Stats 241.3
Probability Theory
Summary
Probability
Axioms of Probability
A probability measure P is defined on S by defining for each event E, P[E] with the following properties
1. P[E] ≥ 0, for each E.
2. P[S] = 1.
3. if for all ,i i i iii
P E P E E E i j
1 2 1 2P E E P E P E
Finite uniform probability space
Many examples fall into this category
1. Finite number of outcomes
2. All outcomes are equally likely
3.
no. of outcomes in =
total no. of outcomes
n E n E EP E
n S N
: = no. of elements of n A ANote
To handle problems in case we have to be able to count. Count n(E) and n(S).
Techniques for counting
Basic Rule of countingSuppose we carry out k operations in sequence
Letn1 = the number of ways the first operation
can be performed
ni = the number of ways the ith operation can be performed once the first (i - 1) operations have been completed. i = 2, 3, … , k
Then N = n1n2 … nk = the number of ways the k operations can be performed in sequence.
Basic Counting Formulae1. Permutations: How many ways can you order n
objects
n!2. Permutations of size k (< n): How many ways can
you choose k objects from n objects in a specific order
!
= 1 1!n k
nP n n n k
n k
3. Combinations of size k ( ≤ n): A combination of size k chosen from n objects is a subset of size k where the order of selection is irrelevant. How many ways can you choose a combination of size k objects from n objects (order of selection is irrelevant)
n k
nC
k
1 2 1!
! ! 1 2 1
n n n n kn
n k k k k k
Important Notes
1. In combinations ordering is irrelevant. Different orderings result in the same combination.
2. In permutations order is relevant. Different orderings result in the different permutations.
Rules of Probability
The additive rule
P[A B] = P[A] + P[B] – P[A B]
and
if P[A B] = P[A B] = P[A] + P[B]
The additive rule for more than two events
then
and if Ai Aj = for all i ≠ j.
11
n n
i i i ji i ji
P A P A P A A
i j ki j k
P A A A
1
1 21n
nP A A A
11
n n
i iii
P A P A
The Rule for complements
for any event E
1P E P E
Conditional Probability,Independence
andThe Multiplicative Rule
The conditional probability of A given B is defined to be:
P A BP A B
P B
if 0P B
if 0
if 0
P A P B A P AP A B
P B P A B P B
The multiplicative rule of probability
and
P A B P A P B
if A and B are independent.
This is the definition of independence
1 2 nP A A A
The multiplicative rule for more than two events
1 2 1 3 2 1P A P A A P A A A
1 2 1n n nP A A A A
Independencefor more than 2 events
Definition:
The set of k events A1, A2, … , Ak are called mutually independent if:
P[Ai1 ∩ Ai2 ∩… ∩ Aim
] = P[Ai1] P[Ai2
] …P[Aim]
For every subset {i1, i2, … , im } of {1, 2, …, k }
i.e. for k = 3 A1, A2, … , Ak are mutually independent if:
P[A1 ∩ A2] = P[A1] P[A2], P[A1 ∩ A3] = P[A1] P[A3],
P[A2 ∩ A3] = P[A2] P[A3],
P[A1 ∩ A2 ∩ A3] = P[A1] P[A2] P[A3]
Definition:
The set of k events A1, A2, … , Ak are called pairwise independent if:
P[Ai ∩ Aj] = P[Ai] P[Aj] for all i and j.
i.e. for k = 3 A1, A2, … , Ak are pairwise independent if:
P[A1 ∩ A2] = P[A1] P[A2], P[A1 ∩ A3] = P[A1] P[A3],
P[A2 ∩ A3] = P[A2] P[A3],
It is not necessarily true that P[A1 ∩ A2 ∩ A3] = P[A1]
P[A2] P[A3]
Bayes Rule for probability
P A P B AP A B
P A P B A P A P B A
Let A1, A2 , … , Ak denote a set of events such that
1 1
i ii
k k
P A P B AP A B
P A P B A P A P B A
An generalization of Bayes Rule
1 2 and k i jS A A A A A
for all i and j. Then
Random Variables
an important concept in probability
A random variable , X, is a numerical quantity whose value is determined be a random experiment
Definition – The probability function, p(x), of a random variable, X.
For any random variable, X, and any real number, x, we define
p x P X x P X x
where {X = x} = the set of all outcomes (event) with X = x.
For continuous random variables p(x) = 0 for all values of x.
Definition – The cumulative distribution function, F(x), of a random variable, X.
For any random variable, X, and any real number, x, we define
F x P X x P X x
where {X ≤ x} = the set of all outcomes (event) with X ≤ x.
Discrete Random Variables
For a discrete random variable X the probability distribution is described by the probability function p(x), which has the following properties
1
2. 1ix i
p x p x
1. 0 1p x
3. a x b
P a x b p x
Graph: Discrete Random Variable
p(x)
a x b
P a x b p x
a b
Continuous random variables
For a continuous random variable X the probability distribution is described by the probability density function f(x), which has the following properties :
1. f(x) ≥ 0
2. 1.f x dx
3. .
b
a
P a X b f x dx
Graph: Continuous Random Variableprobability density function, f(x)
1.f x dx
.b
a
P a X b f x dx
The distribution function F(x)
This is defined for any random variable, X.
F(x) = P[X ≤ x]
Properties
1. F(-∞) = 0 and F(∞) = 1.
2. F(x) is non-decreasing (i. e. if x1 < x2 then F(x1) ≤ F(x2) )
3. F(b) – F(a) = P[a < X ≤ b].
4. p(x) = P[X = x] =F(x) – F(x-)
5. If p(x) = 0 for all x (i.e. X is continuous) then F(x) is continuous.
Here limu x
F x F u
6. For Discrete Random Variables
F(x) is a non-decreasing step function with
u x
F x P X x p u
jump in at .p x F x F x F x x
0 and 1F F
0
0.2
0.4
0.6
0.8
1
1.2
-1 0 1 2 3 4
F(x)
p(x)
7. For Continuous Random Variables Variables
F(x) is a non-decreasing continuous function with
x
F x P X x f u du
.f x F x
0 and 1F F F(x)
f(x) slope
0
1
-1 0 1 2x
To find the probability density function, f(x), one first finds F(x) then .f x F x
Some Important Discrete distributions
The Bernoulli distribution
Suppose that we have a experiment that has two outcomes
1. Success (S)2. Failure (F)
These terms are used in reliability testing.Suppose that p is the probability of success (S) and q = 1 – p is the probability of failure (F)This experiment is sometimes called a Bernoulli Trial
Let 0 if the outcome is F
1 if the outcome is SX
Then 0
1
q xp x P X x
p x
The probability distribution with probability function
is called the Bernoulli distribution
0
1
q xp x P X x
p x
0
0.2
0.4
0.6
0.8
1
0 1
p
q = 1- p
The Binomial distribution
We observe a Bernoulli trial (S,F) n times.
0,1,2, ,x n xnp x P X x p q x n
x
where
Let X denote the number of successes in the n trials.Then X has a binomial distribution, i. e.
1. p = the probability of success (S), and2. q = 1 – p = the probability of failure (F)
The Poisson distribution
• Suppose events are occurring randomly and uniformly in time.
• Let X be the number of events occuring in a fixed period of time. Then X will have a Poisson distribution with parameter .
0,1,2,3,4,!
x
p x e xx
The Geometric distribution
Suppose a Bernoulli trial (S,F) is repeated until a success occurs.
X = the trial on which the first success (S) occurs.
The probability function of X is:
p(x) =P[X = x] = (1 – p)x – 1p = p qx - 1
The Negative Binomial distribution
Suppose a Bernoulli trial (S,F) is repeated until k successes occur.
Let X = the trial on which the kth success (S) occurs.
The probability function of X is:
1 , 1, 2,
1k x kx
p x P X x p q x k k kk
The Hypergeometric distribution
Suppose we have a population containing N objects.
Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b.
Now suppose that n elements are selected from the population at random. Let X denote the elements from group A.
The probability distribution of X is
a b
x n xp x P X x
N
n
Continuous Distributions
Continuous random variables
For a continuous random variable X the probability distribution is described by the probability density function f(x), which has the following properties :
1. f(x) ≥ 0
2. 1.f x dx
3. .
b
a
P a X b f x dx
Graph: Continuous Random Variableprobability density function, f(x)
1.f x dx
.b
a
P a X b f x dx
0
0.1
0.2
0.3
0.4
0 5 10 15
1
b a
a b
f x
x0
0.1
0.2
0.3
0.4
0 5 10 15
1
b a
a b
f x
x
1
b a
a b
f x
x
Continuous Distributions
The Uniform distribution from a to b
1
0 otherwise
a x bf x b a
The Normal distribution (mean , standard deviation )
2
221
2
x
f x e
0
0.1
0.2
-2 0 2 4 6 8 10
The Exponential distribution
0
0 0
xe xf x
x
The Weibull distribution
A model for the lifetime of objects that do age.
The Weibull distribution with parameters and.
1 0x
f x x e x
The Weibull density, f(x)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4 5
( = 0.5, = 2)
( = 0.7, = 2)
( = 0.9, = 2)
The Gamma distribution
An important family of distributions
The Gamma distribution
Let the continuous random variable X have density function:
1 0
0 0
xx e xf x
x
Then X is said to have a Gamma distribution with parameters and .
Graph: The gamma distribution
0
0.1
0.2
0.3
0.4
0 2 4 6 8 10
( = 2, = 0.9)
( = 2, = 0.6)
( = 3, = 0.6)
Comments
1. The set of gamma distributions is a family of distributions (parameterized by and ).
2. Contained within this family are other distributionsa. The Exponential distribution – in this case = 1, the
gamma distribution becomes the exponential distribution with parameter . The exponential distribution arises if we are measuring the lifetime, X, of an object that does not age. It is also used a distribution for waiting times between events occurring uniformly in time.
b. The Chi-square distribution – in the case = /2 and = ½, the gamma distribution becomes the chi- square (2) distribution with degrees of freedom. Later we will see that sum of squares of independent standard normal variates have a chi-square distribution, degrees of freedom = the number of independent terms in the sum of squares.
Expectation
Let X denote a discrete random variable with probability function p(x) (probability density function f(x) if X is continuous) then the expected value of X, E(X) is defined to be:
i ix i
E X xp x x p x
E X xf x dx
and if X is continuous with probability density function f(x)
Expectation of functionsLet X denote a discrete random variable with probability function p(x) then the expected value of X, E[g (X)] is defined to be:
x
E g X g x p x
E X g x f x dx
and if X is continuous with probability density function f(x)
Moments of a Random Variable
the kth moment of X :
kk E X
-
if is discrete
if is continuous
k
x
k
x p x X
x f x dx X
• The first moment of X , = 1 = E(X) is the center of gravity of the distribution of X.
• The higher moments give different information regarding the distribution of X.
the kth central moment of X
0 k
k E X
-
if is discrete
if is continuous
k
x
k
x p x X
x f x dx X
Moment generating functions
Definition
Let X denote a random variable, Then the moment generating function of X , mX(t) is defined by:
if is discrete
if is continuous
tx
xtX
Xtx
e p x X
m t E ee f x dx X
Properties1. mX(0) = 1
0 derivative of at 0.k thX Xm k m t t 2.
kk E X
2 33211 .
2! 3! !kk
Xm t t t t tk
3.
continuous
discrete
k
kk k
x f x dx XE X
x p x X
4. Let X be a random variable with moment generating function mX(t). Let Y = bX + a
Then mY(t) = mbX + a(t)
= E(e [bX + a]t) = eatE(e X[ bt ])
= eatmX (bt)
5. Let X and Y be two independent random variables with moment generating function mX(t) and mY(t) .
Then mX+Y(t) = E(e [X + Y]t) = E(e Xt e Yt)
= E(e Xt) E(e Yt)
= mX (t) mY (t)
6. Let X and Y be two random variables with moment generating function mX(t) and mY(t) and two distribution functions FX(x) and FY(y) respectively.
Let mX (t) = mY (t) then FX(x) = FY(x).
This ensures that the distribution of a random variable can be identified by its moment generating function
M. G. F.’s - Continuous distributions
Name
Moment generating function MX(t)
Continuous Uniform
ebt-eat
[b-a]t
Exponential t
for t <
Gamma t
for t <
2
d.f.
1
1-2t /2
for t < 1/2
Normal et+(1/2)t22
M. G. F.’s - Discrete distributions
Name
Moment generating
function MX(t)
Discrete Uniform
et
N etN-1et-1
Bernoulli q + pet Binomial (q + pet)N
Geometric pet
1-qet
Negative Binomial
pet
1-qet k
Poisson e(et-1)
Note:
The distribution of a random variable X can be described by:
probability function if is discrete1.
probability density function if is continuous
p x X
f x X
3. Moment generating function:
if is discrete
if is continuous
tx
xtX
Xtx
e p x X
m t E ee f x dx X
2. Distribution function:
if is discrete
if is continuous
u x
x
p u X
F xf u du X
Summary of Discrete Distributions
Name
probability function p(x)
Mean
Variance
Moment generating
function MX(t)
Discrete Uniform p(x) =
1N x=1,2,...,N
N+12
N2-112
et
N etN-1et-1
Bernoulli p(x) =
p x=1q x=0
p pq q + pet
Binomial p(x) =
N
x pxqN-x Np Npq (q + pet)N
Geometric p(x) =pqx-1 x=1,2,... 1p
qp2
pet
1-qet
Negative Binomial p(x) =
x-1
k-1 pkqx-k
x=k,k+1,...
kp
kqp2
pet
1-qet k
Poisson p(x) =
x
x! e- x=1,2,... e(et-1)
Hypergeometric
p(x) =
A
x
N-A
n-x
N
n
n
A
N n
A
N
1-AN
N-n
N-1 not useful
Summary of Continuous Distributions
Name
probability density function f(x)
Mean
Variance
Moment generating function MX(t)
Continuous Uniform
otherwise
bxaabxf
0
1)(
a+b2
(b-a)2
12 ebt-eat
[b-a]t
Exponential
00
0)(
x
xlexf
lx
1
12
t
for t <
Gamma
f(x) =
00
0)( f(x)
1
x
xexaG
l lxaa
2
t
for t <
2
d.f.
f(x) = (1/2)
(/2) x e-(1/2)x x ? 0
0 x < 0
1
1-2t /2
for t < 1/2
Normal f(x) =
1
2 e-(x-)2/22
2 et+(1/2)t22
Weibull
f(x) = x e-x x ? 0
0 x < 0
( )+1
( )+2 -[ ]( )+1
not avail.
Jointly distributed Random variables
Multivariate distributions
Discrete Random Variables
The joint probability function;
p(x,y) = P[X = x, Y = y]
1. 0 , 1p x y
2. , 1x y
p x y
3. , ,P X Y A p x y ,x y A
Continuous Random Variables
Definition: Two random variable are said to have joint probability density function f(x,y) if
1. 0 ,f x y
2. , 1f x y dxdy
3. , ,P X Y A f x y dxdy A
Marginal and conditional distributions
Marginal Distributions (Discrete case):
Let X and Y denote two random variables with joint probability function p(x,y) then
the marginal density of X is
,Xy
p x p x y
the marginal density of Y is
,Yx
p y p x y
Marginal Distributions (Continuous case):
Let X and Y denote two random variables with joint probability density function f(x,y) then
the marginal density of X is
,Xf x f x y dy
the marginal density of Y is
,Yf y f x y dx
Conditional Distributions (Discrete Case):
Let X and Y denote two random variables with joint probability function p(x,y) and marginal probability functions pX(x), pY(y) then
the conditional density of Y given X = x
,
Y XX
p x yp y x
p x
conditional density of X given Y = y
,
X YY
p x yp x y
p y
Conditional Distributions (Continuous Case):
Let X and Y denote two random variables with joint probability density function f(x,y) and marginal densities fX(x), fY(y) then
the conditional density of Y given X = x
,
Y XX
f x yf y x
f x
conditional density of X given Y = y
,
X YY
f x yf x y
f y
The bivariate Normal distribution
Let
2 2
1 1 1 1 2 2 2 2
1 1 2 2
1 2 2
2
,1
x x x x
Q x x
1 21
,2
1 2 21 2
1, e
2 1
Q x xf x x
where
This distribution is called the bivariate Normal distribution.
The parameters are 1, 2 , 1, 2 and
Surface Plots of the bivariate Normal distribution
2. The marginal distribution of x2 is Normal with mean 2 and standard deviation 2.
1. The marginal distribution of x1 is Normal with mean 1 and standard deviation 1.
Marginal distributions
Conditional distributions
1. The conditional distribution of x1 given x2 is Normal with:
andmean
standard deviation
11 2 212
2
x
2112 1
2. The conditional distribution of x2 given x1 is Normal with:
andmean
standard deviation
22 1 121
1
x
2221 1
Independence
Two random variables X and Y are defined to be independent if
Definition:
, X Yp x y p x p y if X and Y are discrete
, X Yf x y f x f y if X and Y are continuous
multivariate distributions
k ≥ 2
Definition
Let X1, X2, …, Xn denote n discrete random variables, then
p(x1, x2, …, xn )
is joint probability function of X1, X2, …, Xn if
1
12. , , 1n
nx x
p x x
11. 0 , , 1np x x
1 13. , , , ,n nP X X A p x x 1, , nx x A
Definition
Let X1, X2, …, Xk denote k continuous random variables, then
f(x1, x2, …, xk )
is joint density function of X1, X2, …, Xk if
1 12. , , , , 1n nf x x dx dx
11. , , 0nf x x
1 1 13. , , , , , ,n n nP X X A f x x dx dx A
The Multinomial distribution
Suppose that we observe an experiment that has k possible outcomes {O1, O2, …, Ok } independently n times.
Let p1, p2, …, pk denote probabilities of O1, O2, …, Ok respectively.
Let Xi denote the number of times that outcome Oi occurs in the n repetitions of the experiment.
is called the Multinomial distribution
1 21 1 2
1 2
! , ,
! ! !kxx x
n kk
np x x p p p
x x x
1 21 2
1 2
kxx xk
k
np p p
x x x
The joint probability function of:
The Multivariate Normal distributionRecall the univariate normal distribution
2121
2
x
f x e
the bivariate normal distribution
221
22 12
2
1 ,
2 1
x xx x y yx xx x y y
x y
f x y e
The k-variate Normal distribution
112
1 / 2 1/ 2
1 , ,
2k kf x x f e
x μ x μx
where
1
2
k
x
x
x
x
1
2
k
μ
11 12 1
12 22 2
1 2
k
k
k k kk
Marginal distributions
Definition
Let X1, X2, …, Xq, Xq+1 …, Xk denote k discrete random variables with joint probability function
p(x1, x2, …, xq, xq+1 …, xk )
1
12 1 1 , , , ,q n
q q nx x
p x x p x x
then the marginal joint probability function
of X1, X2, …, Xq is
Definition
Let X1, X2, …, Xq, Xq+1 …, Xk denote k continuous random variables with joint probability density function
f(x1, x2, …, xq, xq+1 …, xk )
12 1 1 1 , , , ,q q n q nf x x f x x dx dx
then the marginal joint probability function
of X1, X2, …, Xq is
Conditional distributions
Definition
Let X1, X2, …, Xq, Xq+1 …, Xk denote k discrete random variables with joint probability function
p(x1, x2, …, xq, xq+1 …, xk )
1
1 11 11 1
, , , , , ,
, ,k
q q kq q kq k q k
p x xp x x x x
p x x
then the conditional joint probability function
of X1, X2, …, Xq given Xq+1 = xq+1 , …, Xk = xk is
Definition
Let X1, X2, …, Xq, Xq+1 …, Xk denote k continuous random variables with joint probability density function
f(x1, x2, …, xq, xq+1 …, xk )
then the conditional joint probability function
of X1, X2, …, Xq given Xq+1 = xq+1 , …, Xk = xk is
Definition
11 11 1
1 1
, , , , , ,
, ,k
q q kq q kq k q k
f x xf x x x x
f x x
Definition
Let X1, X2, …, Xq, Xq+1 …, Xk denote k continuous random variables with joint probability density function
f(x1, x2, …, xq, xq+1 …, xk )
then the variables X1, X2, …, Xq are independent of Xq+1, …, Xk if
Definition – Independence of sets of vectors
1 1 1 1 1 , , , , , ,k q q q k q kf x x f x x f x x
A similar definition for discrete random variables.
Definition
Let X1, X2, …, Xk denote k continuous random variables with joint probability density function
f(x1, x2, …, xk )
then the variables X1, X2, …, Xk are called mutually independent if
Definition – Mutual Independence
1 1 1 2 2 , , k k kf x x f x f x f x
A similar definition for discrete random variables.
Expectation
for multivariate distributions
Definition
Let X1, X2, …, Xn denote n jointly distributed random variable with joint density function
f(x1, x2, …, xn )
then
1, , nE g X X
1 1 1, , , , , ,n n ng x x f x x dx dx
Some Rules for Expectation
1 11. , ,i i n nE X x f x x dx dx
i i i ix f x dx
Thus you can calculate E[Xi] either from the joint distribution of
X1, … , Xn or the marginal distribution of Xi.
1 1 1 12. n n n nE a X a X b a E X a E X b
The Linearity property
1 1, , , ,q q kE g X X h X X
In the simple case when k = 2
3. (The Multiplicative property) Suppose X1, … , Xq
are independent of Xq+1, … , Xk then
1 1, , , ,q q kE g X X E h X X
E XY E X E Y
if X and Y are independent
Some Rules for Variance
2 2 2Var X XX E X E X
Ex:
2
11P X k
k
32
4P X
Tchebychev’s inequality
83
9P X
154
16P X
1. Var Var Var 2Cov ,X Y X Y X Y
where Cov , = X YX Y E X Y
Cov , 0X Y
and Var Var VarX Y X Y
Note: If X and Y are independent, then
The correlation coefficient XY
Cov , Cov ,=
Var Varxy
X Y
X Y X Y
X Y
:
1. If and are independent than 0.XYX Y Properties
2. 1 1XY
if there exists a and b such thatand 1XY
1P Y bX a
whereXY = +1 if b > 0 and XY = -1 if b< 0
2 22. Var Var Var 2 Cov ,aX bY a X b Y ab X Y
Some other properties of variance
1 13. Var n na X a X
2 21 1Var Varn na X a X
1 2 1 2 1 12 Cov , 2 Cov ,n na a X X a a X X
2 3 2 3 2 22 Cov , 2 Cov ,n na a X X a a X X
1 12 Cov ,n n n na a X X
2
1
Var 2 Cov ,n
i i i j i ji
a X a a X X
4. Variance: Multiplicative Rule for independent random variables
Suppose that X and Y are independent random variables, then:
2 2X YVar XY Var X Var Y Var Y Var X
Mean and Variance of averages
Let1
1 n
ii
X Xn
Let X1, … , Xn be n mutually independent random variables each having mean and standard deviation (variance 2).
Then X E X
and2
2X Var X
n
The Law of Large Numbers
Let1
1 n
ii
X Xn
Let X1, … , Xn be n mutually independent random variables each having mean
Then for any > 0 (no matter how small)
1 as P X P X n
Conditional Expectation:
Let X1, X2, …, Xq, Xq+1 …, Xk denote k continuous random variables with joint probability density function
f(x1, x2, …, xq, xq+1 …, xk )
then the conditional joint probability function
of X1, X2, …, Xq given Xq+1 = xq+1 , …, Xk = xk is
Definition
11 11 1
1 1
, , , , , ,
, ,k
q q kq q kq k q k
f x xf x x x x
f x x
Let U = h( X1, X2, …, Xq, Xq+1 …, Xk )
then the Conditional Expectation of U
given Xq+1 = xq+1 , …, Xk = xk is
Definition
1 1 1 11 1 , , , , , ,k q q k qq q kh x x f x x x x dx dx
1 , , q kE U x x
Note this will be a function of xq+1 , …, xk.
A very useful rule
E U E E U y y
Var U E Var U Var E U y yy y
Then
1 1Let , , , , , ,q mU g x x y y g x y
Let (x1, x2, … , xq, y1, y2, … , ym) = (x, y) denote q + m random variables.
Functions of Random Variables
Methods for determining the distribution of functions of Random Variables
1. Distribution function method
2. Moment generating function method
3. Transformation method
Distribution function method
Let X, Y, Z …. have joint density f(x,y,z, …)Let W = h( X, Y, Z, …)First step
Find the distribution function of WG(w) = P[W ≤ w] = P[h( X, Y, Z, …) ≤ w]
Second stepFind the density function of Wg(w) = G'(w).
Use of moment generating functions
1. Using the moment generating functions of X, Y, Z, …determine the moment generating function of W = h(X, Y, Z, …).
2. Identify the distribution of W from its moment generating function
This procedure works well for sums, linear combinations, averages etc.
Let x1, x2, … denote a sequence of independent random variables
1 2 1 2
=n nS x x x x x xm t m t m t m t m t
SumsLet S = x1 + x2 + … + xn then
1 1 2 2 1 21 2=
n n nL a x a x a x x x x nm t m t m a t m a t m a t
Linear CombinationsLet L = a1x1 + a2x2 + … + anxn then
Arithmetic MeansLet x1, x2, … denote a sequence of independent random variables coming from a distribution with moment generating function m(t)
1 2
1 1 1
1 1 1
nx
x x xn n n
m t m t m t m t m tn n n
1 2Let , thennx x xx
n
nt
mn
The Transformation Method
Theorem
Let X denote a random variable with probability density function f(x) and U = h(X).
Assume that h(x) is either strictly increasing (or decreasing) then the probability density of U is:
1
1 ( )( )
dh u dxg u f h u f x
du du
The Transfomation Method(many variables)
Theorem
Let x1, x2,…, xn denote random variables with joint probability density function
f(x1, x2,…, xn )
Let u1 = h1(x1, x2,…, xn).u2 = h2(x1, x2,…, xn).
un = hn(x1, x2,…, xn).
define an invertible transformation from the x’s to the u’s
Then the joint probability density function of u1, u2,…, un is given by:
11 1
1
, ,, , , ,
, ,n
n nn
d x xg u u f x x
d u u
1, , nf x x J
where
1
1
, ,
, ,n
n
d x xJ
d u u
Jacobian of the transformation
1 1
1
1
detn
n n
n
dx dx
du du
dx dx
du du
Some important results
Distribution of functions of random variables
The method used to derive these results will be indicated by:
1. DF - Distribution Function Method.
2. MGF - Moment generating function method
3. TF - Transformation method
Student’s t distribution
Let Z and U be two independent random variables with:
1. Z having a Standard Normal distribution
and
2. U having a 2 distribution with degrees of freedom
then the distribution of:Z
tU
12 2
( ) 1t
g t K
12
where
2
K
is:
DF
The Chi-square distribution
Let Z1, Z2, … , Zv be v independent random variables having a Standard Normal distribution, then
has a 2 distribution with degrees of freedom.
2
1i
i
U Z
MGF
DF for = 1
for > 1
Distribution of the sample mean
Let x1, x2, …, xn denote a sample from the normal distribution with mean and variance 2.
and standard deviation x xn
then
has a Normal distribution with:
1
n
ii
xx
n
MGF
If x1, x2, …, xn is a sample from a distribution with mean , and standard deviations then if n is large the sample meanx
The Central Limit theorem
22x n
and variance
x has a normal distribution with mean
standard deviation xn
MGF
Distribution of sums of Gamma R. V.’s
Let X1, X2, … , Xn denote n independent random variables each having a gamma distribution with parameters
(,i), i = 1, 2, …, n.
Then W = X1 + X2 + … + Xn has a gamma distribution with
parameters (, 1 + 2 +… + n).
Distribution of a multiple of a Gamma R. V.
Suppose that X is a random variable having a gamma distribution with parameters (,).
Then W = aX has a gamma distribution with parameters (/a, ). MGF
MGF
Distribution of sums of Binomial R. V.’s
Let X1, X2, … , Xk denote k independent random variables each having a binomial distribution with parameters
(p,ni), i = 1, 2, …, k.
Then W = X1 + X2 + … + Xk has a binomial distribution with
parameters (p, n1 + n2 +… + nk).
Distribution of sums of Negative Binomial R. V.’s
Let X1, X2, … , Xn denote n independent random variables each having a negative binomial distribution with parameters
(p,ki), i = 1, 2, …, n.
Then W = X1 + X2 + … + Xn has a negative binomial distribution
with parameters (p, k1 + k2 +… + kn). MGF
MGF
Beyond Stats 241
Courses that can be taken
after Stats 241
Statistics
What is Statistics?
It is the major mathematical tool of scientific inference – methods for drawing conclusion from data.
Data that is to some extent corrupted by some component of random variation (random noise)
In both Statistics and Probability theory we are concerned with studying random phenomena
In probability theory
The model is known and we are interested in predicting the outcomes and observations of the phenomena.
modeloutcomes and observations
In statistics
The model is unknown
the outcomes and observations of the phenomena have been observed.
We are interested in determining the model from the observations
modeloutcomes and observations
Example - Probability
A coin is tossed n = 100 times
We are interested in the observation, X, the number of times the coin is a head.
Assuming the coin is balanced (i.e. p = the probability of a head = ½.)
100100 1 12 2
x x
p x P X xx
100100 1 for 0, 1, , 1002 xx
Example - Statistics
We are interested in the success rate, p, of a new surgical procedure.
The procedure is performed n = 100 times.
X, the number of successful times the procedure is performed is 82.
The success rate p is unknown.
If the success rate p was known.
Then
1001001
xxp x P X x p px
This equation allows us to predict the value of the observation, X.
In the case when the success rate p was unknown.
Then the following equation is still true the success rate
1001001
xxp x P X x p px
We will want to use the value of the observation, X = 82 to make a decision regarding the value of p.
Introductory Statistics Courses Non calculus Based
Stats 244.3 Stats 245.3
Calculus Based Stats 242.3
Stats 244.3 Statistical concepts and techniques including graphing of distributions, measures of location and variability, measures of association, regression, probability, confidence intervals, hypothesis testing. Students should consult with their department before enrolling in this course to determine the status of this course in their program. Prerequisite(s):A course in a social science or Mathematics A30.
Stats 245.3An introduction to basic statistical methods including frequency distributions, elementary probability, confidence intervals and tests of significance, analysis of variance, regression and correlation, contingency tables, goodness of fit. Prerequisite(s):
MATH 100, 101, 102, 110 or STAT 103.
Stats 242.3Sampling theory, estimation, confidence intervals, testing hypotheses, goodness of fit, analysis of variance, regression and correlation.
Prerequisite(s):MATH 110, 116 and STAT 241.
Stats 244 and 245
• do not require a calculus prerequisite
• are Recipe courses
Stats 242
• does require calculus and probability (Stats 241) as a prerequisite
• More theoretical class – You learn techniques for developing statistical procedures and thoroughly investigating the properties of these procedures
Statistics Courses beyond Stats 242.3
STAT 341.3
Probability and Stochastic Processes 1/2(3L-1P) Prerequisite(s): STAT 241.
Random variables and their distributions; independence; moments and moment generating functions; conditional probability; Markov chains; stationary time-series.
STAT 342.3 Mathematical Statistics 1(3L-1P) Prerequisite(s): MATH 225 or 276; STAT 241 and 242.
Probability spaces; conditional probability and independence; discrete and continuous random variables; standard probability models; expectations; moment generating functions; sums and functions of random variables; sampling distributions; asymptotic distributions. Deals with basic probability concepts at a moderately rigorous level.Note: Students with credit for STAT 340 may not take this course for credit.
STAT 344.3 Applied Regression Analysis 1/2(3L-1P) Prerequisite(s): STAT 242 or 245 or 246 or a comparable course in statistics.
Applied regression analysis involving the extensive use of computer software. Includes: linear regression; multiple regression; stepwise methods; residual analysis; robustness considerations; multicollinearity; biased procedures; non-linear regression.Note: Students with credit for ECON 404 may not take this course for credit. Students with credit for STAT 344 will receive only half credit for ECON 404.
STAT 345.3 Design and Analysis of Experiments 1/2(3L-1P) Prerequisite(s): STAT 242 or 245 or 246 or a comparable course in statistics.
An introduction to the principles of experimental design and analysis of variance. Includes: randomization, blocking, factorial experiments, confounding, random effects, analysis of covariance. Emphasis will be on fundamental principles and data analysis techniques rather than on mathematical theory.
STAT 346.3 Multivariate Analysis 1/2(3L-1P) Prerequisite(s): MATH 266, STAT 241, and 344 or 345.
The multivariate normal distribution, multivariate analysis of variance, discriminant analysis, classification procedures, multiple covariance analysis, factor analysis, computer applications.
STAT 347.3 Non Parametric Methods 1/2(3L-1P) Prerequisite(s): STAT 242 or 245 or 246 or a comparable course in statistics.
An introduction to the ideas and techniques of non-parametric analysis. Includes: one, two and K samples problems, goodness of fit tests, randomness tests, and correlation and regression.
STAT 348.3 Sampling Techniques 1/2(3L-1P) Prerequisite(s): STAT 242 or 245 or 246 or a comparable course in statistics.
Theory and applications of sampling from finite populations. Includes: simple random sampling, stratified random sampling, cluster sampling, systematic sampling, probability proportionate to size sampling, and the difference, ratio and regression methods of estimation.
STAT 349.3 Time Series Analysis 1/2(3L-1P) Prerequisite(s): STAT 241, and 344 or 345.
An introduction to statistical time series analysis. Includes: trend analysis, seasonal variation, stationary and non-stationary time series models, serial correlation, forecasting and regression analysis of time series data.
STAT 442.3 Statistical Inference 2(3L-1P) Prerequisite(s): STAT 342.
Parametric estimation, maximum likelihood estimators, unbiased estimators, UMVUE, confidence intervals and regions, tests of hypotheses, Neyman Pearson Lemma, generalized likelihood ratio tests, chi-square tests, Bayes estimators.
STAT 443.3 Linear Statistical Models 2(3L-1P) Prerequisite(s): MATH 266, STAT 342, and 344 or 345.
A rigorous examination of the general linear model using vector space theory. Includes: generalized inverses; orthogonal projections; quadratic forms; Gauss-Markov theorem and its generalizations; BLUE estimators; Non-full rank models; estimability considerations.