Now we'll move onto an essential building block of Monte Carlo simulations: how
to model normal distribution curves. Since we didn't cover this in the
earlier "worksheet based simulations" section of this tutorial, we'll cover that
here, in addition to two VBA approaches to the same problem. This set of
examples is intended to tie together and reinforce many of the concepts we've
covered so far throughout the entire tutorial, in addition to introducing the
normal distribution modeling technique.
At the heart of the technique is the worksheet function NormInv(). The
function takes the three arguments probability, mean, standard deviation.
So, to generate random numbers with a mean of 10 and standard deviation of 3 is
as simple as entering the following formula into a worksheet cell:
=NormInv(Rand(), 10, 3)
In the three examples that follow, we'll use the Scholastic Aptitude Test (SAT)
as the basis of our problem. We want to model a set of SAT scores.
Currently the three-section SAT is calibrated to have a mean total score of 1500
with a standard deviation of 300. The minimum score is 600 and the maximum
is 2400. (Each section has a minimum of 200 and maximum of 800, mean of
500 and standard deviation of 100). Every SAT score is a multiple of 10
(i.e., 1240, 1250, etc.)
Thus the input screen for our worksheet-only SAT model is quite straightforward:
Calculating the SAT score using the NormInv() function requires an addition
step, since we need to set the minimum and maximum scores to 600 and 2400,
respectively, and make sure each score is a multiple of 10. The following
line of code gives a normally-distributed SAT score according to these
where "SAT1Mean" and "SAT1StdDev" are named ranges referring to cells D14 and
So, using the "worksheet-only" Excel Monte Carlo method, we'll enter that
formula into a series of cells, each cell generating one datapoint (or
iteration) of the model. Since our desired final results will be displayed
in a histogram, grouping scores rounded to the nearest multiple of 100, we'll
add a score "Range" column next to the raw score containing the formula:
with the "D20" reference changing accordingly for each iteration of the model.
An alternative to the separate "Score" and "Range" fields would have been to
combine the two formulas into a single one, performing the entire calculation in
a single cell. The formula would read:
A minor difference is the first versus second approach is that the first
approach rounds scores ending in "50" to the next higher 100, while the second
approach rounds down.