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Review for exam II—fall 2013

Review for exam II—fall 2013. October 16, 2013. Format for exam. 30 -- 40 multiple choice 3 sets of discussion questions Identical in format to previous exam. Bring…/Don’t Bring…. Bring… Scantron sheet Pencil, eraser, calculator Don’t Bring… Paper PDAs, Pocket PC’s, tablets,

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Review for exam II—fall 2013

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  1. Review for exam II—fall 2013 October 16, 2013

  2. Format for exam • 30 -- 40 multiple choice • 3 sets of discussion questions • Identical in format to previous exam

  3. Bring…/Don’t Bring… • Bring… • Scantron sheet • Pencil, eraser, calculator • Don’t Bring… • Paper • PDAs, Pocket PC’s, tablets, • Programmable, high memory storage devices

  4. We Covered: • Burns Chs 4, 7, 8 • Larson and Gray 5, 6, 7

  5. Estimation • Weakest link • History database • By analogy • Use models • Top down—uses…..XXXXXXX?? • Bottom up – uses…..XXX?? • Reconciliation of top down and bottom up

  6. Parkinson’s Law • What is it?? The length of time To do a task Fills up the time Allotted for it

  7. Processes

  8. More Processes

  9. What are the processes that make up the cost management knowledge area? • Estimate Costs • Determine Budget • Control Costs

  10. What are the processes that make up the quality management knowledge area? • Plan Quality • Perform Quality Assurance • Perform Quality Control

  11. Methodologies • Waterfall – Document-driven—CASE tools • Spiral—risk-driven—invented by Barry Bhoem • RAD—superior to SAD • Agile/Iterative—increasingly popular • Scrum • RUP

  12. Process Map Waterfall Few risks, late integration and testing Low Ceremony High Ceremony Little Doc, light process discipline Heavy Doc, heavy process discipline, CCB Iterative Risk Driven, Continuous integration and testing

  13. The Waterfall Staircase Definition of Requirements Analysis Design Construction System Integration Testing Acceptance Testing Implementation Operation

  14. Figure 2-3. Spiral Model of Software Dev.

  15. What is the homegound for Waterfall? • Stable requirements – few changes • Large monolithic app—can’t be broken up • Difficult development • Mathematical algorithms • Compliers • Database engines • Artificial intelligence apps

  16. Home Ground for Agile • Unstable/unknown requirements • Rapid technological change • An environment conducive to learning • An environment accommodative of change

  17. The Transform Model • Did it work? NO!!! Writing a specification was not different from coding

  18. We also covered • Probabilistic PERT (formulas will be given to you) • Each task (activity) requires three time estimates – Optimistic, Most likely, Pessimistic • Crashing

  19. Further comments • Burns 6-13: Almost all of you did this wrong—you did not follow my solution in class, nor did you follow the solution provided in Chapter 6 of Burns

  20. Further Recitation • What is meant by feasibility? • What are three kinds of feasibility? • General, economic (financial), technical • What is meant by a risky decision? • State probabilities are KNOWN • Upside only, downside only, or both • What is meant by an uncertain decision? • How can decision theory help us make better project decisions?

  21. Know MS Project Navigation • What tool is used to specify subordination? • What tool is used to link tasks sequentially? • What tool is used to assign resources? • How do you enter resources and their hourly rates? • How do you show COST on the Entry table? • How do you specify durations? • What enables you to see ES, EF, LS, LF, Slack

  22. Topics--Chapter 6 • Must understand what Bayesian revision does for you • Will have to do analyses exactly like that in Chapter 6 • DMUU—Decision Making under Uncertainty • DMUR—Decision Making under Risk

  23. Decision Making under Uncertainty • Choose alternatives—must be mutually exclusive • Choose states—must be mutually exclusive and collectively exhaustive • For each alternative/state pair specify a payoff • What are the decision criteria? They are based on the DM’s attitude toward the situation—pessimist, optimist, regretist, etc

  24. To solve a problem like 6-10, you must • Do both DMUU and DMUR • Recall that EPPI and EVPI are not decision criteria, like EV* (maximal expected value) and ER* (minimal expected regret)

  25. To solve a problem like 6-12 or 6-13, you must • First, solve the decision problem without any additional information • Determine the optimal choice • Compute EPPI and EVPI • Second, perform Bayesian revision • Third, solve decision problem assuming a positive predictor state (consultant predicts SUCCESS) • Fourth, solve decision problem assuming a negative predictor state (consultant predicts FAILURE) • Compute EPSI and EVSI

  26. Remember… • EPPI = expected payoff of perfect information is not a decision criterion … tells us the payoff of perfect information • EVPI = EPPI – EV*(WITHOUT ADD’TL INFORMATION) … tells us how much perfect information would be valued • EVPI = expected value of PERFECT information • EPSI = expected payoff of sample information • EVSI = EPSI – EV*(WITHOUT ADD’TL INFORMATION) • EVSI = expected value of sample information… tells us how much sample information would be valued—how much we might be willing to pay for additional information

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