Lecture 1. Making Sense of Data: Data Variation David R. Merrell 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 Statistics is the use of data to reduce uncertainty about potential observations

Course Information Web site Heinz Front Page.htm Data files r:/academic/90786

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

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

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

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,...)

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

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

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

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)

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

What’s the Use of Data? Description Evaluation Estimation

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

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

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?

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

Batting Average of 263 major league baseball players

Compressed Data Values Median0.263 Minimum0.196 Maximum0.353 Range0.155 Mode0.250 Mean0.263 Standard Deviation0.023

Batting Average of 263 major league baseball players Median Maximum Minimum 0.196

Next Time... Data Compression for One Variable