Overview of Statistics Chapter 1 Chapter Contents 1.1 What is Statistics? 1.2 Why Study Statistics? 1.3 Statistics in Business 1.4 Statistical Challenges 1.5 Critical Thinking

Overview of Statistics Chapter 1 Chapter Learning Objectives LO1-1: Define statistics and explain some of its uses in business. LO1-2: List reasons for a business student to study statistics. LO1-3: State the common challenges facing business professionals using statistics. LO1-4: List and explain common statistical pitfalls.

1.1 What is Statistics? Chapter 1 LO1-1 LO1-1: Define statistics and explain some of its uses in business. Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. It is also called data science, where data modeling, analysis and . decision making are considered a trilogy of activities. Statistics takes the politics, territorial struggles and personalities out of decision making and focuses people on the issues. A statistic is a single measure (number) used to summarize a sample data set; for example, the average height of students in a university. 1-3

1.1 What is Statistics? Chapter 1 LO1-1 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. Average and Max are the two statistics here. Discuss Mini Case Vail Resorts: How would the relative importance of each feature be calculated? Features: Pass price, which resorts to include, number of ski days on pass, black out dates How would the price of $579 of the Epic Pass be determined? 1-4

Two primary kinds of statistics: 1.1 What is Statistics? Chapter 1 Two primary kinds of statistics: Descriptive statistics – the collection, organization, presentation, and summary of data. Charts, Graphs. Chapts 1-8. Inferential statistics – generalizing from a sample to a population, estimating unknown parameters, drawing conclusions, making decisions. Chapts 8-9. 1-5

1.1 What is Statistics? LO1-1 Chapter 1 1-6

1.2 Why Study Statistics? Chapter 1 LO1-2 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.

1.2 Why Study Statistics? Chapter 1 LO1-2 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.

Communication Computer Skills 1.2 Why Study Statistics? Chapter 1 LO1-2 Chapter 1 Communication Computer Skills Understanding the language of statistics facilitates communication across linguistic and cultural boundaries and improves problem solving. The use of spreadsheets for data analysis and word processors or presentation software for reports improves upon your existing skills. We need to be able to do data analysis and reporting without the help of experts. 1-9

Information Management 1.2 Why Study Statistics? LO1-2 Chapter 1 Information Management Technical Literacy Statistics helps summarize small and large amounts of data and reveal underlying relationships. See trends and patterns emerge from the large amounts of raw data. Career opportunities are in growth industries propelled by advanced technology. The use of statistical software increases your technical literacy. Allows you to make verifiable claims about products you are selling or a service you offer. 1-10

1.2 Why Study Statistics? Chapter 1 LO1-2 Process Improvement Statistics helps firms oversee their suppliers, monitor their internal operations, and identify problems. Allows you to monitor processes and measure improvement. Example Process: Teaching a College Course. How would we measure it? What improvements may be suggested once it is measured? 1-11

Auditing 1.3 Uses of Statistics Chapter 1 LO1-1 Marketing Sample from over 12,000 invoices to estimate the proportion of incorrectly paid invoices. How large should the sample be so that sample estimate is close enough to the true proportion? Identify likely repeat customers for Amazon.com and suggest co-marketing opportunities based on a database of 5 million Internet purchases. 1-12

1.3 Uses of Statistics Quality Improvement Chapter 1 LO1-1 Health Care Evaluate 100 incoming patients using a 42-item physical and mental assessment questionnaire. Are the scores different by the 2 therapists? Which questions predict the patients’ length of stay? Initiate a triple inspection program, setting penalties for workers who produce poor-quality output, calls for “zero defects”. The program fails. Why? 1-13

1.3 Uses of Statistics Medicine Chapter 1 LO1-1 Purchasing Determine the defect rate of a shipment and whether that rate has changed significantly over time. 200 DVD players have 4 defects, historic rate 0.005 (or 5 out of a thousand). Determine whether a new drug is really better than the placebo or if the difference is due to chance. How close are the sample means to the population means? 1-14

Product Warranty 1.3 Uses of Statistics Operations Management LO1-1 Chapter 1 Operations Management Product Warranty Manage inventory by forecasting consumer demand. Lots of product types (over 50,000). How many of each should be ordered? What if we order too many? Or too few? Determine the average dollar cost of engine warranty claims on a new hybrid engine. In-class Questions: 1.1 1-15

1.4 Statistical Challenges LO1-4 Chapter 1 LO1-4: State the common challenges facing business professionals using statistics. The Ideal Data Analyst Is technically current (e.g., software-wise). Communicates well. Is proactive. 1-16

1.4 Statistical Challenges LO1-4 Chapter 1 The Ideal Data Analyst Has a broad outlook. Is flexible. Focuses on the main problem. Meets deadlines 1-17

1.4 Statistical Challenges LO1-4 Chapter 1 The Ideal Data Analyst Knows his/her limitations and is willing to ask for help. Can deal with imperfect information. Has professional integrity. 1-18

1.4 Statistical Challenges LO1-4 Chapter 1 Statistics in the real world can be messy and ambiguous. Imperfect Data and Practical Constraints Assume a new air bag is being tested for safety with children. Old design may be safer in some situations and the new one in others. Crash tests are expensive and number of data points are limited. Sensor failures in crash test dummies may yield incomplete information in some crashes. What should you do? 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. You may give conditional answers starting with “It depends on…”. 1-19

1.4 Statistical Challenges LO1-4 Chapter 1 Business Ethics 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. 1-20

1.4 Statistical Challenges LO1-4 Chapter 1 Business Ethics Some broad ethical responsibilities of business are (continued) Standing behind warranties. Advertising in a factual and informative manner. Encouraging employees to ask questions and voice concerns about the company’s business practices (whistle blowers). Being responsible for accurately reporting information to management. 1-21

1.4 Statistical Challenges LO1-4 Chapter 1 Upholding Ethical Standards Ethical standards for the data analyst: Know and follow accepted procedures. Maintain data integrity. Carry out accurate calculations. 1-22

1.4 Statistical Challenges LO1-4 Chapter 1 Upholding Ethical Standards Ethical standards for the data analyst (continued): Report procedures faithfully. Protect confidential information. Avoid giving in to pressure to interpret a study in a positive way. Cite sources. Acknowledge sources of financial support. 1-23

1.4 Statistical Challenges LO1-4 Chapter 1 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. 1-24

1.4 Statistical Challenges LO1-4 Chapter 1 Communicating with Numbers 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. Charts and graphs should be near the paragraph explaining them, not at the end. Discussion: Look at US Trademarks Data Set. 1-25

1.4 Statistical Challenges LO1-4 Chapter 1 NASA Mini Case In 1986, Shuttle Challenger exploded after takeoff, because its O-rings had become brittle due to colder temperatures the night before. What relationship is important to investigate here? O-rings were in 2 layers. The second layer had suffered no erosion in warmer temperatures, but in colder temperatures, had suffered erosion 4 out of 13 times. What is the ethical question here? 1-26

1.5 Critical Thinking Chapter 1 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. This is different from anecdotal data. Examples in policy. Statistical tools are used to compare prior ideas with empirical data, but pitfalls do occur. 1-27

1.5 Critical Thinking LO1-5 Chapter 1 Pitfall 1: Making Conclusions about a Large Population from a Small Sample LO1-5: List and explain common statistical pitfalls. Be careful about making generalizations from small samples (e.g., a group of 10 patients who showed improvement). Smoking doesn’t hurt you because of the example of Aunt Harriet. Sampling will be done in chapter 8. 1-28

Pitfall 2: Making Conclusions About Populations from Nonrandom Samples 1.5 Critical Thinking LO1-4 Chapter 1 Pitfall 2: Making Conclusions About Populations from Nonrandom Samples Be careful about making generalizations based on studies of special groups (e.g., studying heart attack patients without defining matched control group). “All old people eat greasy food at diners.” 1-29

Pitfall 3: Conclusions From Rare Events 1.5 Critical Thinking LO1-4 Chapter 1 Pitfall 3: Conclusions From Rare Events Pitfall 4: Using Poor Survey Methods Be careful about drawing strong inferences from events that are not surprising when looking at the entire population (e.g., winning the lottery). Winning the jackpot in a casino. Millions play, someone will win the jackpot. Happens a lot in investing. Be careful about using poor sampling methods or vaguely worded questions (e.g., anonymous survey or quiz). “How many of you learned statistics in High School?” 1-30

Pitfall 5: Assuming a Causal Link Based on Observations 1.5 Critical Thinking LO1-4 Chapter 1 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 12 p.m. and 2 p.m.). If A precedes B does not mean A necessarily causes B. Usually we need carefully controlled experiments to show causality. Just a statistical association is not enough. E.g., My age and the US Gross Domestic Product are highly correlated, but there is no causal link. 1-31

1.5 Critical Thinking LO1-4 Chapter 1 Pitfall 6: Generalization to Individuals from Observations about Groups Avoid reading too much into statistical generalizations (e.g., men are taller than women). Happens to minority groups a lot. Also tends to happen when discussing other cultures. 1-32

Pitfall 7: Unconscious Bias 1.5 Critical Thinking LO1-4 Chapter 1 Pitfall 7: Unconscious Bias Pitfall 8: Significance versus Importance Be careful about unconsciously or subtly allowing bias to alter handling of data (e.g., heart disease in men vs. women). Or a belief that people in a certain ethnic group are less honest than another group. If sample size is large, small differences may be significant. 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). In-class Questions: 1.8, 1.13, 1.14, 1.20 , 1.27 (GMAT) 1-33

Homework Questions 1.2 1.10 1.22 1.24 1.28