As many models as categories of the dependent variable are obtained. An observation is associated to the category that has an equation with the highest value.
従属変数がrundefの時:内部変数UREF(初期値=0)の値が指定されたものとみなす。
When the dependent variable is RUNDEF: The value of the internal variable UREF(initial value 0) is considered as specified.
In the General tab,select the Taste and Sweetness columns as dependent variables, and the Panelist and Product columns as explanatory qualitative variables..
Prédictions and residuals: For each observation, the value of the dependent variable, the predictions, the residuals and the standardized residuals are displayed.
従属変数(またはモデルする変数)は、ここではYield(収穫)です。
The Dependent variable(or variable to model) is here the"Yield".
この数値変数は独立変数と従属変数間の関係を説明しています。
The numeric variable describes the relationship between an independent variable and a dependent variable.
従属変数フィールドでは、マウスでspeciesを選択します。
In the Dependent variable(s) field, select with the mouse the species.
R'²決定係数)は、説明変数によって説明された従属変数の変動の%を示します。
The R'²(coefficient of determination)indicates the% of variability of the dependant variable which is explained by the explanatory variables.
R2(決定係数)は、説明変数によって説明された従属変数の変動の%を示します。
The R²(coefficient of determination)indicates the% of variability of the dependent variable which is explained by the explanatory variables.
各モデルのR2.R²が1なら,モデルの従属変数(Y)と説明変数(X)の間に線形の関係がある.。
The R² of each of the models If the R² is 1,then there is a linear relationship between the dependent variable of the model(the Y) and the explanatory variables(the Xs).
The map that displays the dependent variables on the c vectors and the explanatory variables on the w* vectors allows visualizing the global relationship between the variables..
Additionally, these transformations assist inconverting non-linear relationships between independent variables and the dependent variable into a linear relationship- the customer behavior often requested by the business.
For instance, if y= x+4 it means that xis the independent variable while y is the dependent variable while 4 is a constant since it is not affected by change of values of x and y.
The monotone regression tool(MONANOVA) combines a monotonic transformation of the responses to a linear regression as a way to improve the linear regression results.It is well suited to ordinal dependent variables.
Correspondence between the categories of the response variable and the probabilities:This table shows which categories of the dependent variable have been assigned probabilities 0 and 1.
And then you immediately see that even if this wasn't a squared here, you would be multiplying the y times dy dx,and that also makes it non-linear because you're multiplying the dependent variable times the derivative of itself.
Where yi is the value observed for the dependent variable for observation i, xki is the value taken by variable k for observation i, and ei is the error of the model.
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