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Regression Analysis

Regression Analysis. Unscheduled Maintenance Issue:. 36 flight squadrons Each experiences unscheduled maintenance actions (UMAs) UMAs costs $1000 to repair, on average. You’ve got the Data… Now What?. Unscheduled Maintenance Actions (UMAs). What do you want to know?.

philip-mclaughlin
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Regression Analysis

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  1. Regression Analysis

  2. Unscheduled Maintenance Issue: • 36 flight squadrons • Each experiences unscheduled maintenance actions (UMAs) • UMAs costs $1000 to repair, on average.

  3. You’ve got the Data… Now What? Unscheduled Maintenance Actions (UMAs)

  4. What do you want to know? • How many UMAs will there be next month? • What is the average number of UMAs ?

  5. Sample Mean

  6. Sample Standard Deviation

  7. UMA Sample Statistics

  8. UMAs Next Month 95% Confidence Interval

  9. Average UMAs 95% Confidence Interval

  10. Model: Cost of UMAs for one squadron If the cost per UMA = $1000, the Expected cost for one squadron = $60,000

  11. Model: Total Cost of UMAs Expected Cost for all squadrons = 60 * $1000 * 36 = $2,160,000

  12. Model: Total Cost of UMAs Expected Cost for all squadrons = 60 * $1000 * 36 = $2,160,000 How confident are we about this estimate?

  13. ~ 95% mean (=60) standard error =12/36 = 2

  14. ~56 ~58 60 ~62 ~64 (1 standard unit = 2) ~ 95%

  15. 95% Confidence Interval on our estimate of UMAs and costs • 60 + 2(2) = [56, 64] • low cost: 56 * $1000 * 36 = $2,016,000 • high cost: 64 * $1000 * 36 = $2,304,000

  16. What do you want to know? • How many UMAs will there be next month? • What is the average number of UMAs ? • Is there a relationship between UMAs and and some other variable that may be used to predict UMAs? • What is that relationship?

  17. Relationships • What might be related to UMAs? • Pilot Experience ? • Flight hours ? • Sorties flown ? • Mean time to failure (for specific parts) ? • Number of landings / takeoffs ?

  18. Regression: • To estimate the expected or mean value of UMAs for next month: • look for a linear relationship between UMAs and a “predictive” variable • If a linear relationship exists, use regression analysis

  19. Regression analysis: describes and evaluates relationships between one variable (dependent or explained variable), and one or more other variables (called the independent or explanatory variables).

  20. What is a good estimating variable for UMAs? • quantifiable • predictable • logical relationship with dependent variable • must be a linear relationship: Y = a + bX

  21. Sorties

  22. Pilot Experience

  23. Sample Statistics

  24. Describing the Relationship • Is there a relationship? • Do the two variables (UMAs and sorties or experience) move together? • Do they move in the same direction or in opposite directions? • How strong is the relationship? • How closely do they move together?

  25. Positive Relationship

  26. Strong Positive Relationship

  27. Negative Relationship

  28. Strong Negative Relationship

  29. No Relationship

  30. Relationship?

  31. Correlation Coefficient • Statistical measure of how closely two variables are moving together in a coordinated fashion • Measures strength and direction • Value ranges from -1.0 to +1.0 • +1.0 indicates “perfect” positive linear relation • -1.0 indicates “perfect” negative linear relation • 0 indicates no relation between the two variables

  32. Correlation Coefficient

  33. Sorties vs. UMAs r = .9788

  34. Experience vs. UMAs r = .1896

  35. Correlation Matrix

  36. A Word of Caution... • Correlation does NOT imply causation • It simply measures the coordinated movement of two variables • Variation in two variables may be due to a third common variable • The observed relationship may be due to chance alone

  37. What is the Relationship? • In order to use the correlation information to help describe the relationship between two variables we need a model • The simplest one is a linear model:

  38. Fitting a Line to the Data

  39. One Possibility Sum of errors = 0

  40. Another Possibility Sum of errors = 0

  41. Which is Better? • Both have sum of errors = 0 • Compare sum of absolute errors:

  42. Fitting a Line to the Data

  43. One Possibility Sum of absolute errors = 6

  44. Another Possibility Sum of absolute errors = 6

  45. Which is Better? • Sum of the absolute errors are equal • Compare sum of errors squared:

  46. The Correct Relationship: Y = a + bX + U Y systematic random 100 90 80 70 60 50 X 100 110 120 130

  47. The correct relationship: • Y = a + bX + U Y systematic random 100 90 80 70 60 50 X 100 110 120 130

  48. Least-Squares Method • Penalizes large absolute errors • Y- intercept: • Slope:

  49. Assumptions • Linear relationship: • Errors are random and normally distributed with mean = 0 and variance = • Supported by Central Limit Theorem

  50. Least Squares Regression for Sorties and UMAs

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