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Exploring Statistics: Definitions, Layout & Homework

An exploration of statistics including definitions, course layout, homework assignments, and a breakdown of topics. Learn about measurements, validity, reliability, bias, and practice exercises. Dive into section quizzes and review problems.

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Exploring Statistics: Definitions, Layout & Homework

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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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