1 Pertemuan 01 PENDAHULUAN: Data dan Statistika Matakuliah: I0262-Statiatik Probabilitas Tahun:...
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Transcript of 1 Pertemuan 01 PENDAHULUAN: Data dan Statistika Matakuliah: I0262-Statiatik Probabilitas Tahun:...
1
Pertemuan 01
PENDAHULUAN: Data dan Statistika
Matakuliah : I0262-Statiatik Probabilitas
Tahun : 2007
2
Outline Materi:
• Peranan dan Jangkauan Statistika
• Diagram Dahan dan Daun
• Sebaran Frekuensi
3
Business Basic Statistics
Introduction and Data Collection
4
PERANAN DAN Jangkauan Statistika
• Why a Manager Needs to Know About Statistics
• The Growth and Development of Modern Statistics
• Some Important Definitions
• Descriptive Versus Inferential Statistics
5
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
(continued)
6
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
7
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
8
Some Important Definitions
• A Population (Universe) is the Whole Collection of Things 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
9
Population and Sample
Population Sample
Use parameters to summarize features
Use statistics to summarize features
Inference on the population from the sample
10
Statistical Methods
• Descriptive Statistics– Collecting and describing data
• Inferential Statistics– Drawing conclusions and/or making decisions
concerning a population based only on sample data
11
Descriptive Statistics
• Collect Data– E.g., Survey
• Present Data– E.g., Tables and graphs
• Characterize Data– E.g., Sample Mean = iX
n
12
Inferential Statistics
• Estimation– E.g., Estimate the
population mean weight using the sample mean weight
• Hypothesis Testing– E.g., Test the claim that
the population mean weight is 120 pounds
Drawing conclusions and/or making decisions concerning a population based on sample results.
13
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 Alternative Courses of Action
• To Satisfy Curiosity
14
Data Sources
Observation
Experimentation
Survey
Print or Electronic
Data Sources
15
Types of Data
Categorical(Q ualitative)
Discrete Continuous
Num erical(Q uantitative)
D ata
16
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
17
Reasons for Drawing a Sample
• 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
18
Types of Sampling Methods
Quota
Samples
Non-Probability Samples
(Convenience)
Judgement Chunk
Probability Samples
Simple Random
Systematic
Stratified
Cluster
19
Probability Sampling
• Subjects of the Sample are Chosen Based on Known Probabilities
Probability Samples
Simple Random
Systematic Stratified Cluster
20
Organizing Numerical Data
2 144677
3 028
4 1
Numerical Data
Ordered Array
Stem and LeafDisplay
Frequency DistributionsCumulative 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
21
• Data in RawRaw Form (as Collected): 24, 26, 24, 21, 27, 27, 30, 41, 32, 38
• Data in Ordered ArrayOrdered Array from Smallest to Smallest 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
22
Tabulating and Graphing Numerical Data
O g ive
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
Stem and LeafDisplay
Histograms Ogive
Tables
41, 24, 32, 26, 27, 27, 30, 24, 38, 21
21, 24, 24, 26, 27, 27, 30, 32, 38, 41
Frequency DistributionsCumulative Distributions
Polygons
23
Tabulating Numerical Data: Frequency Distributions
• 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
24
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
25
Graphing Numerical Data: The Histogram
Histogram
0
3
65
4
2
001234567
5 15 25 35 45 55 More
Fre
qu
en
cy
Data in Ordered Array: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 MidpointsClass Boundaries
26
Graphing Numerical Data: The Frequency Polygon
Frequency
0
1
2
3
4
5
6
7
5 15 25 35 45 55 More
Class 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
27
Tabulating Numerical Data: 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
Data in Ordered Array:12, 13, 17, 21, 24, 24, 26, 27, 27, 30, 32, 35, 37, 38, 41, 43, 44, 46, 53, 58
28
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
29
Graphing Bivariate Numerical Data (Scatter Plot)
Mutual Funds Scatter Plot
0
10
20
30
40
0 10 20 30 40
Net Asset Values
Tota
l Yea
r to
Dat
e R
etur
n (%
)
30
Tabulating and Graphing Univariate Categorical Data
Categorical Data
Tabulating Data
The Summary Table
Graphing Data
Pie Charts
Pareto DiagramBar Charts
31
Graphing Univariate Categorical Data
0 1 0 2 0 3 0 4 0 5 0
S to c k s
B o n d s
S a vin g s
C D
Categorical Data
Tabulating Data
The Summary Table
Graphing Data
Pie Charts
Pareto DiagramBar Charts
0
5
1 0
1 5
2 0
2 5
3 0
3 5
4 0
4 5
S to c k s B o n d s S a vin g s C D
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
32
Bar Chart(for an Investor’s Portfolio)
Investor's Portfolio
0 10 20 30 40 50
Stocks
Bonds
CD
Savings
Amount in K$
33
Pie Chart (for an Investor’s Portfolio)
Percentages are rounded to the nearest percent
Amount Invested in K$
Savings
15%
CD 14%
Bonds
29%
Stocks
42%
34
Pareto Diagram
Axis for line graph shows
cumulative % invested
Axis for bar
chart shows
% 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%