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Body Composition – Questions & Detailed Answers
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Body Composition – Questions & Detailed Answers
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Body Composition – Questions & Detailed AnswersBody density ✔️Ans - total body mass expressed relative to total body
volume; varies from person to person because the percentage of
components vary; reference man was 1.064
Fat-free body (FFB) ✔️Ans - all residual lipid-free chemicals and tissues
including water, muscle, bone, connective tissue, and internal organs
Lean body mass ✔️Ans - fat-free mass plus essential lipids
Correlation ✔️Ans - measures the strength of association or degree of the
relationship between two variables; how well two things relate to each
other; strong = 0.8 or greater; r = 0-1; + or - is direction only; generally
linear data
Coefficient of determination ✔️Ans - the square of the correlation
coefficient and represents the amount of variance shared by variables X
and Y; N% of the variability is explained by Y and 100-N% is unexplained;
r^2
Regression ✔️Ans - used to predict one variable from one or more other
variables; aka prediction
Bivariate regression ✔️Ans - predicting one thing from something else;
must know the relationship between the two variables; on a graph, find a
line of best fit
Regression line ✔️Ans - the line that minimizes the squared deviations
from the line
Multiple regression ✔️Ans - predicting one thing from many variables
Polynomial regression ✔️Ans - one of the variables has a square; puts a
curve in the line of best fit
, Basics of regression analysis ✔️Ans - Y=a+bX where Y = dependent
(criterion) variable; X = independent (predictor) variable variable; a = y-
intercept, b = slope
T-test ✔️Ans - tests the difference between the means; Paired = same
group tested in all tests (want small SEE, TE and non-significant
difference); Independent = different group of people are tested on different
tests
Standard Error of Estimate ✔️Ans - on average, the distance that people
are away from the regression line (line of best fit); smaller is better,
because then people are closer to that line
Total error ✔️Ans - how far away people are from the line of identity;
smaller is better; will generally be larger
Line of Identity ✔️Ans - slope = 1 and y-intercept = 0; if the data points
are on or very close, then the prediction is very good
Cross-validation ✔️Ans - use a smaller group of people and repeat the
study to see if the new equation (prediction) works again
Criteria for Evaluating Prediction Equations ✔️Ans - 1. Reference
method? multiple component model
2.Sample size? minimum of 20 people per variable
3.Applicability of equation (general or population-specific) depends on the
population and intent
4.Cross-validated? yes
5.Size of r and R2? closer to >0.9
6.Size of SEE and TE? small
7.Constant error? Avg. predicted score close to avg. reference score? t-test
8.Individual error (residual scores) bland-Altman plots
Constant error ✔️Ans - on average, how close were the means
How much error is acceptable ✔️Ans - SEE/TE Body Fat < 3.5; body
density <0.0080
Individual error ✔️Ans - can be really far off even if the means look great
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