DecisionTools Suite 7.5 Industrial (cracked) - download
Everyone would like answers to these types of questions. Armed with that kind of information, you could take a lot of guesswork out of big decisions and plan strategies with confidence. With the DecisionTools Suite, you can answer these questions and more – right in your Excel spreadsheet.
The DecisionTools Suite is an integrated set of programs for risk analysis and decision making under uncertainty that runs in Microsoft Excel. The DecisionTools Suite includes @RISK, which adds risk analysis to Excel using Monte Carlo simulation, PrecisonTree for visual decision tree analysis, TopRank for what-if analysis, NeuralTools and StatTools for data analysis, and RISKOptimizer and Evolver for optimization. Rounding out the Suite is BigPicture for mind mapping, diagramming, and data exploration.
Risk analysis is systematic use of available information to determine how often specified events may occur and the magnitude of their consequences.
Risks are typically defined as negative events, such as losing money on a venture or a storm creating large insurance claims. However, the process of risk analysis can also uncover potential positive outcomes. By exploring the full space of possible outcomes for a given situation, a good risk analysis can both identify pitfalls and uncover new opportunities.
Risk analysis can be performed qualitatively or quantitatively. Qualitative risk analysis generally involves assessing a situation by instinct or “gut feel,” and is characterized by statements like, “That seems too risky” or “We’ll probably get a good return on this.” Quantitative risk analysis attempts to assign numeric values to risks, either by using empirical data or by quantifying qualitative assessments. We will focus on quantitative risk analysis.
A quantitative risk analysis can be performed a couple of different ways. One way uses single-point estimates, or is deterministic in nature. Using this method, an analyst may assign values for discrete scenarios to see what the outcome might be in each. For example, in a financial model, an analyst commonly examines three different outcomes: worst case, best case, and most likely case, each defined as follows:
Worst case scenario – All costs are the highest possible value, and sales revenues are the lowest of possible projections. The outcome is losing money.
Best case scenario – All costs are the lowest possible value, and sales revenues are the highest of possible projections. The outcome is making a lot of money.
Most likely scenario – Values are chosen in the middle for costs and revenue, and the outcome shows making a moderate amount of money.
There are several problems with this approach:
It considers only a few discrete outcomes, ignoring hundreds or thousands of others.
It gives equal weight to each outcome. That is, no attempt is made to assess the likelihood of each outcome.
Interdependence between inputs, impact of different inputs relative to the outcome, and other nuances are ignored, oversimplifying the model and reducing its accuracy.
Yet despite its drawbacks and inaccuracies, many organizations operate using this type of analysis.
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