90 (25 49 ) 25 ( 30 60 ) 5 25 28 29 32 33 37 38 46 47 49 250 03-3283201 1420 E-mailt476@ms24.hinet.net 1
( 80) 20 30 (Timiras,1972) 16 (Lowrey,1978) (86) 35 35 35 ( 30 60 ) 6 65 6 20 21 65 (25 49 ) 3997 2
19 24 25 49 50 65 (25 49 ) (cluster) ( 90) (Ward s Minimum Variance) () R (semipartial R-square; SPRSQ) ( 82) () R (RSQ) ( 82) () Cubic (Cubic clustering criterion; CCC) ( 82) () F (pseudo F;PSF) ( 82) ( ) ( ) (Scheffe method) SAS 8.1 SPSS 10.0 30 60 3
( ) 25 125.44 1.5425 125.9 0.30 ( ) 39 () Pearson.467 25 49 25 29 30 34 35 40 44 4
( ) 30 60 30 60 Pearson.911 25 49 25 24 1 24 38 39 ( ) 10 25 27 28 3 2 25 37 13 SPRSQ RSQ CCC PSF SPRSQ RSQ (SPRSQ:0.14~0.58RSQ:0.58~0)CCC PSF (CCC:-0.94PSF:31.4) 25 37 38 49 ( v25~v37v38~v49) SPRSQ:0.01RSQ:0.83CCC:-1.10PSF:23.90 SPRSQ RSQ CCC PSF ( ) 5
(~ ) SPRSQ RSQ CCC PSF 24 2 0.002 0.998 --- 21.8 ~ ~ ~ ~ ~ ~ 10 3 0.011 0.926 --- 20.8 9 3 0.011 0.915 --- 21.4 8 4 0.014 0.901 --- 22.0 7 4 0.016 0.885 --- 23.1 6 7 0.020 0.865 --- 24.4 5 9 0.039 0.827-1.10 23.9 4 9 0.045 0.781-1.30 25.0 3 12 0.067 0.714-1.00 27.5 2 13 0.137 0.577-0.94 31.4 1 25 0.577 0 0 --- 25 37 12 (25 37 ) 25 37 38 49 25 28 29 32 33 37 38 46 47 49 6
25 28 29 32 33 37 38 46 47 49 (α 0.05) *p<0.05 F 26850.08 4 6712.52 29.81* 898871.49 3992 225.17 925721.57 3996 17340.50 4 4335.13 25.51* 678533.03 3992 169.97 695873.53 3996 20640.67 4 5160.25 28.99* 710514.53 3992 177.99 731155.2 3996 *p<0.05 F 0.95 4 0.24 60.00* 15.18 3992 0.004 16.13 3996 8045.50 4 2011.13 19.05 421477.61 3992 105.58 429523.11 3996 7
*p<0.05 F 26850.08 4 6712.52 29.81* 898871.49 3992 225.17 925721.57 3996 17340.50 4 4335.13 25.51* 678533.03 3992 169.97 695873.53 3996 20640.67 4 5160.25 28.99* 710514.53 3992 177.99 731155.2 3996 8
(Scheffe method ) 30 60 0.02-0.29-1.59-2.57 2.55* 3.99* 5.16* 7.11* -0.31-1.61-2.59 30 1.44* 2.61* 4.56* -1.29-2.28 1.17* 3.12* -0.99 1.95* -0.20-1.05-1.56-2.18 4.49* 7.17* 9.43* 12.63* -0.85-1.35-1.98 60 2.68* 4.93* 8.13* -0.50-1.13 2.26* 5.46* -0.63 3.20* 0.03-0.28 1.23 1.63 1.45 1.75 2.18* 4.20* -0.58 0.93 1.34 0.31 0.73 2.75* 1.51 1.92 0.43 2.44* 0.40 2.02 0.01* 0.02* 0.03* 0.05* -0.32-0.59-1.28-0.88 0.01* 0.02* 0.03* -.28-0.97-0.56 0.01* 0.02* -0.69-0.28 0.01* 0.40-0.95-1.49-1.23-0.62-0.54-0.28 0.34 0.26 0.88 0.61 ( 81) 2 ( ˆω ) 2.97 2.54 2.90 5.86 19.03 15.50 17.35 8.92 2.85 20 9
90 25 28 29 32 33 37 38 46 47 49 (1991) (87) (86) 198-209 (90)SAS (85)SPSSSASBMDP Timiras, P.S.(1972) : Developmental Physiology and aging. New York: Macmillan. Lowrey, G.H.(1978):Growth and Development of Children. Chicago: Year Book. 10
A Study of Dividing The Group by Age in Male s Health-Related Fitness Hsu Po-Yang NCPES ABSTRACT The objective of this study was to use the national health-related fitness data of male adults, the age ranging from 25 to 49 years old, including 9 measurement indexes (systolic pressure, diastolic pressure, static heart rate, height, body weight, 30s sit-ups, 60s sit-ups, sit and reach, and cardiorespiratory ) The study used 5 year-old as the group to find the optimal age in dividing the group by utilizing hierarchical clustering analysis, and one-way ANOVA. The results showed that the best way to divide group was by adopting the age ranges of first group, second group, third group, forth group, and fifth group, from 25 to 28 years old, 29 to 32 years old, 33 to 37 years old, 38 to 46 years old, and 47 to 49 years old, respectively. key words health-related fitness, hierarchical clustering, one-way ANOVA 11