Examples of using Response variable in English and their translations into Greek
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Official/political
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Computer
Estimates of toxicity for response variables e.g.
Yield: this response variable is the biomass at the end of the test minus the starting biomass.
Estimates of toxic endpoints for response variables e.g.
This Testing Method describes two response variables, as member countries have different preferences and regulatory needs.
Statistical method used to test the hypotheses that changes in levels for each x factor cause changes in response variable Y.
O Obtained by observing response variable at regular time periods.
The notion of a"unique effect" is appealing when studying a complex system where multiple interrelated components influence the response variable.
This assumes that the errors of the response variables are uncorrelated with each other.
The response variables in the control and treatment group may be analysed using a statistical test to compare means, e.g. a Student''t's t-test.
Note, however, that in these cases the response variable y is still a scalar.
Gt; the mean of the response variable is a linear combination of the parameters(regression coefficients) and the predictor variables. .
The general linear model considers the situation when the response variable Y is not a scalar but a vector.
This response variable should not be used for comparing the sensitivity to toxicants among duckweed species or even different clones.
Various models have been created that allow for heteroscedasticity,i.e. the errors for different response variables may have different variances.
For this method, growth rates andyield are response variables derived from measuring biomass directly or any of the surrogates mentioned.
Generalized linear models allow for an arbitrary link function g that relates the mean of the response variable to the predictors, i.e. E(y)= g(β′x).
Model-based statistics are from a mixed effects linear model using CFA-72h as the response variable, fixed effect factors for treatment, period and treatment sequence, and subject within treatment sequence as a random effect.
Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variables and their relationship.
Linear regression: Data are modeled to fit a straight line Often uses the least-square method to fit the line Multiple regression:allows a response variable Y to be modeled as a linear function of multidimensional feature vector Log-linear model: approximates discrete multidimensional probability distributions.
The condition that the errors are uncorrelated with the regressors will generally be satisfied in an experiment, but in the case of observational data,it is difficult to exclude the possibility of an omitted covariate z that is related to both the observed covariates and the response variable.
However, a range of 10 to 20% appears to be appropriate(depending on the response variable chosen), and preferably both the EC10 and EC20 should be reported.
The existence of such a covariate will generally lead to a correlation between the regressors and the response variable, and hence to an inconsistent estimator of β.
This trick is used, for example,in polynomial regressionwhich uses linear regression to fit the response variable as an arbitrary polynomial function up to a given rank of a predictor variable. .
Generalized linear models allow for an arbitrary link function, g, that relates the mean of the response variable(s) to the predictors: E( Y)= g- 1( X B){\displaystyle E( Y)= g^{ -1}( XB)}.
For example, weighted least squares is a method for estimating linear regression models when the response variables may have different error variances, possibly with correlated errors.
A fitted linear regression model can be used to identify the relationship between a single predictor variable xj and the response variable y when all the other predictor variables in the model are"held fixed".
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.
Regression and Log-Linear Models Linear regression: Data are modeled to fit a straight line Often uses the least-square method to fit the line Multiple regression:allows a response variable Y to be modeled as a linear function of multidimensional feature vector Log-linear model: approximates discrete multidimensional probability distributions April 6, 2017 Data Mining.