![]() ![]() How PrecisionTree Works Build a Decision Tree Decision trees provide a formal structure in which decisions and chance events are linked in sequence from left to right. With this capability, collaborative decision-making just got much easier, bridging the gap between quantitative probabilistic analysis and expert knowledge. PrecisionTree now has the ability to export quantitative decision tree models directly to BigPicture, turning them into stunning visual maps that can be shared with any Excel user for presentation and discussion. Offering unparalleled visual diagramming and reporting capabilities, BigPicture is becoming widely used for analyzing, discussing, and sharing information about complex decisions. New in Version 7 – View and Share Decision Trees in BigPicture » Palisade’s BigPicture is the mind mapping and data exploration add-in now included with the DecisionTools Suite. PrecisionTree has also been fully translated into, and. This helps you identify and calculate the value of all possible alternatives, so you can choose the best option with confidence. Decision trees are quantitative diagrams with nodes and branches representing different possible decision paths and chance events. ![]() With PrecisionTree, you can visually map out, organize, and analyze decisions using decision trees, right in Microsoft Excel. ![]() Visual Decision Analysis in Your Spreadsheet Have you ever been faced with a complex, multi-stage decision like what is the best strategy for testing and drilling for oil, or should we build a new plant or buy an existing one? What about bidding on a new project – what should you bid, and how should you react to your opponent’s bid? Or perhaps you are faced with determining the best litigation strategy is in a legal dispute, or the best series of medical tests and procedures to maximize a patient’s chance of recovery? PrecisionTree helps you tackle these types of complex, sequential decisions. All the combinations of inputs and to describe each one explicitly in a decision table. ![]()
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