1. 1 BA 555 Practical Business Analysis Simulation Using @Risk Agenda

2. 2 Example 3. IRR (p.93) Is there more than 80% chance that the IRR exceeds the discount rate of 15%?

3. 3 Choosing a Probability Distribution (p.88) Without past observations With very limited amount of information, try to estimate Max, Min, and Most Likely values and use =RiskPert (or =RiskTriang) With more information (e.g., percentiles), look for a distribution that reflects the information. With past observations Click the Define Distributions icon and New Fit. @Risk will find a distribution that fits the data.

4. 4 Example 5 Software Development Project (p.97) A software development project consists of 6 activities. What is the expected duration for the entire project?

5. 5 =RiskSimTable( { v1, v2, …, vn} ) The function specifies a list of values (v1, v2, …, vn) which will be used sequentially in individual simulations executed during a sensitivity simulation. The number of simulations executed must be less than or equal to the number of arguments.

6. 6 Example 4. Stock Portfolio (p. 95) =RiskNormal( mu, sigma, RiskCorrMat( correlation matrix, position) )

7. 7 New Product Design Product Attributes Customer Scoring Weights

8. 8 New Product Design Which new brand will maximize the market share?

9. 9 Example 6: Binomial Experiment (pp. 99 – 104) The experiment consists of n identical trials. There are only two possible outcomes on each trial. We will denote one outcome by S (for Success) and the other by F (for Failure). The probability of S remains the same from trial to trial. This probability is denoted by p, and the probability of F is denoted by 1 – p. The trials are independent. The binomial random variable X is the number of S’s in n trials. Example: Flip a fair coin 5 times. X = # of tails in 5 trials.

10. 10 Example 6: Binomial Probability Distribution (pp. 99 – 104) @Risk function = RiskBinomial( n, p ) Binomial Probability