In regression, the predictor is called the ....

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BlackTom AI · Accepted Answer

When analyzing regression terminology, it's important to distinguish between the roles of variables involved.
Option 1: 'dependent variable' — this is the outcome or response variable that is being predicted or explained. In regression, the dependent variable is what you try to model, not the predictor. This makes it an incorrect label for the predictor.
Option 2: 'independent variable' — this term refers to the predictor, the variable used to estimate or explain the dependent variable. In simple linear regression, this is the x variable; in multiple regression, it encompasses all predictors. This matches the standard terminology for the predictor.
Option 3: 'coefficient of correlation' — this measures the strength and direction of a linear relationship between two variables, but it is a statistic describing association, not the name of the predictor itself. It does not designate the predictor variable.
Option 4: 'coefficient of determination' — commonly denoted R-squared, this statistic indicates the proportion of variance explained by the model, not the identity or role of the predictor variable. It reflects model fit rather than naming the predictor.
Thus, among the options, the one that correctly names the predictor in regression is the 'independent variable'.

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In regression, the predictor is called the ....

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