BCOR 1020 Business Statistics

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BCOR 1020Business Statistics

• Lecture 1 January 15, 2008

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Overview

• Introduction
• Syllabus
• Course Expectations
• Clickers
• Chapter 1
• Key Definitions
• Why Study Statistics?
• Uses of Statistics
• Statistical Challenges
• Written Reports and Presentations
• Statistical Pitfalls
• An Evolving Field

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Introduction

• About the Instructor
• Syllabus Overview of Course Topics
• Course Expectations (My Expectations)
• Office Hours
• Instructor
• TAs

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Chapter 1 Key Definitions

• Statistics is the science of collecting,
  organizing, analyzing, interpreting, and
  presenting data.
• Statistics is the science of making inferences
  about and entire population based of a sample
  from that population.

Sample statistics are computed Size n
Population Characterized by Parameters Size N
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Chapter 1 Key Definitions

• A statistic is a single measure (number) used to
  summarize a sample data set. For example, the
  average height of students in this class.
• Two primary uses for statistics
• Descriptive statistics the collection,
  organization, presentation and summary of data.
  (Computational/Mechanical)
• Inferential statistics generalizing from a
  sample to a population, estimating unknown
  parameters, drawing conclusions, making
  decisions. (Analytical typically using
  probability theory)

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Chapter 1 Why Study Statistics?

• Your textbook cites the following reasons
• 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.
• 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.
• 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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Chapter 1 Uses of Statistics

• As mentioned earlier, there are generally two
  primary uses of statistics
• Descriptive (early chapters)
• Inferential (later chapters)

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Chapter 1 Uses of Statistics

• Some specific examples from business
• 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.
• Purchasing Determine the defect rate of a
  shipment and whether that rate has changed
  significantly over time.
• Forecasting Manage inventory by forecasting
  consumer demand.

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Clickers Relevance of Statistics1

• Based on what has been discussed so far,
• do you feel that Statistics will be important
• in your future career?
• A Yes
• B No

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Clickers Relevance of Statistics2

• Based on what has been discussed so far,
• how important do you feel Statistics will be
• in your future career?
• A very important
• B important
• C somewhat important
• D not important

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Chapter 1 Statistical Challenges

• Working with Imperfect Data State any
  assumptions and limitations and use generally
  accepted statistical tests to detect unusual data
  points or to deal with missing data.
• Dealing with Practical Constraints You will face
  constraints on the type and quantity of data you
  can collect.
• Upholding Ethical Standards Know and follow
  accepted procedures, maintain data integrity,
  carry out accurate calculations, report
  procedures, protect confidentiality, cite sources
  and financial support.
• Using Consultants Hire consultants at the
  beginning of the project, when your team lacks
  certain skills or when an unbiased or informed
  view is needed.

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Chapter 1 Written Reports and Presentations

• In this course, you will be required to submit
  written
• reports for two projects. In your career, you
  will be
• required to submit written reports often and to
  give
• oral presentations occasionally.
• Your textbook has very good advice for presenting
• statistical information, both in written reports
  and in
• oral presentations.
• Read and use these sections (beginning with
  section 1.5)!!!

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Chapter 1 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 consumers).
• Pitfall 2 Making Conclusions from Nonrandom
  Samples
• Be careful about making generalizations from
  retrospective studies of special groups (e.g.,
  the first 50 potential customers on a mail-list
  or your best 50 customers).
• 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).

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Chapter 1 Statistical Pitfalls

• Pitfall 4 Using Poor Survey Methods
• Be careful about using poor sampling methods or
  vaguely worded questions (e.g., anonymous survey
  or quiz).
• 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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Chapter 1 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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Chapter 1 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)
• Data Mining
• Computer-intensive statistics
• Design of experiments
• Statistical Quality Process Control
• Robust product design