Applying Multiple Regression Analyses to Compare the Regression Predicted and Measured Body Weight Using Food Quantity and Sleep Score as Inputs over a 6.5-Year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 547)

Abstract

Gerald C Hsu

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted body weight as the output (dependent variable) by using his foods quantity and sleep score as inputs (independent variables). Since 5/1/2015, the author has been collecting various data related to his food nutrition (~0.5 million data) and sleep conditions. The Food Details (FD) category includes both food quantity (m9a) and food quality (nutrition, m9b).

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