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"I've worked with simulation in business for over 20 years, and Allman really nails it with this book. I admit that I own his previous book on structured finance cash flows, but I was surprised by what I found in here. He addresses the fundamental questions of how decision makers react to simulations and his read was very much in accordance with what I've experienced myself. When it came to the nuts and bolts of describing the different types of simulation analysis the book becomes incredibly detailed. There is working code and models for a fantastic array of the most common simulation problems. If you're so inclined, the book very carefully steps through the tricky math needed to really understand the theory behind stochastic modeling in finance. If you're preparing models that include any kind of randomization or stochastic modeling component, this book is a must-read, a tremendous value and time-saver." ? David Brode of The Brode Group
A practical guide to understanding and implementing financial simulation modeling
As simulation techniques become more popular among the financial community and a variety of sub-industries, a thorough understanding of theory and implementation is critical for practitioners involved in portfolio management, risk management, pricing, and capital budgeting. Financial Simulation Modeling in Excel contains the information you need to make the most informed decisions possible in your professional endeavors.
Financial Simulation Modeling in Excel contains a practical, hands-on approach to learning complex financial simulation methodologies using Excel and VBA as a medium. Crafted in an easy to understand format, this book is suitable for anyone with a basic understanding of finance and Excel. Filled with in-depth insights and expert advice, each chapter takes you through the theory behind a simulation topic and the implementation of that same topic in Excel/VBA in a step-by-step manner.
Created for those with some background in finance and experience in Excel, this reliable resource shows you how to effectively perform sound financial simulation modeling, even if you've yet to do extensive modeling up to this point in your professional or academic career.
This lively and engaging textbook explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The author, David A. Freedman, explains the basic ideas of association and regression, and takes you through the current models that link these ideas to causality. The focus is on applications of linear models, including generalized least squares and two-stage least squares, with probits and logits for binary variables. The bootstrap is developed as a technique for estimating bias and computing standard errors. Careful attention is paid to the principles of statistical inference. There is background material on study design, bivariate regression, and matrix algebra. To develop technique, there are computer labs with sample computer programs. The book is rich in exercises, most with answers. Target audiences include advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modeling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. Features of the book: - authoritative guidance from a well-known author with wide experience in teaching, research, and consulting - careful analysis of statistical issues in substantive applications - no-nonsense, direct style - versatile structure, enabling the text to be used as a text in a course, or read on its own - text that has been thoroughly class-tested at Berkeley - background material on regression and matrix algebra - plenty of exercises, most with solutions - extra material for instructors, including data sets and code for lab projects (available from Cambridge University Press) - many new exercises and examples - reorganized, restructured, and revised chapters to aid teaching and understanding
The use of simulation modeling and analysis is becoming increasingly more popular as a technique for improving or investigating process performance. This book is a practical, easy-to-follow reference that offers up-to-date information and step-by-step procedures for conducting simulation studies. It provides sample simulation project support material, including checklists, data-collection forms, and sample simulation project reports and publications to facilitate practitioners' efforts in conducting simulation modeling and analysis projects.
A basic text-workbook for the food preparation lab portion of the ``foodservice fundamentals'' course. Twenty-two compact chapters offer information on cooking procedures and food categories. Features small quantity recipes with simple ingredient, equipment and procedure lists, mise en place (prep) sheet for all recipes, review exercises and glossaries of key terminology with definitions.
Geophysics, or physics modelling of geological phenomena, is as old and as - tablished as geoscience itself. The statistical physics modelling of various g- physical phenomena, earthquake in particular, is comparatively recent. This bookintendstocovertheserecentdevelopmentsinmodellingvariousgeoph- ical phenomena, including the imposing classic phenomenon of earthquakes, employing various statistical physical ideas and techniques. This ?rst book on statistical physics modelling of geophysical phenomena contains extensive - viewsbyalmostalltheleadingexpertsinthe?eldandshouldbewidelyuseful to the graduate students and researchers in geoscience and statistical physics. It grew out of the lecture notes from a workshop on "Models of Earthquakes: Physics Approaches", held in Saha Institute of Nuclear Physics, Kolkata, - der the auspices of its Centre for Applied Mathematics and Computational Science in December 2005. The book is divided in four parts. In the ?rst part, tutorial materials are introduced. Chakrabarti introduces the fracture nucleation processes, their (extreme) statistics in disordered solids, in ?bre bundle models and in the two fractal overlap models of earthquakes. In the next two chapters, Hemmer et al. and Kun et al. review the avalanche or quake statistics and the bre- ing dynamics in simple (mean-?eld like) ?bre bundle models and in their extended versions, respectively. Hansen and Mathiesen discuss the scale - variance properties of the random and fractured surfaces.
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