Chapter 1
Introduction
to Statistics
with Excel
© 2002 Thomson / South-Western
Slide 1-1
Learning Objectives
• Define statistics
• Become aware of a wide range of
applications of statistics in business
• Differentiate between descriptive and
inferential statistics
• Classify numbers by level of data and
understand why doing so is important
• Become aware of the statistical analysis
capabilities of Excel
© 2002 Thomson / South-Western
Slide 1-2
What is Statistics?
• Science of gathering, analyzing,
interpreting, and presenting data
• Branch of mathematics
• Course of study
• Facts and figures
• A death
• Measurement taken on a sample
• Type of distribution being used to
analyze data
© 2002 Thomson / South-Western
Slide 1-3
Population Versus Sample
• Population — the whole
– a collection of persons, objects, or items
under study
• Census — gathering data from the
entire population
• Sample — a portion of the whole
– a subset of the population
© 2002 Thomson / South-Western
Slide 1-4
Population
© 2002 Thomson / South-Western
Slide 1-5
Population and Census Data
© 2002 Thomson / South-Western
Identifier
Color
MPG
RD1
RD2
RD3
RD4
RD5
BL1
BL2
GR1
GR2
GY1
GY2
GY3
Red
Red
Red
Red
Red
Blue
Blue
Gree
n
Gree
n
Gray
Gray
Gray
12
10
13
10
13
27
24
35
35
15
18
17
Slide 1-6
Sample and Sample Data
© 2002 Thomson / South-Western
Identifier
Color
MPG
RD2
Red
10
RD5
Red
13
GR1
Gree
n
35
GY2
Gray
18
Slide 1-7
Descriptive vs. Inferential Statistics
• Descriptive Statistics — using data
gathered on a group to describe or
reach conclusions about that same
group only
• Inferential Statistics — using sample
data to reach conclusions about the
population from which the sample was
taken
© 2002 Thomson / South-Western
Slide 1-8
Parameter vs. Statistic
• Parameter — descriptive measure of
the population
– Usually represented by Greek letters
• Statistic — descriptive measure of a
sample
– Usually represented by Roman letters
© 2002 Thomson / South-Western
Slide 1-9
Symbols for Population Parameters
 denotes population parameter

2
denotes population variance
 denotes population standard deviation
© 2002 Thomson / South-Western
Slide 1-10
Symbols for Sample Statistics
x denotes sample mean
S
2
denotes sample variance
S denotes sample standard deviation
© 2002 Thomson / South-Western
Slide 1-11
Process of Inferential Statistics
Calculate x
Population
to estimate 
Sample

x
(parameter )
(statistic )
Select a
random sample
© 2002 Thomson / South-Western
Slide 1-12
Levels of Data Measurement
•
•
•
•
Nominal - Lowest level of measurement
Ordinal
Interval
Ratio - Highest level of measurement
© 2002 Thomson / South-Western
Slide 1-13
Nominal Level Data
• Numbers are used to classify or
categorize
Example: Employment Classification
– 1 for Educator
– 2 for Construction Worker
– 3 for Manufacturing Worker
Example: Ethnicity
– 1 for African-American
– 2 for Anglo-American
– 3 for Hispanic-American
– 4 for Oriental-American
© 2002 Thomson / South-Western
Slide 1-14
Ordinal Level Data
• Numbers are used to indicate rank or order
– Relative magnitude of numbers is meaningful
– Differences between numbers are not comparable
Example: Taste test ranking of three brands of soft
drink
Example: Position within an organization
– 1 for President
– 2 for Vice President
– 3 for Plant Manager
– 4 for Department Supervisor
– 5 for Employee
© 2002 Thomson / South-Western
Slide 1-15
Example of Ordinal Measurement
1
6
f
i
2
4
3
5
© 2002 Thomson / South-Western
n
i
s
h
Slide 1-16
Ordinal Data
Faculty and staff should receive preferential
treatment for parking space.
Strongly
Agree
1
Agree
2
© 2002 Thomson / South-Western
Neutral
3
Disagree
4
Strongly
Disagree
5
Slide 1-17
Interval Level Data
• Distances between consecutive integers
are equal
– Relative magnitude of numbers is
meaningful
– Differences between numbers are
comparable
– Location of origin, zero, is arbitrary
– Vertical intercept of unit of measure
transform function is not zero
Examples: Fahrenheit Temperature, Calendar
Time, Monetary Units
© 2002 Thomson / South-Western
Slide 1-18
Ratio Level Data
• Highest level of measurement
– Relative magnitude of numbers is meaningful
– Differences between numbers are
comparable
– Location of origin, zero, is absolute (natural)
– Vertical intercept of unit of measure transform
function is zero
Examples: Height, Weight, and Volume
Monetary Variables, such as Revenues, and
Expenses
Financial ratios, such as P/E Ratio, Inventory
Turnover
© 2002 Thomson / South-Western
Slide 1-19
Usage Potential of Various
Levels of Data
Ratio
Interval
Ordinal
Nominal
© 2002 Thomson / South-Western
Slide 1-20
Data Level, Operations,
and Statistical Methods
Data Level
Meaningful Operations
Statistical
Methods
Nominal
Classifying and Counting
Nonparametric
Ordinal
All of above plus Ranking
Nonparametric
Interval
All of above plus Addition,
Subtraction, Multiplication,
and Division
Parametric
Ratio
All of the above
Parametric
© 2002 Thomson / South-Western
Slide 1-21
Qualitative vs Quantitative Data
Qualitative Data is data of the nominal or
ordinal level that classifies by a label or
category. The labels may be numeric or
nonnumeric.
Quantitative Data is data of the interval
or ratio level that measures on a naturally
occurring numeric scale.
© 2002 Thomson / South-Western
Slide 1-22
Discrete and Continuous Data
Discrete Data is numeric data in which the
values can come only from a list of specific
values. Discrete data results from a
counting process.
Continuos Data is numeric data that can
take on values at every point over a given
interval. Continuous data result from a
measuring process.
© 2002 Thomson / South-Western
Slide 1-23
Summary of Data Classifications
Data
Data
nal
Nominal
Ordinal
Ordinal
Qualitative
(Categorical)
Qualitative
Nonnumeric
Nonnumeric
Numeric
Numeric
Discrete
Discrete
© 2002 Thomson / South-Western
Interval
Interl
Ratio
Ratio
Quantitative
Quantitative
Numeric
Numeric
Discrete or
Discrete
or
Continuous
Continuous
Slide 1-24