Problema 3 del capĆtulo 2
setwd("C:/Users/mylen/OneDrive/Escritorio/PostInvestig/DExperimental")
df=read.csv("p23c2datos.csv",sep=";")
df
## Mujer Hombre
## 1 75 74
## 2 77 72
## 3 78 77
## 4 79 76
## 5 77 76
## 6 73 73
## 7 78 75
## 8 79 73
## 9 78 74
## 10 80 75
t.test(df$Mujer,df$Hombre)
##
## Welch Two Sample t-test
##
## data: df$Mujer and df$Hombre
## t = 3.5254, df = 16.851, p-value = 0.002626
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.163304 4.636696
## sample estimates:
## mean of x mean of y
## 77.4 74.5
df2=stack(df)
names(df2)=c("Y","Genero")
df2
## Y Genero
## 1 75 Mujer
## 2 77 Mujer
## 3 78 Mujer
## 4 79 Mujer
## 5 77 Mujer
## 6 73 Mujer
## 7 78 Mujer
## 8 79 Mujer
## 9 78 Mujer
## 10 80 Mujer
## 11 74 Hombre
## 12 72 Hombre
## 13 77 Hombre
## 14 76 Hombre
## 15 76 Hombre
## 16 73 Hombre
## 17 75 Hombre
## 18 73 Hombre
## 19 74 Hombre
## 20 75 Hombre
boxplot(Y~Genero,data=df2,col=c("blue","yellow"))
Problema 23 del capĆtulo 2
setwd("C:/Users/mylen/OneDrive/Escritorio/PostInvestig/DExperimental")
df=read.csv("p23c2datos.csv",sep=";")
df
## Mujer Hombre
## 1 75 74
## 2 77 72
## 3 78 77
## 4 79 76
## 5 77 76
## 6 73 73
## 7 78 75
## 8 79 73
## 9 78 74
## 10 80 75
t.test(df$Mujer,df$Hombre)
##
## Welch Two Sample t-test
##
## data: df$Mujer and df$Hombre
## t = 3.5254, df = 16.851, p-value = 0.002626
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.163304 4.636696
## sample estimates:
## mean of x mean of y
## 77.4 74.5
df2=stack(df)
names(df2)=c("Y","Genero")
df2
## Y Genero
## 1 75 Mujer
## 2 77 Mujer
## 3 78 Mujer
## 4 79 Mujer
## 5 77 Mujer
## 6 73 Mujer
## 7 78 Mujer
## 8 79 Mujer
## 9 78 Mujer
## 10 80 Mujer
## 11 74 Hombre
## 12 72 Hombre
## 13 77 Hombre
## 14 76 Hombre
## 15 76 Hombre
## 16 73 Hombre
## 17 75 Hombre
## 18 73 Hombre
## 19 74 Hombre
## 20 75 Hombre
boxplot(Y~Genero,data=df2,col=c("blue","yellow"))
setwd("C:/Users/mylen/OneDrive/Escritorio/PostInvestig/DExperimental")
Problema 12 del capĆtulo 2
x=c(28.3,26.8,26.6,26.5,28.1,24.8,27.4,26.2,29.4,28.6,24.9,25.2,30.4,27.7,27.0,26.1,28.1,26.9,28.0,27.6,25.6,29.5,27.6,27.3,26.2,27.7,27.2,25.9,26.5,28.3,26.5,29.1,23.7,29.7,26.8,29.5,28.4,26.3,28.1,28.7,27.0,25.5,26.9,27.2,27.6,25.5,28.3,27.4,28.8,25.0,25.3,27.7,25.2,28.6,27.9,28.7)
n=length(x)
media=mean(x)
std=sd(x)
media
## [1] 27.24643
std
## [1] 1.430444
hist(x,col="blue", breaks=15, freq=FALSE)
lines(density(x),col="orange",lwd=2)
tmin=qt(0.025,n-1)
tmax=qt(0.975,n-1)
tmin
## [1] -2.004045
tmax
## [1] 2.004045
media+tmin*std/sqrt(n)
## [1] 26.86335
media+tmax*std/sqrt(n)
## [1] 27.6295
(n-1)*var(x)/qchisq(0.025,n-1)
## [1] 3.091899
(n-1)*var(x)/qchisq(0.975,n-1)
## [1] 1.454363
Problema 16 del capĆtulo 2
x=c(1.81,1.97,1.93,1.97,1.85,1.99,1.95,1.93,1.85,1.87,1.98,1.93,1.96,2.02,2.07,1.92,1.99,1.93)
n=length(x)
media=mean(x)
std=sd(x)
media
## [1] 1.94
std
## [1] 0.06462562
hist(x,col="blue", breaks=15, freq=FALSE)
lines(density(x),col="orange",lwd=2)
tmin=qt(0.005,n-1)
tmax=qt(0.995,n-1)
tmin
## [1] -2.898231
tmax
## [1] 2.898231
media+tmin*std/sqrt(n)
## [1] 1.895853
media+tmax*std/sqrt(n)
## [1] 1.984147
tmax*std/sqrt(n)
## [1] 0.04414702
tmin*std/sqrt(n)
## [1] -0.04414702
(n-1)*var(x)/qchisq(0.005,n-1)
## [1] 0.01246222
(n-1)*var(x)/qchisq(0.995,n-1)
## [1] 0.001987767
boxplot(x)
Problema 24 del capĆtulo 2
Tb=c(17.2,17.5,18.6,15.9,16.4,17.3,16.8,18.4,16.7,17.6)
Ta=c(21.4,20.9,19.8,20.4,20.6,21.0,20.8,19.9,21.1,20.3)
t.test(Tb,Ta,paired=F)
##
## Welch Two Sample t-test
##
## data: Tb and Ta
## t = -10.797, df = 14.995, p-value = 1.811e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.047267 -2.712733
## sample estimates:
## mean of x mean of y
## 17.24 20.62
df=data.frame(Tb=Tb,Ta=Ta)
df
## Tb Ta
## 1 17.2 21.4
## 2 17.5 20.9
## 3 18.6 19.8
## 4 15.9 20.4
## 5 16.4 20.6
## 6 17.3 21.0
## 7 16.8 20.8
## 8 18.4 19.9
## 9 16.7 21.1
## 10 17.6 20.3
df=stack(df)
names(df)=c("Y","Temperatura")
df
## Y Temperatura
## 1 17.2 Tb
## 2 17.5 Tb
## 3 18.6 Tb
## 4 15.9 Tb
## 5 16.4 Tb
## 6 17.3 Tb
## 7 16.8 Tb
## 8 18.4 Tb
## 9 16.7 Tb
## 10 17.6 Tb
## 11 21.4 Ta
## 12 20.9 Ta
## 13 19.8 Ta
## 14 20.4 Ta
## 15 20.6 Ta
## 16 21.0 Ta
## 17 20.8 Ta
## 18 19.9 Ta
## 19 21.1 Ta
## 20 20.3 Ta
boxplot(Y~Temperatura,data=df)
modelo=aov(Y~Temperatura,data=df)
summary(modelo)
## Df Sum Sq Mean Sq F value Pr(>F)
## Temperatura 1 57.12 57.12 116.6 2.71e-09 ***
## Residuals 18 8.82 0.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tk=TukeyHSD(modelo)
tk
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Y ~ Temperatura, data = df)
##
## $Temperatura
## diff lwr upr p adj
## Ta-Tb 3.38 2.722307 4.037693 0
plot(tk)
qqnorm(modelo$residuals)
qqline(modelo$residuals,col='orange')
shapiro.test(modelo$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo$residuals
## W = 0.98544, p-value = 0.9841
Problema 11 del capĆtulo 3
setwd("C:/Users/mylen/OneDrive/Escritorio/PostInvestig/DExperimental")
df=read.csv("11cap3.csv",sep=";")
df
## Marca Y
## 1 1 72
## 2 2 55
## 3 3 64
## 4 1 65
## 5 2 59
## 6 3 74
## 7 1 67
## 8 2 68
## 9 3 61
## 10 1 75
## 11 2 70
## 12 3 58
## 13 1 62
## 14 2 53
## 15 3 51
## 16 1 73
## 17 2 50
## 18 3 69
df$Marca=factor(df$Marca)
str(df)
## 'data.frame': 18 obs. of 2 variables:
## $ Marca: Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
## $ Y : int 72 55 64 65 59 74 67 68 61 75 ...
boxplot(Y~Marca,data=df)
modelo=aov(Y~Marca,data=df)
summary(modelo)
## Df Sum Sq Mean Sq F value Pr(>F)
## Marca 2 296.3 148.17 2.793 0.0931 .
## Residuals 15 795.7 53.04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
qqnorm(modelo$residuals)
qqline(modelo$residuals)
shapiro.test(modelo$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo$residuals
## W = 0.96797, p-value = 0.7589
library("car")
## Loading required package: carData
leveneTest(Y~Marca,data=df)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 2 0.5288 0.5999
## 15
plot(modelo$residuals)
abline(h=0)
plot(modelo$fitted.values, modelo$residuals)
abline(h=0)
plot(df$Marca,modelo$residuals)
abline(h=0)
Problema 12 del capĆtulo 3
setwd("C:/Users/mylen/OneDrive/Escritorio/PostInvestig/DExperimental")
df=read.csv("cap3p12.csv",sep=";")
df
## Tratamiento tiempo
## 1 Control 213
## 2 Control 214
## 3 Control 204
## 4 Control 208
## 5 Control 212
## 6 Control 200
## 7 Control 207
## 8 T2 76
## 9 T2 85
## 10 T2 74
## 11 T2 78
## 12 T2 82
## 13 T2 75
## 14 T2 82
## 15 T3 57
## 16 T3 67
## 17 T3 55
## 18 T3 64
## 19 T3 61
## 20 T3 63
## 21 T3 63
## 22 T4 82
## 23 T4 85
## 24 T4 92
## 25 T4 87
## 26 T4 79
## 27 T4 90
df$Tratamiento=factor(df$Tratamiento)
boxplot(tiempo~Tratamiento,data=df)
modelo=aov(tiempo~Tratamiento,data=df)
summary(modelo)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 3 94420 31473 1493 <2e-16 ***
## Residuals 23 485 21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tk=TukeyHSD(modelo)
tk
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tiempo ~ Tratamiento, data = df)
##
## $Tratamiento
## diff lwr upr p adj
## T2-Control -129.42857 -136.21990802 -122.63723 0.0000000
## T3-Control -146.85714 -153.64847945 -140.06581 0.0000000
## T4-Control -122.45238 -129.52102819 -115.38373 0.0000000
## T3-T2 -17.42857 -24.21990802 -10.63723 0.0000018
## T4-T2 6.97619 -0.09245676 14.04484 0.0539588
## T4-T3 24.40476 17.33611467 31.47341 0.0000000
plot(tk)
qqnorm(modelo$residuals)
qqline(modelo$residuals)
shapiro.test(modelo$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo$residuals
## W = 0.95141, p-value = 0.232
library(car)
leveneTest(tiempo~Tratamiento,data=df)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.1943 0.8992
## 23
plot(modelo$residuals)
abline(h=0)
plot(df$Tratamiento,modelo$residuals)
abline(h=0)
plot(modelo$fitted.values, modelo$residuals)
abline(h=0)
Problema 14 capĆtulo 3
df=read.csv("c3p14.csv",sep=";")
df
## Tratamiento Ppdef
## 1 ConTrat 5.3
## 2 ConTrat 2.2
## 3 ConTrat 4.0
## 4 ConTrat 1.1
## 5 ConTrat 4.0
## 6 ConTrat 2.0
## 7 ConTrat 4.0
## 8 ConTrat 3.0
## 9 ConTrat 2.6
## 10 ConTrat 3.1
## 11 ConTrat 2.1
## 12 ConTrat 2.1
## 13 ConTrat 5.1
## 14 ConTrat 1.2
## 15 ConTrat 4.1
## 16 ConTrat 3.3
## 17 ConTrat 4.1
## 18 ConTrat 2.1
## 19 ConTrat 3.2
## 20 ConTrat 4.0
## 21 ConTrat 5.1
## 22 ConTrat 2.0
## 23 ConTrat 2.2
## 24 ConTrat 3.0
## 25 ConTrat 4.1
## 26 SinTrat 8.0
## 27 SinTrat 8.7
## 28 SinTrat 13.2
## 29 SinTrat 11.3
## 30 SinTrat 7.2
## 31 SinTrat 4.5
## 32 SinTrat 8.2
## 33 SinTrat 6.6
## 34 SinTrat 9.1
## 35 SinTrat 9.2
## 36 SinTrat 6.7
## 37 SinTrat 10.2
## 38 SinTrat 12.2
## 39 SinTrat 10.6
## 40 SinTrat 16.3
## 41 SinTrat 13.3
## 42 SinTrat 9.2
## 43 SinTrat 5.2
## 44 SinTrat 6.4
## 45 SinTrat 6.2
## 46 SinTrat 7.2
## 47 SinTrat 8.0
## 48 SinTrat 17.2
## 49 SinTrat 4.8
## 50 SinTrat 12.3
str(df)
## 'data.frame': 50 obs. of 2 variables:
## $ Tratamiento: chr "ConTrat" "ConTrat" "ConTrat" "ConTrat" ...
## $ Ppdef : num 5.3 2.2 4 1.1 4 2 4 3 2.6 3.1 ...
df$Tratamiento=factor(df$Tratamiento)
str(df)
## 'data.frame': 50 obs. of 2 variables:
## $ Tratamiento: Factor w/ 2 levels "ConTrat","SinTrat": 1 1 1 1 1 1 1 1 1 1 ...
## $ Ppdef : num 5.3 2.2 4 1.1 4 2 4 3 2.6 3.1 ...
df
## Tratamiento Ppdef
## 1 ConTrat 5.3
## 2 ConTrat 2.2
## 3 ConTrat 4.0
## 4 ConTrat 1.1
## 5 ConTrat 4.0
## 6 ConTrat 2.0
## 7 ConTrat 4.0
## 8 ConTrat 3.0
## 9 ConTrat 2.6
## 10 ConTrat 3.1
## 11 ConTrat 2.1
## 12 ConTrat 2.1
## 13 ConTrat 5.1
## 14 ConTrat 1.2
## 15 ConTrat 4.1
## 16 ConTrat 3.3
## 17 ConTrat 4.1
## 18 ConTrat 2.1
## 19 ConTrat 3.2
## 20 ConTrat 4.0
## 21 ConTrat 5.1
## 22 ConTrat 2.0
## 23 ConTrat 2.2
## 24 ConTrat 3.0
## 25 ConTrat 4.1
## 26 SinTrat 8.0
## 27 SinTrat 8.7
## 28 SinTrat 13.2
## 29 SinTrat 11.3
## 30 SinTrat 7.2
## 31 SinTrat 4.5
## 32 SinTrat 8.2
## 33 SinTrat 6.6
## 34 SinTrat 9.1
## 35 SinTrat 9.2
## 36 SinTrat 6.7
## 37 SinTrat 10.2
## 38 SinTrat 12.2
## 39 SinTrat 10.6
## 40 SinTrat 16.3
## 41 SinTrat 13.3
## 42 SinTrat 9.2
## 43 SinTrat 5.2
## 44 SinTrat 6.4
## 45 SinTrat 6.2
## 46 SinTrat 7.2
## 47 SinTrat 8.0
## 48 SinTrat 17.2
## 49 SinTrat 4.8
## 50 SinTrat 12.3
boxplot(Ppdef~Tratamiento,data=df)
modelo=aov(Ppdef~Tratamiento,data=df)
summary(modelo)
## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 1 467.0 467.0 73.14 3.27e-11 ***
## Residuals 48 306.5 6.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tk=TukeyHSD(modelo)
tk
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ppdef ~ Tratamiento, data = df)
##
## $Tratamiento
## diff lwr upr p adj
## SinTrat-ConTrat 6.112 4.675016 7.548984 0
qqnorm(modelo$residuals)
qqline(modelo$residuals)
shapiro.test(modelo$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo$residuals
## W = 0.94382, p-value = 0.01913
library(car)
leveneTest(Ppdef~Tratamiento,data=df)
## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 12.969 0.0007491 ***
## 48
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(modelo$residuals)
abline(h=0)
plot(modelo$fitted.values, modelo$residuals)
abline(h=0)
