Require assistance with possible multiple regression analysis

In summary, the speaker is interested in finding more efficient ways to determine individuals' body fat percentage. They are using a method of measuring body segments and underwater weighing for 252 participants. Their goal is to generate a predictive equation for estimating percentage body fat, and they are unsure if they should use multiple regression or simple linear regression. The speaker is informed that in a machine learning environment, the terms are interchangeable and the underlying model is the same. It is the way the coefficients show up that determines if the model is linear regression, not the shape of the line or curve being fit.
  • #1
Dants
7
0
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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  • #2
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.
 

FAQ: Require assistance with possible multiple regression analysis

What is multiple regression analysis?

Multiple regression analysis is a statistical technique used to examine the relationship between a dependent variable and two or more independent variables. It allows researchers to determine how much of the variation in the dependent variable can be explained by the independent variables.

When is multiple regression analysis used?

Multiple regression analysis is commonly used in scientific research to analyze complex relationships between variables. It is often used in fields such as psychology, economics, and social sciences to identify patterns and make predictions.

What are the steps involved in conducting a multiple regression analysis?

The first step is to define the research question and identify the dependent and independent variables. Then, the data is collected and organized. The next step is to test for assumptions, such as normality and linearity. After that, the regression model is built and the statistical significance of each independent variable is determined. Finally, the results are interpreted and conclusions are drawn.

What are the advantages of using multiple regression analysis?

Multiple regression analysis allows researchers to examine the impact of multiple independent variables on a dependent variable, which can provide a more comprehensive understanding of the relationship between variables. It also allows for the identification of confounding variables and the control of their effects.

What are the limitations of multiple regression analysis?

One limitation of multiple regression analysis is that it assumes a linear relationship between the variables. If the relationship is non-linear, the results may not be accurate. Additionally, the analysis can be affected by outliers and missing data. It also cannot establish causation, only correlation, between variables.

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