Chapter 7

Sampling and Sampling Distributions

Statistika

Descriptive statistics Collecting, presenting, and describing

data Inferential statistics

Drawing conclusions and/or making decisions concerning a population based only on sample data

Tools of Business Statistics

Population vs. Sample

a b c d

ef gh i jk l m n

o p q rs t u v w

x y z

Population Sample

b c

g i n

o r u

y

Less time consuming than a census Less costly to administer than a census It is possible to obtain statistical results of a

sufficiently high precision based on samples.

Why Sample?

Every object in the population has an equal chance of being selected

Objects are selected independently Samples can be obtained from a table of random

numbers or computer random number generators

A simple random sample is the ideal against which other sample methods are compared

Simple Random Samples

Making statements about a population by examining sample resultsSample statistics Population parameters (known) Inference (unknown, but can

be estimated from

sample evidence)

Inferential Statistics

Sample Population

Estimation e.g., Estimate the population mean

weight using the sample mean weight

Hypothesis Testing e.g., Use sample evidence to test

the claim that the population mean weight is 120 pounds

Inferential StatisticsDrawing conclusions and/or making decisions

concerning a population based on sample results.

A sampling distribution is a distribution of all of the possible values of a statistic for a given size sample selected from a population

Seperti distribusi sampling rata-rata

Sampling Distributions

Sampling Distributions ofSample Means

Sampling Distributions

Sampling Distribution of

Sample Mean

Sampling Distribution of

Sample Proportion

Sampling Distribution of

Sample Variance

Assume there is a population … Population size N=4 Random variable, X,

is age of individuals Values of X:

18, 20, 22, 24 (years)

Developing a Sampling Distribution

A B C D

Developing a Sampling Distribution

.25

0 18 20 22 24 A B C D

Uniform Distribution

P(x)

x

Summary Measures for the Population Distribution:

214

24222018

NX

μ i

2.236N

μ)(Xσ

2i

Now consider all possible samples of size n = 2

1st 2nd Observation Obs 18 20 22 24 18 18,18 18,20 18,22 18,24

20 20,18 20,20 20,22 20,24

22 22,18 22,20 22,22 22,24

24 24,18 24,20 24,22 24,24

16 possible samples (sampling with replacement)

1st 2nd Observation Obs 18 20 22 24 18 18 19 20 21

20 19 20 21 22

22 20 21 22 23

24 21 22 23 24

Developing a Sampling Distribution

16 Sample Means

Sampling Distribution of All Sample Means

1st 2nd Observation Obs 18 20 22 24 18 18 19 20 21

20 19 20 21 22

22 20 21 22 23

24 21 22 23 24

18 19 20 21 22 23 240

.1

.2

.3 P(X)

X

Sample Means Distribution

16 Sample Means

_

Developing a Sampling Distribution

(no longer uniform)

_

Summary Measures of this Sampling Distribution:

Developing aSampling Distribution

iX 18 19+19 20 24E(X) 21 μ

N 16

2i

X

2 2 2

(X μ)σ

N

(18-21) (19-21) (24-21) 1.5816

Comparing the Population with its Sampling Distribution

18 19 20 21 22 23 240

.1

.2

.3 P(X)

X 18 20 22 24 A B C D

0

.1

.2

.3

PopulationN = 4

P(X)

X _

1.58σ 21μ XX 2.236σ 21μ

Sample Means Distributionn = 2

_

Let X1, X2, . . . Xn represent a random sample from a population

The sample mean value of these observations is defined as

Expected Value of Sample Mean

n

1iiX

n1X

Different samples of the same size from the same population will yield different sample means

A measure of the variability in the mean from sample to sample is given by the Standard Error of the Mean:

Note that the standard error of the mean decreases as the sample size increases

Standard Error of the Mean

nσσX

If a population is normal with mean μ and standard deviation σ, the sampling distribution of mean sampel (x-bar) is also normally distributed with

If the Population is Normal

μμX n

σσX

σx (μ, )n

Z-value for the sampling distribution of :

Z-value for Sampling Distributionof the Mean

where: = sample mean= population mean= population standard deviation

n = sample size

Xμσ

nσ

μ)X(σ

μ)X(ZX

We can apply the Central Limit Theorem: Even if the population is not normal, …sample means from the population will be

approximately normal as long as the sample size is large enough.

Properties of the sampling distribution:

and

If the Population is not Normal

μμx n

σσx

Central Limit Theorem

n↑As the sample size gets large enough…

the sampling distribution becomes almost normal regardless of shape of population

x

If the Population is not Normal

Population Distribution

Sampling Distribution (becomes normal as n increases)

Central Tendency

Variation

x

x

Larger sample size

Smaller sample size

Sampling distribution properties:

μμx

nσσx

xμ

μ

For most distributions, n > 25 will give a sampling distribution that is nearly normal

For normal population distributions, the sampling distribution of the mean is always normally distributed

How Large is Large Enough?

Dipunyai rata-rata nilai stat μ = 8 dan dev standard σ = 3. Diambil sampel random berukuran n = 36.

Berapakah probabilitas rata-rata sampel terletak antara 7.8 dan 8.2?

Sampel mean ~N(8, σ = 0.5)

Example

0.38300.5)ZP(-0.5

363

8-8.2

nσ

μ- μ

363

8-7.8P 8.2) μ P(7.8 XX

Dipunyai rata-rata berat timbang susu merk XYZ μ = 500,1 gr dan dev standard σ =1.3. Diambil sampel random berukuran n = 36.

Berapakah probabilitas rata-rata sampel kurang dari 500 gram ?

Sampel mean ~N(500,1, σ = 0.217)

Example

x -μ 500-500,1P(x 500) P σ 1,3n 36

P(Z 0.4615) 0.32