Práctica 1
Mujer<-c(75,77,78,79,77,73,78,79,78,80)
Hombre<-c(74,72,77,76,76,73,75,73,74,75)
Sexo<-c(rep("mujer",10),rep("Hombre",10))
Temp<-c(Mujer,Hombre)
datos<-data.frame(Sexo=Sexo,T=Temp)
resultado <- t.test(Mujer, Hombre,
alternative = "two.sided",
var.equal = FALSE)
print(resultado)
##
## Welch Two Sample t-test
##
## data: Mujer and 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
datos$Sexo<-factor(datos$Sexo)
boxplot(T~Sexo,data=datos,col=c("orange","green"))
library(car)
## Cargando paquete requerido: carData
leveneTest(T~Sexo,data=datos,center=mean)
## Levene's Test for Homogeneity of Variance (center = mean)
## Df F value Pr(>F)
## group 1 0.2085 0.6534
## 18
Problema 2
actual<-c(1.88, 1.84, 1.83, 1.90, 2.19, 1.89, 2.27, 2.03, 1.96,
1.98, 2.00, 1.92, 1.83, 1.94, 1.94, 1.95, 1.93, 2.01)
nuevo<-c(1.87, 1.90, 1.85, 1.88, 2.18, 1.87, 2.23, 1.97, 2.00,
1.98, 1.99, 1.89, 1.78, 1.92, 2.02, 2.00, 1.95, 2.05)
resultado <- t.test(actual, nuevo,
alternative = "two.sided",paired=TRUE,
var.equal = FALSE)
print(resultado)
##
## Paired t-test
##
## data: actual and nuevo
## t = -0.23874, df = 17, p-value = 0.8142
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.02186024 0.01741580
## sample estimates:
## mean difference
## -0.002222222
desgaste<-c(264, 260, 258, 241, 262, 255,208, 220, 216, 200, 213, 206,220, 263, 219, 225, 230, 228,217, 226, 215, 227, 220, 222)
tipoCuero<-c(rep("A",6),rep("B",6),rep("C",6),rep("D",6))
datos<-data.frame(tipoCuero=tipoCuero,desgaste=desgaste)
datos
## tipoCuero desgaste
## 1 A 264
## 2 A 260
## 3 A 258
## 4 A 241
## 5 A 262
## 6 A 255
## 7 B 208
## 8 B 220
## 9 B 216
## 10 B 200
## 11 B 213
## 12 B 206
## 13 C 220
## 14 C 263
## 15 C 219
## 16 C 225
## 17 C 230
## 18 C 228
## 19 D 217
## 20 D 226
## 21 D 215
## 22 D 227
## 23 D 220
## 24 D 222
datos$tipoCuero<-factor(datos$tipoCuero)
modelo<-aov(desgaste~tipoCuero,data=datos)
summary(modelo)
## Df Sum Sq Mean Sq F value Pr(>F)
## tipoCuero 3 7019 2339.8 22.75 1.18e-06 ***
## Residuals 20 2056 102.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(desgaste~tipoCuero,data=datos,col=c("orange","green","red","purple"))
plot(modelo)
hist(modelo$residuals,breaks=5)
TukeyHSD(modelo)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = desgaste ~ tipoCuero, data = datos)
##
## $tipoCuero
## diff lwr upr p adj
## B-A -46.166667 -62.552998 -29.780336 0.0000008
## C-A -25.833333 -42.219664 -9.447002 0.0014117
## D-A -35.500000 -51.886331 -19.113669 0.0000349
## C-B 20.333333 3.947002 36.719664 0.0118160
## D-B 10.666667 -5.719664 27.052998 0.2926431
## D-C -9.666667 -26.052998 6.719664 0.3742863
Regresión lineal
x1<-seq(0,10,length=10)
y<-0.8+2.3*x1
y<-y+rnorm(10,0,1.5)
plot(x1,y)
modelo<-lm(y~x1)
summary(modelo)
##
## Call:
## lm(formula = y ~ x1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0995 -0.8693 0.6083 1.1143 1.4373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5805 0.9299 1.70 0.128
## x1 2.3492 0.1568 14.98 3.88e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.582 on 8 degrees of freedom
## Multiple R-squared: 0.9656, Adjusted R-squared: 0.9613
## F-statistic: 224.5 on 1 and 8 DF, p-value: 3.884e-07
yajustado<-modelo$fitted.values
plot(y~x1)
lines(x1,yajustado)
