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Lecture 1. Making Sense of Data: Data Variation

Lecture 1. Making Sense of Data: Data Variation. David R. Merrell 90-786 Intermediate Empirical Methods for Public Policy and Management. Making Sense of Data: Data Variation. Introductions Instructor: David R. Merrell TA s: Max Hernandez-Toso and Hao Xu Course Content: USEFUL STATISTICS.

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Lecture 1. Making Sense of Data: Data Variation

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  1. Lecture 1. Making Sense of Data: Data Variation David R. Merrell 90-786 Intermediate Empirical Methods for Public Policy and Management

  2. Making Sense of Data: Data Variation • Introductions • Instructor: David R. Merrell • TA s: Max Hernandez-Toso and Hao Xu • Course Content: USEFUL STATISTICS Statistics is the use of data to reduce uncertainty about potential observations

  3. Course Information • Web site • http://Duncan.heinz.cmu.edu/GeorgeWeb/ Heinz 90-786 Front Page.htm • Data files • r:/academic/90786

  4. Making Sense of Data • Motivation in management and policy • What is data? • What’s the use of data? • Data variation

  5. Motivation for Statistical Input • Managerial Decision Making • Changes in societal or organizational conditions • Differences between observations and expectations • Policy Making • Impact of changing the system

  6. What is Data? • Unit of analysis • Number of variables • one, two, more than two • Level of measurement / kind of data • Nominal, Ordinal, Interval

  7. Unit of analysis • Focus of attention: a case that can be be separately and uniquely identified • person (student, woman, tenant, .. • place (city, street intersection, river, … • object (car, power plant, ...) • organization (school, corporation, …) • incident (birth, election) • time period(day, season, year, ...)

  8. Variables • Characteristics, attributes, and occurrences observed about each unit of analysis • Require specific step-by-step procedure to obtain values for the variable

  9. Examples • Driver's license application study • Unit of analysis: people who apply for a driver's license. • Outcome variable: License issued or not • Other variables: Applicant's age, sex, and race • Snowfall in Pittsburgh • Units of analysis: Snowstorms • Outcome variable: depth of the snowfall from each storm • Other variables: date of snowstorm, temperature

  10. Nominal data • Classifies outcomes by categories • Categories must be mutually exclusive and exhaustive • Examples: • Marital status, region of the country, religion, occupation, school district, place of birth, blood type

  11. Ordinal data • Classifies outcomes by ranked categories • Examples: • Officers in the U.S. Army can be classified as: • 1 = general 5 = captain • 2 = colonel 6 = first lieutenant • 3 = lieutenant colonel 7 = second lieutenant • 4 = major • Education (highest diploma or degree attained)

  12. Interval data • Classifies outcomes on a continuous scale • Examples: • Scholastic Aptitude Test (SAT) score • Consumer Price Index (CPI) • Time of day

  13. What’s the Use of Data? • Description • Evaluation • Estimation

  14. Description • Summary of observations • In February, 1997 the M1A money supply in Taiwan rose 6.46% over February, 1996 • Housing starts in June, 1996, rose to a seasonally adjusted rate of 1,480,000 units from a revised 1,461,000 in May

  15. Evaluation • Comparison of observed state of affairs against expectations • Expectations are based on: ethical norms, managerial plans and budgets

  16. Estimation • Uses observations to assess an attribute of a population or to predict future values. • A new charter school in Boston raised test scores an average of 7 percentile points. • How would other charter schools do? • How will this charter school do in the future?

  17. Data Variation: Data Compression and Display • Boxplots • Five number summary • minimum • lower quartile point • median • upper quartile point • maximum

  18. Batting Average of 263 major league baseball players

  19. Compressed Data Values Median 0.263 Minimum 0.196 Maximum 0.353 Range 0.155 Mode 0.250 Mean 0.263 Standard Deviation 0.023

  20. Batting Average of 263 major league baseball players Maximum 0.352 Median 0.263 Minimum 0.196

  21. Next Time ... • Data Compression for One Variable

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