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Applied Statistics in Business & Economics, 4th edition
Chapter Contents 1.1 1.2 1.3 1.4 1.5
David P. Doane and Lori E. Seward Prepared by Lloyd R. Jaisingh
What is Statistics? Why Study Statistics? Uses of Statistics Statistical Challenges Critical Thinking
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Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
McGraw-Hill/Irwin
Chapter 1
Overview of Statistics
A PowerPoint Presentation Package to Accompany
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Chapter Learning Objectives
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Overview of Statistics
1.1 What is Statistics? LO1-1: Define statistics and explain some of its uses in business.
LO1-1: Define statistics and explain some of its uses in business.
• Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data.
LO1-2: List reasons for a business student to study statistics. LO1-3: State the common challenges facing business professionals using statistics.
• A statistic is a single measure (number) used to summarize a sample data set; for example, the average height of students in a university.
LO1-4: List and explain common statistical pitfalls.
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1.1 What is Statistics?
• For the height of students, a graduation gown manufacturer may need to know the average height for the length of the gowns or an architect may need to know the maximum height to design the height of the doorways of the classrooms.
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1.2 Why Study Statistics
LO1-2: List reasons for a business student to study statistics. • Knowing statistics will make you a better consumer of other people's data. • You should know enough to handle everyday data problems, to feel confident that others cannot deceive you with spurious arguments, and to know when you've reached the limits of your expertise.
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1.2 Why Study Statistics • Statistical knowledge gives a company a competitive advantage against organizations that cannot understand their internal or external market data. • Mastery of basic statistics gives an individual manager a competitive advantage as one works one s way through the promotion process, or when one moves to a new employer. • Here are some reasons to study statistics.
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1.2 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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1.2 Why Study Statistics?
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1.2 Why Study Statistics?
Information Management
Process Improvement
Statistics helps summarize small and large amounts of data and reveal underlying relationships.
Statistics helps firms oversee their suppliers, monitor their internal operations, and identify problems.
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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1.3 Uses of Statistics?
1.3 Uses of Statistics?
Two primary kinds of 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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1.3 Uses of Statistics?
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Health Care
Auditing Sample from over 12,000 invoices to estimate the proportion of incorrectly paid invoices.
Evaluate 100 incoming patients using a 42-item physical and mental assessment questionnaire.
Marketing Quality Improvement
Identify likely repeat customers for Amazon.com and suggest co-marketing opportunities based on a database of 5 million Internet purchases.
Initiate a triple inspection program, setting penalties for workers who produce poor-quality output.
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1.3 Uses of Statistics?
Purchasing
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1.3 Uses of Statistics?
Operations Management
Determine the defect rate of a shipment and whether that rate has changed significantly over time.
Manage inventory by forecasting consumer demand.
Product Warranty Medicine
Determine the average dollar cost of engine warranty claims on a new hybrid engine.
Determine whether a new drug is really better than the placebo or if the difference is due to chance.
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1.4 Statistical Challenges LO1-3: State the common challenges facing
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1.4 Statistical Challenges
The Ideal Data Analyst
business professionals using statistics. • Has a broad outlook. • Is flexible.
The Ideal Data Analyst
• Focuses on the main problem. • Meets deadlines
• Is technically current (e.g., software-wise). • Communicates well. • Is proactive.
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1.4 Statistical Challenges
The Ideal Data Analyst
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1.4 Statistical Challenges
Imperfect Data and Practical Constraints • Knows his/her limitations and is willing to ask for help.
State any assumptions and limitations and use generally accepted statistical tests to detect unusual data points or to deal with missing data. You will face constraints on the type and quality of data you can collect.
• Can deal with imperfect information. • Has professional integrity.
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1.4 Statistical Challenges
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Business Ethics
Business Ethics
Some broad ethical responsibilities of business are (continued)
Some broad ethical responsibilities of business are • Treating customers in a fair and honest manner. • Complying with laws that prohibit discrimination. • Ensuring that products and services meet safety regulations.
• Standing behind warranties. • Advertising in a factual and informative manner. • Encouraging employees to ask questions and voice concerns about the company s business practices. • Being responsible for accurately reporting information to management.
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1.4 Statistical Challenges
Upholding Ethical Standards
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1.4 Statistical Challenges
Upholding Ethical Standards Ethical standards for the data analyst (continued):
Ethical standards for the data analyst:
• • • •
• Know and follow accepted procedures. • Maintain data integrity. • Carry out accurate calculations.
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Report procedures faithfully. Protect confidential information. Cite sources. Acknowledge sources of financial support.
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1.4 Statistical Challenges
Using Consultants
1.4 Statistical Challenges
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Communicating with Numbers
Hire consultants at the beginning of the project, when your team lacks certain skills or when an unbiased or informed view is needed.
• Numbers have meaning only when communicated in the context of a certain situation. • Presentation should be such that managers will quickly understand the information they need to use in order to make good decisions.
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1.4 Statistical Challenges
Skills Needed for Success in Business
1.5 Critical Thinking •
(Table 1.1)
For initial job success
For long-range job success
Common weaknesses
Report-writing
Managerial accounting
Communication skills
Accounting principles
Managerial economics
Writing skills
Mathematics
Managerial finance
Immaturity
Statistics
Oral communication Unrealistic expectations
Statistics is an essential part of critical thinking because it allows us to test an idea against empirical evidence.
• Empirical data represent data collected through observation and experiments. • Statistical tools are used to compare prior ideas with empirical data, but pitfalls do occur.
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1.5 Critical Thinking
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Pitfall 2: Making Conclusions from Nonrandom Samples
LO1-4: List and explain common statistical pitfalls.
Be careful about making generalizations from small samples and from retrospective studies of special groups (e.g., heart attack patients).
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).
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1.5 Critical Thinking
Pitfall 3: Conclusions From Rare Events
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Pitfall 5: Assuming a Causal Link Based on Observations
Be careful about drawing strong inferences from events that are not surprising when looking at the entire population (e.g., winning the lottery).
Be careful about drawing conclusions when no cause-and-effect link exists (e.g., most shark attacks occur between 12 p.m. and 2 p.m.).
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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1.5 Critical Thinking
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1.5 Critical Thinking
Pitfall 7: Unconscious Bias
Pitfall 6: Generalization to Individuals from Observations about Groups
Be careful about unconsciously or subtly allowing bias to color handling of data (e.g., heart disease in men vs. women).
Avoid reading too much into statistical generalizations (e.g., men are taller than women).
Pitfall 8: Significance versus Importance 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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