Section 6
Multiple Regression
By Boundless
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Multiple regression is used to find an equation that best predicts the
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The purpose of a multiple regression is to find an equation that best predicts the
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The results of multiple regression should be viewed with caution.
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Standard multiple regression involves several independent variables predicting the dependent variable.
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In regression analysis, an interaction may arise when considering the relationship among three or more variables.
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The goal of polynomial regression is to model a non-linear relationship between the independent and dependent variables.
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Dummy, or qualitative variables, often act as independent variables in regression and affect the results of the dependent variables.
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A regression model that contains a mixture of quantitative and qualitative variables is called an Analysis of Covariance (ANCOVA) model.
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Multilevel (nested) models are appropriate for research designs where data for participants are organized at more than one level.
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Stepwise regression is a method of regression modeling in which the choice of predictive variables is carried out by an automatic procedure.
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There are a number of assumptions that must be made when using multiple regression models.
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Some problems with multiple regression include multicollinearity, variable selection, and improper extrapolation assumptions.