( )E , the expected value of the squared
difference between estimator ˆ and the “target” parameter .
Exercise: Prove*
that 2
MSE = Variance + Bias .
Comment: A parameter estimator ˆ is defined to be unbiased if ˆ[ ]E , i.e.,
Bias = 0. In this case, MSE = Variance, so that if ˆ minimizes MSE, it then follows
that it has the smallest variance of any estimator. Such a highly desirable estimator is
called MVUE (Minimum Variance Unbiased Estimator). It can be shown that the
estimators X and 2
S (of and 2
, respectively) are MVUE, but finding such an
estimator ˆ for a general parameter can be quite difficult in practice. Often, one
must settle for either not having minimum variance or having a small amount of bias.
*
using the basic properties of mathematical expectation given earlier
POPULATION
Parameter
SAMPLE
Statistic ˆ
ˆc
ˆ ˆ[ ]Ea
ˆ[ ]Eb
Vector interpretation
2 2 2
[ ] [ ] [ ]E E E
c = a +b
c a b
Ismor Fischer, 5/26/2016 4.1-9
Related (but not identical) to this is the idea that of all linear combinations
1 1 2 2 n nc x c x c x of the data 1 2{ , , , }nx x x (such as X , with 1 2 1/nc c c n)
which are also unbiased, the one that minimizes MSE is called BLUE (Best Linear
Unbiased Estimator). It can be shown that, in addition to being MVUE (as stated
above), X is also BLUE. To summarize,
MVUE gives: Min Variance among all unbiased estimators
≤ Min Variance among linear unbiased estimators
= Min MSE among linear unbiased estimators (since MSE = Var + Bias2
),
given by BLUE (by def).
The Venn diagram below depicts these various relationships.
Comment: If MSE 0 as n , then ˆ is said to have mean square convergence
to . This in turn implies “convergence in probability” (via “Markov's Inequality,”
also used in proving Chebyshev’s Inequality), i.e., ˆ is a consistent estimator of .
Unbiased Linear
Minimum
MSE
Minimum
Variance
BLUE
MVUE
X
2
S
Minimum variance
among linear
unbiased estimators
Minimum variance
among all unbiased
estimators
Ismor Fischer, 5/26/2016 4.1-10
10%
20%
30%
40%
20%
30%
50%
Experiment 4 - revisited: Recall the previous example, where X1 and X2 represent
the daily number of calories consumed in two populations, respectively.
Population 1 Population 2
Case 1: First suppose that X1 and X2 are statistically independent, as shown in the joint probability
distribution given in the table below. That is, each cell probability is equal to the product of the
corresponding row and column marginal probabilities. For example, P(X1 = 2300 ∩ X2 = 2200) = .02,
but this is equal to the product of the column marginal P(X1 = 2300) = .1 with the row marginal
P(X2 = 2200) = .2. Note that the marginal distributions for X1 and X2 remain the same as above, as can
be seen from the single-underlined values for X1, and respectively, the double-underlined values for X2.
X1 = # calories for Pop 1
2300 2400 2500 2600
X2=#calories
forPop2
2200 .02 .04 .06 .08 .20
2300 .03 .06 .09 .12 .30
2400 .05 .10 .15 .20 .50
.10 .20 .30 .40 1.00
2300
2400
2500
2600
2200
2300
2400
x p1(x)
2300 0.1
2400 0.2
2500 0.3
2600 0.4
Mean(X1) = µ1 = 2500 cals;
Var(X1) = σ1
2
= 10000 cals2
x p2(x)
2200 0.2
2300 0.3
2400 0.5
Mean(X2) = µ2 = 2330 cals;
Var(X2) = σ2
2
= 6100 cals2
Ismor Fischer, 5/26/2016 4.1-11
Now imagine that we wish to compare the two populations, by considering the
probability distribution of the calorie difference D = X1 – X2 between them. (The sum
S = X1 + X2 is similar, and left as an exercise.)
As an example, there are two possible ways that D = 300 can occur, i.e., two possible
outcomes corresponding to the event D = 300: Either A = “X1 = 2500 and X2 = 2200”
or B = “X1 = 2600 and X2 = 2300,” that is, A ⋃ B. For its probability, recall that
( ) ( ) ( ) ( ).P A B P A P B P A B However, events A and B are disjoint, for they
cannot both occur simultaneously, so that the last term is P(A ⋂ B) = 0. Thus,
( ) ( ) ( )P A B P A P B with P(A) = .06 and P(B) = .12 from the joint distribution.
Mean(D) = µD =
(–100)(.05) + (0)(.13) + (100)(.23) +
(200)(.33) + (300)(.18) + (400)(.08)
= 170 cals
i.e., µD = µ1 – µ2 (Check this!)
Var(D) = σD
2
=
(–270)2
(.05) + (–170)2
(.13) + (–70)2
(.23)
+ (30)2
(.33) + (130)2
(.18) + (230)2
(.08)
= 16100 cals2
i.e., σD
2
= σ1
2
+ σ2
2
(Check this!)
Events
D = d
Sample Space
Outcomes in the form of ordered pairs (X1, X2)
Probabilities
from joint distribution
D = –100: (2300, 2400) .05
D = 0: (2300, 2300), (2400, 2400) .13 = .03 + .10
D = +100: (2300, 2200), (2400, 2300), (2500, 2400) .23 = .02 + .06 + .15
D = +200: (2400, 2200), (2500, 2300), (2600, 2400) .33 = .04 + .09 + .20
D = +300: (2500, 2200), (2600, 2300) .18 = .06 + .12
D = +400: (2600, 2200) .08
.05
.13
.23
.33
.18
.08
Ismor Fischer, 5/26/2016 4.1-12
Case 2: Now assume that X1 and X2 are not statistically independent, as given in the
joint probability distribution table below.
X1 = # calories for Pop 1
2300 2400 2500 2600
X2=#calories
forPop2
2200 .01 .03 .07 .09 .20
2300 .02 .05 .10 .13 .30
2400 .07 .12 .13 .18 .50
.10 .20 .30 .40 1.00
The events “D = d” and the corresponding sample space of outcomes remain unchanged,
but the last column of probabilities has to be recalculated, as shown. This results in a
slightly different probability histogram (Exercise) and parameter values.
Mean(D) = µD = (–100)(.07) + (0)(.14) + (100)(.19) + (200)(.33) + (300)(.18) + (400)(.08)
= 170 cals, i.e., µD = µ1 – µ2.
Var(D) = σD
2
= (–270)2
(.07) + (–170)2
(.14) + (–70)2
(.19) + (30)2
(.31) + (130)2
(.20) + (230)2
(.09)
= 18517 cals2
It seems that “the mean of the difference is equal to the difference in the means” still
holds, even when the two populations are dependent. But the variance of the difference
is no longer necessarily equal the sum of the variances, as with independent populations.
Events
D = d
Sample Space
Outcomes in the form of ordered pairs (X1, X2)
Probabilities
from joint distribution
D = –100: (2300, 2400) .07
D = 0: (2300, 2300), (2400, 2400) .14 = .02 + .12
D = +100: (2300, 2200), (2400, 2300), (2500, 2400) .19 = .01 + .05 + .13
D = +200: (2400, 2200), (2500, 2300), (2600, 2400) .31 = .03 + .10 + .18
D = +300: (2500, 2200), (2600, 2300) .20 = .07 + .13
D = +400: (2600, 2200) .09
Ismor Fischer, 5/26/2016 4.1-13
These examples illustrate a general principle that can be rigorously proved with mathematics.
GENERAL FACT ~
Comments:
 These formulas actually apply to both discrete and continuous variables (next section).
 The difference relations will play a crucial role in 6.2 - Two Samples inference.
 If X and Y are dependent, then the two bottom relations regarding the variance also
involve an additional term, Cov(X, Y), the population covariance between X and Y.
See problems 4.3/29 and 4.3/30 for details.
 The variance relation can be interpreted visually via the Pythagorean Theorem,
which illustrates an important geometric connection, expanded in the Appendix.]
Certain discrete distributions (or discrete models) occur so frequently in practice, that
their properties have been well-studied and applied in many different scenarios. For
instance, suppose it is known that a certain population consists of 45% males (and thus
55% females). If a random sample of 250 individuals is to be selected, then what is the
probability of obtaining exactly 100 males? At most 100 males? At least 100 males?
What is the “expected” number of males? This is the subject of the next topic:
Mean(X + Y) = Mean(X) + Mean(Y) and Mean(X – Y) = Mean(X) – Mean(Y)
In addition, if X and Y are independent random variables,
Var(X + Y) = Var(X) + Var(Y) and Var(X – Y) = Var(X) + Var(Y).
X
Y
D
Ismor Fischer, 5/26/2016 4.1-14
POPULATION = Women diagnosed
with breast cancer in Dane County,
1996-2000
Among other things, this study
estimated that the rate of “breast cancer
in situ (BCIS),” which is diagnosed
almost exclusively via mammogram, is
approximately 12-13%. That is, for any
individual randomly selected from this
population, we have a binary variable
1, with probability 0.12
BCIS
0, with probability 0.88.
In a random sample of 100n breast
cancer diagnoses, let
X = # BCIS cases (0,1,2, ,100) .
Questions:
 How can we model the probability
distribution of X, and under what
assumptions?
 Probabilities of events, such as
( 0),P X ( 20),P X ( 20),P X
etc.?
 Mean # BCIS cases = ?
 Standard deviation of # BCIS cases = ?
Full article available online at this link.
Ismor Fischer, 5/26/2016 4.1-15
Binomial Distribution (Paradigm model = coin tosses)
(H H H H H) (H H T H H) (H T H H H) (H T T H H) (T H H H H) (T H T H H) (T T H H H) (T T T H H)
(H H H H T) (H H T H T) (H T H H T) (H T T H T) (T H H H T) (T H T H T) (T T H H T) (T T T H T)
(H H H T H) (H H T T H) (H T H T H) (H T T T H) (T H H T H) (T H T T H) (T T H T H) (T T T T H)
(H H H T T) (H H T T T) (H T H T T) (H T T T T) (T H H T T) (T H T T T) (T T H T T) (T T T T T)
Random Variable: X = “# Heads in n = 5 independent tosses (0, 1, 2, 3, 4, 5)”
Events: “X = 0” = Exercise #(X = 0) = 5
0
= 1
“X = 1” = Exercise #(X = 1) = 5
1
= 5
“X = 2” = Exercise #(X = 2) = 5
2
= 10
“X = 3” = see above #(X = 3) = 5
3
= 10
“X = 4” = Exercise #(X = 4) = 5
4
= 5
“X = 5” = Exercise #(X = 5) = 5
5
= 1
Recall: For x = 0, 1, 2, …, n, the combinatorial symbol
n
x
– read “n-choose-x” – is
defined as the value
n!
x! (n x)!
, and counts the number of ways of rearranging x objects
among n objects. See Appendix > Basic Reviews > Perms & Combos for details.
Note:
n
r
is computed via the mathematical function “nCr” on most calculators.
Binary random variable: Probability:
1, Success (Heads) with P(Success) =
Y =
0, Failure (Tails) with P(Failure) = 1
Experiment: n = 5 independent coin tosses
Sample Space S = {(H H H H H), …, (T T T T T)} #(S) = 25
= 32
Ismor Fischer, 5/26/2016 4.1-16
Total Area = 1
Probabilities:
First assume the coin is fair ( = 0.5 1 = 0.5), i.e., equally likely elementary
outcomes H and T on a single trial. In this case, the probability of any event A above
can thus be easily calculated via P(A) = #(A) / #(S).
x P(X = x) =
1
25
5
x
0 1/32 = 0.03125
1 5/32 = 0.15625
2 10/32 = 0.312500
3 10/32 = 0.312500
4 5/32 = 0.15625
5 1/32 = 0.03125
Now consider the case where the coin is biased (e.g., = 0.7 1 = 0.3).
Calculating P(X = x) for x = 0, 1, 2, 3, 4, 5 means summing P(all its outcomes).
Example: P(X = 3) =
outcome via independence of H, T
P(H H H T T) = (0.7)(0.7)(0.7)(0.3)(0.3) = (0.7)3
(0.3)2
+ P(H H T H T) = (0.7)(0.7)(0.3)(0.7)(0.3) = (0.7)3
(0.3)2
+ P(H H T T H) = (0.7)(0.7)(0.3)(0.3)(0.7) = (0.7)3
(0.3)2
+ P(H T H H T) = (0.7)(0.3)(0.7)(0.7)(0.3) = (0.7)3
(0.3)2
+ P(H T H T H) = (0.7)(0.3)(0.7)(0.3)(0.7) = (0.7)3
(0.3)2
+ P(H T T H H) = (0.7)(0.3)(0.3)(0.7)(0.7) = (0.7)3
(0.3)2
+ P(T H H H T) = (0.3)(0.7)(0.7)(0.7)(0.3) = (0.7)3
(0.3)2
+ P(T H H T H) = (0.3)(0.7)(0.7)(0.3)(0.7) = (0.7)3
(0.3)2
+ P(T H T H H) = (0.3)(0.7)(0.3)(0.7)(0.7) = (0.7)3
(0.3)2
+ P(T T H H H) = (0.3)(0.3)(0.7)(0.7)(0.7) = (0.7)3
(0.3)2
via disjoint outcomes,
=
5
3 (0.7)3
(0.3)2
Ismor Fischer, 5/26/2016 4.1-17
Hence, we similarly have…
x
0
5
0
(0.7)0
(0.3)5
= 0.00243
1
5
1
(0.7)1
(0.3)4
= 0.02835
2
5
2
(0.7)2
(0.3)3
= 0.13230
3
5
3
(0.7)3
(0.3)2
= 0.30870
4
5
4
(0.7)4
(0.3)1
= 0.36015
5
5
5
(0.7)5
(0.3)0
= 0.16807
Example: Suppose that a certain medical procedure is known to have a 70%
successful recovery rate (assuming independence). In a random sample of n = 5
patients, the probability that three or fewer patients will recover is:
Method 1: P(X 3) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3)
= 0.00243 + 0.02835 + 0.13230 + 0.30870 = 0.47178
Method 2: P(X 3) = 1 [ P(X = 4) + P(X = 5) ]
= 1 [0.36015 + 0.16807 ] = 1 – 0.52822 = 0.47178
Example: The mean number of patients expected to recover is:
= E[X] = 0 (0.00243) + 1 (0.02835) + 2 (0.13230) + 3 (0.30870) + 4 (0.36015) + 5 (0.16807)
= 3.5 patients
This makes perfect sense for n = 5 patients with a = 0.7 recovery probability, i.e.,
their product. In the probability histogram above, the “balance point” fulcrum
indicates the mean value of 3.5.
Total Area = 1
P(X = x) =
5
x
(0.7)x
(0.3)5 x
Ismor Fischer, 5/26/2016 4.1-18
General formulation:
The Binomial Distribution
Let the discrete random variable X = “# Successes in n independent Bernoulli trials
(0, 1, 2, …, n),” each having constant probability P(Success) = , and hence
P(Failure) = 1 . Then the probability of obtaining any specified number of
successes x = 0, 1, 2, …, n, is given by the pmf p(x):
P(X = x) =
n
x
x
(1 ) n x
.
We say that X has a Binomial Distribution, denoted X ~ Bin(n, ).
Furthermore, the mean = n , and the standard deviation = n (1 ) .
Example: Suppose that a certain spontaneous medical condition affects 1% (i.e., = 0.01)
of the population. Let X = “number of affected individuals in a random sample of n = 300.”
Then X ~ Bin(300, 0.01), i.e., the probability of obtaining any specified number x = 0, 1, 2,
…, 300 of affected individuals is:
P(X = x) =
300
x
(0.01)x
(0.99)300 x
.
The mean number of affected individuals is = n = (300)(0.01) = 3 expected cases, with a
standard deviation of = (300)(0.01)(0.99) = 1.723 cases.
Probability Table for Binomial Dist.
x p(x) =
n
x
x
(1 ) n x
0
0 0
0
(1 )nn
1
1 1
1
(1 )nn
2
2 2
2
(1 )nn
etc. etc.
n (1 )n nnn
n
1
Exercise: In order to be a valid distribution,
the sum of these probabilities must = 1. Prove it.
Hint: First recall the Binomial Theorem:
How do you expand the algebraic expression
( )n
a b for any n = 0, 1, 2, 3, …? Then replace
a with , and b with 1 – . Voilà!
Ismor Fischer, 5/26/2016 4.1-19
Comments:
 The assumption of independence of the trials is absolutely critical! If not satisfied – i.e.,
if the “success” probability of one trial influences that of another – then the Binomial
Distribution model can fail miserably. (Example: X = “number of children in a particular
school infected with the flu”) The investigator must decide whether or not independence
is appropriate, which is often problematic. If violated, then the correlation structure
between the trials may have to be considered in the model.
 As in the preceding example, if the sample size n is very large, then the computation of
n
x
for x = 0, 1, 2, …, n, can be intensive and impractical. An approximation to the
Binomial Distribution exists, when n is large and is small, via the Poisson Distribution
(coming up…).
 Note that the standard deviation = n (1 ) depends on the value of . (Later…)
Ismor Fischer, 5/26/2016 4.1-20
How can we estimate the parameter , using a sample-based statistic ˆ ?
Example: If, in a sample of n = 50 randomly selected individuals, X = 36 are female,
then the statistic ˆ =
X
n
=
36
50
= 0.72 is an estimate of the true probability that a
randomly selected individual from the population is female. The probability of
selecting a male is therefore estimated by 1 ˆ = 0.28 .
Binary random variable
1, Success with probability
Y =
0, Failure with probability 1
POPULATION
Experiment: n independent trials
SAMPLE
0/1 0/1 0/1 0/1 0/1 0/1 … 0/1
(y1, y2, y3, y4, y5, y6, …, yn)
y1 + y2 + y3 + y4 + y5 + … + yn
Let X = # Successes in n trials ~ Bin(n, )
(n X = # Failures in n trials).
Therefore, dividing by n…
X
n
= proportion of Successes in n trials
ˆ = p ( = y , as well)
and hence…
q = 1 p = proportion of Failures in n trials.
Ismor Fischer, 5/26/2016 4.1-21
Poisson Distribution (Models rare events)
Assume:
1. All the occurrences of E are independent in the interval.
2. The mean number of expected occurrences of E in the interval is proportional
to T, i.e., = T. This constant of proportionality is called the rate of the
resulting Poisson process.
Then…
Examples: # bee-sting fatalities per year, # spontaneous cancer remissions per year,
# accidental needle-stick HIV cases per year, hemocytometer cell counts
Discrete Random Variable:
X = # occurrences of a (rare) event E, in a given interval
of time or space, of size T. (0, 1, 2, 3, …)
T0
The Poisson Distribution
The probability of obtaining any specified number x = 0, 1, 2, … of
occurrences of event E is given by the pmf p(x):
P(X = x) =
e x
x!
where e = 2.71828… (“Euler’s constant”).
We say that X has a Poisson Distribution, denoted X ~ Poisson( ).
Furthermore, the mean is = T, and the variance is 2
= T also.
Ismor Fischer, 5/26/2016 4.1-22
Area = 1
Area = 1
Example (see above): Again suppose that a certain spontaneous medical condition E
affects 1% (i.e., = 0.01) of the population. Let X = “number of affected individuals
in a random sample of T = 300.” As before, the mean number of expected occurrences
of E in the sample is = T = (0.01)(300) = 3 cases. Hence X ~ Poisson(3), and the
probability that any number x = 0, 1, 2, … of individuals are affected is given by:
P(X = x) =
e 3
3 x
x!
which is a much easier formula to work with than the previous one. This fact is
sometimes referred to as the Poisson approximation to the Binomial Distribution,
when T (respectively, n) is large, and (respectively, ) is small. Note that in this
example, the variance is also 2
= 3, so that the standard deviation is = 3 = 1.732,
very close to the exact Binomial value.
x
Binomial
P(X = x) =
300
x
(0.01)x
(0.99)300 x
Poisson
P(X = x) =
e 3
3 x
x!
0 0.04904 0.04979
1 0.14861 0.14936
2 0.22441 0.22404
3 0.22517 0.22404
4 0.16888 0.16803
5 0.10099 0.10082
6 0.05015 0.05041
7 0.02128 0.02160
8 0.00787 0.00810
9 0.00258 0.00270
10 0.00076 0.00081
etc. 0 0
Ismor Fischer, 5/26/2016 4.1-23
Why is the Poisson Distribution a good approximation to the
Binomial Distribution, for large n and small ?
Rule of Thumb: n 20 and 0.05; excellent if n 100 and 0.1.
Let pBin(x) =
n
x
x
(1 ) n x
and pPoisson(x) =
e x
x!
, where = n .
We wish to show formally that, for fixed , and x = 0, 1, 2, …, we have:
lim pBin(x) = pPoisson(x).
Proof: By elementary algebra, it follows that…
pBin(x) =
n
x
x
(1 ) n x
=
n!
x! (n x)!
x
(1 ) n
(1 ) x
=
1
x!
n (n 1) (n 2) ... (n x + 1) x
1
n
n
(1 ) x
=
1
x!
n (n 1) (n 2) ... (n x + 1)
nx nx x
1
n
n
(1 ) x
=
1
x!
n
n
n 1
n
n 2
n
…
n x + 1
n
(n )x
1
n
n
(1 ) x
=
1
x!
1 1
1
n
1
2
n
… 1
x 1
n
x
1
n
n
(1 ) x
As n ,
0,
1
x!
1(1)(1) … (1) = 1 x
e 1 x
= 1
=
e x
x!
= pPoisson(x). QED
n
0
Siméon Poisson
(1781 - 1840)
Ismor Fischer, 5/26/2016 4.1-24
Classical Discrete Probability Distributions
Binomial (probability of finding x “successes” and n – x “failures” in n independent trials)
Negative Binomial (probability of needing x independent trials to find k successes)
Hypergeometric (modification of Binomial to sampling without replacement from “small” finite populations, relative to n.)
Multinomial (generalization of Binomial to k categories, rather than just two)
Poisson (“limiting case” of Binomial, with n and 0, such that n = , fixed)
X = # occurrences of a rare event (i.e., 0) among many (i.e., n large), with fixed mean = n
p(x) = P(X = x) =
e x
x!
, x = 0, 1, 2, …
X = # independent Bernoulli trials for k successes (each with probability ), k = 1, 2, 3, …
p(x) = P(X = x) = x 1
k 1
k
(1 ) x k
, x = k, k + 1, k + 2, …
Geometric: X = # independent Bernoulli trials for k = 1 success
p(x) = P(X = x) = (1 ) x 1
, x = 1, 2, 3, …
For i = 1, 2, 3, …, k,
Xi = # outcomes in category i (each with probability i), in n independent Bernoulli trials, n = 1, 2, 3, …
1 2 3 1k
p(x1, x2, …, xk) = P(X1 = x1, X2 = x2, …, Xk = xk) =
n!
x1! x2! … xk!
1 2
1 2
kxx x
k ,
xi = 0, 1, 2, …, n with x1 + x2 + … + xk = n
X = # successes (each with probability ) in n independent Bernoulli trials, n = 1, 2, 3, …
p(x) = P(X = x) = n
x
x
(1 ) n x
, x = 0, 1, 2, …, n
X = # successes in n random trials taken from a population of size N containing d successes, n >
N
10
p(x) = P(X = x) =
d
x
N d
n x
N
n
, x = 0, 1, 2, …, d