Titlebook: Energy Risk Modeling; Applied Modeling Met Nigel Costa Lewis Book 2005 Palgrave Macmillan, a division of Macmillan Publishers Limited 2005

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Energy Risk Modeling978-0-230-52378-4Series ISSN 2946-2010 Series E-ISSN 2946-2029

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https://doi.org/10.1007/b117251stics is to reduce the original sample into a handful of more understandable metrics without distorting or losing too much of the valuable information contained in the individual observations. We begin by collecting . observations {.., ..,…,..} on the price return random variable .. These measuremen

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Static charging by spray electrification,collected from a sample. Such generalizations are about an unobserved population. A population consists of all values (past and future) of the random variable of interest. In most circumstances the exact value of a population parameter such as the mean or variance will be unknown, and we will have t

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https://doi.org/10.1007/978-1-4471-1669-1wledge about the underlying probability distributions generating returns is used both in pricing models and risk management. The selected probability distribution(s) can have a significant impact on the calculated Value at Risk measure of a company’s exposure from trading floor transactions and in t

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Statical and Geomechanical Modelsor characteristic of a sample. Another important element of applied statistical modeling of energy risks concerns the relationship between two or more price or other variables. Generally, a risk manager will be interested in whether above (below) average values of one variable tend to be associated

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Classification of Structural Elements,finance. Given its importance and frequency of use, this chapter provides a hands-on introduction to applied regression modeling. The emphasis is on using R to analyze some simple data sets contained in the R package. The objective is to give you a feel for regression techniques using simple (nonene

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Stiffness Changes and Reanalysis,ct more than one independent variable to influence the dependent variable. It allows us to explore the relationship between several independent and a single dependent variable. We also discuss multivariate regression which arises when we have several dependent variables dependent on the same (or som

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https://doi.org/10.1007/978-94-007-6113-1How can we assess the validity of the pre-specified regression model, which will provide the basis of statistical inference and practical decisions? It turns out that statistical inference concerning the linear regression model depends crucially on the “validity” of the underlying statistical assump

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