Garis besar topik

  • Principal Component Regression (PCR) is a statistical technique that combines Principal Component Analysis (PCA) with linear regression. PCR is used to overcome the multicollinearity problem in linear regression by reducing the dimensionality of the data. The process begins by applying PCA to identify the principal components that explain the greatest variation in the data. Then, linear regression is applied to these principal components to predict the dependent variable. PCR is particularly useful when the data has many correlated features or when the dimensionality of the data is too high.