Which Method Best Identifies Influential Factors: PCA or MLR?
Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) each offer distinct advantages and are suited to different analytical goals when investigating influential factors:
Principal Component Analysis is well-suited for exploring high-dimensional datasets and can reveal latent patterns and structural relationships within the data. However, PCA does not establish explicit causal relationships between variables.
Multiple Linear Regression enables the quantification of the specific relationships between independent and dependent variables, including the estimation of effect sizes. Nevertheless, when applied to high-dimensional data, MLR may suffer from multicollinearity, which can affect the stability and interpretability of the regression coefficients.
Therefore, the choice between these methods should be guided by the research objective and the characteristics of the dataset. PCA is preferable when the focus is on uncovering the underlying structure or dimensionality of the data, while MLR is more suitable when the aim is to assess the direct influence of predictor variables on an outcome.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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