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10 Terms
1
Multiple Regression
A statistical technique used to model the effects of multiple predictor variables on an outcome variable.
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2
Partial Correlation
A measure of the relationship between two variables while holding a third variable constant.
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3
Semi-Partial Correlation
A measure that indicates the relationship between an X variable and a Y variable while holding the covariation of X with a third variable constant.
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4
Outcome Variable
The variable that is being predicted or explained in a regression analysis, often denoted as Y.
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5
Predictor Variable
The variable(s) that are used to predict or explain the outcome variable, often denoted as X.
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6
Multicollinearity
A situation in regression analysis where predictor variables are highly correlated with each other, which can complicate the interpretation of the model.
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7
Residual Variation
The variation in the outcome variable that cannot be explained by the predictor variables; often referred to as error.
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8
Background Variable
A variable that may influence the relationship between a predictor and the outcome, but is not the focus of the main analysis.
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9
Correlational Research Design
A research design that examines the relationships between variables without manipulating them.
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10
Complex Research Questions
Questions that involve multiple factors affecting an outcome variable, requiring more advanced analysis than simple bivariate relationships.
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