BCOR 1020 Business Statistics Lecture 1 – January 15, 2008

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

Introduction About the Instructor Syllabus & Overview of Course Topics Course Expectations (My Expectations) Office Hours –Instructor –TAs

Chapter 1 – Key Definitions Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data.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.Statistics is the science of making inferences about and entire population based of a sample from that population. Population: Characterized by Parameters Size = N Sample: statistics are computed Size = n

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.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: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)

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.

Chapter 1 – Uses of Statistics As mentioned earlier, there are generally two primary uses of statistics: –Descriptive (early chapters) –Inferential (later chapters) Statistics Describing Data Making Inferences from Samples Visual Displays Numerical Summaries Estimating Parameters Testing Hypotheses

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.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.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.Purchasing: Determine the defect rate of a shipment and whether that rate has changed significantly over time. Forecasting: Manage inventory by forecasting consumer demand.Forecasting: Manage inventory by forecasting consumer demand.

Clickers – Relevance of Statistics 1 Based on what has been discussed so far, do you feel that Statistics will be important in your future career? A = Yes B = No

Clickers – Relevance of Statistics 2 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

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.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.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.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.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.

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)!!!

Chapter 1 – Statistical Pitfalls Pitfall 1: Making Conclusions about a Large Population from a Small Sample: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: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).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: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).

Chapter 1 – Statistical Pitfalls Pitfall 4: Using Poor Survey Methods: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: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:Pitfall 6: Making Generalizations about Individuals from Observations about Groups: –Avoid reading too much into statistical generalizations (e.g., men are taller than women).

Chapter 1 – Statistical Pitfalls Pitfall 7: Unconscious BiasPitfall 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 ResultPitfall 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).

Chapter 1 – An Evolving Field Statistics is a relatively young field, having been developed mostly during the 20 th century.Statistics is a relatively young field, having been developed mostly during the 20 th century. Its mathematical frontiers continue to expand with the aid of computers.Its mathematical frontiers continue to expand with the aid of computers. Major recent developments includeMajor recent developments include Exploratory data analysis (EDA) Exploratory data analysis (EDA) Data Mining Data Mining Computer-intensive statistics Computer-intensive statistics Design of experiments Design of experiments Statistical Quality & Process Control Statistical Quality & Process Control Robust product design Robust product design