Translation of "Predictor variable" in German

The variables which form the basis (input) of the model are called predictor variables, the variable which is to be estimated by the model is called the response variable.

The U.S. Bureau of Labour Statistics has a Local Area Unemployment Statistics program that similarly uses Current Population Survey estimates of unemployment as the dependent variable, and information from local unemployment insurance records as the predictor variable.

A predictor variable of soft models cannot be assigned to a single measured variables, but is rather some combination of several measured variables.

The central element ofpredictive analyticsis the predictor, a variable that can be measured for an individual or other entity to predict future behavior.

The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behaviour.

The predictor variables show an additive effect on the response variable.

This allows you to control for correlations between predictor variables and error terms.

In the case of continuous predictor variables, the number of states are infinite.

Metadata proved to be relevant predictor variables, too.

Is a linear model sufficient for describing the relationship between predictors and target variables?

It provides answers to questions concerning the relative importance of independent (or predictor) variables, e.g. is company size or the presence of an occupational health department more important in predicting levels of health activity ?

In order to optimize a given problem, one has to identify a response function which defines the relationship between the independent predictor variables and the "goal" of the optimization.

Then we carry out multiple regression analysis, focusing on the variables we want to use as predictors (explanatory variables).

Linear models describe a continuous response variable as a function of one or more predictor variables.

Indicators and predictor variables are developed on the crown scale with high resolution data (1 - 6.5 m resolution per Pixel) and are then upscaled to the landscape scale (~30 m pixel resolution) for area-wide monitoring in southern Ecuador.

In the figure below you see an example of a hypothetical variable selection procedure, resulting in the "best" model of 5 predictor variables.

In the first phase, we will lay down the methodological conditions necessary to approach these questions and identify the most promising predictor variables.

The theoretical covariance between SNP genotypes can be used to filter the whole set of SNPs in order to remain at less but representative predictor variables.

Linear regression is a statistical modeling technique used to describe a continuous response variable as a function of one or more predictor variables.

In the case of calibration problems there are two aspects which should be considered prior to developing a calibration model of the data: (1) Is a linear model sufficient to describe the relationship between predictors and target variables, or (2) is it necessary to use a non-linear model in order to set up a model. Again, this decision is easy, and is made even more complicated by extensive noise in the data.