Economic modeling is present in the activity of those competent in processing information in the economic field.
An important category of economic phenomena occur in an environment invaded by uncertainty, a factor that can prevent the employee from making the right decision, generating profit.
The approach of the economic environment in which uncertainty plays an essential role presupposes a special competence on the part of the one who makes this enterprise, reason for which this volume is addressed to the student, bachelor or master student in economics and even to all those who want to acquire this specialization.
Appropriate means in overcoming the effects of uncertainty exist, and these means are also acquired by accumulating knowledge from probability theory or mathematical statistics.
Economic modeling receives a first-rate support from probability theory, which being a mathematical theory of empirical origin has as object of study the modeling of phenomena with at least one random component, the attribute "random" certifying the presence of uncertainty in the evolution of the phenomenon.
Secondly, economic modeling receives from mathematical statistics a consistent offer of techniques and methods, specially created to elaborate in conditions of uncertainty decisions rigorously substantiated in fields such as: trade, tourism, marketing, finance, agriculture, industry, property and personal insurance, economic forecast for different terms, medicine and enumeration could continue.
This volume is formally made up of two chapters, numbered by 5 and 6, respectively, thus marking the continuation of the numbering started in the first volume. This continuation is justified especially by the fact that the results presented in the first volume constitute the foundation of the knowledge presented in this second volume.
The knowledge of probability theory presented in Chapter 5 is grouped in the following sections: introduction to probability theory, mathematical modeling of an experiment, developments of the probabilistic model, classical probabilistic models, random variables, numerical characteristics of random variables, characteristic function, one-dimensional classical distributions, strings of random variables.
The object of chapter 6 is formed by the sections: selection theory, estimation theory, verification of statistical hypotheses.
The content of chapter 5 introduces the reader to a space in which the probabilistic thinking and inventiveness of the probabilistic mathematician has built notions and theoretical tools adequate to quantify the chance to be realized in an uncertain environment of a random phenomenon. The one who accumulates the knowledge offered in this chapter acquires the competence to probabilistically model the economic phenomena affected by uncertainty.
The presentation in Chapter 6 is made in a framework provided by mathematical modeling in general and probabilistic modeling in particular. The presentation emphasizes, whenever necessary, the distance between the probabilistic model built on the basis of hypotheses and the modeled statistical reality, the distance that requires the specification of modeling errors caused by the presence of uncertainty. The accumulation of knowledge in this chapter generates the competence to think and model statistically-inferentially.
Each section ends with a summary, followed by a list of representative concepts and notions, and each chapter ends with solved applications and applications proposed for solving, with the corresponding answers.
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