Práctica
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 T
## 1 Mujer 75
## 2 Mujer 77
## 3 Mujer 78
## 4 Mujer 79
## 5 Mujer 77
## 6 Mujer 73
## 7 Mujer 78
## 8 Mujer 79
## 9 Mujer 78
## 10 Mujer 80
## 11 Hombre 74
## 12 Hombre 72
## 13 Hombre 77
## 14 Hombre 76
## 15 Hombre 76
## 16 Hombre 73
## 17 Hombre 75
## 18 Hombre 73
## 19 Hombre 74
## 20 Hombre 75
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("green","pink"))
plot(modelo)
shapiro.test(modelo$residuals)
##
## Shapiro-Wilk normality test
##
## data: modelo$residuals
## W = 0.88326, p-value = 0.00967
hist(modelo$residuals,breaks = 5)
TukeyHSD(modelo)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = desgaste ~ tipoCuero, data = datos)
##
## $tipoCuero
## diff lwr upr p adj
## B-A -46.166667 -62.552998 -29.780336 0.0000008
## C-A -25.833333 -42.219664 -9.447002 0.0014117
## D-A -35.500000 -51.886331 -19.113669 0.0000349
## C-B 20.333333 3.947002 36.719664 0.0118160
## D-B 10.666667 -5.719664 27.052998 0.2926431
## D-C -9.666667 -26.052998 6.719664 0.3742863
Tregresión Lineal
x1<-seq(0,10,length=10)
y<-0.8+2.3*x1
y<-rnorm(10,0,1.5)
plot(x1,y)
modelo<-lm(y~x1)
summary(modelo)
##
## Call:
## lm(formula = y ~ x1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.88267 -0.59232 -0.03026 0.30488 1.14501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001949 0.454208 -0.004 0.997
## x1 -0.073949 0.076573 -0.966 0.362
##
## Residual standard error: 0.7728 on 8 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: -0.007541
## F-statistic: 0.9326 on 1 and 8 DF, p-value: 0.3625
yajustado<-modelo$fitted.values
plot(x1,y)
lines(x1,yajustado,col="red",lwd=2)
