Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals. Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.

Find the model parameters β such that X*β with predictor X becomes as close to response Y as possible, with least squares residuals. Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.

Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals. Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.

Find the model parameters β such that their linear combination with all predictor-arrays in X become as close to their response in Y as possible, with least squares residuals. Uses an orthogonal decomposition and is therefore more numerically stable than the normal equations but also slower.