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Welcome!!
ECON 2113-001
Business Statistics
Fall Semester, 2007
Professor Chris Brown
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Chapter
1
Overview of Statistics
What is Statistics?
Why Study Statistics?
Uses of Statistics
Statistical Pitfalls
Statistics: An Evolving Field
McGraw-Hill/Irwin
© 2008 The McGraw-Hill Companies, Inc. All rights reserved.
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What is Statistics?
• Statistics is the science of collecting, organizing,
analyzing, interpreting, and presenting data.
• A statistic is a single measure (number) used to
summarize a sample data set. For example, the
average height of students in this class.
• A statistician is an expert with at least a master’s
degree in mathematics or statistics or a trained
professional in a related field.
McGraw-Hill/Irwin
© 2008 The McGraw-Hill Companies, Inc. All rights reserved.
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Why Study Statistics?
 Communication
• Understanding the language of statistics facilitates
communication and improves problem solving.
 Computer Skills
• The use of spreadsheets for data analysis and word
processors or presentation software for reports
improves upon your existing skills.
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Why Study Statistics?
 Information Management
• Statistics help summarize large amounts of data
and reveal underlying relationships.
 Technical Literacy
• Career opportunities are in growth industries
propelled by advanced technology. The use of
statistical software increases your technical
literacy.
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Why Study Statistics?
 Career Advancement
• Statistical literacy can enhance your career mobility.
 Quality Improvement
• Statistics helps firms oversee their suppliers,
monitor their internal operations and identify
problems.
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Uses of Statistics
 Two primary uses for statistics:
• Descriptive statistics – the collection, organization,
presentation and summary of data.
• Inferential statistics – generalizing from a sample
to a population, estimating unknown parameters,
drawing conclusions, making decisions.
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Uses of Statistics
 Overview of Statistics
Statistics
Describing
Data
Visual
Displays
Numerical
Summaries
Making Inferences
from Samples
Estimating
Parameters
Testing
Hypotheses
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Uses of Statistics
 Auditing
• Sample from over 12,000 invoices to estimate the
proportion of incorrectly paid invoices.
 Marketing
• Identify likely repeat customers for Amazon.com
and suggests co-marketing opportunities based
on a database of 5 million Internet purchases.
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Uses of Statistics
 Health Care
• Evaluate 100 incoming patients using a 42-item
physical and mental assessment questionnaire.
 Purchasing
• Determine the defect rate of a shipment and
whether that rate has changed significantly over
time.
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Uses of Statistics
 Medicine
• Determine whether a new drug is really better
than the placebo or if the difference is due to
chance.
 Forecasting
• Manage inventory by forecasting consumer
demand.
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Statistical Pitfalls
 Pitfall 1: Making Conclusions about a Large
Population from a Small Sample
• Be careful about making generalizations from
small samples (e.g., a group of 10 patients).
 Pitfall 2: Making Conclusions from
Nonrandom Samples
• Be careful about making generalizations from
retrospective studies of special groups (e.g.,
heart attack patients).
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Statistical Pitfalls
 Pitfall 3: Attaching Importance to Rare
Observations from Large Samples
• Be careful about drawing strong inferences from
events that are not surprising when looking at the
entire population (e.g., winning the lottery).
 Pitfall 4: Using Poor Survey Methods
• Be careful about using poor sampling methods or
vaguely worded questions (e.g., anonymous
survey or quiz).
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How many
students
know how to
invert a matrix?
Is this a good
survey
technique?
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Statistical Pitfalls
 Pitfall 5: Assuming a Causal Link Based on
Observations
• Be careful about drawing conclusions when no
cause-and-effect link exists (e.g., most shark
attacks occur between 12p.m. and 2p.m.).
 Pitfall 6: Making Generalizations about
Individuals from Observations about Groups
• Avoid reading too much into statistical
generalizations (e.g., men are taller than
women).
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Statistical Pitfalls
 Pitfall 7: Unconscious Bias
• Be careful about unconsciously or subtly allowing
bias to color handling of data (e.g., heart disease
in men vs. women).
 Pitfall 8: Attaching Practical Importance to
Every Statistically Significant Study Result
• Statistically significant effects may lack practical
importance (e.g., Austrian military recruits born in
the spring average 0.6 cm taller than those born
in the fall).
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Statistics: An Evolving Field
• Statistics is a relatively young field, having been
developed mostly during the 20th century.
• Its mathematical frontiers continue to expand with the
aid of computers.
• Major recent developments include
- Exploratory data analysis (EDA)
- Computer-intensive statistics
- Design of experiments
- Robust product design
- Advanced Bayesian methods
- and more