As one of the important statistical methods, quantile regression(QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...

This results in a considerable simplification of the model, especially if there are more than three categories in the dependent variable or if there are multiple predictors. The three alternative ...

Predicting car prices using multiple linear regression. This project uses real-world automotive data to train a machine learning model capable of estimating car prices based on technical ...

A novel framework is proposed that combines multiresonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction ... Ridge Regression modeling. Systematic ...

K was the variable with the highest MSE oob, indicating larger prediction errors (to be confirmed by the validation of the models). Validation of models generated with stepwise multiple linear ...

Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data ...

We evaluated predictors of poor outcomes (mRS >2) despite successful recanalization (TICI ≥2b) in the North American Solitaire Stent Retriever Acute Stroke (NASA) registry. Methods Logistic regression ...

These predictors ... model using multiple predictors to make accurate predictions for real workloads. The weights of the predictors in CloudInsight are determined at runtime with their accuracy for ...