Transcript Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples
• A parameter is a number that describes the population. It is a fixed
number, but in practice we do not know its value.
• A statistic is a function of the sample data, i.e., it is a quantity
whose value can be calculated from the sample data. It is a random
variable with a distribution function. Statistics are used to make
inference about unknown population parameters.
• The random variables X1, X2,…, Xn are said to form a (simple)
random sample of size n if the Xi’s are independent random
variables and each Xi has the sample probability distribution. We say
that the Xi’s are iid.
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Example – Sample Mean and Variance
• Suppose X1, X2,…, Xn is a random sample of size n from a population
with mean μ and variance σ2.
•
The sample mean is defined as
1 n
X   Xi.
n i 1
• The sample variance is defined as
1 n
2


S 
X

X
.

i
n  1 i 1
2
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Goals of Statistics
• Estimate unknown parameters μ and σ2.
• Measure errors of these estimates.
• Test whether sample gives evidence that parameters are (or are
not) equal to a certain value.
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Sampling Distribution of a Statistic
• The sampling distribution of a statistic is the distribution of values
taken by the statistic in all possible samples of the same size from
the same population.
• The distribution function of a statistic is NOT the same as the
distribution of the original population that generated the original
sample.
• The form of the theoretical sampling distribution of a statistic will
depend upon the distribution of the observable random variables in
the sample.
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Sampling from Normal population
• Often we assume the random sample X1, X2,…Xn is from a normal
population with unknown mean μ and variance σ2.
• Suppose we are interested in estimating μ and testing whether it is
equal to a certain value. For this we need to know the probability
distribution of the estimator of μ.
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Claim
• Suppose X1, X2,…Xn are i.i.d normal random variables with
unknown mean μ and variance σ2 then
 2 

X ~ N   ,
n 

• Proof:
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Recall - The Chi Square distribution
• If Z ~ N(0,1) then, X = Z2 has a Chi-Square distribution with
parameter 1, i.e., X ~  21 .
• Can proof this using change of variable theorem for univariate
random variables.
• The moment generating function of X is
1/ 2
 1 
mX t   

1

2
t


• If X1 ~ 2v1  , X 2 ~ 2v2  , , X k ~ 2vk  , all independent then
k
T   X i ~  2k v
i 1
1 i
• Proof…
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Claim
• Suppose X1, X2,…Xn are i.i.d normal random variables with mean μ
and variance σ2. Then, Z i  X i   are independent standard normal

variables, where i = 1, 2, …, n and
 Xi   
2
Z

~





n 
i



i 1
i 1
n
2
n
• Proof: …
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t distribution
• Suppose Z ~ N(0,1) independent of X ~ χ2(n). Then, T 
Z
X /v
~ t v  .
• Proof:
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Claim
• Suppose X1, X2,…Xn are i.i.d normal random variables with mean μ
and variance σ2. Then,
X 
~ tn1
S/ n
• Proof:
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F distribution
• Suppose X ~ χ2(n) independent of Y ~ χ2(m). Then,
X /n
~ Fn ,m 
Y /m
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Properties of the F distribution
• The F-distribution is a right skewed distribution.
• Fm,n  
1
Fn,m 
i.e. PFn,m 
 1
1 
1


 a  P

 P Fm,n   
F

a

  n ,m  a 
• Can use Table 7 on page 796 to find percentile of the F- distribution.
• Example…
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The Central Limit Theorem
• Let X1, X2,…be a sequence of i.i.d
random variables with E(Xi) = μ < ∞
n
and Var(Xi) = σ2 < ∞. Let S n   X i
i 1
 S n  n

lim
P


z
  PZ  z   z  for - ∞ < x < ∞
Then, n 
  n

where Z is a standard normal random variable and Ф(z)is the cdf for the
standard normal distribution.
•
This is equivalent to saying that Z n 
Z ~ N(0,1).
•
S n  n
converges in distribution to
 n
 Xn  


  x 
lim
P

x
Also, n 

 n

i.e. Z n 
Xn  
converges in distribution to Z ~ N(0,1).
 n
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Example
• Suppose X1, X2,…are i.i.d random variables and each has the Poisson(3)
distribution. So E(Xi) = V(Xi) = 3.


• The CLT says that P X1   X n  3n  x 3n  x as n  ∞.
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Examples
• A very common application of the CLT is the Normal approximation to the
Binomial distribution.
• Suppose X1, X2,…are i.i.d random variables and each has the Bernoulli(p)
distribution. So E(Xi) = p and V(Xi) = p(1- p).


• The CLT says that P X 1    X n  np  x np1  p  x as n  ∞.
• Let Yn = X1 + … + Xn then Yn has a Binomial(n, p) distribution.

So for large n, PYn  y   P Yn  np 

 np1  p 
 y  np 
y  np 

 



np1  p  
 np1  p  
• Suppose we flip a biased coin 1000 times and the probability of heads on
any one toss is 0.6. Find the probability of getting at least 550 heads.
• Suppose we toss a coin 100 times and observed 60 heads. Is the coin fair?
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