This book examines the use of models in the decisionmaking process. The book is divided into seven main parts. Part I - Events and Relations: Alternative Views contains two chapters that look at unknown entities and their manifestations from alternative views of causality and probability. Part II - Events and Relations: Linear Representation contains five chapters that look at the empirical representation of events and relations through linear representations, while Part III contains four chapters that examine the empirical representation of events and relations through the use of nonlinear representations. Part IV - Events and Relations: Underlying Processes contains five chapters they discuss the underlying processes which generate the different probability distributions: Bernoulli trials, Poisson processes, and Markov chains. Part V - Decisions and Consequences: Experimental and Physical, contains two chapters that examine the consequences of two types of decisions, false acceptance and false rejection. Part VI - Decisions and Consequences: Inference and Implications, contains three chapters that explore the inferences on which decisions are based: point estimation and interval estimation, t-distribution, and confidence intervals. Part VII - Decisions and Consequences: Randomness and Risks, contains two chapters that examine the use of randomness in the decisionmaking process. The final section of the book, Conclusion, discusses the process of making better decisions through the use of better models. Appendixes, tables, and index