Business Statistics:
A First Course
Seventh Edition
Chapter 1
Introduction and Data Collection
Chap 1-1
Learning Objectives
In this chapter you learn:

How Statistics is used in business

The sources of data used in business

The types of data used in business
Chap 1-2
What is statistics?

A branch of mathematics taking and
transforming numbers into useful information for
decision makers

Methods for processing & analyzing numbers

Methods for helping reduce the uncertainty
inherent in decision making
Chap 1-3
Why Study Statistics?
Decision Makers Use Statistics To:




Present and describe business data and information properly
Draw conclusions about large groups of individuals or items,
using information collected from subsets of the individuals or
items.
Make reliable forecasts about a business activity
Improve business processes
Chap 1-4
Types of Statistics


Statistics
The branch of mathematics that transforms data into
useful information for decision makers.
Descriptive Statistics
Inferential Statistics
Collecting, summarizing,
presenting and analyzing data
Drawing conclusions and/or
making decisions concerning a
population based only on sample
data
Chap 1-5
Descriptive Statistics

Collect data


Present data


e.g., Survey
e.g., Tables and graphs
Characterize data

X

e.g., Sample mean =
i
n
Chap 1-6
Descriptive Statistics
Descriptive Statistics - methods of organizing,
summarizing, and presenting data in an
informative way.
EXAMPLE 1: The United States government reports the
population of the United States was 179,323,000 in
1960; 203,302,000 in 1970; 226,542,000 in 1980;
248,709,000 in 1990, and 265,000,000 in 2000.
EXAMPLE 2: According to the Bureau of Labor Statistics,
the average hourly earnings of production workers was
$17.90 for April 2008.
Chap 1-7
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 about a large group of
individuals based on a subset of the large group.
Chap 1-8
Basic Vocabulary of Statistics
VARIABLE
A variable is a characteristic of an item or individual.
DATA
Data are the different values associated with a variable.
OPERATIONAL DEFINITIONS
Data values are meaningless unless their variables have operational
definitions, universally accepted meanings that are clear to all associated
with an analysis.
Chap 1-9
Basic Vocabulary of Statistics
POPULATION
A population consists of all the items or individuals about which
you want to draw a conclusion.
SAMPLE
A sample is the portion of a population selected for analysis.
PARAMETER
A parameter is a numerical measure that describes a characteristic
of a population.
STATISTIC
A statistic is a numerical measure that describes a characteristic of
a sample.
Chap 1-10
Population vs. Sample
Population
Measures used to describe the
population are called parameters
Sample
Measures computed from
sample data are called statistics
Chap 1-11
Population versus Sample
Parameter and Statistic
Parameter
A descriptive measure of the population is called
a Parameter usually denoted by Greek Letters
Population mean
2
Population Variance
Population Standard Deviation



12
Population versus Sample
Statistic
A descriptive measure of a sample is known as a
statistic, usually denoted by Roman Letters

Sample Mean
x
Sample Variance s2
Sample Standard Deviation s
13
Why Collect Data?

A marketing research analyst needs to assess the
effectiveness of a new television advertisement.

A pharmaceutical manufacturer needs to determine
whether a new drug is more effective than those currently
in use.

An operations manager wants to monitor a manufacturing
process to find out whether the quality of the product
being manufactured is conforming to company standards.

An auditor wants to review the financial transactions of a
company in order to determine whether the company is in
compliance with generally accepted accounting
principles.
Chap 1-14
Sources of Data
 Primary Sources: The data collector is the one using the data
for analysis
 Data from a political survey
 Data collected from an experiment
 Observed data
 Secondary Sources: The person performing data analysis is
not the data collector
 Analyzing census data
 Examining data from print journals or data published on the internet.
Chap 1-15
Sources of data fall into four
categories

Data distributed by an organization or an
individual

A designed experiment

A survey

An observational study
Chap 1-16
Types of Variables

Categorical (qualitative) variables have values that
can only be placed into categories, such as “yes” and
“no.”

Numerical (quantitative) variables have values that
represent quantities.
Chap 1-17
Types of Data
Data
Categorical
Numerical
Examples:



Marital Status
Political Party
Eye Color
(Defined categories)
Discrete
Examples:


Number of Children
Defects per hour
(Counted items)
Continuous
Examples:


Weight
Voltage
(Measured characteristics)
Chap 1-18
Four Levels of Measurement
Nominal level - data that is classified
into categories and cannot be
arranged in any particular order.
EXAMPLES: eye color, gender,
religious affiliation.
Ordinal level – data arranged in
some order, but the differences
between data values cannot be
determined or are meaningless.
EXAMPLE: During a taste test of 4 soft drinks,
Mellow Yellow was ranked number 1, Sprite
number 2, Seven-up number 3, and Orange
Crush number 4.
Interval level - similar to the ordinal
level, with the additional property that
meaningful amounts of differences
between data values can be
determined. There is no natural zero
point.
EXAMPLE: Temperature on the
Fahrenheit scale.
Ratio level - the interval level with an
inherent zero starting point.
Differences and ratios are meaningful
for this level of measurement.
EXAMPLES: Monthly income of surgeons, or
distance traveled by manufacturer’s
representatives per month.
Data Measurement Scale


Nominal Data: A categorical data, weakest of all
data measurements. Only counting the number
of occurrence (frequencies) are possible. E.g.
various brands of watches, subject code
number, roll number etc.
Ordinal or Rank Data: Numbers are used to
rank objects or attributes.eg: excellent, good,
fair, poor. In ordinal data distance between
ranks cannot be measured.
Chap 1-20
Types of Ordinal Data


Interval Data: Distance between objects can be
measured. E.g. Frozen food distributors are
concerned with temperature, which is an
interval measurement. Basic arithmetic
operations are possible.
Ratio Data: It is the highest level of
measurement. It has fixed zero point.
Examples: Business data such as cost,
revenue, profit, market share etc.
Chap 1-21
Summary of the Characteristics for Levels of
Measurement
Chapter Summary
In this chapter, we have
 Reviewed why a manager needs to know statistics
 Introduced key definitions:
 Population vs. Sample
 Primary vs. Secondary data types
 Categorical vs. Numerical data
 Examined descriptive vs. inferential statistics
 Reviewed data types
Chap 1-23