Matakuliah
Tahun
Versi
: I0272 – Statistik Probabilitas
: 2005
: Revisi
Pertemuan 01
Pendahuluan
1
Learning Outcomes
Pada akhir pertemuan ini, diharapkan mahasiswa
akan mampu :
• Mahasiswa akan dapat menjelaskan cara
menentukan data pencilan dengan
diagram kotak-garis.
2
Outline Materi
• Peranan dan jangkauan statistika
• Sebaran frekuensi
• Diagram dahan dan daun
3
Data and Statistics
•
•
•
•
•
Applications in Business and Economics
Data
Data Sources
Descriptive Statistics
Statistical Inference
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Applications in
Business and Economics
• Accounting
Public accounting firms use statistical sampling
procedures when conducting audits for their clients.
• Finance
Financial analysts use a variety of statistical
information, including price-earnings ratios and
dividend yields, to guide their investment
recommendations.
• Marketing
Electronic point-of-sale scanners at retail checkout
counters are being used to collect data for a variety
of marketing research applications.
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Applications in
Business and Economics
• Production
A variety of statistical quality control charts
are used to monitor the output of a
production process.
• Economics
Economists use statistical information in
making forecasts about the future of the
economy or some aspect of it.
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Data
•
•
•
•
Elements, Variables, and Observations
Scales of Measurement
Qualitative and Quantitative Data
Cross-Sectional and Time Series Data
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Data and Data Sets
• Data are the facts and figures that are
collected, summarized, analyzed, and
interpreted.
• The data collected in a particular study
are referred to as the data set.
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Elements, Variables, and Observations
• The elements are the entities on which
data are collected.
• A variable is a characteristic of interest
for the elements.
• The set of measurements collected for a
particular element is called an
observation.
• The total number of data values in a
data set is the number of elements
multiplied by the number of variables.
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Data, Data Sets,
Elements, Variables, and Observations
Variables
Company
Dataram
EnergySouth
Keystone
LandCare
Psychemedics
Elements
Stock
Exchange
Annual Earn/
Sales($M) Sh.($)
AMEX
OTC
NYSE
NYSE
AMEX
Data Set
73.10
74.00
365.70
111.40
17.60
0.86
1.67
0.86
0.33
0.13
Datum
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Scales of Measurement
• Scales of measurement include:
– Nominal
– Ordinal
– Interval
– Ratio
• The scale determines the amount of
information contained in the data.
• The scale indicates the data
summarization and statistical analyses
that are most appropriate.
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Scales of Measurement
• Nominal
– Data are labels or names used to identify
an attribute of the element.
– A nonnumeric label or a numeric code may
be used.
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Scales of Measurement
• Nominal
– Example:
Students of a university are classified by the
school in which they are enrolled using a
nonnumeric label such as Business, Humanities,
Education, and so on.
Alternatively, a numeric code could be used for the
school variable (e.g. 1 denotes Business, 2
denotes Humanities, 3 denotes Education, and so
on).
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Scales of Measurement
• Ordinal
– The data have the properties of nominal
data and the order or rank of the data is
meaningful.
– A nonnumeric label or a numeric code may
be used.
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Scales of Measurement
• Ordinal
– Example:
Students of a university are classified by their
class standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used for the
class standing variable (e.g. 1 denotes Freshman,
2 denotes Sophomore, and so on).
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Scales of Measurement
• Interval
– The data have the properties of ordinal data
and the interval between observations is
expressed in terms of a fixed unit of measure.
– Interval data are always numeric.
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Scales of Measurement
• Interval
– Example:
Melissa has an SAT score of 1205, while Kevin
has an SAT score of 1090. Melissa scored 115
points more than Kevin.
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Scales of Measurement
• Ratio
– The data have all the properties of interval
data and the ratio of two values is
meaningful.
– Variables such as distance, height, weight,
and time use the ratio scale.
– This scale must contain a zero value that
indicates that nothing exists for the variable
at the zero point.
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Qualitative Data
• Qualitative data are labels or names used
to identify an attribute of each element.
• Qualitative data use either the nominal or
ordinal scale of measurement.
• Qualitative data can be either numeric or
nonnumeric.
• The statistical analysis for qualitative data
are rather limited.
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Quantitative Data
• Quantitative data indicate either how
many or how much.
– Quantitative data that measure how many
are discrete.
– Quantitative data that measure how much
are continuous because there is no
separation between the possible values for
the data..
• Quantitative data are always numeric.
• Ordinary arithmetic operations are
meaningful only with quantitative data.
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Cross-Sectional and Time
Series Data
• Cross-sectional data are collected at the
same or approximately the same point in
time.
– Example: data detailing the number of
building permits issued in June 2000 in each
of the counties of Texas
• Time series data are collected over
several time periods.
– Example: data detailing the number of
building permits issued in Travis County,
Texas in each of the last 36 months
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Data Sources
• Internet
– The Internet has become an important source
of data.
– Most government agencies, like the Bureau of
the Census (www.census.gov), make their
data available through a web site.
– More and more companies are creating web
sites and providing public access to them.
– A number of companies now specialize in
making information available over the
Internet.
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• Selamat Belajar Semoga Sukses.
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