Traitement des eaux usées industriellesLe traitement des eaux usées industrielles décrit les procédés utilisés pour traiter les eaux usées produites par les industries en tant que sous-produits indésirables. Après traitement, les eaux usées industrielles (ou effluents) traitées peuvent être réutilisées ou rejetées dans un égout sanitaire ou une eau de surface dans l'environnement. La plupart des industries produisent des eaux usées. Les tendances récentes ont été de minimiser une telle production ou de recycler les eaux usées traitées dans le processus de production.
Plan factorielthumb|right|Expériences statistiques : à gauche, un plan factoriel et, à droite, la surface de réponse obtenue par la méthode des surfaces de réponses En statistiques, un plan factoriel est une expérience qui consiste à choisir des valeurs pour chacun des facteurs en faisant varier simultanément tous les facteurs, de façon exhaustive ou non. Le nombre d'essais peut alors devenir très grand, i.e. on a une explosion combinatoire. Une telle expérience permet l'étude de l'effet de chaque variable sur le processus, ainsi que l'étude de la dépendance entre les variables.
Potentiel hydrogèneLe potentiel hydrogène, noté pH, est une mesure de l'activité chimique des protons ou ions hydrogène en solution. Notamment, en solution aqueuse, ces ions sont présents sous forme d'ions hydronium (ion hydraté, ou ). Le pH sert à mesurer l’acidité ou la basicité d’une solution. Ainsi, dans un milieu aqueux à : une solution de pH = 7 est dite neutre ; une solution de pH < 7 est dite acide ; plus son pH diminue, plus elle est acide ; une solution de pH > 7 est dite basique ; plus son pH augmente, plus elle est basique.
Dependent and independent variablesDependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.
Fractional factorial designIn statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.
Glossary of experimental designA glossary of terms used in experimental research. Statistics Experimental design Estimation theory Alias: When the estimate of an effect also includes the influence of one or more other effects (usually high order interactions) the effects are said to be aliased (see confounding). For example, if the estimate of effect D in a four factor experiment actually estimates (D + ABC), then the main effect D is aliased with the 3-way interaction ABC. Note: This causes no difficulty when the higher order interaction is either non-existent or insignificant.
Errors-in-variables modelsIn statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples.
Variable discrèteIn mathematics and statistics, a quantitative variable may be continuous or discrete if they are typically obtained by measuring or counting, respectively. If it can take on two particular real values such that it can also take on all real values between them (even values that are arbitrarily close together), the variable is continuous in that interval. If it can take on a value such that there is a non-infinitesimal gap on each side of it containing no values that the variable can take on, then it is discrete around that value.
Groundwater pollutionGroundwater pollution (also called groundwater contamination) occurs when pollutants are released to the ground and make their way into groundwater. This type of water pollution can also occur naturally due to the presence of a minor and unwanted constituent, contaminant, or impurity in the groundwater, in which case it is more likely referred to as contamination rather than pollution.
Omitted-variable biasIn statistics, omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to those that were included. More specifically, OVB is the bias that appears in the estimates of parameters in a regression analysis, when the assumed specification is incorrect in that it omits an independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables.
