Linear models dominate quantitative thinking in investments. The weighted sum of a relevant set of factors, typically determined from a regression or discriminant analysis, determines model expectation. This approach is quite different from the heuristic style of decision making people tend to prefer. For example, when a patient is admitted to a trauma center, the attending physician determines the course of treatment for the emergency by going through a set of hierarchical decisions. Some things are more important than others, and they get considered first. Is there a pulse? is clearly a question to be asked first. Then follow questions of decreasing significance in deciding the course of treatment for the patient. The decision model for the physician is a decision tree, with an appropriate set of "if -then" rules. The decision tree is determined in a heuristic fashion based on experience and is preferred to a quantitative, weighted-sum approach, as in regression or discriminant analysis. The best investment decision making is more along the lines of using the right decision tree than using the right linear model.