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Titlebook: Applied Data Analysis and Modeling for Energy Engineers and Scientists; T. Agami Reddy,Gregor P. Henze Textbook 2023Latest edition The Edi

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期刊全称Applied Data Analysis and Modeling for Energy Engineers and Scientists
影响因子2023T. Agami Reddy,Gregor P. Henze
视频video
发行地址All twelve chapters greatly revised with six chapters extensively expanded and updated.Primarily covers classical data analysis methods while also treating machine learning and data mining methods.Inc
图书封面Titlebook: Applied Data Analysis and Modeling for Energy Engineers and Scientists;  T. Agami Reddy,Gregor P. Henze Textbook 2023Latest edition The Edi
影响因子.Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is presented, and a formal structure is proposed meant to classify the various assessment m
Pindex Textbook 2023Latest edition
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发表于 2025-3-21 20:19:07 | 显示全部楼层
e also treating machine learning and data mining methods.Inc.Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experim
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https://doi.org/10.1007/978-3-031-22556-7erent in the nature of the process itself. Probability theory allows idealized behavior of different types of systems to be modeled and provides the mathematical underpinning of statistical inference. In that respect, it can be viewed as pertaining to the forward modeling domain. Both the primary vi
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https://doi.org/10.1007/978-3-031-22556-7curacy of the raw data collected are addressed. These involve limit and balance checks, outlier rejection, and ways to handle missing data. Subsequently, salient statistical measures to describe and summarize datasets are presented. The importance of exploratory data analysis is highlighted along wi
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Peter Medway,John Hardcastle,David Crookedge of probability and probability distributions. More specifically, such statistical inferences involve point estimation, confidence interval estimation, hypothesis testing of means and variances from two or more samples, analysis of variance methods, goodness of fit tests, and correlation analysi
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,The Three Schools—What We Have Learned,essor variables. The process involves both identifying the model functional form and estimating the parameters of the model. Models linear in their parameters, the focus of this chapter, are widely used, with the . (OLS) method of estimating model parameters being the most frequent. The . with one r
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English Theatre and Social Abjectionnder different changes/variations during the manufacturing process (called “treatments”). . is the term used to denote the series of planned experiments to be undertaken to compare the effect of one or more treatments or interventions on a response variable. The analysis of such data once collected
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