MHB Require assistance with possible multiple regression analysis

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To estimate body fat percentage more efficiently, the discussion focuses on using the circumference measurements of ten body segments and underwater weighing data from 252 participants. The goal is to create a predictive equation for body fat estimation. It is suggested that multiple regression is appropriate for this analysis, as it can incorporate multiple predictors like age and body segment measurements. The distinction between multiple and simple linear regression is clarified, emphasizing that the underlying model remains linear regardless of the complexity of the relationship. This approach aims to facilitate a straightforward estimation of body fat percentage.
Dants
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I am interested in determining more efficient ways of determining individuals' body fat percentage. To do this, I measure the circumference of a number of segments (10 of them) of the body and determine the person's percentage body fat through underwater weighing. I have done this for 252 total participants, and I have recorded the age for the participants.

My goal is to facilitate the simple estimation of percentage body fat. I wish to generate a predictive equation the best estimate of percentage body fat. Am I correct to assume that I should be using multiple regression or simple linear regression?

Thanks,

Dants
 
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In a machine learning environment, the terms are essentially interchangeable, and it doesn't matter. The underlying model is the same either way. In fact, any kind of regression in which the coefficients show up linearly is still linear regression, even if the shape of the line or curve being fit is not straight. It's how the coefficients show up that determine if the model is a linear regression model, not the shape of the line or curve.
 
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