Different techniques for analyzing project risk require different input variables and assumptions. Suppose you are using the scenario analysis technique to evaluate project risk. You wow change in the model to evaluate the effect of the input factors on the expected value. Danielle is a risk analyst. She is conducting a sensitivity analysis to evaluate the riskiness of a new project that her company is considering investing in. Her risk analysis report includes the sensitivity curve shown on the graph. NPV (Millions of dollars) Base Case Base Case Cost of Capital 9 12 15 COST OF CAPITAL (Percent) This curve implies that the project is not very sensitive to changes in cost of capital. The project's NPV is likely to if the cost of capital increases to 15%. Along with the sensitivity analysis, Danielle is including a scenario analysis for the project in her report, giving the probability of the project generating a negative NPV. Her report includes the following information about the scenario analysis: Data Collected Probability (P) Outcome Pessimistic Most likely Optimistic NPV; -$5.62 million $7.94 million $16.45 million 0.4 0.6 Probability Data for z 0.03 0.06 0.09 0.3336 0.3228 0.3121 0.2643 0.2546 0.2451 0.2033 0.1949 0.1867 0.1515 0.1446 0.1379 0.35 0.30 0.8 0.35 Complete the missing information in Danielle's report: The expected net present value of the project is million. Standard deviation of the net present value (the NPV of the project is likely to vary by) Assuming that probability distribution is normal, the value of z is Thus, the project has a chance to generate an NPV of less than $0