Examples of using Explanatory variables in English and their translations into Japanese
{-}
-
Colloquial
-
Ecclesiastic
-
Computer
-
Programming
Next, the X/ Explanatory variables should be selected.
Show the properties of the groups using explanatory variables;
Explanatory variables are often represented on the X axis.
Likewise it is similar for the other explanatory variables.
In ANOVA, explanatory variables are often called factors.
In this particular case we have two quantitative explanatory variables.
With MANOVA, explanatory variables are often called factors.
The first table gives basic statistics for the dependent and explanatory variables.
The quantitative explanatory variables are the"Height" and the"Age".
The specificity of ANCOVA is that it mixes qualitative and quantitative explanatory variables.
When you select Explanatory Variables, there are a couple of things to consider.
Moreover, as in ANCOVA,it's possible to mix qualitative and quantitative explanatory variables.
The quantitative explanatory variables are the concentration of the two components C1and C2.
The R² obtained with a regression betweenX1 and all the other explanatory variables included in the model.
Explanatory variables are gasoline prices( G), gross domestic product( Y), population( P), and other factors( X).
In the case of a model with p explanatory variables, the OLS regression model writes:.
The estimation results are generallyrobust when they use some alternative dependent and explanatory variables.
XLSTAT allows you to take into account explanatory variables through a linear model. Three different approaches are possible:.
When reading a research article,it is good practice to identify the response and explanatory variables used.
The results enable us to determine whether or not the explanatory variables bring significant information(null hypothesis H0) to the model.
The choice of a statistical model can alsobe guided by the shape of the relationships between the dependent and explanatory variables.
P0(baseline probability): The probability that Y=1 when all explanatory variables are set to their mean value.
The only constraint is that it can only be executed toexamine the difference between the levels of two-level qualitative explanatory variables.
The results enable us to determine whether or not the explanatory variables bring significant information(null hypothesis H0) to the model.
It is therefore adapted to situations where we are looking for a largenumber of genes which are likely affected by the explanatory variables.
The results enable us to determine whether or not the explanatory variables bring significant information(null hypothesis H0) to the model.
For that reason, and also in order to handle the cases where there a lot of explanatory variables, other methods have been developed.
The results enable us to determine whether or not the explanatory variables bring significant information(null hypothesis H0) to the model.