through thick and thin by:mark bergman thomas bursey jay laporte paul miller aaron sinz
TRANSCRIPT
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Through Thick and Thin
By: Mark Bergman
Thomas Bursey
Jay LaPorte
Paul Miller
Aaron Sinz
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Measurement MethodsMeasurement Methods
1.1. Hydrostatic (Underwater) Hydrostatic (Underwater) WeighingWeighing
2.2. Skin Fold MeasurementsSkin Fold Measurements
3.3. Ultrasound MeasurementsUltrasound Measurements
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Through Thick and ThinThrough Thick and Thin(Statistical Model)(Statistical Model)
I.I. IntroductionIntroduction
II.II. Siri’s Equation and DataSiri’s Equation and Data
III.III. Elements of Regression Elements of Regression AnalysisAnalysis
IV.IV. Regression Analysis of Body Regression Analysis of Body Fat DataFat Data
V.V. DemonstrationsDemonstrations
VI.VI. ConclusionConclusion
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Body DensityBody Density
Body Density = WA/[(WA-WW)/c.f. - LV]
WA = Weight in air (kg)
WW = Weight in water (kg)
c.f. = Water correction factor (=1 at 39.2 deg F as one-gram of water occupies exactly one cm^3 at this temperature, =.997 at 76-78 deg F)
LV = Residual Lung Volume (liters)
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Proportion of Fat TissueProportion of Fat Tissue
D = Body Density (gm/cm^3)D = Body Density (gm/cm^3)
A = Proportion of lean body tissueA = Proportion of lean body tissue
B = Proportion of fat tissue(A + B =1)B = Proportion of fat tissue(A + B =1)
a = Density of lean body tissue (gm/cm^3)a = Density of lean body tissue (gm/cm^3)
b = Density of fat tissue (gm/cm^3)b = Density of fat tissue (gm/cm^3)
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Proportion of Fat TissueProportion of Fat Tissue
D = 1/[(A/a) + (B/b)]D = 1/[(A/a) + (B/b)]
B = (1/D)*[a*b/(a-b)]-[b/(a-b)]B = (1/D)*[a*b/(a-b)]-[b/(a-b)]
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Estimates Estimates a =1.10 gm/cm^3 and a =1.10 gm/cm^3 and
b =0.90 gm/cm^3b =0.90 gm/cm^3
Percentage of Body Fat = 495 /D - 450Percentage of Body Fat = 495 /D - 450
Siri’s EquationSiri’s Equation
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Elements of Regression AnalysisElements of Regression Analysis
Simple RegressionSimple Regression
Multiple RegressionMultiple Regression
xbby 10
nnxbxbby ....110
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Elements of Regression AnalysisElements of Regression AnalysisRegression AssumptionsRegression Assumptions
1.1. The population satisfies the equationThe population satisfies the equation
2.2. The true residuals are mutually independentThe true residuals are mutually independent
3.3. The true residuals all have the same varianceThe true residuals all have the same variance
4.4. The true residuals all have a normal distribution The true residuals all have a normal distribution with mean zerowith mean zero
xBBy 10
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Elements of Regression AnalysisElements of Regression Analysis
Sum of SquaresSum of Squares
Mean of SquaresMean of Squares
Coefficient of DeterminationCoefficient of Determination
totalrestotal SSSSSSR /)(2
resresres
regregreg
dfSSMS
dfSSMS
/
/
2)( yySS itotal
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Elements of Regression AnalysisElements of Regression Analysis
F-RatioF-Ratio
T-RatioT-Ratio
ii seBt /ˆ
resreg MSMSF /
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The Best Predictor For Simple Regression Using Excel
Simple Regression
Abdomen Circumference
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Abdomeny = 0.6313x - 39.28
R2 = 0.6617
0
10
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30
40
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0 20 40 60 80 100 120 140 160
Percent Body Fat
Ab
dom
en C
ircu
mfe
ren
ce
Series1
Linear (Series1)
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The Worst Predictor For Simple Regression Using Excel
Ankle Circumference
Simple Regression
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Ankle Cirumferencey = 1.3133x - 11.189
R2 = 0.0707
0
5
10
15
20
25
30
35
40
45
50
0 5 10 15 20 25 30 35 40
Ankle Cirumference
Per
cen
t Bo
dy F
at
Series1
Linear (Series1)
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Single Predictors from Best to worst1. Abdomen Circumference (R^2 = .6617) 2. Chest Circumference (R^2 = .4937) 3. Hip Circumference (R^2 = .3909) 4. Weight (R^2 = .3751) 5. Thigh Circumference (R^2 = .3132) 6. Knee Circumference (R^2 = .2587) 7. Biceps (extended) Circumference (R^2 = .2433) 8. Neck Circumference (R^2 = .2407) 9. Forearm Circumference (R^2 = .1306) 10. Wrist Circumference (R^2 = .1201) 11. Age (R^2 = .0849) 12. Height (R^2 = .0800) 13. Ankle Circumference (R^2 = .0707)
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Best Single Predictor Equation And The Average Percent Difference From
The Given Data
y = .6313(abdomen) – 39.28
Average Difference = 3.9163
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Multiple Regression Using SPSS
Model Summary
.866a .749 .736 4.307Model1
R R SquareAdjustedR Square
Std. Error ofthe Estimate
Predictors: (Constant), WRIST, AGE, HEIGHT, ANKLE,FOREARM, ABDOMEN, BICEPS, KNEE, NECK, THIGH,CHEST, HIP, WEIGHT
a.
Coefficientsa
-17.775 17.361 -1.024 .307
5.840E-02 .033 .088 1.791 .075
-9.01E-02 .054 -.316 -1.682 .094
-7.20E-02 .096 -.032 -.750 .454
-.467 .233 -.136 -2.008 .046
-2.61E-02 .099 -.026 -.263 .793
.961 .087 1.239 11.078 .000
-.215 .146 -.184 -1.471 .142
.237 .144 .149 1.643 .102
2.610E-02 .242 .008 .108 .914
.170 .222 .034 .767 .444
.191 .172 .069 1.116 .266
.444 .199 .107 2.227 .027
-1.620 .535 -.180 -3.027 .003
(Constant)
AGE
WEIGHT
HEIGHT
NECK
CHEST
ABDOMEN
HIP
THIGH
KNEE
ANKLE
BICEPS
FOREARM
WRIST
Model1
B Std. Error
UnstandardizedCoefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: BODYFATa.
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Multiple Regression And The Affects of Removing a Predictor
1.All Predictors (R^2 = .749)2. Abdomen Circumference (R^2 = .620)
3. Chest Circumference (R^2 = .749) 4. Hip Circumference (R^2 = .749)
5. Weight (R^2 = .746) 6. Thigh Circumference (R^2 = .746) 7. Knee Circumference (R^2 = .749)
8. Biceps (extended) Circumference (R^2 = .748) 9. Neck Circumference (R^2 = .745)
10. Forearm Circumference (R^2 = .744) 11. Wrist Circumference (R^2 = .739)
12. Age (R^2 = .745) 13. Height (R^2 = .748)
14. Ankle Circumference (R^2 = .748)
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ALL 1 0.749AGE 2 0.745WEIGHT 3 0.746HEIGHT 4 0.748NECK 5 0.745CHEST 6 0.749ABDOMEN 7 0.620HIP 8 0.749THIGH 9 0.746KNEE 10 0.749ANKLE 11 0.748BICEPS 12 0.748FORARM 13 0.744WRIST 14 0.739
Removing Predictors
0.0000.2000.400
0.6000.800
1 2 3 4 5 6 7 8 9 10 11 12 13 14
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The Best Predictors Using The Percent Of Significance1. Abdomen Circumference (Sig. = .000)
2. Wrist Circumference (Sig. = .003)
3. Forearm Circumference (Sig. = .024)
4. Neck Circumference (Sig. = .044)
5. Age (Sig. = .056)
6. Weight (Sig. = .100)
7. Thigh Circumference (Sig. = .103)
8. Hip Circumference (Sig. = .156)
9. Biceps (extended) Circumference (Sig. = .290)
10. Ankle Circumference (Sig. = .433)
11. Height (Sig. = .469)
12. Chest Circumference (Sig. = .810)
13. Knee Circumference (Sig. = .950)
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The Best Three Predictor Models For Multiple Regression
Top Three:
1. Abdomen Circumference, Wrist Circumference, Weight (R^2 = .728)
2. Weight, Abdomen Circumference, Neck Circumference (R^2 = .724)
3. Abdomen Circumference, Weight, Height (R^2 = .721)
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Best Multiple Predictor Equation And The Average Percent Difference From The Given Data
Average Difference = 3.58
body fat = abdomen (.975) – weight (.114) – wrist (1.245) – 27.930
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Body Fat DemonstrationBody Fat Demonstration
Using the best model from our Regression Using the best model from our Regression AnalysisAnalysis
body fat = abdomen (.975) – weight (.114) – wrist (1.245) – 27.93
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The Best 3 Predictors are the
• Abdomen
• Weight
• Wrist
Measuring the PredictorsMeasuring the Predictors
Abdomen and Wrist are measured in Centimeters (cm)
Weight is measured in pounds
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Measuring the AbdomenMeasuring the AbdomenMake sure that the heels are together before applying the tapeline.
Then measure approximately 3” below the waistline.
Measure the abdomen circumference (cm).
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Measuring the WeightMeasuring the WeightWeight should be taken with an accurate weighing scale.
Record the persons weight in pounds.
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Measuring the WristMeasuring the Wrist
Measurement should be taken between hand and wrist bone.
Measure the wrist circumference (cm).
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Calculating the Body Fat %Calculating the Body Fat %
Body fat = A (.975) – W (.114) – P(1.245) – 27.93
A = abdomen circumference (cm)
P = wrist circumference (cm)
W = weight (lbs)
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What Does This Mean ?What Does This Mean ?The normal range for men is 15-18%
Age Excellent Good Fair Poor19-24 10.8 % 14.9 % 19.0 % 23.3 % 25-29 12.8 % 16.5 % 20.3 % 24.4 %30-34 14.5 % 18.0 % 21.5 % 25.2 %35-39 16.1 % 19.4 % 22.6 % 26.1 %40-44 17.5 % 20.5 % 23.6 % 26.9 %45-49 18.6 % 21.5 % 24.5 % 27.6 %50-54 19.8 % 22.7 % 25.6 % 28.7 %55-59 20.2 % 23.2 % 26.2 % 29.3 %60 + 20.3 % 23.5 % 26.7 % 29.8 %
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ReferencesReferences Dr. Steve DeckelmanDr. Steve Deckelman A Course in Mathematical ModelingA Course in Mathematical Modeling
– By Douglas Mooney and Randall By Douglas Mooney and Randall SwiftSwift
http://lib.stat.cmu.edu/datasets/bodyfathttp://lib.stat.cmu.edu/datasets/bodyfat