Monte Carlo Analysis in DataGraph

This webinar will teach you how to conduct a basic Monte Carlo Analysis using DataGraph.

Monte Carlo Analysis allows you to represent variability and uncertainly in what would otherwise be a single, deterministic calculation. This approach is used in multiple fields such as financial analysis and health risk assessment. The basic approach involves generating distributions of data and using those to calculate results.

In this webinar, we provide some background information and demonstrate this technique using the two examples summarized below.

  • 00:00 – Introduction
  • 01:29 – Background
  • 08:42 – Basic Example
  • 24:01 – Discrete and Continuous
  • 33:00 – Q&A
  • 41:19 – Demo for Weibull Distribution
  • 44:17 – Next time …

Basic Monte Carlo

The first example we explore in DataGraph is a basic Monte Carlo analysis. In this example a simple deterministic calculation is used to demonstrate how each variable can be represented using a distribution.

In DataGraph, the distributions are generated using the nrand function. The distributions are used to calculated outcomes that can be analyzed to determine the mean and standard deviation of the result, along with specific percentiles.

Marketing Example

The second example discussed in this webinar is a Monte Carlo simulation of profit. This is an example was published on Medium (Application of Monte Carlo Simulation in Marketing, October 2020). In that article, code is provide to conduct the simulation in R. We use the same example here in DataGraph.

This is example uses a function to estimate the distributions of profit using discrete and continuous input distributions. The discrete distribution is created using the rand function and the continuous distribution is generated using the nrand function.

The result is a simulation of net profit.

Example Files

Details on how these examples are created are covered in the webinar. DataGraph files for each example can be access using File > On-line examples from within DataGraph. They are saved in the Analysis section.

References

Thomopoulos, Nick T. Essentials of Monte Carlo Simulation Statistical Methods for Building Simulation Models. New York, NY: Springer, 2013.

Hariharan. Application of Monte Carlo Simulation in Marketing, Medium.com, Oct 12, 2020. https://medium.com/analytics-vidhya/application-of-monte-carlo-simulation-in-marketing-811f9e6b07e4

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