Quantitative Analysis: An IntroductionCRC Press, 1999. gada 22. febr. - 592 lappuses Written in a lecture format with solved problems at the end of each chapter, this book surveys quantitative modeling and decision analysis techniques. It serves to familiarize the reader with quantitative techniques utilized in planning and optimizing complex systems, as well as students experiencing the subject for the first time. It can be used by students of business and public administration without a background in calculus as well as engineers with significant scientific training. It allows the reader to comprehend the material through examples and problems and also demonstrates the value and shortcomings of many methods. Quantitative Analysis: An introduction developed out of the author's experience teaching the material to students at the University of California Los Angeles, California State University, Northridge, and the University of Southern California, Los Angeles. |
Saturs
DECISION ANALYSIS | 45 |
Introduction | 91 |
4 | 98 |
6 | 106 |
7 | 112 |
BreakEven Analysis Additional Solved Problems | 119 |
INVENTORY DECISION ANALYSIS | 125 |
PROJECT MANAGEMENT PERTCPM | 159 |
FORECASTING | 231 |
MULTIPLE REGRESSION | 267 |
LINEAR PROGRAMMING LP | 311 |
QUEUEING | 377 |
SIMULATION | 409 |
APPENDICES | 435 |
B Management of Information Systems | 459 |
Solutions to Exercises | 481 |
Project Management PERTCPM Additional Solved Problems | 183 |
SIMPLE REGRESSION | 195 |
6 | 213 |
References | 551 |
Index 571 | |
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action Answer autocorrelation calculators cell Chi-Square coefficient computed constant constraints corner point correlation critical path decision maker decision tree decision variables degrees of freedom dependent design matrix determine deterministic Durbin-Watson statistic Earliest Completion ECT for Task error terms estimated Example expected number Expected Value Decision expected waiting line Exponential distribution Exponential Smoothing F-statistic feasible region fixed cost follows Holding Cost independent variables input inventory cycle Least Squares Least Squares line linear programming linear trend Method of Least moving average forecasts Normal distribution Note objective function optimal solution output past demand data payoff Poisson distribution probability Problem provides queueing system R2-statistic Random Numbers Random Variable reorder point Safety Stock sample mean server service completion simple moving average Simplex Method simulation standard deviation statistic stochastic Table test design matrix tests/month Total Annual Cost unexplained deviation variance waiting line length zero