General management - introduction to statistics

1. Copyright 2011 John Wiley & Sons, Inc. 1
Copyright 2011 John Wiley & Sons, Inc.
Applied Business Statistics, 7th ed.
by Ken Black
Chapter 1
Introduction
to Statistics

2. Copyright 2011 John Wiley & Sons, Inc. 2
What is statistics?
Become aware of the varied applications of statistics
in business.
Differentiate between descriptive and inferential
statistics.
Identify types of variables.
Learning Objectives

3. Copyright 2011 John Wiley & Sons, Inc. 3
Statistics in Business
Accounting — auditing and cost estimation
Economics — local, regional, national, and international
economic performance
Finance — investments and portfolio management
Management — human resources, compensation, and quality
management
Management Information Systems — performance of systems
which gather, summarize, and disseminate information to
various managerial levels
Marketing — market analysis and consumer research
International Business — market and demographic analysis

4. Copyright 2011 John Wiley & Sons, Inc. 4
Science of gathering, presenting, analyzing, and
interpreting data
Uses mathematics and probability
Branches of statistics:
Descriptive – graphical or numerical summaries of data
Inferential – making a decision based on data
What is Statistics?

5. Copyright 2011 John Wiley & Sons, Inc. 5
Population — the whole
a collection of all persons, objects, or items under study
Census — gathering data from the entire population
Sample — gathering data on a subset of the
population
Use information about the sample to infer about the
population
Population Versus Sample

6. Copyright 2011 John Wiley & Sons, Inc. 6
Population

7. Copyright 2011 John Wiley & Sons, Inc. 7
Identifier Color MPG
RD1 Red 12
RD2 Red 10
RD3 Red 13
RD4 Red 10
RD5 Red 13
BL1 Blue 27
BL2 Blue 24
GR1 Green 35
GR2 Green 35
GY1 Gray 15
GY2 Gray 18
GY3 Gray 17
Population and Census Data

8. Copyright 2011 John Wiley & Sons, Inc. 8
Sample and Sample Data
Identifier Color MPG
RD2 Red 10
RD5 Red 13
GR1 Green 35
GY2 Gray 18

9. Copyright 2011 John Wiley & Sons, Inc. 9
Copyright 2011 John Wiley & Sons, Inc. 9
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
parameter
population
denotes

variance
population
denotes
2

 denotes populationstandard deviation
mean
sample
denotes
x
variance
sample
denotes
s2
deviation
standard
sample
denotes
s

10. Copyright 2011 John Wiley & Sons, Inc. 10
Copyright 2011 John Wiley & Sons, Inc. 10
Process of Inferential Statistics
)
(parameter
Population
1.

)
(statistic
x
Sample
3.

estimate
to
x
Use
4.
sample
random
a
Select
2.

11. Copyright 2011 John Wiley & Sons, Inc. 11
Copyright 2011 John Wiley & Sons, Inc. 11
Statistics in Business
Inferences about parameters made under conditions
of uncertainty (which are always present in statistics)
Uncertainty can be caused by
Randomness in selection of a sample
lack of knowledge about the source of the inferences
change in conditions not accounted for

12. Copyright 2011 John Wiley & Sons, Inc. 12
Copyright 2011 John Wiley & Sons, Inc. 12
Statistics in Business
Probability is used in statistics
To estimate the level of confidence in a confidence
interval
To calculate the p-value in hypothesis testing

13. Copyright 2011 John Wiley & Sons, Inc. 13
Nominal — In nominal measurement the values just
"name" the attribute uniquely.
No ordering of the cases is implied.
For example, a persons gender is nominal. It doesn’t
matter whether you call them boys vs. girls or males vs.
females or XY vs. XX chromosomes.
Another example is religion – Catholic, Protestant,
Muslim, etc.
Levels of Data Measurement

14. Copyright 2011 John Wiley & Sons, Inc. 14
Nominal Level Data
Numbers are used to classify or categorize
Example: Employment Classification
1 for Educator
2 for Construction Worker
3 for Manufacturing Worker

15. Copyright 2011 John Wiley & Sons, Inc. 15
Ordinal - A variable is ordinal measurable if ranking is
possible for values of the variable.
For example, a gold medal reflects superior performance to
a silver or bronze medal in the Olympics.
You also can’t say a gold and a bronze medal average out to
a silver medal, though.
Preference scales are typically ordinal – how much do you
like this cereal? Like it a lot, somewhat like it, neutral,
somewhat dislike it, dislike it a lot.
Levels of Data Measurement

16. Copyright 2011 John Wiley & Sons, Inc. 16
Ordinal Level Data
Numbers are used to indicate rank or order
Relative magnitude of numbers is meaningful
Differences between numbers are not comparable
Example: Ranking productivity of employees
Example: Position within an organization
1 for President
2 for Vice President
3 for Plant Manager
4 for Department Supervisor
5 for Employee

17. Copyright 2011 John Wiley & Sons, Inc. 17
Faculty and staff should receive preferential
treatment for parking space.
Ordinal Data
1 2 3 4 5
Strongly
Agree
Agree Strongly
Disagree
Disagree
Neutral

18. Copyright 2011 John Wiley & Sons, Inc. 18
Interval - In interval measurement the distance
between attributes does have meaning.
Numerical data typically fall into this category
For example, when measuring temperature (in Fahrenheit),
the distance from 30-40 is same as the distance from 70-80.
The interval between values is interpretable.
Levels of Data Measurement

19. Copyright 2011 John Wiley & Sons, Inc. 19
Ratio — in ratio measurement there is always a
reference point that is meaningful
This means that you can construct a meaningful fraction
(or ratio) with a ratio variable.
In applied social research most "count" variables are ratio,
for example, the number of clients in past six months.
Levels of Data Measurement

20. Copyright 2011 John Wiley & Sons, Inc. 20
Highest level of measurement
Relative magnitude of numbers is meaningful
Differences between numbers are comparable
Location of origin, zero, is absolute (natural)
Examples: Height, Weight, and Volume
Example: Monetary Variables, such as Profit and Loss,
Revenues, Expenses, Financial ratios - such as P/E
Ratio, Inventory Turnover, and Quick Ratio.
Ratio Level Data

21. Copyright 2011 John Wiley & Sons, Inc. 21
Ratio Level Data
Parametric statistics – requires that the data be
interval or ratio
Non Parametric – used if data are nominal or ordinal
Non parametric statistics can also be used to analyze
interval
or ratio data

22. Copyright 2011 John Wiley & Sons, Inc. 22
Copyright 2011 John Wiley & Sons, Inc. 22
Data Level
Nominal
Ordinal
Interval
Ratio
Meaningful Operations
Classifying and Counting
All of the above plus Ranking
All of the above plus Addition,
Subtraction, Multiplication, and
Division (including means,
standard deviations, etc.)
All of the above
Data Level, Operations, and
Statistical Methods

23. Copyright 2011 John Wiley & Sons, Inc. 23
Metric and nonmetric data
Metric Data
Interval and ratio level data are usually gathered by
precise instruments often used in production and
engineering processes, so called the metric data, or
sometimes referred to as quantitative data
Non-metric Data
Nominal and ordinal data are often derived from
imprecise measurements such as the categorization
of people or objects, or the ranking of items, so
called the non-metric data, sometimes also called
qualitative data

24. Copyright 2011 John Wiley & Sons, Inc. 24
Variables, Measurement, and Data
 Variable
A characteristics of any entity being studied that is
capable of taking on different values
 Measurement
A standard process used to assign numbers to a
particular attribute or characteristics of a variable
 Data
Recorded measurement against the variable

25. Copyright 2011 John Wiley & Sons, Inc. 25