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Lecture Notes |
Videos |
Excel |
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Excel Tutorial |
Blackboard Access
Video |
Atomic Learning |
| 1. Overview |
Course Syllabus &
video |
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| 0. Breakeven Analysis |
Breakeven Analysis, What if, Data Table, P Q problems |
Ch1.htm (5 mins) analytic models,
Breakeven by
Cameron (6 mins) and
by About.com
(2.5 mins), |
Breakeven for
Part S Part 1, One and
Two Way Tables |
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| 1. Decision Analysis |
LectureDAReview.pdf ipot and cancer examples: basic probabilities, prior,
conditional, joint, and posterior probabilities |
DecisionSA.html
(11:39 min) video: payoff table, Excel@ Data/What If/Data Table in
sensitivity analysis |
Decision 1T1f08 /
EVwSI |
| Chapter 15 in Ragsdale Text |
LectureDecisionhandout.pdf examples from text and summary of
LectureDecison.xlsx. Examples: airport, Colonial Motors, Grant
Proposal , iPot and Cancer
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EVwoPI, EVPI, EVwSI, EVwoSI, EVSI, Efficiency,
payoff tables, Decision Trees, Excel@ Data/What If/Data Table in
sensitivity analysis
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LectureDecison.xlsx- Computing posterior probabilities, MaxiMAX,
MiniMAX, MaxiMIN(pages 729 to 731), EMV, EVwPI, |
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DecisionNotesSpr09.pdf Test 1 Fall 08 with examples of buyers,
and selling ice cream versus soft drinks |
it covers both
LectureDAReview.pdf for probability review and
LectureDecisionhandout.pdf for decision anslysis |
Decision 2
T1Sum08 |
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DecisionNoteT1F08.pdf
identified buyers & compute posterior probabilities |
DAReviewSum.htm (5 mins)
video on details of posterior
probabilities in DecisionNoteT1F08.pdf |
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DAProbReview.htm
(17 mins) video from first part of
DecisionNotesT1F08.pdf |
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DAPack01Lump |
DecisionT1F08.htm (37 mins)
video from second part of
DecisionNotesT1F08.pdf
or pages 32 to 36 |
to compute MV, EVwPI, EVwoPI, EVPI,
EVwSI, EVwoSI, EVSI, and Efficiency using the selling ice cream
versus soft drinks example |
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DecisionTree.swf
10 min video to use
treelan.xla for
decision tree |
treeplan.xla: Excel@
add ins for Decision Trees |
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| LP, Optimization and
Business Analytics |
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PremSolv or
new versions for PC and Mac |
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LP Graphics notes |
LP
Applications |
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LP
lecture Excel@ |
| 2. Introduction to Modeling |
Algebra reviews & LP Graphic solutions.pdf algebra review and
LP graphic solutions. |
Algebra Review (30 mins) LP graphic solution |
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LP Graphic solution & use of
Solver.pdf LP graphic solution and use of
solver. |
LP Graphic Solution and Excel@ Solver (26 mins) video for
LP Graphic solution & use of
Solver |
LP Graphics.html
(60 mins) video, long version of
LP Graphic Solution |
| 3. LP Sensitivity Analysis |
LP sensitivity
analysis.pdf |
SA 1:
LPSA.pptx(2).html
(50 mins) video |
LP
SA Notes (16 pages)Updated 3/2010, the rest are the same. |
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SA 2:
LPSALecture2.html
(48 mins) video |
LP SA
Lecture Example 3/2/10 |
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LP and ILP models.pdf
examples in Ch.3 up to Multi -
periods Cash Flow Problem. You may get Data Evolvement and Ch.6 for Integer LP
here. |
ILP.html (70 mins)
video Ch. 3 and Ch.6 of Integer LP |
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| 4. LP Modeling
Applications |
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LP Inventory and Cash flow
LPModelingProCash.html
(67 mins) video |
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| 5. Regression |
LectureRegression.pdf regression, linear models, equations, hypothesis
test for slope and intercept with p-values, confidence intervals, and
select right models in multiple regression application |
Regression.swf
(10 mins) use Data/Data analysis/Regression to
get Summary Output tables |
LectureRegression.xls
and RegT1P1F08xls
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LectureRegressionSum.pdf is 5-page hand-written summary of
regression in class lectures with sales versus advertisement
expenditure and month |
RegTrend.swf
(3 mins) to use Excel@ for regression,
including the use of =CORREL(), =INTERCEPT(), =SLOPE(),
=RSQ(), and =TREND() |
Regression 1T1F08
&
xls |
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RegressionNotesSpr09.pdf
is the hand written regression lecture with car sales versus week
and interest rates |
RegTrend2.swf
(3 mins) to use Excel@
for more of regression, including more on =TREND() |
Regression 2
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| 6. Queuing or Waiting Line
Models |
QueueSummary.pdf (by Visio)
two pages to summarize 95% of queuing models. |
LectureQueue.pdf JMU Bookstore, pg 9 to 11 for s >=2. |
Queue.xlsx |
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msPack5QueueF8T2 and
add some details in lecture
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LectureQueueT2F08.htm
(40 mins) video from
LectureQueueT2F08.pdf to cover POISSION and EXPONENTIAL
distributions, and M/M/1 and M/M/s queuing models. |
Q.xls try out M/M/1 model |
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QueuingHandout.pdf
questions in JMU Bookstore example, but no answers. |
Queue.html
(65 mins) is the video made during class with
QueuingHandout.pdf
and LectureQueue.pdf |
to.cover
the same contents as
LectureQueueT2F08.htm
with different examples or applications, except,
queue.html also covers
M/G/s queue at the very end. |
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| 7. Simulation |
Visio-SIMChart.pdf one
page summary |
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simlec.xls short examples to use =VLOOKUP(), =NORMINV(), =IF(), =MIN(),
=MAX(), =AVERAGE(), =COUNTIF() and MonteCarlito. |
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SIM Lecture.pdf 29
pages business logics,
inventory and queuing. |
SIM.htm (31 mins): video for
SIM Lecture.pdf. |
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Simulation models:
detailed examples and answers in
inventory and queuing. |
sim2.html (59 mins) video
of class in fall 08, based on Simulation models |
LectureSIM.xlsx examples used in lecture
SIM Lecture.pdf and
Simulation models |
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Simulation Handout:
abbreviated version of
Simulation models
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Required
supplemental readings
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MonteCarlito_v1_05.xls
to make simulation real and easy |
| 8. Forecasting |
FcstLecture.pdf - page 1 of the 19 pages - summary |
Fcst1.swf
(12 mins) to cover ExcelTutor291.xls: Forecast, Regression and
Use of Solver |
ExcelTutor291.xls: SMA, WMA, EXP, =SUMPRODUCT(), =AVERAGE(), =STDEV(), and
=COUNT() |
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Forecast 1 Problems /
xls /answers
- first part of
FcstNotesSpr09.pdf. |
WMAwSolver.swf
(3 mins): to find optimal weights with Excel Solver |
=SUMPRODUCT() for WMA,
=SUMXMY2()/COUNT() for MSE |
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Forecast 2 Problem - second part
of
FcstNotesSpr09.pdf. |
EXPwSolver.swf
(3 mins): to use Excel@ to find optimal alpha (α)
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=SUMXMY2()/COUNT() for MSE |
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FcstNotesSpr09.pdf - Cars sold in T1F08 and Speeding Tickets in
T1Sum08 with hand written and Excel solutions |
FcstLecture.pdf - 19 pages: SMA, WMA, EXP, MAD/MAE, MAPE, SSE,
MSE, =SUMPRODUCT(), =SUMXMY2()/COUNT() |
Regression, Use of Solver to find optimal weights
and alpha, etc. |
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