# Modeling Monte Carlo simulations for life outcomes

Hello all, this is my first post!

I read Douglas Hubbard’s excellent book, “How To Measure Anything,” in which he describes how to use Monte Carlo simulations to model the likelihood of different event outcomes in Excel.

The formula he uses is =NORMINV (RAND(), mean value of outcome, (max value of outcome - min value of outcome)/3.29)

However, this formula assumes that outcomes occur in a standard distribution. What if my distribution is not standard?

For example, let’s say I’m doing a Monte Carlo to see how much my house might sell for. The max expected value is \$500,000 and the min value is \$400,000. However, the likeliest outcome is not in the middle, but slightly lower – \$430,000 rather than the mean of \$450,000, depending upon how buyers respond to repairs that are needed.

So I can’t use NORMINV to calculate this. Any idea how I would model this calculation instead? Thanks!

Hi Noamb.

I liked his book and see QS applications in the approach where you identify all the variables that you believe are influencing something in your life and then use calibrated expert opinion to reduce the set of things you really need to measure.

Excel will calculate the higher moments useful for defining those distributions (mean, variance, skew, kurtosis). Excel can be expanded by add-ins to model non-normal distributions. See @Risk or Crystal Ball.