Choosing an Appropriate Distribution
Choose the right distributions for your @RISK or Monte Carlo simulations. Improve accuracy & results! Learn distribution selection techniques.
Course Content
Choosing an Appropriate Distribution is a beginner-level Udemy course taught by Fernando Hernandez. It runs 2h, is taught in English, and includes a certificate of completion. Learners rate it 4.5 out of 5 from 17 reviews, with 155 students enrolled. It is a paid course, first published July 2020.
Are you a current @RISK user looking to enhance your simulation skills? Or perhaps a business analyst, financial analyst, economist, or scientific researcher interested in leveraging Monte Carlo simulation? This course is designed for you, whether you're a beginner or an experienced @RISK professional. It's also an excellent introduction to Monte Carlo simulation methodology for those new to the concept.
This course directly addresses a critical challenge modelers face: choosing the right distributions for variables within their Monte Carlo simulation models. The principle of GIGO (Garbage In, Garbage Out) is paramount in simulation. Accurate results depend on selecting distributions that realistically reflect the statistical properties of your input variables. Poorly chosen distributions will lead to unreliable and questionable model outputs.
We begin by exploring decision trees as a structured approach to selecting appropriate distributions. The world of statistical distributions is vast – @RISK offers access to over 97 different distribution functions. And the possibilities don't stop there! You'll learn how to create your own custom distributions to perfectly fit your modeling needs.
Key topics include:
- Monte Carlo Simulation Fundamentals
- @RISK Distribution Selection
- Decision Tree Methodologies for Distribution Choice
- Understanding Statistical Distributions
- Creating Custom Distributions in @RISK
- Avoiding GIGO in your Models
- Building Robust Simulation Models
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