Tarea clase 2

Uniforme

set.seed(2022)
Uniforme<-runif(100,-1,1) #Datos 
MU<-mean(Uniforme); MU # Media

## [1] -0.001150162

MinU<-min(Uniforme);MinU # Minimo

## [1] -0.9962759

MaxU<-max(Uniforme);MaxU # Maximo

## [1] 0.9953095

medU<-median(Uniforme);medU # Mediana

## [1] 0.04859939

varU<-var(Uniforme);varU # Varianza

## [1] 0.2862362

desvU<-sd(Uniforme);desvU # Desviacion estandar

## [1] 0.5350105

cuartiles<-quantile(Uniforme,c(0.25,0.75))
Q1<-cuartiles[1];Q1

##        25% 
## -0.4539225

Q3<-cuartiles[2];Q3

##      75% 
## 0.488441

boxplot(Uniforme,col = "red",main="Distribución uniforme",horizontal = T)
text(MinU,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medU,1.3, "Mediana")
text(MaxU,1.2, "Max")
points(MU,1,col="orange",pch=16)
arrows(x0 =MU ,y0 =1 ,x1 =MU ,y1 =0.58,lty=2,col="orange",lwd=1)
text(MU+0.05,0.55, "Media",col="orange")

hist(Uniforme,breaks = 20)
abline(v=MU,col="red",lwd=2)
abline(v=MU+c(desvU,-desvU),col="green",lwd=2)
abline(v=MU+c(2*desvU,-2*desvU),col="blue",lwd=2)

Beta

set.seed(2022)
Beta<-rbeta(100,2,5) #Datos 
MBe<-mean(Beta); MBe # Media

## [1] 0.326895

MinBe<-min(Beta);MinBe # Minimo

## [1] 0.008053305

MaxBe<-max(Beta);MaxBe # Maximo

## [1] 0.7729642

medBe<-median(Beta);medBe # Mediana

## [1] 0.316835

varBe<-var(Beta);varBe # Varianza

## [1] 0.03077504

desvBe<-sd(Beta);desvBe # Desviacion estandar

## [1] 0.1754281

cuartiles<-quantile(Beta,c(0.25,0.75))
Q1<-cuartiles[1];Q1

##       25% 
## 0.1920439

Q3<-cuartiles[2];Q3

##       75% 
## 0.4501092

boxplot(Beta,col = "red",main="Distribución Beta",horizontal = T)
text(MinBe,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medBe,1.3, "Mediana")
text(MaxBe,1.2, "Max")
points(MBe,1,col="orange",pch=16)
arrows(x0 =MBe ,y0 =1 ,x1 =MBe ,y1 =0.58,lty=2,col="orange",lwd=1)
text(MBe+0.05,0.55, "Media",col="orange")

hist(Beta,breaks = 20)
abline(v=MBe,col="red",lwd=2)
abline(v=MBe+c(desvBe,-desvBe),col="green",lwd=2)
abline(v=MBe+c(2*desvBe,-2*desvBe),col="blue",lwd=2)

Log normal

set.seed(2022)
Lnormal<-rlnorm(100,5,0.2) #Datos
MLn<-mean(Lnormal); MLn # Media

## [1] 155.7501

MinLn<-min(Lnormal);MinLn # Minimo

## [1] 83.08583

MaxLn<-max(Lnormal);MaxLn # Maximo

## [1] 264.4057

medLn<-median(Lnormal);medLn # Mediana

## [1] 153.604

varLn<-var(Lnormal);varLn # Varianza

## [1] 1020.853

desvLn<-sd(Lnormal);desvLn # Desviacion estandar

## [1] 31.95079

cuartiles<-quantile(Lnormal,c(0.25,0.75))
Q1<-cuartiles[1];Q1

##      25% 
## 135.1096

Q3<-cuartiles[2];Q3

##      75% 
## 176.4831

boxplot(Lnormal,col = "red",main="Distribución Lognormal",horizontal = T)
text(MinLn,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medLn,1.3, "Mediana")
text(MaxLn,1.2, "Max")
points(MLn,1,col="orange",pch=16)
arrows(x0 =MLn ,y0 =1 ,x1 =MLn ,y1 =0.58,lty=2,col="orange",lwd=1)
text(MLn+0.05,0.55, "Media",col="orange")

hist(Lnormal,breaks = 20)
abline(v=MLn,col="red",lwd=2)
abline(v=MLn+c(desvLn,-desvLn),col="green",lwd=2)
abline(v=MLn+c(2*desvLn,-2*desvLn),col="blue",lwd=2)

T-student

set.seed(2022)
t<-rt(100,15) #Datos 
Mt<-mean(t); Mt # Media

## [1] -0.02534641

Mint<-min(t);Mint # Minimo

## [1] -3.753892

Maxt<-max(t);Maxt # Maximo

## [1] 2.563128

medt<-median(t);medt # Mediana

## [1] 0.1846227

vart<-var(t);vart # Varianza

## [1] 1.155848

desvt<-sd(t);desvt # Desviacion estandar

## [1] 1.075104

cuartiles<-quantile(t,c(0.25,0.75))
Q1<-cuartiles[1];Q1

##       25% 
## -0.702622

Q3<-cuartiles[2];Q3

##      75% 
## 0.802149

boxplot(t,col = "red",main="Distribución T-Student",horizontal = T)
text(Mint,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medt,1.3, "Mediana")
text(Maxt,1.2, "Max")
points(Mt,1,col="orange",pch=16)
arrows(x0 =Mt ,y0 =1 ,x1 =Mt ,y1 =0.58,lty=2,col="orange",lwd=1)
text(Mt+0.05,0.55, "Media",col="orange")

hist(t,breaks = 20)
abline(v=Mt,col="red",lwd=2)
abline(v=Mt+c(desvt,-desvt),col="green",lwd=2)
abline(v=Mt+c(2*desvt,-2*desvt),col="blue",lwd=2)

Binomial

set.seed(2022)
Binomial<-rbinom(100,5,0.5) #Datos
MB<-mean(Binomial); MB # Media

## [1] 2.49

MinB<-min(Binomial);MinB # Minimo

## [1] 0

MaxB<-max(Binomial);MaxB# Maximo

## [1] 5

medB<-median(Binomial);medB # Mediana

## [1] 3

varB<-var(Binomial);varB # Varianza

## [1] 1.080707

desvB<-sd(Binomial);desvB # Desviacion estandar

## [1] 1.039571

cuartiles<-quantile(Binomial,c(0.25,0.75))
Q1<-cuartiles[1];Q1

## 25% 
##   2

Q3<-cuartiles[2];Q3

## 75% 
##   3

boxplot(Binomial,col = "red",main="Distribución binomial",horizontal = T)
text(MinB,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medB,1.3, "Mediana")
text(MaxB,1.2, "Max")
points(MB,1,col="orange",pch=16)
arrows(x0 =MB ,y0 =1 ,x1 =MB ,y1 =0.58,lty=2,col="orange",lwd=1)
text(MB+0.05,0.55, "Media",col="orange")

hist(Binomial)
abline(v=MB,col="red",lwd=2)
abline(v=MB+c(desvB,-desvB),col="green",lwd=2)
abline(v=MB+c(2*desvB,-2*desvB),col="blue",lwd=2)

Poisson

set.seed(2022)
Poison<-rpois(100,3) #Datos 
MP<-mean(Poison); MP # Media

## [1] 2.92

MinP<-min(Poison);MinP # Minimo

## [1] 0

MaxP<-max(Poison);MaxP # Maximo

## [1] 9

medP<-median(Poison);medP # Mediana

## [1] 3

varP<-var(Poison);varP # Varianza

## [1] 2.458182

desvP<-sd(Poison);desvP # Desviacion estandar

## [1] 1.567859

cuartiles<-quantile(Poison,c(0.25,0.75))
Q1<-cuartiles[1];Q1

## 25% 
##   2

Q3<-cuartiles[2];Q3

## 75% 
##   4

boxplot(Poison,col = "red",main="Distribución Poison",horizontal = T)
text(MinP,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medP,1.3, "Mediana")
text(MaxP,1.2, "Max")
points(MP,1,col="orange",pch=16)
arrows(x0 =MP ,y0 =1 ,x1 =MP,y1 =0.58,lty=2,col="orange",lwd=1)
text(MP+0.05,0.55, "Media",col="orange")

hist(Poison)
abline(v=MP,col="red",lwd=2)
abline(v=MP+c(desvP,-desvP),col="green",lwd=2)
abline(v=MP+c(2*desvP,-2*desvP),col="blue",lwd=2)

Hipergeometrica

set.seed(2022)
Hiper<-rhyper(100,10,20-10,5) #Datos 
MH<-mean(Hiper); MH # Media

## [1] 2.49

MinH<-min(Hiper);MinH # Minimo

## [1] 0

MaxH<-max(Hiper);MaxH # Maximo

## [1] 5

medH<-median(Hiper);medH # Mediana

## [1] 3

varH<-var(Hiper);varH # Varianza

## [1] 0.8786869

desvH<-sd(Hiper);desvH # Desviacion estandar

## [1] 0.937383

cuartiles<-quantile(Hiper,c(0.25,0.75))
Q1<-cuartiles[1];Q1

## 25% 
##   2

Q3<-cuartiles[2];Q3

## 75% 
##   3

boxplot(Hiper,col = "red",main="Distribución hipergeometria",horizontal = T)
text(MinH,1.2, "Min")
text(Q1,0.7, "Q1")
text(Q3,0.7, "Q3")
text(medH,1.3, "Mediana")
text(MaxH,1.2, "Max")
points(MH,1,col="orange",pch=16)
arrows(x0 =MH ,y0 =1 ,x1 =MH ,y1 =0.58,lty=2,col="orange",lwd=1)
text(MH+0.05,0.55, "Media",col="orange")

hist(Hiper,breaks = 20)
abline(v=MH,col="red",lwd=2)
abline(v=MH+c(desvH,-desvH),col="green",lwd=2)
abline(v=MH+c(2*desvH,-2*desvH),col="blue",lwd=2)

Tarea clase 2

Valentina Osorio

30/4/2021