Examples of using Predictor variables in English and their translations into Greek
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Colloquial
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Medicine
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Computer
C is the number of predictor variables;
It can handle a mixture of categorical and continuous predictors andincorporates interactions among predictor variables.
The neuron stores the values of the predictor variables for the case along with the target value.
Otherwise, we have a condition known as perfect multicollinearity in the predictor variables.
Assuming that there are p predictor variables, LOGEST fits an equation of the following form.
The meaning of the expression"held fixed" may depend on how the values of the predictor variables arise.
Evaluations should be seen both as predictor variables and as dependent variables. .
Because the predictor variables are treated as fixed values see abovelinearity is really only a restriction on the parameters.
Gt; the mean of the response variable is a linear combination of the parameters(regression coefficients) and the predictor variables.
This essentially means that the predictor variables x can be treated as fixed values, rather than random variables. .
This means that the indicate with the reaction variable is a linear mix of the parameters(regression coefficients) as well as predictor variables.
This means, for example, that the predictor variables are assumed to be error-free- that is, not contaminated with measurement errors.
A two-way ANOVA test is a statistical test used to determine the effect of two nominal predictor variables on a continuous outcome variable. .
This two-stage procedure first reduces the predictor variables using principal component analysis then uses the reduced variables in an OLS regression fit.
Errors-in-variables models(or"measurement error models")extend the traditional linear regression model to allow the predictor variables X to be observed with error.
Principal component regression(PCR) is used when the number of predictor variables is large, or when strong correlations exist among the predictor variables.
The predictor variables themselves can be arbitrarily transformed, and in fact multiple copies of the same underlying predictor variable can be added, each one transformed differently.
Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variables and their relationship.
The extension to multiple and/or vector-valued predictor variables(denoted with a capital"X") is known as"multiple linear regression", also known as"multivariable linear regression".
A two-way ANOVA test is a statistical test used to determine the effect of two nominal predictor variables on a continuous outcome variable. .
The predictor variables themselves is usually arbitrarily reworked, and in reality various copies of the same underlying predictor variable could be included, every one transformed in a different way.
While it often works well in practice,there is no general theoretical reason that the most informative linear function of the predictor variables should lie among the dominant principal components of the multivariate distribution of the predictor variables.
The predictor variables themselves can be arbitrarily transformed, Dating algorithm ted talk in fact multiple copies of the same underlying predictor variable can be added, each one transformed differently.
However, it has been argued that in many cases multiple regression analysis fails to clarify the relationships between the predictor variables and the response variable when the predictors are correlated with each other and are not assigned following a study design.
If the experimenter directly sets the values of the predictor variables according to a study design, the comparisons of interest may literally correspond to comparisons among units whose predictor variables have been"held fixed" by the experimenter.
This can be triggered by having two ormore perfectly correlated predictor variables(e.g. if the same predictor variable is mistakenly given twice, either without transforming one of the copies or by transforming one of the copies linearly).
Neural networks are applicable in virtually every situation in which a relationship between the predictor variables(independents, inputs) and predicted variables(dependents, outputs) exists, even when that relationship is very complex and not easy to articulate in the usual terms of"correlations" or"differences between groups.".
In some cases,it can literally be interpreted as the causal effect of an intervention that is linked to the value of a predictor variable.