Related Experiment Video
Updated: Mar 2, 2026

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
Validity of anthropometric regression equations for predicting changes in body fat of obese females
Douglas L Ballor1,2, Victor L Katch1,2
1Behnke Laboratory for Body Composition Research, The University of Michigan, Ann Arbor, Michigan 48109-2214.
Abstract:
The validity of ten popular anthropometric percent fat prediction equations for estimating changes in percentage of body fat for obese females was studied. Thirty-one obese females (mean ± SEM, %fat = 36.7 ± 1.1%, body mass = 75.6 ± 1.7 kg, age = 32.8 ± 1.1 years) participated in a diet-only, diet-plus-exercise, or exercise-only program. Subjects lost 2.7 ± 0.3 fat percentage points and 3.0 ± 0.3 kg body mass during the 8-week study. While many of the equations had acceptable validity before and after body mass loss, when applied to the prediction of changes in body fat none of the equations was acceptable. It was concluded that use of anthropometric prediction equations to estimate individual percent fat change scores results in large errors and is not recommended.
Related Concept Videos
Drug Dosing: Obese Patients
Obesity
Pharmacokinetics in Obese Patients: Drug Absorption and Distribution
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Regression Toward the Mean
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

