What Does PCR Stand For?

PCR stands for Principal Component Regression

Principal Component Regression (PCR) is a statistical technique that combines principal component analysis and multiple linear regression. It is used to analyze the relationship between a dependent variable and multiple independent variables, particularly when the independent variables are highly correlated or when there are more predictors than observations. PCR first reduces the dimensionality of the dataset by extracting principal components, which are linear combinations of the original variables, and then fits a linear regression model using these components. This approach helps to mitigate issues of multicollinearity and improves the model's predictive accuracy. PCR is widely utilized in fields such as finance, bioinformatics, and social sciences for effective data analysis and interpretation.

Other Meanings for PCR