Better Forecasting by Projecting Ranges of Outcomes

Course Overview:

This course provides a practical framework for incorporating probability analysis into your financial projections, equipping you with the tools and techniques to make more informed and robust financial forecasts. By incorporating probability analysis into your financial projections, you can better understand and manage risk to make more informed decisions.
 
Accounting is the language of business. Statistics is the language of uncertainty and risk. Financial analysts and decision-makers must speak both accounting and statistics; otherwise, we ignore uncertainty and risk.
 
This is a very high-level overview of probabilistic analysis. I'll focus more on concepts than calculations. My hope is that these concepts improve your company's understanding and measurement of uncertainty so that you can make better decisions.
 
In most forecasts and budgets, great effort is spent on determining the "right input number" or a few "right numbers" for the projection model. The assumption is that if we get the right inputs, we'll get the right output. In stochastic analysis, the model inputs are a range of numbers with assumptions about how those numbers are distributed throughout that range. Projections that provide ranges of outcomes and probabilities of those outcomes give us a much more complete picture of an uncertain and risky future.
 
The first lesson explores the benefits of probabilistic analysis. The second lesson briefly covers a few basic statistics concepts. We'll then look at S-curves and flying bars. These are ways to display a range of data. Next is an overview of Monte Carlo simulation. We'll then explore the implementation process for simulation analysis.

• Recall the definitions of possibility and probability
• Identify key aspects of distribution curves and flying bar charts
• Recognize methods and tools for probabilistic modeling