ECON 3790
Statistics for Business and Economics
Instructor: Ou Hu, Ph.D., CFA
Youngstown State University
Summer 2014
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Chapter 1
Data and Statistics
Outline –
 Statistics in the world of business - examples
 What does business statistics entail?
 Data set as the object of statistics
 Scales of measurement
 Classifications of data and statistics
 Data sources
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Statistics in the Business and Economy
 Examples
• The U.S. real GDP has grown about 1.8% in the past
decade;
• The sales of iPhone accounts for about 25% in the
global smartphone market;
• In the U.S., a person with 4-year college education
earns averagely twice as much as the one with a high
school degree does;
• By the time the lecture was prepared, the Dow Jones
Industrial Average reached its historical highest point of
16167.97 on December 18, 2013.
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What does business statistics entail?
 Statistics is the art and science of collecting,
presenting, analyzing, and interpreting
data.
 In business, statistics is not just about
numbers and mathematics, but a tool that
analyzes the available data and helps make
informed and better business decisions.
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Data Set as the Object of Statistics
 The structure of a data set:
• Elements – the entities on which data are
collected;
• Variables – characteristics of the elements;
• Observations – the set of measurements for an
element.
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Data Set – An Example
Variables
Company
Apple Inc.
Boeing
Netflix
Starbucks
Wal-Mart
Exchange
Nasdaq
NYSE
Nasdaq
Nasdaq
NYSE
Market Capitalization
Market Risk (Beta)
($Billion)
496
0.63
102
0.94
23
1.79
59
0.75
252
0.3
An Observation
Names of
Elements
Data Set
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Scales of Measurement
 Data of different scales of measurement
require different statistical analyses.
 There are four scales of measurement:
•
•
•
•
Nominal
Ordinal
Interval
Ratio
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Nominal Scale
 Names or labels that show the attributes of
elements, variables such as
• Names of companies;
• Gender of employees;
 Data of nominal scale can be numeric. ( For
instance, ‘0’ denotes female and ‘1’ denotes
male.)
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Ordinal Scale
 It has the properties of nominal scale and
the order or rank matters. For example,
• Customer service rating ( poor, average, good,
outstanding);
 Variables of ordinal scale can assume
numeric data values.
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Interval Scale
 It has the properties of ordinal scale and the
interval/difference between values is measured in
the same unit. For example,
• Temperatures – oF or oC
• SAT scores
 Data of interval scale are always numeric.
 For interval data, zeros do not mean nothing. For
instance, a temperature of 0 degree does not mean
there is no temperature.
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Ratio Scale
 It has the properties of interval scale and the
ratio of of two values are meaningful. For
example,
• Weight, height, distance, time, income, etc.
 Data of ratio scale are always numeric.
 For ratio data, zeros do mean nothing. For
instance, a profit of zero $ means there is no
profit.
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Q1.1 – Which of the following variables uses
the ordinal scale of measurement?
a.
b.
c.
d.
Paint colors
Social Security Numbers
Letter grades (A,B,C,…)
Monthly return of S&P 500 Index
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Q1.1 – Which of the following variables uses
the ordinal scale of measurement?
a. Paint colors
b. Social Security Numbers
c. Letter grades (A,B,C,…)
d. Monthly return of S&P 500 Index
Answer: c
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Classifications of Data
 Qualitative vs. Quantitative data
• Nominal and ordinal data are qualitative, while
interval and ratio data are quantitative.
• Mathematical operations don’t apply to
qualitative data even if they are numeric.
 Cross-Sectional vs. Time Series data
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Classifications of Statistics
 Descriptive Statistics
• To summarize data in an informative way;
• Demonstrate patterns;
• Use tables, graphs, and numerical measures.
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Classifications of Statistics
 Inferential Statistics
• A population is the entire set of data in a
particular study.
Its characteristics are called population parameters.
The study of a population is probably very daunting,
time-consuming, and costly.
• A sample is a subset of a population.
Its characteristics are called sample statistics.
The study of a sample is much more manageable.
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Inferential Statistics
To estimate population parameters based on sample statistics
Sample
Statistics
Population
Parameters
Sample mean: X
Population mean: 
Sample variance: S 2
Population variance: 
Sample proportion: P
Population proportion: P
2
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Data Sources
 Nowadays, the public can get easy access to
business and economics data online. For
instance:
• Yahoo! Finance
• Federal Reserve Economic Data
• Census Bureau
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