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Choosing an Appropriate Distribution

  Choosing an Appropriate Distribution

Udemy Coupon ED | Choosing an Appropriate Distribution

You can express uncertainty about any variable by using a probability distribution. You can select a distribution based on available data, on the judgment of a knowledgeable expert, or on a combination of data and judgment. Get Discount

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What you'll learn
  • How to select the best distribution for your @RISK models
Requirements
  • Basic knowledge of @RISK and Excel
 Description
 
Current @RISK users, both novices and experts, business and financial analysts, economists, statisticians, scientific researchers using @RISK or any other Monte Carlo simulation platform may greatly benefit by taking this course.  Students who also want to be introduced to @RISK as a general simulation methodology will benefit from this course.
It is intended to answer the common question modelers have whenever they are building a model: How to choose appropriate distributions for the variables, or “moving parts” of a Monte Carlo simulation model they are attempting to build. The principle of GIGO (“garbage in, garbage out”) applies here dramatically well. Build a model with appropriate distributions that clearly reflect the statistical nature of your variables and you will end up with a robust model to withstand reality testing. Build a model with lousily chosen distributions and your model will be as weak and questionable as any of your input variables.
This course starts by introducing a decision tree as a structure to help decide on multiple distributions. The world of statistical distribution functions is endless. @RISK uses some 97 different distribution functions to choose from. And this is not the end of it, since you can create, as we will show, your own distributions.
Who this course is for:
  • Users of @RISK or of any other Monte Carlo simulation software

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