SlideShare ist ein Scribd-Unternehmen logo
1 von 33
• To minimize background noise and audio feedback, the meeting’s global
microphone is muted – except for mine.
• Please also mute the audio controls on your side.
• At certain points in the presentation I may announce that I’ll be fielding
questions. When announced, please type your question into the
GoToMeeting chat window.
• Of course, you may pre-emptively type your questions in.
• We have a lot of material to cover, so the presentation will proceed at a
rapid clip. You are always welcome to e-mail or call to review anything
that you may have questions about or would like to see again.
• Today’s webinar will be recorded and posted on the Palisade website
under the “TRAINING & EVENTS” menu.
Webinar Notes
Andrew J Sich, Regional Sales Manager
asich@palisade.com; (800) 432-7475 x330
@RISK Unchained!
Seldom used yet highly valued features you
probably didn’t know about
Presentation Objectives
(1) Learn about your partner: Palisade Corp. (appx. 5 minutes)
(3) For those new to @RISK . . .
A walk-through on how to apply 3 fundamentals to
Run a Monte Carlo simulation on your spreadsheet,
and how to interpret the results. (appx. 15 minutes)
(5) For everyone: New features in @RISK Version
(appx. 10 minutes)
(2) For those new to Monte Carlo simulation . . .
An introduction (appx. 5 minutes)
(4) For everyone: Seldom used @RISK features – valuable
for answering the boss’s barrage of ‘What If’ questions.
(appx. 25 minutes)
(6) More: Stochastic Time Series and MS Project Integration
Palisade Corporation is the maker of the market leading
risk and decision analysis software @RISK.
@RISK is the flagship product within the DecisionTools Suite
which also includes the following MS Excel add-ins:
l PrecisionTree l StatTools l RISKOptimizer
l TopRank l NeuralTools l Evolver
@RISK.
DecisionTools Suite,
London
Sydney
Ithaca, NY
Rio de Janiero
Tokyo
Palisade Global Presence
Cuenca
6 global offices and 8 language versions
l English l 中国语言 l Français l Deutsch
l 日本語 l Português l Русский язык l Español
l Certified Microsoft Partner
Palisade Recognition
l Intel Software Partner
l Accredited PMI Registered Education Provider
l Approved Education Provider for the AACE
l Sponsor of Continuing Professional Education
on The National Registry of CPE Sponsors
l Primary Monte Carlo simulation tool at the majority of
academic institutions in North America and Europe
94% of Fortune 500
companies Partner
with Palisade.
Risk Analysis Modeling using
Monte Carlo Simulation
Risk Modeling: The Old Way
 Single Point Estimates
Do you make multi-million dollar decisions based on 1 number?
 3-Point Estimates or Scenario Analyses
Do you make multi-million dollar decisions based on 3 numbers?
 Manual What-if Analyses
Will you mandate that your analysts run a hundred scenarios for your
million dollar decision? How much will that cost you?!?
What if something changes? What if many things change,
and change at different times? Managing risk then becomes
cumbersome, time consuming, and error laden.
Probability Distributions
The direct result of Monte Carlo simulations,
which are absolutely necessary for making
defensible business decisions and for managing risk.
Risk Modeling: The New Way
Probability distributions furnish you with the full range of
possible outcomes, how likely those outcomes are to occur,
and identifies those items that impact your bottom line most
significantly and by how much.
What is Monte Carlo simulation?!?!
At its core, Monte Carlo simulation is a virtual experiment
– repeated hundreds, thousands, even millions of times –
all the while generating random samples, bound by a set
of parameters that you define, from each repetition of that
experiment.
It’s really not rocket science.
Those random samples are then collected, organized,
and analyzed to help you understand something about
the behavior of that process or system.
What is Monte Carlo simulation?!?!
Let’s use a common example to illustrate
. . . rolling a pair of dice . . .
What happens when we roll the dice 48 times . . . and collect the results?
Imagine we have only a vague
idea of how 2 dice behave when
rolled.
Let’s model it!
We define the parameters as:
“2 dice”
each with
“6 possible outcomes”
What is Monte Carlo simulation?!?!
We get a bunch of numbers . . .
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
1 4 5 3 6 9 4 3 7 6 6 12
2 2 4 2 4 6 2 2 4 3 4 7
3 3 6 1 1 2 3 1 4 1 1 2
4 1 5 6 2 8 1 6 7 6 2 8
1 6 7 5 3 8 6 5 11 5 3 8
6 5 11 2 5 7 5 4 9 2 5 7
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
6 1 7 3 4 7 3 3 6 3 4 7
4 2 6 6 2 8 6 1 7 6 2 8
1 3 4 2 3 5 2 6 8 2 3 5
2 4 6 4 1 5 4 1 5 4 1 5
3 5 8 5 5 10 5 2 7 5 6 11
5 6 11 1 5 6 1 6 7 1 5 6
What is Monte Carlo simulation?!?!
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
1 4 5 3 6 9 4 3 7 6 6 12
2 2 4 2 4 6 2 2 4 3 4 7
3 3 6 1 1 2 3 1 4 1 1 2
4 1 5 6 2 8 1 6 7 6 2 8
1 6 7 5 3 8 6 5 11 5 3 8
6 5 11 2 5 7 5 4 9 2 5 7
Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total
6 1 7 3 4 7 3 3 6 3 4 7
4 2 6 6 2 8 6 1 7 6 2 8
1 3 4 2 3 5 2 6 8 2 3 5
2 4 6 4 1 5 4 1 5 4 1 5
3 5 8 5 5 10 5 2 7 5 6 11
5 6 11 1 5 6 1 6 7 1 5 6
We then collect those numbers . . .
5 9 7 12
4 6 4 7
6 2 4 2
5 8 7 8
7 8 11 8
11 7 9 7
7 7 6 7
6 8 7 8
4 5 8 5
6 5 5 5
8 10 7 11
11 6 7 6
. . . and then condense the numbers into just the totals . . .
What is Monte Carlo simulation?!?!
Next . . . we organize the figures . . .
5 9 7 12
4 6 4 7
6 2 4 2
5 8 7 8
7 8 11 8
11 7 9 7
7 7 6 7
6 8 7 8
4 5 8 5
6 5 5 5
8 10 7 11
11 6 7 6
7
7
7
7
7 8
5 6 7 8
5 6 7 8
5 6 7 8
4 5 6 7 8 11
4 5 6 7 8 11
2 4 5 6 7 8 9 11
2 4 5 6 7 8 9 10 11 12
What is Monte Carlo simulation?!?!
The result is a chart that illustrates something about the
behavior of rolling dice.
7
7
7
7
7 8
5 6 7 8
5 6 7 8
5 6 7 8
4 5 6 7 8 11
4 5 6 7 8 11
2 4 5 6 7 8 9 11
2 4 5 6 7 8 9 10 11 12
12
F 10
r
e 8
q
u 6
n
c 4
y
2
0
2 3 4 5 6 7 8 9 10 11 12
What is Monte Carlo simulation?!?!
Let’s review . . .
Monte Carlo simulation is a virtual experiment that
repeats a process or project or situation a large number
of times, and generates a large number of random
samples bound by a specific set of parameters.
Those random samples are collected and then
organized and analyzed to help you understand the
behavior of a simple or complex system or process.
Benefits of Using @RISK
 Peace of Mind
Scrutinized, accepted, and lauded for 30+ years at all levels
of academia, commercial industry, government & military.
 Make Defensible Business Decisions
Transform decision making discussions by illustrating
“ranges”, “probabilities”, and “likelihood of occurrence” –
and leave poor decisions arising from single point
estimates to historians.
 Ease of Use
Work in the familiar environment of MS Excel with native
Excel functions that allow full cell referencing capabilities.
@RISK Demo
Fundamentals
Set-up your model and run a simulation.
@RISK Demo
Reports
Present your modeling results in a variety of ways with attractive,
easy to interpret, and time saving charts, graphs, and reports.
Risk Register
Capture and quantify event frequency and severity.
X
RiskMakeInput
Summarize layered @RISK distributions in the Sensitivity Analysis.
Treat cells with formulas or references as simulation inputs.
Filter Simulation Results
Observe simulation results within a range of percentiles.
Stress Analysis
Analyze the effect of stressing @RISK distributions
between a set of percentiles, like the tails.
“Stressing” a distribution restricts samples drawn from a distribution
to values between a specified pair of percentiles, e.g. the left or
right tail. Compare the results of a simulation with stressed inputs
to a baseline simulation with no stress.
Advanced Sensitivity Analysis
Analyze the effect of any input at a specific value on an @RISK Output.
@RISK runs a separate simulation for any number of isolated
inputs, across a range of values. Inputs may be @RISK
distributions or even cells with no @RISK function.
RiskLock
Lock specified inputs from sampling.
Locking a cell with an @RISK input at its expected value, or any
subsection of cells, lets you observe the effects of uncertainty of
remaining or unlocked @RISK inputs.
RiskSimtable
Run a set of sequential simulations for different scenarios
for side-by-side comparison.
@RISK Goal Seek
Find the value of an input that leads to a desired simulated Goal.
Similar to Excel’s Goal Seek. However, unlike Excel’s Goal Seek,
@RISK's Goal Seek utilizes multiple simulations to find the
adjustable cell value that achieves your specified target.
Efficient Frontier Analysis
with RISKOptimizer
Optimization competing goals under uncertainty.
Affectionately referred to as a solver on steroids.
New in Version
Correlation Copulas
Control correlation patterns of two dependent,
or correlated, @RISK inputs.
New in Version
Data Viewer
View any Excel data with @RISK’s graphing engine.
No simulation necessary.
New in Version
Select any variable or data set and instantly create histograms,
cumulative charts, scatterplots, trend, or box plots, for consistency
in presentation of data.
Thank You!
We’ll now field questions . . .
. . . via the GoToMeeting chat mechanism.
@RISK Unchained Webinar

Weitere ähnliche Inhalte

Ähnlich wie @RISK Unchained Webinar

7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
EngFaisalAlrai
 
MONTE CARLO SimulationChapter 14 PowerPointManagement
 MONTE CARLO SimulationChapter 14 PowerPointManagement MONTE CARLO SimulationChapter 14 PowerPointManagement
MONTE CARLO SimulationChapter 14 PowerPointManagement
TatianaMajor22
 
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docxProblem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
sleeperharwell
 
You shouldneverdo
You shouldneverdoYou shouldneverdo
You shouldneverdo
daniil3
 
Columnar processing for SQL-on-Hadoop: The best is yet to come
Columnar processing for SQL-on-Hadoop: The best is yet to comeColumnar processing for SQL-on-Hadoop: The best is yet to come
Columnar processing for SQL-on-Hadoop: The best is yet to come
Wang Zuo
 
GA.-.Presentation
GA.-.PresentationGA.-.Presentation
GA.-.Presentation
oldmanpat
 

Ähnlich wie @RISK Unchained Webinar (20)

7 QC Tools and Problem Solving Presentation.pdf
7 QC Tools and Problem Solving Presentation.pdf7 QC Tools and Problem Solving Presentation.pdf
7 QC Tools and Problem Solving Presentation.pdf
 
7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
7_QC_Tools_and_Problem_Solving_Presentation_1656881575.pdf
 
MONTE CARLO SimulationChapter 14 PowerPointManagement
 MONTE CARLO SimulationChapter 14 PowerPointManagement MONTE CARLO SimulationChapter 14 PowerPointManagement
MONTE CARLO SimulationChapter 14 PowerPointManagement
 
Exact Real Arithmetic for Tcl
Exact Real Arithmetic for TclExact Real Arithmetic for Tcl
Exact Real Arithmetic for Tcl
 
205250 crystall ball
205250 crystall ball205250 crystall ball
205250 crystall ball
 
Free2 play soft launch obtaining tangible results through action-oriented a...
Free2 play soft launch   obtaining tangible results through action-oriented a...Free2 play soft launch   obtaining tangible results through action-oriented a...
Free2 play soft launch obtaining tangible results through action-oriented a...
 
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docxProblem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
Problem Set 5FIN 424, Winter 2017Robert Novy-MarxT.docx
 
Visualizing Your Startup Pitch Deck
Visualizing Your Startup Pitch DeckVisualizing Your Startup Pitch Deck
Visualizing Your Startup Pitch Deck
 
Shap
ShapShap
Shap
 
You shouldneverdo
You shouldneverdoYou shouldneverdo
You shouldneverdo
 
Moved to https://slidr.io/azzazzel/web-application-performance-tuning-beyond-xmx
Moved to https://slidr.io/azzazzel/web-application-performance-tuning-beyond-xmxMoved to https://slidr.io/azzazzel/web-application-performance-tuning-beyond-xmx
Moved to https://slidr.io/azzazzel/web-application-performance-tuning-beyond-xmx
 
Columnar processing for SQL-on-Hadoop: The best is yet to come
Columnar processing for SQL-on-Hadoop: The best is yet to comeColumnar processing for SQL-on-Hadoop: The best is yet to come
Columnar processing for SQL-on-Hadoop: The best is yet to come
 
Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)
Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)
Monte Carlo Simulations (UC Berkeley School of Information; July 11, 2019)
 
Stochastic Modeling for Valuation and Risk Management
Stochastic Modeling for Valuation and Risk ManagementStochastic Modeling for Valuation and Risk Management
Stochastic Modeling for Valuation and Risk Management
 
Week8
Week8Week8
Week8
 
Acceptance Test-Driven Development: Mastering Agile Testing
Acceptance Test-Driven Development: Mastering Agile TestingAcceptance Test-Driven Development: Mastering Agile Testing
Acceptance Test-Driven Development: Mastering Agile Testing
 
Verification challenges and methodologies - SoC and ASICs
Verification challenges and methodologies - SoC and ASICsVerification challenges and methodologies - SoC and ASICs
Verification challenges and methodologies - SoC and ASICs
 
Beyond Breakpoints: A Tour of Dynamic Analysis
Beyond Breakpoints: A Tour of Dynamic AnalysisBeyond Breakpoints: A Tour of Dynamic Analysis
Beyond Breakpoints: A Tour of Dynamic Analysis
 
GA.-.Presentation
GA.-.PresentationGA.-.Presentation
GA.-.Presentation
 
Know your platform. 7 things every scala developer should know about jvm
Know your platform. 7 things every scala developer should know about jvmKnow your platform. 7 things every scala developer should know about jvm
Know your platform. 7 things every scala developer should know about jvm
 

@RISK Unchained Webinar

  • 1. • To minimize background noise and audio feedback, the meeting’s global microphone is muted – except for mine. • Please also mute the audio controls on your side. • At certain points in the presentation I may announce that I’ll be fielding questions. When announced, please type your question into the GoToMeeting chat window. • Of course, you may pre-emptively type your questions in. • We have a lot of material to cover, so the presentation will proceed at a rapid clip. You are always welcome to e-mail or call to review anything that you may have questions about or would like to see again. • Today’s webinar will be recorded and posted on the Palisade website under the “TRAINING & EVENTS” menu. Webinar Notes
  • 2. Andrew J Sich, Regional Sales Manager asich@palisade.com; (800) 432-7475 x330 @RISK Unchained! Seldom used yet highly valued features you probably didn’t know about
  • 3. Presentation Objectives (1) Learn about your partner: Palisade Corp. (appx. 5 minutes) (3) For those new to @RISK . . . A walk-through on how to apply 3 fundamentals to Run a Monte Carlo simulation on your spreadsheet, and how to interpret the results. (appx. 15 minutes) (5) For everyone: New features in @RISK Version (appx. 10 minutes) (2) For those new to Monte Carlo simulation . . . An introduction (appx. 5 minutes) (4) For everyone: Seldom used @RISK features – valuable for answering the boss’s barrage of ‘What If’ questions. (appx. 25 minutes) (6) More: Stochastic Time Series and MS Project Integration
  • 4. Palisade Corporation is the maker of the market leading risk and decision analysis software @RISK. @RISK is the flagship product within the DecisionTools Suite which also includes the following MS Excel add-ins: l PrecisionTree l StatTools l RISKOptimizer l TopRank l NeuralTools l Evolver @RISK. DecisionTools Suite,
  • 5. London Sydney Ithaca, NY Rio de Janiero Tokyo Palisade Global Presence Cuenca 6 global offices and 8 language versions l English l 中国语言 l Français l Deutsch l 日本語 l Português l Русский язык l Español
  • 6. l Certified Microsoft Partner Palisade Recognition l Intel Software Partner l Accredited PMI Registered Education Provider l Approved Education Provider for the AACE l Sponsor of Continuing Professional Education on The National Registry of CPE Sponsors l Primary Monte Carlo simulation tool at the majority of academic institutions in North America and Europe
  • 7. 94% of Fortune 500 companies Partner with Palisade.
  • 8. Risk Analysis Modeling using Monte Carlo Simulation
  • 9. Risk Modeling: The Old Way  Single Point Estimates Do you make multi-million dollar decisions based on 1 number?  3-Point Estimates or Scenario Analyses Do you make multi-million dollar decisions based on 3 numbers?  Manual What-if Analyses Will you mandate that your analysts run a hundred scenarios for your million dollar decision? How much will that cost you?!? What if something changes? What if many things change, and change at different times? Managing risk then becomes cumbersome, time consuming, and error laden.
  • 10. Probability Distributions The direct result of Monte Carlo simulations, which are absolutely necessary for making defensible business decisions and for managing risk. Risk Modeling: The New Way Probability distributions furnish you with the full range of possible outcomes, how likely those outcomes are to occur, and identifies those items that impact your bottom line most significantly and by how much.
  • 11. What is Monte Carlo simulation?!?! At its core, Monte Carlo simulation is a virtual experiment – repeated hundreds, thousands, even millions of times – all the while generating random samples, bound by a set of parameters that you define, from each repetition of that experiment. It’s really not rocket science. Those random samples are then collected, organized, and analyzed to help you understand something about the behavior of that process or system.
  • 12. What is Monte Carlo simulation?!?! Let’s use a common example to illustrate . . . rolling a pair of dice . . . What happens when we roll the dice 48 times . . . and collect the results? Imagine we have only a vague idea of how 2 dice behave when rolled. Let’s model it! We define the parameters as: “2 dice” each with “6 possible outcomes”
  • 13. What is Monte Carlo simulation?!?! We get a bunch of numbers . . . Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total 1 4 5 3 6 9 4 3 7 6 6 12 2 2 4 2 4 6 2 2 4 3 4 7 3 3 6 1 1 2 3 1 4 1 1 2 4 1 5 6 2 8 1 6 7 6 2 8 1 6 7 5 3 8 6 5 11 5 3 8 6 5 11 2 5 7 5 4 9 2 5 7 Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total 6 1 7 3 4 7 3 3 6 3 4 7 4 2 6 6 2 8 6 1 7 6 2 8 1 3 4 2 3 5 2 6 8 2 3 5 2 4 6 4 1 5 4 1 5 4 1 5 3 5 8 5 5 10 5 2 7 5 6 11 5 6 11 1 5 6 1 6 7 1 5 6
  • 14. What is Monte Carlo simulation?!?! Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total 1 4 5 3 6 9 4 3 7 6 6 12 2 2 4 2 4 6 2 2 4 3 4 7 3 3 6 1 1 2 3 1 4 1 1 2 4 1 5 6 2 8 1 6 7 6 2 8 1 6 7 5 3 8 6 5 11 5 3 8 6 5 11 2 5 7 5 4 9 2 5 7 Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total Die 1 Die 2 Total 6 1 7 3 4 7 3 3 6 3 4 7 4 2 6 6 2 8 6 1 7 6 2 8 1 3 4 2 3 5 2 6 8 2 3 5 2 4 6 4 1 5 4 1 5 4 1 5 3 5 8 5 5 10 5 2 7 5 6 11 5 6 11 1 5 6 1 6 7 1 5 6 We then collect those numbers . . . 5 9 7 12 4 6 4 7 6 2 4 2 5 8 7 8 7 8 11 8 11 7 9 7 7 7 6 7 6 8 7 8 4 5 8 5 6 5 5 5 8 10 7 11 11 6 7 6 . . . and then condense the numbers into just the totals . . .
  • 15. What is Monte Carlo simulation?!?! Next . . . we organize the figures . . . 5 9 7 12 4 6 4 7 6 2 4 2 5 8 7 8 7 8 11 8 11 7 9 7 7 7 6 7 6 8 7 8 4 5 8 5 6 5 5 5 8 10 7 11 11 6 7 6 7 7 7 7 7 8 5 6 7 8 5 6 7 8 5 6 7 8 4 5 6 7 8 11 4 5 6 7 8 11 2 4 5 6 7 8 9 11 2 4 5 6 7 8 9 10 11 12
  • 16. What is Monte Carlo simulation?!?! The result is a chart that illustrates something about the behavior of rolling dice. 7 7 7 7 7 8 5 6 7 8 5 6 7 8 5 6 7 8 4 5 6 7 8 11 4 5 6 7 8 11 2 4 5 6 7 8 9 11 2 4 5 6 7 8 9 10 11 12 12 F 10 r e 8 q u 6 n c 4 y 2 0 2 3 4 5 6 7 8 9 10 11 12
  • 17. What is Monte Carlo simulation?!?! Let’s review . . . Monte Carlo simulation is a virtual experiment that repeats a process or project or situation a large number of times, and generates a large number of random samples bound by a specific set of parameters. Those random samples are collected and then organized and analyzed to help you understand the behavior of a simple or complex system or process.
  • 18. Benefits of Using @RISK  Peace of Mind Scrutinized, accepted, and lauded for 30+ years at all levels of academia, commercial industry, government & military.  Make Defensible Business Decisions Transform decision making discussions by illustrating “ranges”, “probabilities”, and “likelihood of occurrence” – and leave poor decisions arising from single point estimates to historians.  Ease of Use Work in the familiar environment of MS Excel with native Excel functions that allow full cell referencing capabilities.
  • 19. @RISK Demo Fundamentals Set-up your model and run a simulation.
  • 20. @RISK Demo Reports Present your modeling results in a variety of ways with attractive, easy to interpret, and time saving charts, graphs, and reports.
  • 21. Risk Register Capture and quantify event frequency and severity. X
  • 22. RiskMakeInput Summarize layered @RISK distributions in the Sensitivity Analysis. Treat cells with formulas or references as simulation inputs.
  • 23. Filter Simulation Results Observe simulation results within a range of percentiles.
  • 24. Stress Analysis Analyze the effect of stressing @RISK distributions between a set of percentiles, like the tails. “Stressing” a distribution restricts samples drawn from a distribution to values between a specified pair of percentiles, e.g. the left or right tail. Compare the results of a simulation with stressed inputs to a baseline simulation with no stress.
  • 25. Advanced Sensitivity Analysis Analyze the effect of any input at a specific value on an @RISK Output. @RISK runs a separate simulation for any number of isolated inputs, across a range of values. Inputs may be @RISK distributions or even cells with no @RISK function.
  • 26. RiskLock Lock specified inputs from sampling. Locking a cell with an @RISK input at its expected value, or any subsection of cells, lets you observe the effects of uncertainty of remaining or unlocked @RISK inputs.
  • 27. RiskSimtable Run a set of sequential simulations for different scenarios for side-by-side comparison.
  • 28. @RISK Goal Seek Find the value of an input that leads to a desired simulated Goal. Similar to Excel’s Goal Seek. However, unlike Excel’s Goal Seek, @RISK's Goal Seek utilizes multiple simulations to find the adjustable cell value that achieves your specified target.
  • 29. Efficient Frontier Analysis with RISKOptimizer Optimization competing goals under uncertainty. Affectionately referred to as a solver on steroids. New in Version
  • 30. Correlation Copulas Control correlation patterns of two dependent, or correlated, @RISK inputs. New in Version
  • 31. Data Viewer View any Excel data with @RISK’s graphing engine. No simulation necessary. New in Version Select any variable or data set and instantly create histograms, cumulative charts, scatterplots, trend, or box plots, for consistency in presentation of data.
  • 32. Thank You! We’ll now field questions . . . . . . via the GoToMeeting chat mechanism.