Introduction People with epilepsy are at higher risk for hypertension, diabetes, hyperlipidaemia and obesity than the US general population. It is unknown whether these risk factors translate to ...
Neter, J., Wasserman, W. and Kutner, M.H. (1983) Applied Linear Regression Models. Richard D. Irwin.
ABSTRACT: This study explores a comprehensive statistical model for analyzing rental apartment prices per square meter in Munich, Germany. The research investigates key quantitative and qualitative ...
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Why I Prefer Python for Data Analysis
Python makes calculations quick, easy, and graphical.
Many businesses are just beginning to grapple with the impact of artificial intelligence, but some have been using machine learning (ML) and other emerging technologies for over a decade. Also: Most ...
Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
Linear to Logistic regression Introduction In this lesson, you'll be introduced to the logistic regression model. You'll start with an introductory example using linear regression, which you've seen ...
ABSTRACT: This study investigates the impact of marketing mix elements—Product, Price, Promotion, and Place (4Ps)—on the revenue and profit of Nigerian Breweries Plc (NBL) from 2013 to 2022, using ...
Abstract: Almost all industrial wind turbine failures are caused by bearing degeneration. As a critical part of this functionality, precisely estimating the remaining usable life (RUL) of the bearings ...
This repository is for downscaling physical fields using multivariate linear regression. Here the model is applied to downscale significant wave height (SWH) in the Black Sea using low-resolution data ...
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