Assertion: regression's variable | /r/HasProperty | predictor
Facets:
value: in the model
type: LOCATION
value: sometimes
type: TEMPORAL
value: from the independent variable
type: OTHER
Source sentences:
text: What we have is a list of average monthly rainfall for the last 24 months in column B, which is our independent variable (predictor), and the number of umbrellas sold in column C, which is the dependent variable.
text: The independent variables can be called exogenous variables, predictor variables, or regressors.
text: Using the regression equation, the dependent variable may be predicted from the independent variable.
text: This includes regression in which the independent variable (the predictor) or response variables are images, curves, or graphs.
text: These variables are also called predictors.
text: When doing prediction, the independent variable is called the predictor and the dependent variable is called the criterion.
text: Categorical variables, also called indicator variables, are converted into dummy variables by assigning the levels in the variable some numeric representation.
text: Standardized variables (either the predicted values or the residuals) have a mean of zero and standard deviation of one.
text: This variable is also sometimes known as the predicted variable , but this should not be confused with predicted values , which are denoted y ^ {\displaystyle {\hat {y}}}
text: Y. The variable we are basing our predictions on is called the predictor variable and is referred to as X.
text: The independent variables may also be referred to as the predictor variables or regressors.
text: In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value).
text: Binary logistic regression is used to predict the odds of being a case based on the values of the independent variables (predictors).
text: It estimates relationship between a dependent variable (target) and one or more independent variable (predictors) where dependent variable is categorical/nominal.
text: The /DEPENDENT subcommand indicates the dependent variable, and the variables following /METHOD=ENTER are the predictors in the model (in this case we only have one predictor).
text: The independent variables are also called exogenous variables, predictor variables or regressors.
text: The independent variables are also called exogenous variables, predictor variables or regressors.
text: This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.