# CS代考程序代写 database University of California, Los Angeles Department of Statistics

University of California, Los Angeles Department of Statistics

Statistics 100B Instructor: Nicolas Christou

The Central Limit Theorem

Suppose that a sample of size n is selected from a population that has mean μ and standard deviation σ. Let X1, X2, · · · , Xn be the n observations that are independent and identically distributed (i.i.d.). Define now the sample mean and the total of these n observations as follows:

̄ ni=1Xi X=n

n

T = Xi

i=1

The central limit theorem states that the sample mean X ̄ follows approximately the normal

distribution with mean μ and standard deviation σ , where μ and σ are the mean and stan- n

dard deviation of the population from where the sample was selected. The sample size n has to be large (usually n ≥ 30) if the population from where the sample is taken is nonnormal. If the population follows the normal distribution then the sample size n can be either small or large.

To summarize: X ̄ ∼ N(μ, σ ). n

̄

Example: Let X be a random variable with μ = 10 and σ = 4. A sample of size 100 is taken

from this population. Find the probability that the sample mean of these 100 observations is

less than 9. We write P(X ̄ < 9) = P(z < 9−10) = P(z < −2.5) = 0.0062 (from the standard √4
100
normal probabilities table).
Similarly the central limit theorem states that sum T follows approximately the normal distribution, T ∼ N(nμ,√nσ), where μ and σ are the mean and standard deviation of the population from where the sample was selected.
√ To transform T into z we use: z = T−nμ
nσ
Example: Let X be a random variable with μ = 10 and σ = 4. A sample of size 100 is taken from this population. Find the probability that the sum of these 100 observations is
√
less than 900. We write P(T < 900) = P(z < 900−100(10)) = P(z < −2.5) = 0.0062 (from
100(4)
the standard normal probabilities table).
Below you can find some applications of the central limit theorem.
1
√
√
x ̄−μ TotransformXintozweuse:z= σ √
n
EXAMPLE 1
A large freight elevator can transport a maximum of 9800 pounds. Suppose a load of cargo containing 49 boxes must be transported via the elevator. Experience has shown that the weight of boxes of this type of cargo follows a distribution with mean μ = 205 pounds and standard deviation σ = 15 pounds. Based on this information, what is the probability that all 49 boxes can be safely loaded onto the freight elevator and transported?
EXAMPLE 2
From past experience, it is known that the number of tickets purchased by a student standing in line at the ticket window for the football match of UCLA against USC follows a distribution that has mean μ = 2.4 and standard deviation σ = 2.0. Suppose that few hours before the start of one of these matches there are 100 eager students standing in line to purchase tickets. If only 250 tickets remain, what is the probability that all 100 students will be able to purchase the tickets they desire?
EXAMPLE 3
Suppose that you have a sample of 100 values from a population with mean μ = 500 and with standard deviation σ = 80. a. What is the probability that the sample mean will be in the interval (490, 510)?
b. Give an interval that covers the middle 95% of the distribution of the sample mean.
EXAMPLE 4
The amount of regular unleaded gasoline purchased every week at a gas station near UCLA follows the normal distribution with mean 50000 gallons and standard deviation 10000 gallons. The starting supply of gasoline is 74000 gallons, and there is a scheduled weekly delivery of 47000 gallons.
a. Find the probability that, after 11 weeks, the supply of gasoline will be below 20000 gallons.
b. How much should the weekly delivery be so that after 11 weeks the probability that the supply is below 20000 gallons
is only 0.5%?
Solutions:
EXAMPLE 1
We are given n = 49, μ = 205, σ = 15. The elevator can transport up to 9800 pounds. Therefore these 49 boxes will be safely transported if they weigh in total less than 9800 pounds. The probability that the total weight of these 49 boxes is less than
) = P(z < −2.33) = 1−0.9901 = 0.0099.
We are given that μ = 2.4, σ = 2, n = 100. There are 250 tickets available, so the 100 students will be able to purchase the
) =
9800 pounds is P(T < 9800) = P(z < √
9800−49(205)
4915
tickets they want if all together ask for less than 250 tickets. The probability for that is P (T < 250) = P (z < √
P (z < 0.5) = 0.6915. EXAMPLE 3
We are given μ = 500, σ = 80, n = 100.
a. P(490

1110000

0.2643.

b. Let A be the unknown schedule delivery. Now the total gasoline purchased must be more than 74000 + 11 × A − 20000. We want this with probability 0.5%, or P (T > 74000 + 11A − 20000) = 0.005. The z value that corresponds to this

⇒ A = 52854.8. The weekly delivery must be 52854.8

probability is 2.575. So, 2.575 = √ gallons.

1110000

74000+11A−20000−11(50000)

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Central limit theorem – proof

For the proof below we will use the following theorem.

Theorem:

Let Xn be a random variable with moment generating function MXn (t) and X be a random variable with moment generating function MX(t). If

lim MXn(t)=MX(t) n→∞

then the distribution function (cdf) of Xn converges to the distribution function of X as n → ∞.

Central limit theorem:

If X1, X2, · · · , Xn are i.i.d. (independent and identically distributed) random variables having the

same distribution with mean μ, variance σ2, and moment generating function MX (t), then if n → ∞

the limiting distribution of the random variable Z = T−nμ (where T = X +X +···+X ) is the σ√n 12n

standard normal distribution N(0,1). Proof:

T−nμ nμ √t−√tt

MZ(t)=MT−nμ(t)=Eeσ n =e σ n MT( √ ) σ√n σn

But T = X1 + X2 + ··· + Xn. From earlier discussion the mgf of the sum is equal to the product of the individual mgf. Here each Xi has mgf MX (t). Therefore,

t t n MT(σ√n)= MX(σ√n)

and so MZ(t) is equal to nμ

−√t tn MZ(t) = e σ n MX(σ√n)

One way to find the limit of MZ (t) as n → ∞ is to consider the logarithm of MZ (t):

√

lnMZ(t)=− σ t+nlnMX(σ√n)

nμt

Expanding M ( t ), using the following (also see handout on mgf)

X σ√n

MX(t)=P(x)+ t xP(x)+t2 x2P(x)+t3 x3P(x)+···

x 1! x 2! x 3! x

√ t (t)2 (t)3

we get

lnMZ(t)=− nμt+nln1+σ√nEX+ σ√n EX2+ σ√n EX3+···

σ 1! 2! 3!

3

Nowusingtheseriesexpansionofln(1+y)=y−y2 +y3 −y4 +···where

tt2t3 y=σ√nEX+(σ√n) EX2+(σ√n) EX3+···weget:

234

1! 2! 3!

√ t (t)2 (t)3

lnMZ(t)= − nμt+n σ√nEX+ σ√n EX2+ σ√n EX3+··· σ 1! 2! 3!

t ( t )2 ( t )3 2 − 1 σ√nEX+ σ√n EX2+ σ√n EX3+···

21! 2! 3! t ( t )2 ( t )3 3

+ 1 σ√nEX+ σ√n EX2+ σ√n EX3+··· −··· 31! 2! 3!

EX2 (EX)2 t+ 2σ2 − 2σ2 t2

Factoring out the powers of t we obtain:

√n μ √n EX

lnMZ(t) = − σ + σ

EX3 EX EX2

(EX)3

+ 3σ3√n t3 +···

+ 6σ3√n − 2σ3√n

Because EX = μ and EX2 − (EX)2 = σ2 the last expression becomes

1 EX3 EX EX2 (EX)3 t3

ln MZ(t) = 2t2 + 6 − 2 + 3 σ3√n +···

We observe that as n → ∞ the limit of the previous expression is lim ln MZ(t) = 1t2

n→∞ 2 and therefore

lim M (t)=e1t2. n→∞Z 2

√ But this is the mgf of the standard normal distribution. Therefore the limiting distribution of T −nμ

is the standard normal distribution N(0,1).

♣♥♠♦ ♣♥♠♦ ♣♥♠♦ ♣♥♠♦ ♣♥♠♦

4

σn

Central limit theorem – Examples

Example 1

A large freight elevator can transport a maximum of 9800 pounds. Suppose a load of cargo con- taining 49 boxes must be transported via the elevator. Experience has shown that the weight of boxes of this type of cargo follows a distribution with mean μ = 205 pounds and standard deviation σ = 15 pounds. Based on this information, what is the probability that all 49 boxes can be safely loaded onto the freight elevator and transported?

Example 2

From past experience, it is known that the number of tickets purchased by a student standing in line at the ticket window for the football match of UCLA against USC follows a distribution that has mean μ = 2.4 and standard deviation σ = 2.0. Suppose that few hours before the start of one of these matches there are 100 eager students standing in line to purchase tickets. If only 250 tickets remain, what is the probability that all 100 students will be able to purchase the tickets they desire?

Example 3

Suppose that you have a sample of 100 values from a population with mean μ = 500 and with standard deviation σ = 80.

a. What is the probability that the sample mean will be in the interval (490, 510)?

b. Give an interval that covers the middle 95% of the distribution of the sample mean.

Example 4

The amount of mineral water consumed by a person per day on the job is normally distributed with mean 19 ounces and standard deviation 5 ounces. A company supplies its employees with 2000 ounces of mineral water daily. The company has 100 employees.

a. Find the probability that the mineral water supplied by the company will not satisfy the water demanded by its employees.

b. Find the probability that in the next 4 days the company will not satisfy the water demanded by its employees on at least 1 of these 4 days. Assume that the amount of mineral water consumed by the employees of the company is independent from day to day.

c. Find the probability that during the next year (365 days) the company will not satisfy the water demanded by its employees on more than 15 days.

Example 5

Supply responses true or false with an explanation to each of the following:

a. The probability that the average of 20 values will be within 0.4 standard deviations of the population mean exceeds the probability that the average of 40 values will be within 0.4 standard deviations of the population mean.

b. P(X ̄>4)islargerthanP(X>4)ifX∼N(8,σ).

c. If X ̄ is the average of n values sampled from a normal distribution with mean μ and if c is

any positive number, then P (μ − c ≤ X ̄ ≤ μ + c) decreases as n gets large. 5

Example 6

An insurance company wants to audit health insurance claims in its very large database of trans- actions. In a quick attempt to assess the level of overstatement of this database, the insurance company selects at random 400 items from the database (each item represents a dollar amount). Suppose that the population mean overstatement of the entire database is $8, with population standard deviation $20.

a. Find the probability that the sample mean of the 400 would be less than $6.50.

b. The population from where the sample of 400 was selected does not follow the normal dis-

tribution. Why?

c. Why can we use the normal distribution in obtaining an answer to part (a)?

d. ForwhatvalueofωcanwesaythatP(μ−ω

Example 7

A telephone company has determined that during nonholidays the number of phone calls that pass through the main branch office each hour follows the normal distribution with mean μ = 80000 and standard deviation σ = 35000. Suppose that a random sample of 60 nonholiday hours is selected and the sample mean x ̄ of the incoming phone calls is computed.

a. Describe the distribution of x ̄.

b. Find the probability that the sample mean x ̄ of the incoming phone calls for these 60 hours

is larger than 91970.

c. Is it more likely that the sample average x ̄ will be greater than 75000 hours, or that one hour’s incoming calls will be?

Example 8

Assume that the daily S&P return follows the normal distribution with mean μ = 0.00032 and standard deviation σ = 0.00859.

a. Find the 75th percentile of this distribution.

b. What is the probability that in 2 of the following 5 days, the daily S&P return will be larger

than 0.01?

c. Consider the sample average S&P of a random sample of 20 days.

i. What is the distribution of the sample mean?

ii. What is the probability that the sample mean will be larger than 0.005?

iii. Is it more likely that the sample average S&P will be greater than 0.007, or that one day’s S&P return will be?

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The central limit theorem

The distribution of the sample mean The distribution of the sum Summary

Suppose a population has mean μ and standard deviation σ. Let X1, X2, · · · , Xn be an i.i.d. (independent and identically distributed) sample from this population. This means that E(Xi) = μ, and V ar(Xi) = σ2. Define the following random variables:

̄ X1 + X2 + · · · + Xn ni=1 Xi X=n=n.

and

T = X1 + X2 + · · · + Xn = Xi.

n i=1

Then, for large n (usually n ≥ 30) the following statements are approximately true regardless of the shape of the population:

̄σ X ∼ N(μ, √n)

therefore

Z = X ̄ − μ

√

n

σ

and

T∼N(nμ,σ n)

√

therefore,

Z= σ√n

Note: If the population from where the sample is selected follows the normal distribution, the above statements are exactly true regardless of the sample size. In this case n can be small or large (see next page).

T − nμ

7

Distribution of the sample mean – Sampling from normal distribution

If we sample from normal distribution N(μ,σ) then X ̄ follows exactly the normal distribution with

mean μ and standard deviation σ regardless of the sample size n. In the next figure we see the n

effect of the sample size on the shape of the distribution of X ̄. The first figure is the N(5,2) distribution. The second figure represents the distribution of X ̄ when n = 4. The second figure represents the distribution of X ̄ when n = 16.

N(5, 2)

−3 −1 1 3 5 7 9 11 13

x

2

4

123456789

x

2

16

34567

x

8

√

N(5,

)

N(5,

)

f(x) f(x) f(x)

0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.4 0.6 0.8

Central limit theorem – More problems

Problem 1

Part A:

It is claimed that the histogram below shows the distribution of the sample mean x ̄, when repeated samples of size n = 36 are selected with replacement from the population (2.6,2.8,3.0,3.2,3.4). Clearly explain if there is anything wrong with this histogram.

Part B:

a. What distribution does the sum (total) of 36 observations selected from the same population as above follow?

b. Sketch the histogram (roughly) of the total of repeated samples (with repcalcement) of size 36 selected from the above population. Make sure that you mark off some important values on the horizontal axis.

c. Find the 5th percentile of the distribution of T.

.2

.15

.1

.05

0

2.6 2.8 3 xbar

3.2 3.4

9

Problem 2

A random sample of size n = 100 is selected form a distribution with mean μ = 16 and standard deviation σ = 4. Which one of the graphs below represents the distribution of the sample mean. Please explain your answer.

(a)

13 14 15 16 17 18 19

15.7

15.8

x

(b)

15.9 16.0 16.1

x

(c)

16.0

x

16.2

16.3

15.0

15.5

16.5

17.0

10

f(x) f(x) f(x)

0.0 0.4 0.8 0 1 2 3 4 0.0 0.1 0.2 0.3 0.4

Problem 3

Below you see the probability density function of an exponential distribution with parameter λ = 1 . 2

It is claimed that the second graph is the distribution of the total T when a sample of size n = 100 is selected. Is there anything wrong with this graph?

X ~ exp(λ = 12)

0 2 4 6 8 10

x

Distribution of T

170 175 180 185 190 195 200 205 210 215 220 225 230

T

11

f(T) f(x)

0.00 0.01 0.02 0.03 0.04 0.0 0.1 0.2

0.3 0.4 0.5

The distribution of the sample mean and the central limit theorem An empirical investigation

The central limit theorem states that if a large sample of size n is selected from a population that hasm mean μ and standard deviation σ then the sample mean X ̄ follows approximately:

̄σ X ∼ N(μ, √n)

In this experiment we will roll a die n = 80 times. Using the 80 outcomes we will compute the sample mean. We will repeat until we obtain 500 values of x ̄. At the end we will construct the histogram using the values of x ̄. Here is the population:

X P(X)

1

6 1

6 1

6 1

6 1

6 1

6

This population has μ = 3.5 and σ = 1.71. Why? The summary statistics of the 500 sample means:

. summarize xbar

1 2 3 4 5 6

Variable | Obs Mean Std. Dev.

————-+—————————————————–

Min Max

xbar | 500 3.499725 .1822218 3.025 4

And below you can see the histogram of these 500 sample means:

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