**3. Results and discussion**

#### **3.1. Factors**

put on the special underwear with gym short while the male shall wear the sport short only. Before starting the experiment, subjects were expected to fill in the basic personal information, such as age, gender, weight, and so on. When taking 3D body scanning, the subjects erectly stood on the scanner platform and spread feet 15 cm with arms lifting outward and smooth breathing. Scanning will be completed within 5–10 s. After that the body fat composition of the subjects would be measured. Besides, while taking the metabolic test, subjects sat in a quiet room with physical relaxation. The testing time varies from person to person, and

After scanning with Voxelan 3D laser scanner, Anthroscan (Scanworx) 3D image data processing software was used for obtaining statistical data, importing the scanned.obj cloud point map into Anthroscan, reducing the scale by 1:500 and adjusting X, Y, and Z axes. Therefore, the human body was facing the right side for intelligent repair. After obtaining the complete human body models, human body anthropometric measurements were carried out. Such data and the data of metabolic measurements were imported into the Excel spreadsheet. Spss19.0 was finally used to analyze the data correlation. The images of part of the subjects that were scanned by the 3D scanner and processed by the Anthroscan (Scanworx) 3D image software

about 15 min is required for most subjects.

**Figure 1.** Part of 3D scanning images of female subjects.

**Figure 2.** Part of 3D scanning images of male subjects.

are shown in **Figures 1** and **2**.

62 Body-mass Index and Health

Factor analysis was carried out to analyze the maximum abdominal circumference, waist circumference, chest circumference (horizontal), right thigh circumference, hip circumference, weight, total shoulder width, mid-neck girth, height, waist height, cervical height, hip height, and chest height, so as to verify if the data were appropriate for correlation analysis.

The KMO value is greater than 0.05 and close to 1, sig. = 0.000 < 0.05, so the 13 observed items are suitable for factor analysis (**Table 3**). After preliminary analysis, it was found that the basal metabolic rate was not significantly related to gender, and therefore, 13 representative sizes of the subjects were analyzed. For **Table 4** reveals high communality of each factor, the extracted components can be well described by these variables. Meanwhile, in the light of **Table 5**, the eigenvalues of the first two factors are 6.985 and 3.833, respectively, accounting for 83.596% of the total variance. The first two factors explain the variance of 83.596% of the original 13 factors; hence, we will confirm to extract the two principal components.

In order to name these factors, we rotated the factors so that the coefficients were polarized to 0 and 1. By rotating the factor matrix, the factor can be named and interpreted (**Table 6**). Factor 1 is named the girth factor since it can represent waist girth, maximum belly circumference, bust girth (horizontal), thigh girth (right), buttock girth, weight, across shoulder, and mid-neck girth. Factor 2 is named the height factor as it can represent waist height, neck height, bust height, buttock height, and body height. The coefficient of principal component score is shown in **Table 7**.

Standardized first factor = 0.178 × maximum belly circumference + 0.171 × waist girth + 0.171 × bust girth (horizontal) + 0.166 × thigh girth (right) + 0.154 × buttock girth +0.135 × weight + 0.112 × across shoulder +0.104 × mid-neck girth − 0.017 × body height − 0.043 × waist height − 0.022 × neck height − 0.044 × buttock height − 0.037 × bust height.

Standardized second factor = −0.066 × maximum belly circumference − 0.036 × waist girth − 0.047 × bust girth (horizontal) − 0.059 × thigh girth (right) − 0.015 × buttock girth + 0.047 × weight + 0.023 × across shoulder + 0.051 × mid-neck girth + 0.189 × body height + 0.203 × waist height + 0.196 × neck height + 0.198 × buttock height + 0.200 × bust height.

#### **3.2. Factors and predicted basal metabolic rate**

According to **Figure 3**, the girth and height are highly related to the predicted basal metabolic rate with linear correlation. The correlation coefficients between predicted basal


**Table 3.** Kmo and Bartlett's test.


**Component 1 2**

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Relationship between Human Body Anthropometric Measurements and Basal Metabolic Rate

**Component**

**1 2**

Waist girth 0.928 0.077 Maximum belly circumference 0.919 −0.070 Bust girth (horizontal) 0.911 0.017 Thigh girth (right) 0.865 −0.049 Buttock girth 0.862 0.156 Weight 0.848 0.448 Across shoulder 0.679 0.287 Mid-neck girth 0.674 0.418 Waist height 0.062 0.975 Neck height 0.171 0.972 Bust height 0.094 0.971 Buttock height 0.051 0.948 Body height 0.192 0.945

Maximum belly circumference 0.178 −0.066 Waist girth 0.171 −0.036 Bust girth (horizontal) 0.171 −0.047 Thigh girth (right) 0.166 −0.059 Buttock girth 0.154 −0.015 Weight 0.135 0.047 Across shoulder 0.112 0.023 Mid-neck girth 0.104 0.051 Body height −0.017 0.189 Waist height −0.043 0.203 Neck height −0.022 0.196 Buttock height −0.044 0.198 Bust height −0.037 0.200

**Table 6.** Rotation component matrix.

**Table 7.** Component score coefficient matrix.

**Table 4.** Communalities.


**Table 5.** Analysis on all variances.


**Table 6.** Rotation component matrix.

**Initial Extraction**

**Rotation sums of squared loadings**

**Cumulative %**

**variance**

**Total % of** 

Maximum belly circumference 1.000 0.849 Waist girth 1.000 0.867 Bust girth (horizontal) 1.000 0.830 Thigh girth (right) 1.000 0.750 Buttock girth 1.000 0.768 Weight 1.000 0.919 Across shoulder 1.000 0.544 Mid-neck girth 1.000 0.629 Body height 1.000 0.930 Waist height 1.000 0.954 Neck height 1.000 0.974 Buttock height 1.000 0.901 Bust height 1.000 0.952

**Table 4.** Communalities.

64 Body-mass Index and Health

**Total % of** 

 0.853 6.560 90.156 0.383 2.948 93.103 0.247 1.899 95.003 0.214 1.650 96.652 0.135 1.040 97.693 0.111 0.855 98.548 0.067 0.517 99.065 0.051 0.390 99.455 0.041 0.315 99.770 0.018 0.140 99.910 0.012 0.090 100.000

**Table 5.** Analysis on all variances.

**variance**

**Factor Initial eigenvalues Extraction sums of squared** 

**Cumulative** 

**%**

**loadings**

**Total % of** 

1 6.985 53.729 53.729 6.985 53.729 53.729 5.741 44.165 44.165 2 3.883 29.867 83.596 3.883 29.867 83.596 5.126 39.431 83.596

**variance**

**Cumulative** 

**%**


**Table 7.** Component score coefficient matrix.

**Predicted basal metabolic rate**

Relationship between Human Body Anthropometric Measurements and Basal Metabolic Rate

Sig. (two-sided) 0.000 0.000 N 114 114 114

Sig. (two-sided) 0.000 1.000 N 114 114 114

N 114 114 114

Sig. (two-sided) 0.000 1.000

**Model Sum of squares df Mean square F Sig.** 1 Regression 1100764.555 1 1100764.555 75.320 0.000<sup>a</sup>

2 Regression 2178287.472 2 1089143.736 216.155 0.000<sup>b</sup>

**Model Nonstandardized coefficients Standardized coefficients T Sig.**

1 (Constant) 1551.000 11.322 136.985 0.000 Height index 98.698 11.372 0.634 8.679 0.000 2 (Constant) 1551.000 6.648 233.294 0.000 Height index 98.698 6.678 0.634 14.780 0.000 Girth index 97.650 6.678 0.627 14.624 0.000

**B Std. error Beta**

Pearson's correlation

Pearson's correlation

**Table 8.** Correlation analysis between two factors and predicted basal metabolic rate.

coefficient

coefficient

coefficient

Residual 1636821.445 112 14614.477

Residual 559298.528 111 5038.725

Total 2737586.000 113

Total 2737586.000 113

Predictor variables: height indicator and girth indicator.

Dependent variable: predicted basal metabolic rate.

Dependent variable: predicted basal metabolic rate.

**Table 10.** Regression coefficient.

Predictor variables: height indicator.

Height index Pearson's correlation

Predicted basal metabolic

Circumferential

rate

index

a

b

c

a

**Table 9.** Anova.

**Coefficients<sup>a</sup>**

**Circumferential**

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**Height index**

67

**index**

1 0.627\*\* 0.634\*\*

0.627\*\* 1 0.000

0.634\*\* 0.000 1

**Figure 3.** Simple scatterplot of factors and predicted basal metabolic rate.

metabolic rate and girth index and predicted basal metabolic rate and height index are 0.627 (sig. = 0.000 < 0.01, reject null hypothesis) and 0.634 (sig. = 0.000 < 0.01, reject null hypothesis), respectively. The results unveil that there is a significant correlation between predicted basal metabolic rate and girth index and predicted basal metabolic rate and height index, respectively (**Table 8**).

**Table 9** [(a) predicator variable, height index; (b) predicator variable, height index and circumference index; (c) dependent index, predicted basal metabolic rate] lists the sources of variation, degree of freedom, mean squares, F value, and the significant test of F. The mean squares among group two models are far greater than that within the group. The statistical value of F is 216.155, sig. <0.05, so the regression equation established is valid.

According to **Table 10**, in model 2, dependent variable Y regression on the two independent variables X1 and X2 of the nonstandardized regression coefficients are 98.698 and 97.650, respectively, while T values of the corresponding saliency detection are 14.780 and 14.624, respectively, and the significant level of their regression coefficient (sig.) is 0.000, which is less than 0.05. Hence, it can be deduced that there is a definite linear relationship between the two factors and measured basal metabolic rate.

#### **3.3. Factors and measured basal metabolic rate**

The correlation between the weight index, height index, and predicted basal metabolic rate was analyzed. It can be observed from the scatterplot in **Figure 4** that there is a correlation between weight index, height index, and predicated basal metabolic rate, and the linear trend of the scatterplot is not obvious.

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**Table 8.** Correlation analysis between two factors and predicted basal metabolic rate.


a Predictor variables: height indicator.

b Predictor variables: height indicator and girth indicator.

c Dependent variable: predicted basal metabolic rate.

#### **Table 9.** Anova.

metabolic rate and girth index and predicted basal metabolic rate and height index are 0.627 (sig. = 0.000 < 0.01, reject null hypothesis) and 0.634 (sig. = 0.000 < 0.01, reject null hypothesis), respectively. The results unveil that there is a significant correlation between predicted basal metabolic rate and girth index and predicted basal metabolic rate and height index, respec-

**Table 9** [(a) predicator variable, height index; (b) predicator variable, height index and circumference index; (c) dependent index, predicted basal metabolic rate] lists the sources of variation, degree of freedom, mean squares, F value, and the significant test of F. The mean squares among group two models are far greater than that within the group. The statistical

According to **Table 10**, in model 2, dependent variable Y regression on the two independent variables X1 and X2 of the nonstandardized regression coefficients are 98.698 and 97.650, respectively, while T values of the corresponding saliency detection are 14.780 and 14.624, respectively, and the significant level of their regression coefficient (sig.) is 0.000, which is less than 0.05. Hence, it can be deduced that there is a definite linear relationship between the two

The correlation between the weight index, height index, and predicted basal metabolic rate was analyzed. It can be observed from the scatterplot in **Figure 4** that there is a correlation between weight index, height index, and predicated basal metabolic rate, and the linear trend

value of F is 216.155, sig. <0.05, so the regression equation established is valid.

tively (**Table 8**).

66 Body-mass Index and Health

factors and measured basal metabolic rate.

of the scatterplot is not obvious.

**3.3. Factors and measured basal metabolic rate**

**Figure 3.** Simple scatterplot of factors and predicted basal metabolic rate.


Dependent variable: predicted basal metabolic rate.

**Table 10.** Regression coefficient.

**3.4. Univariate and predicted basal metabolic rate**

Select the maximum abdominal circumference, waist circumference (horizontal), chest circumference, mid-neck girth, right thigh circumference, hip circumference, weight, total shoulder width, and height as independent variables, and select the predicted basal metabolic

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69

According to **Table 12**, except for the right thigh variable, weight, height, hip circumference,

Besides, there is high R square value on model 1, 2, 3, 4, 5, and 6, which unveils the dependent

The F value in model 6 is 358.808, and sig in all models is 0.000, less than 0.01, which has a strong significance. Meanwhile, all the regression variances are greater than residuals, indi-

It can be observed from **Table 15** that the multivariate regression equation should be F = 56. 615 + 15.131 × weight + 6.504 × height − 8.266 × hip circumference + 11.180 × mid-neck girth. However, because the sig. Value of the constant value is 0.772 > 0.1, the constant value is not significant. Therefore, there is no data in the column of constant value, which has been removed. So the standardized equation is Y = 0.771 × weight + 0.268 × height − 0.243 × hip

A linear regression analysis was performed on the nine independent variables and the dependent variable-basal metabolic rate. The nine independent variables are the maximum abdominal circumference, waist circumference (horizontal), chest circumference, mid-neck girth, right thigh circumference, hip circumference, weight, total shoulder width, and height.

1 Weight — Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

— Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

— Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

— Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

— Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

rate as dependent variable, and then regression analysis was performed.

and neck circumference variables are embedded into regression model.

and independent variables are highly correlated (**Table 13**).

circumference + 0.197 × mid-neck girth.

**Variables removed**

**Method**

**Variables entered/removeda**

**Mode Variables entered**

2 Thigh girth (right)

3 Body height

4 Buttock girth

5 Mid-neck girth

**Table 12.** Modeling.

6 — Thigh girth

(right)

**3.5. Univariate and measured basal metabolic rate**

cating the established regression equation is effective (**Table 14**).

**Figure 4.** Simple scatterplot of factors and measured basal metabolic rate.


**Table 11.** Correlation analysis between two factors and measured basal metabolic rate.

The consequence indicates that the correlation coefficient between the measured basal metabolic rate and the height index, the measured basal metabolic rate, and the girth index are 0.303 (sig. = 0.001 < 0.01, reject null hypothesis) and 0.349 (sig. = 0.000 < 0.01, reject null hypothesis), respectively. Accordingly, we can conclude there is an insignificant correlation between the measured basal metabolic rate and the height index, measured basal metabolic rate, and the girth index, respectively (**Table 11**).

#### **3.4. Univariate and predicted basal metabolic rate**

Select the maximum abdominal circumference, waist circumference (horizontal), chest circumference, mid-neck girth, right thigh circumference, hip circumference, weight, total shoulder width, and height as independent variables, and select the predicted basal metabolic rate as dependent variable, and then regression analysis was performed.

According to **Table 12**, except for the right thigh variable, weight, height, hip circumference, and neck circumference variables are embedded into regression model.

Besides, there is high R square value on model 1, 2, 3, 4, 5, and 6, which unveils the dependent and independent variables are highly correlated (**Table 13**).

The F value in model 6 is 358.808, and sig in all models is 0.000, less than 0.01, which has a strong significance. Meanwhile, all the regression variances are greater than residuals, indicating the established regression equation is effective (**Table 14**).

It can be observed from **Table 15** that the multivariate regression equation should be F = 56. 615 + 15.131 × weight + 6.504 × height − 8.266 × hip circumference + 11.180 × mid-neck girth. However, because the sig. Value of the constant value is 0.772 > 0.1, the constant value is not significant. Therefore, there is no data in the column of constant value, which has been removed. So the standardized equation is Y = 0.771 × weight + 0.268 × height − 0.243 × hip circumference + 0.197 × mid-neck girth.

#### **3.5. Univariate and measured basal metabolic rate**

A linear regression analysis was performed on the nine independent variables and the dependent variable-basal metabolic rate. The nine independent variables are the maximum abdominal circumference, waist circumference (horizontal), chest circumference, mid-neck girth, right thigh circumference, hip circumference, weight, total shoulder width, and height.


**Table 12.** Modeling.

The consequence indicates that the correlation coefficient between the measured basal metabolic rate and the height index, the measured basal metabolic rate, and the girth index are 0.303 (sig. = 0.001 < 0.01, reject null hypothesis) and 0.349 (sig. = 0.000 < 0.01, reject null hypothesis), respectively. Accordingly, we can conclude there is an insignificant correlation between the measured basal metabolic rate and the height index, measured basal metabolic rate, and

Sig. (two-sided) 0.000 1.000

**Measured basal metabolic rate**

Sig. (two-sided) 0.001 0.000 N 114 114 114

Sig. (two-sided) 0.001 1.000 N 114 114 114

N 114 114 114

**Circumferential** 

**Height index**

**index**

1 0.303\*\* 0.349\*\*

0.303\*\* 1 0.000

0.349\*\* 0.000 1

the girth index, respectively (**Table 11**).

**Figure 4.** Simple scatterplot of factors and measured basal metabolic rate.

coefficient

coefficient

coefficient

**Table 11.** Correlation analysis between two factors and measured basal metabolic rate.

Circumferential index Pearson's correlation

Height index Pearson's correlation

Pearson's correlation

Measured basal metabolic

68 Body-mass Index and Health

rate


**Model Sum of squares df Mean square F Sig.** 6 Regression 2544352.653 4 636088.163 358.808 0.000<sup>f</sup>

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Residual 193233.347 109 1772.783

Predictor variables: (constant), weight, thigh girth (right), body height.

<sup>d</sup>Predictor variables: (constant), weight, thigh girth (right), body height, buttock girth.

Predictor variables: (constant), weight, body height, buttock girth, mid-neck girth.

Predictor variables: (constant), weight, thigh girth (right), body height, buttock girth, mid-neck girth.

**Model Nonstandardized coefficients Standardized coefficients t Sig.**

1 (Constant) 591.996 44.975 13.163 0.000 Weight 17.615 0.818 0.898 21.546 0.000 2 (Constant) 1164.971 70.904 16.430 0.000 Weight 23.644 0.900 1.205 26.258 0.000 Thigh girth (right) −17.221 1.870 −0.423 −9.210 0.000 3 (Constant) 297.992 206.631 1.442 0.152 Weight 19.243 1.298 0.981 14.829 0.000 Thigh girth (right) −11.486 2.162 −0.282 −5.313 0.000 Body height 4.909 1.109 0.202 4.425 0.000 4 (Constant) 588.554 204.637 2.876 0.005 Weight 20.533 1.248 1.046 16.455 0.000 Thigh girth (right) −5.100 2.525 −0.125 −2.020 0.046 Body height 5.343 1.039 0.220 5.141 0.000 Buttock girth −8.278 1.971 −0.243 −4.199 0.000 5 (Constant) 142.712 222.059 0.643 0.522 Weight 15.874 1.653 0.809 9.603 0.000 Thigh girth (right) −2.024 2.490 −0.050 −0.813 0.418 Body height 6.055 0.991 0.249 6.111 0.000 Buttock girth −7.457 1.860 −0.219 −4.009 0.000 Mid-neck girth 10.517 2.635 0.186 3.991 0.000

**B Std. error Beta**

Total 2737586.000 113

Predictor variables: (constant), weight, thigh girth (right).

Dependent variable: predicted basal metabolic rate.

Predictor variables: (constant), weight.

a

b

c

e

f

g

**Table 14.** Anova.

**Coefficients<sup>a</sup>**

a Predictive variable: (constant), weight.

b Predictive variable: (constant), weight, thigh girth (right).

c Predictive variable: (constant), weight, thigh girth (right), body height.

<sup>d</sup>Predictive variable: (constant), weight, thigh girth (right), body height, buttock girth.

e Predictive variable: (constant), weight, thigh girth (right), body height, buttock girth, mid-neck girth.

f Predictive variable: (constant), weight, body height, buttock girth, mid-neck girth.

g Dependent variable: predicted basal metabolic rate.

**Table 13.** Model summarya .



a Predictor variables: (constant), weight.

b Predictor variables: (constant), weight, thigh girth (right).

c Predictor variables: (constant), weight, thigh girth (right), body height.

<sup>d</sup>Predictor variables: (constant), weight, thigh girth (right), body height, buttock girth.

e Predictor variables: (constant), weight, thigh girth (right), body height, buttock girth, mid-neck girth.

f Predictor variables: (constant), weight, body height, buttock girth, mid-neck girth.

g Dependent variable: predicted basal metabolic rate.

#### **Table 14.** Anova.

**Model R R square Adjusted R square Std. error of the estimate**

 0.898<sup>a</sup> 0.806 0.804 68.926 0.943<sup>b</sup> 0.890 0.888 52.127 0.952<sup>c</sup> 0.906 0.904 48.245 0.959<sup>d</sup> 0.919 0.917 44.965 0.964<sup>e</sup> 0.930 0.927 42.170 0.964<sup>f</sup> 0.929 0.927 42.104

a

b

c

e

f

g

Predictive variable: (constant), weight.

**Table 13.** Model summarya

70 Body-mass Index and Health

Predictive variable: (constant), weight, thigh girth (right).

Dependent variable: predicted basal metabolic rate.

.

Predictive variable: (constant), weight, thigh girth (right), body height.

Residual 532086.048 112 4750.768

Residual 301610.158 111 2717.209

Residual 256036.594 110 2327.605

Residual 220385.397 109 2021.884

Residual 192057.907 108 1778.314

Total 2737586.000 113

Total 2737586.000 113

Total 2737586.000 113

Total 2737586.000 113

Total 2737586.000 113

<sup>d</sup>Predictive variable: (constant), weight, thigh girth (right), body height, buttock girth.

Predictive variable: (constant), weight, body height, buttock girth, mid-neck girth.

Predictive variable: (constant), weight, thigh girth (right), body height, buttock girth, mid-neck girth.

**Model Sum of squares df Mean square F Sig.** 1 Regression 2205499.952 1 2205499.952 464.241 0.000<sup>a</sup>

2 Regression 2435975.842 2 1217987.921 448.250 0.000<sup>b</sup>

3 Regression 2481549.406 3 827183.135 355.379 0.000<sup>c</sup>

4 Regression 2517200.603 4 629300.151 311.244 0.000<sup>d</sup>

5 Regression 2545528.093 5 509105.619 286.286 0.000<sup>e</sup>



**Table 15.** Regression coefficient.


a Dependent variable: measured basal metabolic rate.

**Table 16.** Modeling.


**4. Conclusions**

**Table 19.** Regression coefficient.

a

b

c

a

**Table 18.** Anova.

**Coefficients<sup>a</sup>**

the height factor.

In this study, after undertaking the factor analysis, linear regression analysis, univariate

**1.** There is commonality among three dimensional body measurement data, embracing maximum belly circumference, waist girth, bust girth (horizontal), thigh girth (right), buttock girth, weight, across shoulder, mid-neck girth, waist height, neck height, bust height, buttock height, and body height, which can be well divided into girth and height factor, with the waist girth, maximum belly circumference, bust girth (horizontal), thigh girth (right), buttock girth, weight, across shoulder, and mid-neck girth included in the girth factor, while waist height, neck height, bust height, buttock height, and body height contained in

**2.** Girth and height factors are correlated with the predicted basal metabolic rate as well as the measured basal metabolic rate. They have a significant linear relationship with the

analysis, and other analysis methods, we can draw the following conclusions:

**Model Sum of squares df Mean square F Sig.** 1 Regression 2081624.638 1 2081624.638 32.206 0.000<sup>a</sup>

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2 Regression 2348739.947 2 1174369.974 18.697 0.000<sup>b</sup>

**Model Nonstandardized coefficients Standardized coefficients t Sig.**

1 (Constant) −22.175 287.115 −0.077 0.939 Mid-neck girth 49.413 8.707 0.473 5.675 0.000 2 (Constant) −1128.222 606.438 −1.860 0.065 Mid-neck girth 38.379 10.114 0.367 3.795 0.000 Body height 8.940 4.335 0.199 2.062 0.042

**B Std. error Beta**

Residual 7239032.599 112 64634.220

Residual 6971917.290 111 62810.066

Total 9320657.237 113

Total 9320657.237 113

Measure variables: (constant), mid-neck girth, body height.

Measure variables: (constant), mid-neck girth.

Dependent variable: measured basal metabolic rate.

Dependent variable: measured basal metabolic rate.

a Measure variables: (constant), mid-neck girth.

b Measure variables: (constant), mid-neck girth, body height.

**Table 17.** Model summary.

According to **Table 16**, the mid-neck girth and body height variables can be embedded into the model. The goodness of fit of model 2 is better than model 1, but the R value of the model 2 is 0.252, which indicates that independent variable can explain the change of dependent variable 25.2% (**Table 17**). In the model regression analysis, the goodness of fit is general. In addition, for the probability of F value greater than F critical value (sig.) which is about 0.000, we can deduce that there are correlations between measured basal metabolic rate and the mid-neck girth, measured basal metabolic rate, and body height, respectively (**Table 18**).

In the light of **Table 19**, since P values of the two independent variables are 0.000 and 0.042, respectively, the mid-neck girth and body height are related to the basal metabolic rate. Meanwhile, after considering all the factors of the independent variable, we can deduce the final regression equation: Y = −1128.222 + 38.379 × mid-neck girth + 8.940 × body height.

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a Measure variables: (constant), mid-neck girth.

b Measure variables: (constant), mid-neck girth, body height.

c Dependent variable: measured basal metabolic rate.

#### **Table 18.** Anova.


**Table 19.** Regression coefficient.

#### **4. Conclusions**

According to **Table 16**, the mid-neck girth and body height variables can be embedded into the model. The goodness of fit of model 2 is better than model 1, but the R value of the model 2 is 0.252, which indicates that independent variable can explain the change of dependent variable 25.2% (**Table 17**). In the model regression analysis, the goodness of fit is general. In addition, for the probability of F value greater than F critical value (sig.) which is about 0.000, we can deduce that there are correlations between measured basal metabolic rate and the mid-neck girth, measured basal metabolic rate, and body height, respectively (**Table 18**). In the light of **Table 19**, since P values of the two independent variables are 0.000 and 0.042, respectively, the mid-neck girth and body height are related to the basal metabolic rate. Meanwhile, after considering all the factors of the independent variable, we can deduce the final regression equation: Y = −1128.222 + 38.379 × mid-neck girth + 8.940 × body height.

**Model Nonstandardized coefficients Standardized coefficients t Sig.**

6 (Constant) 56.615 194.877 0.291 0.772 Weight 15.131 1.375 0.771 11.001 0.000 Body height 6.504 0.822 0.268 7.916 0.000 Buttock girth −8.266 1.569 −0.243 −5.269 0.000 Mid-neck girth 11.180 2.502 0.197 4.469 0.000

**Method**

**Model R R square Adjusted R square Std. error of the estimate**

1 0.473<sup>a</sup> 0.223 0.216 254.233 2 0.502<sup>b</sup> 0.252 0.239 250.619

1 Mid-neck girth — Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100) 2 Body height — Stepwise (criteria: probability-of-F-to-enter ≤ 0.005, probability-of-F-to-enter ≥ 0.100)

**B Std. error Beta**

**Coefficients<sup>a</sup>**

72 Body-mass Index and Health

a

a

a

b

**Table 16.** Modeling.

**Table 17.** Model summary.

Dependent variable: predicted basal metabolic rate.

Dependent variable: measured basal metabolic rate.

Measure variables: (constant), mid-neck girth.

Measure variables: (constant), mid-neck girth, body height.

**Variables removed**

**Table 15.** Regression coefficient.

**Variables entered/removeda Model Variables entered**

> In this study, after undertaking the factor analysis, linear regression analysis, univariate analysis, and other analysis methods, we can draw the following conclusions:


predicted basal metabolic rate, whereas there is no significant linear relation between the two factors and the measured basal metabolic rate.

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**3.** There are several variables linearly related to the predicted basal metabolic rate and basal metabolic rate, embracing waist girth, maximum belly circumference, bust girth (horizontal), thigh girth (right), buttock girth, weight, across shoulder, mid-neck girth, and body height. The predicted basal metabolic rate is in a significant correlation with weight, body height, buttock girth, and mid-neck girth, while the basal metabolic rate is correlated with mid-neck girth and body height.
