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HRIR 8011

HRIR 8011. “Statistics is a collection of procedures and principles for gaining and processing information in order to make decisions when faced with uncertainty.” (Utts, p. 3) Objective of HRIR 8011: learning to use information to make good (not lousy) decisions, which requires

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HRIR 8011

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  1. HRIR 8011 • “Statistics is a collection of procedures and principles for gaining and processing information in order to make decisions when faced with uncertainty.” (Utts, p. 3) • Objective of HRIR 8011: learning to use information to make good (not lousy) decisions, which requires • Collecting information (data) • Analyzing data • Interpreting the results of the analyses

  2. Consider… • Employees who are dissatisfied with their job are more likely to vote for a union than employees who are satisfied (HRIR 8071) • Structured interviews are better than open-ended interviews when selecting new employees (HRIR 8031) • An HR manager asks what is the market rate of pay? • An HR manager asks what can I do to reduce absenteeism? • If low paid workers are absent more, do you raise wages?

  3. The Focus of HRIR 8011 • Our focus…the procedures and principles of using information correctly • When Professor Tubre says that you should use a cognitive ability test, question it! How do we know we should use it? • What information is this conclusion based on? • How were the data collected? Does that seem applicable to my situation? • How were the data analyzed? Was that appropriate? What did they miss? • Are the conclusions justified based on the data and the results?

  4. Index Numbers • Index value = 100 X • Price Index Example: if current cost is $3,300 and base period costs is $2,400 then • Price Index = 100 X (3,300/2,400) = 137.5 • Interpretation: the current period is 37.5% percent higher than the base period current value base period value

  5. Time Series Year 1 Year 2 Year 3

  6. Measurement

  7. Measurement • Validity • Reliability • Bias

  8. Seven Measurement Pitfalls • Deliberate bias • Unintentional bias • Desire to please • Asking the uninformed • Unnecessary complexity • Ordering of questions • Confidentiality and anonymity • Source: Jessica M. Utts, Seeing Through Statistics, 2nd ed. (Pacific Grove, CA: Duxbury, 1999), p. 32.

  9. Cumulative Frequency • Recall Eggs R Us Race | Freq. Percent Cumul. ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ African American | 87 15.10 15.10 Asian American | 6 1.04 16.15 Hispanic | 25 4.34 20.49 white | 458 79.51 100.00 ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ Total | 576 100.00

  10. Percentiles • The pth percentile of a sample is the value for which at most p% of the measurements are less than that value and at most (100-p)% of the measurements are greater than that value • Median • Quartiles • Deciles

  11. Box Plot Lower Quartile Median Upper Quartile Smallest Largest { Middle half of the data

  12. Box Plot

  13. A Box Plot in Labor Source: Alan B. Krueger and Alexandre Mas, “Strikes, Scabs, and Tread Separations: Labor Strife and the Production of Defective Bridgestone/Firestone Tires,” Journal of Political Economy 112 (April 2004), pp. 252-289 at 274.

  14. A Simple cdf Example Consider the simple data set: 1, 4, 6, 4, 10, 9, 3, 5 This yields the following relative and cumulative frequencies

  15. A Simple cdf Example 1. To make the cdf, start at zero and move to the right along the x-axis until you come to the first value of x (that is, x=1)

  16. A Simple cdf Example 2. The value x=1 accounts for 0.125 of the cumulative frequency so the cdf jumps up to 0.125

  17. A Simple cdf Example 3. Now continue to the right until you get to the next value (x=3) at which point the cdf jumps up another 0.125 to 0.250.

  18. A Simple cdf Example 4. At x=4, note that the relative frequency is 0.25 (recall that there were two occurrences of 4 in the data set) so the cdf jumps 0.25 to 0.50.

  19. A Simple cdf Example 5. Continuing for the remaining x values yields the completed cdf.

  20. Birth of a Distribution 3 Bins

  21. Birth of a Distribution 7 Bins

  22. Birth of a Distribution 15 Bins

  23. Birth of a Distribution 33 Bins

  24. Birth of a Distribution 1000 Bins

  25. Different Distributions

  26. Even More Distributions

  27. Symmetrical Distributions

  28. Symmetrical Distribution

  29. Positively Skewed

  30. Negatively Skewed

  31. Symmetrical Distribution Will be very important for statistical inference Bell-shaped, symmetrical distribution

  32. Additional Variance Example Cyberland (1st 10 obs) Contrived Sample =13.9 =13.9 =0.30 =1.97

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