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Chapter 1: How do we get “good” data?

Chapter 1: How do we get “good” data?. What does the word “statistics” mean to you?. Definition Applications Where you’ve seen statistics before Your feelings about statistics …. Course Layout. More conceptual than computational I will give reading assignments very often.

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Chapter 1: How do we get “good” data?

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  1. Chapter 1: How do we get “good” data?

  2. What does the word “statistics”mean to you? • Definition • Applications • Where you’ve seen statistics before • Your feelings about statistics …

  3. Course Layout • More conceptual than computational • I will give reading assignments very often. • More frequent smaller quizzes • Book breakdown: • I. Producing Data • II. Organizing Data • III. Chance • IV. Inference

  4. Questionnaire

  5. Opening Day Questionnaire • In groups of 3, compile the data (for #2, #7) and prepare a short report of a couple of things. • Done on whiteboards. • Graphs, tables, statistics, etc. • Color!

  6. Questionnaire • What are the individuals? Variables? • Definitions, p. 5 • Type of study? • Observational? (p. 9) • Experiment? (p. 16)

  7. Homework • Reading, pp. 3-17 • Prepare a dotplot for questionnaire item #9. • See Activity 1.1, p. 4. • Exercises 1.1 and 1.4, pp. 7-8

  8. Comparing Observational Studiesand Experiments • Definitions, p. 9 and p. 16 • Give two examples of each.

  9. Populations and Samples (p. 10) • Population: The whole thing • Sample: A subset of the whole thing • Statistics is usually concerned with taking a sample to infer something about the population. • Census (p. 13): Entire population is included in the sample (or at least there is an attempt to do so).

  10. Exercises • 1.8, p. 13 • 1.12, p. 17

  11. Homework • Read: Statistics in Summary, p. 20 • 1.15, p. 18 • 1.20 and 1.23, p. 21 • Read: pp. 22-35 • Section 1.1 quiz on Thursday • Extra credit opportunity: • Application 1.1, p. 19 • Due on or before 1.18.09 (Monday)

  12. Section 1.2: Measuring • We must have an operational definition of the construct we want to measure. • For example, it’s one thing to say we want to measure intelligence (the construct), but it is quite another to actually measure it (operational definitions). • Valid measure: p. 28

  13. Valid Measurements for … • Physical fitness • Happiness • “Well-educated” • Student “readiness” for college

  14. USDA Statement on Laura Lynn 2% Milk (which does not contain rBGH growth hormone) • “Milk from a cow supplemented with rbGH is not different from that of a non-supplemented cow.” • See sidebar, p. 33 • “The Great One”

  15. Predictive Validity (p. 31) • Application 1.2A, p. 32 • Excel file: Predictive validity for SAT at Rice University • Employment law: • http://www.employment-testing.com/validity.htm • Sonia Sotomayor article in New York (hiring practices for fire fighters): http://www.newyorker.com/reporting/2010/01/11/100111fa_fact_collins?currentPage=all

  16. Measurement Definitions • p. 24: • measure, instrument, units, variable • Exercise 1.24, p. 27

  17. Homework • Look over examples 1.14 and 1.15, p. 30 • Exercises: • 1.31 and 1.32, p. 33 • 1.34, p. 34 • Reading: pp. 34-42

  18. Measurement Validity • We’ve spoken about the need for a measurement to be valid. • Definition, p. 28 • Ways we establish evidence of validity: • Predictive validity (e.g., SAT vs. college GPA) • Face validity: Have a panel of experts (SME) study our instrument for measuring. • There are statistics for measuring this (dissertation, p. 41) • Statistical methods • Correlations with other similar measurements • Use as independent variable in designed experiments

  19. Measurement Reliability (p. 35) • In addition to using valid measurements, our measurements must be reliable. • Reliable=repeatable results • Ways to establish evidence of reliability: • Test-retest • Parallel tests • Statistical methods, including internal consistency evaluations.

  20. Bias (p. 35) • Systematically overstates or understates the true value of a property.

  21. Bias and Reliability

  22. Scales Example

  23. Practice • See Example 1.17, p. 35 • Exercises: • 1.35, p. 39 • 1.42, p. 42 • 1.44, p. 43 • 1.48, p. 44

  24. More practice, section 1.2 • Exercises, pp. 39-40: • 1.37,1.38,1.39,1.41 • Section 1.2 quiz tomorrow (Tuesday)

  25. Section 1.3: Do the numbers make sense? • What they did not tell us … numbers have a context • p. 46 • Are the numbers plausible? • p. 49 • Are the numbers too good to be true? • p. 50 • Fake data? Too precise? • Is the arithmetic right? • p. 51 • Is there a hidden agenda? • p. 53

  26. Section 1.3 problems • pp. 55-58: • 1.55, 1.59, 1.62, 1.64

  27. Chapter 1 Review Exercises • pp. 59-62: • 1.71, 1.73, 1.75, 1.79

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