Xu ly du lieu PhD cho Mr. Giang

Doc du lieu

# Bang 3.22
Giang<-read.csv("https://raw.githubusercontent.com/tuyenhavan/Statistics/master/S%E1%BB%91%20li%E1%BB%87u%20th%C3%B4%20Da%20gop_22.csv",sep = ";",header=T)
head(Giang)
library("dplyr")
library("tidyr")
D1<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(1,7)
head(D1)
library(psych)
describe(D1)
       vars   n    mean    sd median trimmed   mad min  max range  skew kurtosis
Chay_5    1 260 1067.96 42.56   1057 1070.93 31.13 925 1212   287 -1.07     2.83
Nhom*     2 260    2.28  0.85      3    2.35  0.00   1    3     2 -0.57    -1.40
         se
Chay_5 2.64
Nhom*  0.05

Chay tuy suc 5phut

library(psych)
p1<-aov(D1$Chay_5~D1$Nhom)
summary(p1)
             Df Sum Sq Mean Sq F value   Pr(>F)    
D1$Nhom       2  28210   14105   8.222 0.000346 ***
Residuals   257 440905    1716                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p1)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D1$Chay_5 ~ D1$Nhom)

$`D1$Nhom`
            diff       lwr       upr     p adj
G2-G1 -25.500000 -43.58707 -7.412933 0.0029167
G3-G1 -22.907176 -37.32313 -8.491223 0.0006480
G3-G2   2.592824 -13.36193 18.547581 0.9223180
plot(TukeyHSD(p1))

Bat xa tai cho

D2<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(2,7)
p2<-aov(D2$Bat_Xa~D2$Nhom)
summary(p2)
             Df Sum Sq Mean Sq F value   Pr(>F)    
D2$Nhom       2   1334   666.8   7.098 0.000999 ***
Residuals   257  24145    93.9                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p2)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D2$Bat_Xa ~ D2$Nhom)

$`D2$Nhom`
            diff       lwr        upr     p adj
G2-G1 -4.7352941 -8.967926 -0.5026624 0.0239975
G3-G1 -5.2759700 -8.649508 -1.9024314 0.0008073
G3-G2 -0.5406758 -4.274316  3.1929648 0.9378000
plot(TukeyHSD(p2))

Luc bo tay

D3<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(3,7)
p3<-aov(D3$Luc_Bop~D3$Nhom)
summary(p3)
             Df Sum Sq Mean Sq F value  Pr(>F)   
D3$Nhom       2    295  147.72   5.964 0.00294 **
Residuals   257   6366   24.77                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p3)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D3$Luc_Bop ~ D3$Nhom)

$`D3$Nhom`
            diff       lwr         upr     p adj
G2-G1 -2.2098039 -4.383177 -0.03643098 0.0452847
G3-G1 -2.4874844 -4.219730 -0.75523882 0.0023644
G3-G2 -0.2776804 -2.194831  1.63947043 0.9377766
plot(TukeyHSD(p3))

Chay 30m

D4<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(4,7)
p4<-aov(D4$Chay_30m~D4$Nhom)
summary(p4)
             Df Sum Sq Mean Sq F value Pr(>F)  
D4$Nhom       2   2.08  1.0421   2.477  0.086 .
Residuals   257 108.10  0.4206                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p4)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D4$Chay_30m ~ D4$Nhom)

$`D4$Nhom`
             diff         lwr       upr     p adj
G2-G1  0.23397059 -0.04923789 0.5171791 0.1276182
G3-G1  0.18730392 -0.03842200 0.4130298 0.1253522
G3-G2 -0.04666667 -0.29648728 0.2031540 0.8986846
plot(TukeyHSD(p4))

Chay con thoi

D5<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(5,7)
p5<-aov(D5$Con_Thoi~D5$Nhom)
summary(p5)
             Df Sum Sq Mean Sq F value   Pr(>F)    
D5$Nhom       2   7.26   3.632   14.18 1.44e-06 ***
Residuals   257  65.82   0.256                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p5)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D5$Con_Thoi ~ D5$Nhom)

$`D5$Nhom`
           diff         lwr       upr     p adj
G2-G1 0.2398529  0.01886231 0.4608436 0.0297252
G3-G1 0.3970536  0.22091723 0.5731900 0.0000007
G3-G2 0.1572007 -0.03773706 0.3521384 0.1404423
plot(TukeyHSD(p5))

Nam ngua gap bung

D6<-Giang %>% filter( Gioi_Tinh=="Nam") %>% select(6,7)
p6<-aov(D6$Gap_Bung~D6$Nhom)
summary(p6)
             Df Sum Sq Mean Sq F value Pr(>F)  
D6$Nhom       2  105.9   52.93   4.432 0.0128 *
Residuals   257 3068.7   11.94                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p6)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D6$Gap_Bung ~ D6$Nhom)

$`D6$Nhom`
            diff        lwr        upr     p adj
G2-G1 -1.6715686 -3.1805244 -0.1626128 0.0257656
G3-G1 -1.3315603 -2.5342448 -0.1288758 0.0258667
G3-G2  0.3400083 -0.9910545  1.6710712 0.8190514
plot(TukeyHSD(p6))

Nữ

Chay tuy suc 5phut

library(psych)
D1<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(1,7)
p1<-aov(D1$Chay_5~D1$Nhom)
summary(p1)
             Df Sum Sq Mean Sq F value  Pr(>F)   
D1$Nhom       2  13277    6638    5.02 0.00735 **
Residuals   228 301499    1322                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p1)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D1$Chay_5 ~ D1$Nhom)

$`D1$Nhom`
           diff        lwr       upr     p adj
G2-G1 -23.15189 -40.533389 -5.770388 0.0053733
G3-G1 -11.69242 -25.222711  1.837873 0.1053763
G3-G2  11.45947  -3.752613 26.671551 0.1795726
plot(TukeyHSD(p1))

Bat xa tai cho

D2<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(2,7)
p2<-aov(D2$Bat_Xa~D2$Nhom)
summary(p2)
             Df Sum Sq Mean Sq F value Pr(>F)  
D2$Nhom       2    879   439.3   3.813 0.0235 *
Residuals   228  26269   115.2                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p2)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D2$Bat_Xa ~ D2$Nhom)

$`D2$Nhom`
            diff        lwr         upr     p adj
G2-G1 -5.0722496 -10.202836  0.05833717 0.0534239
G3-G1 -4.2378257  -8.231631 -0.24402001 0.0346157
G3-G2  0.8344238  -3.655805  5.32465229 0.8995424
plot(TukeyHSD(p2))

Luc bo tay

D3<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(3,7)
p3<-aov(D3$Luc_Bop~D3$Nhom)
summary(p3)
             Df Sum Sq Mean Sq F value  Pr(>F)   
D3$Nhom       2    195   97.69   5.321 0.00551 **
Residuals   228   4186   18.36                   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p3)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D3$Luc_Bop ~ D3$Nhom)

$`D3$Nhom`
            diff        lwr        upr     p adj
G2-G1 -2.6135468 -4.6615309 -0.5655627 0.0081309
G3-G1 -1.8126744 -3.4068879 -0.2184609 0.0213263
G3-G2  0.8008724 -0.9914989  2.5932437 0.5435942
plot(TukeyHSD(p3))

Chay 30m

D4<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(4,7)
p4<-aov(D4$Chay_30m~D4$Nhom)
summary(p4)
             Df Sum Sq Mean Sq F value  Pr(>F)    
D4$Nhom       2   3.86   1.929   7.143 0.00098 ***
Residuals   228  61.56   0.270                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p4)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D4$Chay_30m ~ D4$Nhom)

$`D4$Nhom`
            diff          lwr       upr     p adj
G2-G1 0.23940066 -0.008971884 0.4877732 0.0615621
G3-G1 0.30886812  0.115527337 0.5022089 0.0006102
G3-G2 0.06946747 -0.147905224 0.2868402 0.7315641
plot(TukeyHSD(p4))

Chay con thoi

D5<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(5,7)
p5<-aov(D5$Con_Thoi~D5$Nhom)
summary(p5)
             Df Sum Sq Mean Sq F value   Pr(>F)    
D5$Nhom       2   3.91  1.9571   7.817 0.000521 ***
Residuals   228  57.08  0.2504                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p5)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D5$Con_Thoi ~ D5$Nhom)

$`D5$Nhom`
             diff         lwr       upr     p adj
G2-G1  0.31250411  0.07333551 0.5516727 0.0064980
G3-G1  0.29578837  0.10961222 0.4819645 0.0006592
G3-G2 -0.01671574 -0.22603324 0.1926018 0.9806251
plot(TukeyHSD(p5))

Nam ngua gap bung

D6<-Giang %>% filter( Gioi_Tinh=="Nu") %>% select(6,7)
p6<-aov(D6$Gap_Bung~D6$Nhom)
summary(p6)
             Df Sum Sq Mean Sq F value Pr(>F)  
D6$Nhom       2   32.7  16.331   2.913 0.0563 .
Residuals   228 1278.2   5.606                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
TukeyHSD(p6)
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = D6$Gap_Bung ~ D6$Nhom)

$`D6$Nhom`
            diff       lwr        upr     p adj
G2-G1 -1.0213465 -2.153070 0.11037680 0.0862705
G3-G1 -0.7865228 -1.667491 0.09444526 0.0908115
G3-G2  0.2348237 -0.755647 1.22529443 0.8417898
plot(TukeyHSD(p6))

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