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Objectives We aimed to understand the factors affecting psychological well-being of patients undergoing haemodialysis (HD). First, we explored how physical symptom severity, emotional distress and ...
Cost Management, Lifecycle Costs, Sustainability, Construction Projects Share and Cite: Kongere, R. , Lucas, S. and Nyakwara, S. (2025) Effect of Sustainable Procurement Practices on Performance of ...
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Multiple Linear Regression from Scratch in Python – Simply Explained
Discover a smarter way to grow with Learn with Jay, your trusted source for mastering valuable skills and unlocking your full potential. Whether you're aiming to advance your career, build better ...
Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
Secondly, it will be shown how the generalized multiple linear regression technique can be used to obtain answers to the question being answered by chi square. Thirdly, the multiple linear regression ...
Based on the proposed millimeter-wave multilayer common-mode filter (CMF) design, this article further presents an innovative parameter analysis method using multiple linear regression equations.
Prediction of preoperative peritoneal cancer index for pseudomyxoma peritonei by multiple linear regression analysis ...
Standardized regression coefficients are difficult to translate into business terms, but they are directly comparable. This allows you to determine which variables in your final model are really ...
If passing multiple quantiles for a quantile regression with the histogram method, there can be cases in which it produces larger predictions for a lower quantile than for a higher quantile. In this ...
Multiple correlation refers to the statistical relationship between one dependent variable and two or more independent variables.
Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes ...
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