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BQT 173
Irwin/McGraw-Hill
BUSINESS
STATISTICS
© Andrew F. Siegel, 1997 and 2000
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l BQT 173l
Business Statistics
CHAPTER 1 The role of Statistical Thinking in
Business
CHAPTER 2 Statistical Concepts and Language
CHAPTER 3 Exploration Data Analysis
CHAPTER 4 Introduction to Statistical Software
Package
CHAPTER 5 Discrete Probability Distributions
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-3
l BQT 173l
Business Statistics
CHAPTER 6 Continuous Probability Distributions
CHAPTER 7 Hypothesis Tests
CHAPTER 8 Analysis of Variance
CHAPTER 9 Simple Linear Regression
CHAPTER 10 Non-Parameteric Statistics
CHAPTER 11 Time Series And Business
Forecasting
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-4
l Chapter 1 l
The role of Statistical Thinking in
Business
1.1 Component of Statistical Thinking
1.2 Definition of Business Statistics
1.3 Descriptive and Inferential Statistics
1.4 Ethical Issues in Statistical Data Analysis
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-5
1.1 Component of Statistical Thinking
Statistical
thinking is the philosophy of
learning and action based on the following
fundamental principles:
1.
2.
3.
all work occurs in a system of interconnected processes
- a process being a chain of activities that turns inputs
into outputs;
variation, which gives rise to uncertainty, exists in all
processes; and
understanding and reducing variation are keys to
success.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.2 Definition of Business Statistics
In
the business world, statistics has these
important specific uses:




To summarize business data
To draw conclusions from those data
To make reliable forecasts about business activities
To improve business processes
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.3 Descriptive and Inferential Statistics
The
statistical methods you use for these tasks
come from one of the two branches of
statistics:
DESCRIPTIVE STATISTICS
Descriptive statistics are the methods that help collect,
summarize, present, and analyze a set of data.
INFERENTIAL STATISTICS
Inferential statistics are the methods that use the data collected
from a small group to draw conclusions about a larger group.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.3 Descriptive and Inferential Statistics
There
are four important uses of statistics in
business:



To visualize and summarize your data (using descriptive
methods)
To reach conclusions about a large group based on data
collected from a small group (using inferential methods)
To make reliable forecasts that are based on statistical
models for prediction (inferential methods)
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-9
1.4 Ethical Issues in Statistical Data
Analysis
There
are a number of possible ways in which
unethical behavior can arise in statistics and
researchers should steer clear of these.
It is relatively simple to manipulate and hide
data, projecting only what one desires and not
what the numbers actually speak.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-10
1.4 Ethical Issues in Statistical Data
Analysis
E.g
1: Data collection can be made inherently
biased by posing the wrong questions that
stimulate strong emotions rather than
objective realities.
This happens all the time when the survey is
aimed to try and prove a viewpoint rather than
find out the truth.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.4 Ethical Issues in Statistical Data
Analysis
E.g
2: Scientists not including data outliers in
their report and analysis to validate their
theory or viewpoint.
This happens both in pure and social sciences.
By obscuring data or taking only the data
points that reinforce a particular theory,
scientists are indulging in unethical behavior.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.4 Ethical Issues in Statistical Data
Analysis
E.g
3: After a broad survey of many
customers, a company might decide to publish
and make available only the numbers and
figures that reflect well on the company and
either totally neglect or not give due
importance to other figures.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-13
1.4 Ethical Issues in Statistical Data
Analysis
E.g
3: After a broad survey of many
customers, a company might decide to publish
and make available only the numbers and
figures that reflect well on the company and
either totally neglect or not give due
importance to other figures.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
1-14
1.4 Ethical Issues in Statistical Data
Analysis
E.g
3 (Cont.): A car might be ranked high on
comfort but low on safety. By showing only
the comfort figures for the car, the company
is, in a way, misleading customers and
shareholders about the real picture.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000
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1.4 Ethical Issues in Statistical Data
Analysis
Surveys
and polls often indulge in unethical
behavior to reinforce a viewpoint. For
example, a survey might not reflect true
public opinion because it is not statistically
significant. However, many surveys do not
publish this along with their poll and this can
be misleading.
Irwin/McGraw-Hill
© Andrew F. Siegel, 1997 and 2000