1 Data Dan Statitiska

8/14/2019 1 Data Dan Statitiska

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Pertemuan 01

PENDAHULUAN:Data dan Statistika

Matakuliah : I0262-Statiatik Probabilitas

Tahun : 2007


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Outline Materi: Peranan dan Jangkauan Statistika

Diagram Dahan dan Daun

Sebaran Frekuensi


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Business Basic Statistics

Introduction and DataCollection


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PERANAN DAN Jangkauan

Statistika

Why a Manager Needs to Know AboutStatistics

The Growth and Development of Modern

Statistics

Some Important Definitions

Descriptive Versus Inferential Statistics


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Peranan dan Jangkauan Statistika

Why Data are Needed Types of Data and Their Sources

Design of Survey Research

Types of Sampling Methods

Types of Survey Errors

(continue

d)


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Why a Manager Needs to Know

About Statistics

To Know How to Properly Present

Information

To Know How to Draw Conclusions about

Populations Based on Sample Information

To Know How to Improve Processes To Know How to Obtain Reliable Forecasts


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The Growth and Development of

Modern Statistics

Needs of government to

collect data on its citizenry

The development of the

mathematics of probability

theory

The advent of the computer


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Some Important Definitions

A Population (Universe) is the Whole Collection ofThings Under Consideration

A Sample is a Portion of the Population Selected

for Analysis

A Parameter is a Summary Measure Computed to

Describe a Characteristic of the Population

A Statistic is a Summary Measure Computed to

Describe a Characteristic of the Sample


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Population and Sample

Population Sample

Use parameters tosummarizefeatures

Use statistics tosummarizefeatures

Inference on the population from the sample


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Statistical Methods

Descriptive Statistics Collecting and describing data

Inferential Statistics

Drawing conclusions and/or making decisions

concerning a population based only on

sample data


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Descriptive Statistics

Collect Data E.g., Survey

Present Data

E.g., Tables and graphs

Characterize Data

E.g., Sample Mean = iX

n


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Inferential Statistics

Estimation

E.g., Estimate the

population mean weightusing the sample mean

weight

Hypothesis Testing

E.g., Test the claim that

the population mean

weight is 120 poundsDrawing conclusions and/or making decisions

concerning a population based on sample results.


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Why We Need Data

To Provide Input to Survey To Provide Input to Study

To Measure Performance of Ongoing

Service or Production Process

To Evaluate Conformance to Standards

To Assist in Formulating AlternativeCourses of Action

To Satisfy Curiosity


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Data Sources

Observation

Experimentation

Survey

Print or Electronic

Data Sources


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Types of Data

C a t e g o

( Q u a l i t

D i s c r C o n t i n

N u m e

( Q u a n t

D a t a


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Design of Survey Research

Choose an Appropriate Mode of Response Reliable primary modes

Personal interview

Telephone interview

Mail survey Less reliable self-selection modes (not appropriate

for making inferences about the population) Television survey

Internet survey

Printed survey in newspapers and magazines

Product or service questionnaires

easons or raw ng a


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easons or raw ng aSample

Less Time Consuming Than a Census

Less Costly to Administer Than a Census Less Cumbersome and More Practical to

Administer Than a Census of the

Targeted Population


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Types of Sampling Methods

Quota

Samples

Non-ProbabilitySamples

(Convenience)

Judgement Chunk

Probability Samples

Simple

Random

Systematic

Stratified

Cluster


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Probability Sampling

Subjects of the Sample are Chosen Basedon Known Probabilities

Probability Samples

Simple

RandomSystematic Stratified Cluster


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Organizing Numerical Data

2 144677

3 028

4 1

Numerical Data

Ordered Array

Stemand Leaf

Display

Frequency Distributions

Cumulative Distributions

Histograms

Polygons

Ogive

Tables

41, 24, 32, 26, 27, 27, 30, 24, 38, 21

21, 24, 24, 26, 27, 27, 30, 32, 38, 41


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Data in RawRawForm (as Collected):24, 26, 24, 21, 27, 27, 30, 41, 32, 38

Data inOrdered ArrayOrdered Array fromSmallest toSmallest to

LargestLargest:21, 24, 24, 26, 27, 27, 30, 32, 38, 41

Stem-and-Leaf Display:

Stem and Leaf Display

(continued)

2 1 4 4 6 7 7

3 0 2 8

4 1

a u a ng an rap ng


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a u a ng an rap ng

Numerical Data

O g i ve

0

20

40

60

80

100

120

10 20 30 40 50 60

0

1

2

3

4

5

6

7

10 20 30 40 50 60

2 144677

3 028

4 1

Numerical Data

Ordered Array

StemandLeaf

Display

Histograms Ogive

Tables

41, 24, 32, 26, 27, 27, 30, 24, 38, 21

21, 24, 24, 26, 27, 27, 30, 32, 38, 41


Cumulative Distributions

Polygons

a u a ng umer ca a a:


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Sort Raw Data in Ascending Order12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

Find Range: 58 - 12 = 46

Select Number of Classes: 5(usually between 5 and 15)

Compute Class Interval (Width): 10 (46/5 then round up)

Determine Class Boundaries (Limits):10, 20, 30, 40, 50, 60

Compute Class Midpoints: 15, 25, 35, 45, 55

Count Observations & Assign to Classes

Frequency Distributions Relative Frequency


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Frequency Distributions, Relative Frequency

Distributions and Percentage Distributions

Class Frequency

10 but under 20 3 .15 15

20 but under 30 6 .30 30

30 but under 40 5 .25 25

40 but under 50 4 .20 20

50 but under 60 2 .10 10

Total 20 1 100

RelativeFrequency

Percentage

Data in Ordered Array:

12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

rap ng umer ca a a:


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rap ng umer ca a a:

The Histogram

Histogram

0

3

6

5

4

2

001

2

3

4

5

6

7

5 15 25 35 45 55 More

Frequenc


12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

No Gaps

Between

Bars

Class MidpointsClassBoundaries

rap ng umer ca a a:


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rap ng umer ca a a:

The Frequency Polygon

Frequency

0

1

2

3

4

5

6

7

5 15 25 35 45 55 More

Class Midpoints


12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58



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a u a ng umer ca a a:Cumulative Frequency

Lower Cumulative CumulativeLimit Frequency % Frequency

10 0 0

20 3 15

30 9 45

40 14 70

50 18 90

60 20 100


12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

Graphing Numerical Data:


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Graphing Numerical Data:The Ogive (Cumulative % Polygon)

Ogive

0

20

40

60

80

100

10 20 30 40 50 60

Class Boundaries (Not Midpoints)

Data in Ordered Array :

12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58

rap ng var a e umer ca


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rap ng var a e umer caData (Scatter Plot)

M u tu a l F u n d s S c a tte

0

1 0

2 0

3 0

4 0

0 1 0 2 0 3 0 4 0

N et Asset Va l

TotalYearto

Date

Return(%)

a u a ng an rap ng


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a u a ng an rap ngUnivariate Categorical Data

Categorical Data

Tabulating Data

The Summary Table

Graphing Data

Pie Charts

Pareto DiagramBar Charts

rap ng n var a e


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rap ng n var a eCategorical Data

0 10 20 3 0 4 0 5 0

S t o c k s

B o n d s

Savings

CD

Categorical Data

Tabulating Data

The Summary Table

Graphing Data

Pie Charts

Pareto DiagramBar Charts

0

5

10

1520

25

30

35

40

45

0

20

40

60

80

100

120

Bar Chart


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Bar Chart(for an Investors Portfolio)

Investor's Portfolio

0 10 20 30 40 50

Stocks

Bonds

CD

Savings

Amount in K$

Pie Chart


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Pie Chart(for an Investors Portfolio)

Percentages arerounded to the

nearest percent

Amount Invested in K$

Savings

15%

CD

14%

Bonds

29%

Stocks

42%

P t Di


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Pareto Diagram

Axis for linegraph

shows

cumulative

% invested

Axis for

bar

chartshows

%

invested

in each

category

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Stocks Bonds Savings CD

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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1 Data Dan Statitiska