A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). A statistical model represents, often in considerably idealized form, the data-generating process. When referring specifically to probabilities, the corresponding term is probabilistic model.
A statistical model is usually specified as a mathematical relationship between one or more random variables and other non-random variables. As such, a statistical model is "a formal representation of a theory" (Herman Adèr quoting Kenneth Bollen).
All statistical hypothesis tests and all statistical estimators are derived via statistical models. More generally, statistical models are part of the foundation of statistical inference.
Informally, a statistical model can be thought of as a statistical assumption (or set of statistical assumptions) with a certain property: that the assumption allows us to calculate the probability of any event. As an example, consider a pair of ordinary six-sided dice. We will study two different statistical assumptions about the dice.
The first statistical assumption is this: for each of the dice, the probability of each face (1, 2, 3, 4, 5, and 6) coming up is 1/6. From that assumption, we can calculate the probability of both dice coming up 5: 1/6 × 1/6 = 1/36. More generally, we can calculate the probability of any event: e.g. (1 and 2) or (3 and 3) or (5 and 6).
The alternative statistical assumption is this: for each of the dice, the probability of the face 5 coming up is 1/8 (because the dice are weighted). From that assumption, we can calculate the probability of both dice coming up 5: 1/8 × 1/8 = 1/64. We cannot, however, calculate the probability of any other nontrivial event, as the probabilities of the other faces are unknown.
The first statistical assumption constitutes a statistical model: because with the assumption alone, we can calculate the probability of any event.
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vignette|Illustration des 4 principales étapes de l'inférence statistique L'inférence statistique est l'ensemble des techniques permettant d'induire les caractéristiques d'un groupe général (la population) à partir de celles d'un groupe particulier (l'échantillon), en fournissant une mesure de la certitude de la prédiction : la probabilité d'erreur. Strictement, l'inférence s'applique à l'ensemble des membres (pris comme un tout) de la population représentée par l'échantillon, et non pas à tel ou tel membre particulier de cette population.
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En statistiques, en économétrie et en apprentissage automatique, un modèle de régression linéaire est un modèle de régression qui cherche à établir une relation linéaire entre une variable, dite expliquée, et une ou plusieurs variables, dites explicatives. On parle aussi de modèle linéaire ou de modèle de régression linéaire. Parmi les modèles de régression linéaire, le plus simple est l'ajustement affine. Celui-ci consiste à rechercher la droite permettant d'expliquer le comportement d'une variable statistique y comme étant une fonction affine d'une autre variable statistique x.
Learn how to describe, model and control urban traffic congestion in simple ways and gain insight into advanced traffic management schemes that improve mobility in cities and highways.
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