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Abstract: Rationality and the "as if" principle - the analysis of expectations - perfect competition - imperfect competition: one-period horizon - imperfect competition: multiperiod horizon - imperfect competition : a dynamic model and some approximations - application to experimental data - application to industry data : southern pine lumber - application to industry data : cement - application to industry data : pneumatic tires - application to industry data : department store shoes.

Abstract: Inquinamento della teoria - Teoria della rappresentazione analitica - Influenza del raggruppamento in classi - Metodi per rendere rappresentative alcune rilevazioni - Introduzione alla teoria dei campioni - Il problema inverso della ricerca statistica - Comparazione dei dati - Piano degli esperimenti - Controllo della produzione.

Abstract: What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and ...; [Read more...] What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-- but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data"--. Part 1. The basics. Mind the semantic gap -- Semantic modeling elements -- Semantic and linguistic phenomena -- Semantic model quality -- Semantic model development -- Part 2. The pitfalls. Bad descriptions -- Bad semantics -- Bad model specification and knowledge acquisition -- Bad quality management -- Bad application -- Bad strategy and organization -- Part 3. The dilemmas. Representation dilemmas -- Expressiveness and content dilemmas -- Evolution and governance dilemmas -- Looking ahead.