One of the side-effects of the recession appeared to be a reduction in the demand for electricity as businesses and consumers alike looked to make savings on their outgoings. However, economic recovery seems to render this trend as temporary, meaning that the global need to tackle energy-consumption is as pressing as ever. BC Hydro, Canada’s […]

# Monthly Archives: July 2010

## A Little Limelight

Limelight–and by this I mean positively glowing publicity– shines only occasionally on quantitative analysis, and rarely on Monte Carlo simulation. But there was, 6 years ago, Michael Lewis’s Moneyball, which established a place for statistical analysis in major league baseball. Now there is Relativity Media, LLC, currently one of the heaviest hitting movie production companies […]

## @RISK Quick Tips: Correlation of Input Variables

This financial risk analysis example demonstrates the use of the Corrmat function to correlate multiple @RISK distributions. The distributions are correlated using a matrix of coefficients that specify the relationship between each pair of functions. The coefficients must be between -1 and +1, with a value of +1 indicating a perfect correlation, 0 indicating no […]

## Sharing Simulation Models – Part III: The @RISK Library

Another great way to collaborate with others is through the @RISK Library. This is a SQL-based database that lets you store customized @RISK functions with the specific parameters that you need. Then others can pull the same @RISK function for their risk analysis models, directly from the standard Define Distribution window. This way you can […]

## @RISK Quick Tips: Use of RiskTheo to Represent Distributions as Discrete Ones

@RISK, risk analysis software using Monte Carlo simulation, has many powerful features that help you create powerful models for decision making under uncertainty. For example, you can use the RiskTheo function in @RISK to determine the parameters of a discrete distribution based on a continuous one. In this example, the RiskTheo functions of @RISK work […]

## Oops! Didn’t see that coming! Part 3

We are pleased to welcome back to my blog consultant and trainer David Roy from Six Sigma Professionals, Inc. Oops! Didn’t see that coming! Part 3 As a continuation from the June blog, we are now covering the “Conceptualize” phase of the ICOV framework of a rigorous new design process as […]

## Sharing Simulation Models – Part II: Saving @RISK Simulations

You can save @RISK risk analysis simulation results directly in your Excel workbook. This makes it easy to pass results around to others. Colleagues can see the benefits of risk analysis using Monte Carlo simulation. Just save your workbook like you normally would, then click Yes when prompted if you want to save simulation results. […]

## @RISK Quick Tips: Oil & Gas: Production and Economic Forecast using Exponential Decline.

@RISK has many applications for oil and gas exploration and production. This quantitative risk analysis model forecasts production, revenues, and present value based on exponential decline. Uncertain input factors include yearly production, decline rate, GOR, price of gas, price of oil, and rate of increase in oil and gas prices. A SimTable function is also […]

## Introduction, by Way of Retraction

Just after I posted my last blog questioning a recent Investopedia column in the San Francisco Chronicle, I had a congenial note from the author of that column, David Harper. His column compared Monte Carlo Simulation with two other methods of calculating Value-at-Risk, and I was concerned that its view of risk and risk analysis […]

## Free Webcast This Thursday: “Why be Normal? Selecting the Best Distribution Models ”

On Thursday, July 15, 2010, Andy Sleeper will present a free live webcast entitled. "Why be Normal? Selecting the Best Distribution Models " Distribution models are important aspects of many types of statistical analysis, including Monte Carlo analysis. The choice of model is vitally important, since the wrong model can be worse than no model […]