Analyse de la variance

En statistique, lanalyse de la variance (terme souvent abrégé par le terme anglais ANOVA : analysis of variance) est un ensemble de modèles statistiques utilisés pour vérifier si les moyennes des groupes proviennent d'une même population. Les groupes correspondent aux modalités d'une variable qualitative (p. ex. variable : traitement; modalités : programme d'entrainement sportif, suppléments alimentaires; placebo) et les moyennes sont calculés à partir d'une variable continue (p. ex. gain musculaire). Ce test s'applique lorsque l'on mesure une ou plusieurs variables explicatives catégorielle (appelées alors facteurs de variabilité, leurs différentes modalités étant parfois appelées « niveaux ») qui ont de l'influence sur la loi d'une variable continue à expliquer. On parle d'analyse à un facteur lorsque l'analyse porte sur un modèle décrit par un seul facteur de variabilité, d'analyse à deux facteurs ou d'analyse multifactorielle sinon. Histoire Ronald Aylmer Fisher prés

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Statistique

La statistique est la discipline qui étudie des phénomènes à travers la collecte de données, leur traitement, leur analyse, l'interprétation des résultats et leur présentation afin de rendre ces don

Ronald Aylmer Fisher

Sir Ronald Aylmer Fisher est un biologiste et statisticien britannique, né à East Finchley le et mort le . Richard Dawkins le considère comme et Anders Hald comme l'homme qui a – . Pour Bradley Efr

Variance (mathématiques)

vignette|Exemple d'échantillons pour deux populations ayant la même moyenne mais des variances différentes. La population en rouge a une moyenne de 100 et une variance de 100 (écart-type = SD = standa

2021

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Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Yuqing Liu

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (ATDF) to precisely predict the stud shear stiffness, where the novel Deep For-est algorithm is integrated with the Sequential Model-Based Optimization method to achieve automatic hyper -parameter optimization. Six variables having causal relationships with shear stiffness were extracted via mechanism and model analysis, including the effect of weld collar that cannot be considered in existing models and subsequently constituting a database of 425 push-out tests. Then the ATDF model was trained by combining the advantages of deep learning, ensemble learning, and auto-tuning techniques. It was approved to significantly outperform representative benchmark models with R values of 0.91 and 0.87 for training and testing sets. The ATDF was subjected to attribute importance analysis, which quantified the stud diameter and concrete elastic modulus as the most significant variables for shear stiffness, with the stud elastic modulus having the minimal effect. The model uncertainty of ATDF was further evaluated, revealing that it had the lowest bias and variability than those in existing empirical or semi-empirical models. Finally, the reliability analysis was conducted and the partial factors of ATDF under specified target reliability were derived.

2022

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The Goals of the Analysis Model

When developing an analysis model, the notation itself does not suffice for determining the content of the analysis model and the end of the analysis process. For this, the goals and intents of the analysis model are used, either implicitly or explicitly. In this report we take a look at various possibilities to define the term analysis and the goals of the analysis model. We start with considering the reference model of the problem solving cycle. We examine the definitions that refer to analysis as the "what" in opposite to the "how", or as the problem definition phase of a problem solving cycle. We then continue with a discussion of various criteria (like technology independence, real word model, unambiguous or essential model) that are often used to specify the goals of an analysis model. Difficulties that arise with these criteria are that they cannot be combined arbitrarily and that some of them are very vague; we propose therefore also some more concrete formulations. In the last part of the report we take a closer look at some misconceptions and intent clashes that are due to a too idealistic model of the software development process. Objective real world models, analysis models that are stable after the analysis phase and analysis models that are equal to the high-level view of the final software system are often attempted, but are normally not realistic. This leads to intent clashes with the desire for a seamless and easy controllable development process. We explain these intent clashes and we also describe the three different approaches taken by software development methods to cope with these intent clashes.

1996

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CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as images, music and social network data.

FIN-403: Econometrics

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications.