Partial Least Squares Regression - Complete Implementation Package

In your submission, please provide the following components to help us better understand your implementation:

1. Problem Statement: Provide a complete description of the problem or research question you addressed using Partial Least Squares Regression (PLSR). This should include the regression objectives, variable relationships, and application context to help us understand your analytical approach. PLSR is particularly useful for handling multicollinearity and high-dimensional data.

2. Raw Data: Submit the original dataset used in your analysis to enable validation of your methodology and results. If your data is subject to confidentiality agreements, please provide a reproducible dataset with similar characteristics that demonstrates the same statistical properties and challenges.

3. Code Implementation: Share your complete code with detailed annotations explaining key algorithm steps. For PLSR implementations, this should include variable standardization, component extraction using covariance maximization, cross-validation procedures for determining optimal components, and regression coefficient calculations. If using third-party libraries (like scikit-learn's PLSRegression or MATLAB's plsregress), ensure proper licensing and provide appropriate references to the implementation framework.

We look forward to your submission and thank you for your contribution!