In multiple regression, the process of isolating confounding variables is called an analysis of covariance.
Confounding variables are those factors that have the potential to alter the relationships between other variables. They may have an impact on elements that are connected causally. They are also known as superfluous or spurious variables. Sometimes it may even be inaccurate in terms of their actual connection or causality. Data on ice cream intake and sunburn will be confounding variables in this case. Because of the association between age and gender and the notions of old and young, gender is another example of a confounding variable.
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