Sampling bias can skew data collection, impacting statistical analysis. It occurs when certain population segments are disproportionately represented, leading to inaccurate conclusions. Understanding bias types like sampling, nonresponse, and response is crucial for reliable results. Minimizing bias mitigates errors, enhancing data quality and decision-making.
Curvilinear Component Analysis (CCA) is a powerful method for dimensionality reduction, preserving local data structure. Unlike PCA, it handles nonlinear relationships, vital for tasks like image compression, speech recognition, and data visualization. In business, CCA aids customer segmentation, fraud detection, and marketing analytics, enhancing decision-making and cost efficiency.
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