Page 19 - Fundamentals of Probability and Statistics for Engineers
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2                      Fundamentals of Probability and Statistics for Engineers

           heads or tails. Random phenomena in scientific areas abound: noise in radio
           signals, intensity of wind gusts, mechanical vibration due to atmospheric dis-
           turbances, Brownian motion of particles in a liquid, number of telephone calls
           made by a given population, length of queues at a ticket counter, choice of
           transportation modes by a group of individuals, and countless others. It is not
           inaccurate to say that randomness is present in any realistic conceptual model
           of a real-world phenomenon.



           1.1  ORGANIZATION OF TEXT

           This book is concerned with the development of basic principles in constructing
           probability models and the subsequent analysis of these models. As in other
           scientific modeling procedures, the basic cycle of this undertaking consists of
           a number of fundamental steps; these are schematically presented in Figure 1.1.
           A basic understanding of probability theory and random variables is central to
           the whole modeling process as they provide the required mathematical machin-
           ery with which the modeling process is carried out and consequences deduced.
           The step from B to C in Figure 1.1 is the induction step by which the structure
           of the model is formed from factual observations of the scientific phenomenon
           under study. Model verification and parameter estimation (E) on the basis of
           observed data (D) fall within the framework of statistical inference. A model



                                              A: Probability and random variables




                  B: Factual observations
                  and nature of scientific    C: Construction of model structure
                      phenomenon





                    D: Observed data      E: Model verification and parameter estimation






                                               F: Model analysis and deduction

                    Figure 1.1 Basic cycle of probabilistic modeling and analysis








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