Course Level: Advanced
Format Requirements: This is a “hands-on” course. Participants will work through structured examples using their own computers. Data sets and files needed for the course will be available for download prior to the conference. Participants should be confident using basic Excel functions covered in the first part of the course, including matrix multiplication functions (MMULT, SUMPRODUCT), and database lookup functions (VLOOKUP) and the use of data tables, and have some experience in building trees and Markov models in Excel
Background: Microsoft Excel has become the tool of choice for quantitative analysis in MBA programs because of its power, flexibility, ease of use, transparency, and widespread availability. However, it has not been fully exploited by the decision analysis community. Many decision problems can be analyzed in Excel. Excel can be used to organize and model input parameters, build small or large models, analyze and graphically display results, and perform sensitivity analysis. Excel is a ubiquitous platform for developing simple or complicated models. Additionally, several common decision analysis software tools integrate with Excel. Improved proficiency with Excel can save time for almost anyone. This is the second of two courses on Excel modeling. In this course we demonstrate how to use Excel to build a number of common types of decision-analytic models. All examples will be done with Excel only and will not require the use of any add-in packages.
Description and Objectives: Course Description and Objectives:
The goals of this short course are to:
- Present advanced spreadsheet functions that will enable participants to build transparent decision-analytic and Markov models in Excel.
- Introduce Monte Carlo methods in Excel without the use of add-ins.
- Present methods for analyzing first order Markov models and for conducting probabilistic sensitivity analysis.
Participants will acquire the following skills from this short course:
- Simulate from uniform, normal, beta and gamma distributions in Excel using the functions rand, norminv, betainv, gammainv.
- Lay out and conduct a first-order simulation in a spreadsheet.
- Conduct probabilistic sensitivity analysis.
- Plot results in a C-E plane and draw simple cost effectiveness acceptability curves.