BOXPLOT: LINGUAGEM R

set.seed(10);a<-rexp(70,rate = 5)
hist(a,freq = F,col=rainbow(15),
     xlab = "R$",ylab = "Densidade",main="Histograma: FDP Exponencial")
curve(dexp(x,5),add = T,
      col="blue",lwd=5)

curve(dexp(x,5),from = min(a),to=max(a),
      col=topo.colors(10),xlab = "R$",ylab = "Densidade",
      main="FDP: Distribuição Exponencial",lwd=4)
lines(density(a),col="turquoise",lwd=4)

legend("topright",legend = c("REAL","FDP"),
       col=c("blue","turquoise"),lwd=4)

boxplot(a,horizontal = T,lwd=3,col="red")

boxplot.stats(a)

$stats
[1] 0.002114238 0.056542631 0.150339324 0.315008370 0.605918628

$n
[1] 70

$conf
[1] 0.1015291 0.1991496

$out
[1] 0.7449669

estatistica<-boxplot.stats(a)
estatistica$stats[3] # Mediana

[1] 0.1503393

median(a) # mediana

[1] 0.1503393

a<-a[-which(a>=estatistica$stats[5])]
boxplot(a,horizontal = T,lwd=3,col="violet")

library(MASS)
library(nortest)
ad.test(a) # Para o teste de normalidade

    Anderson-Darling normality test

data:  a
A = 1.8237, p-value = 0.0001037

fitdistr(a,"lognormal")

    meanlog       sdlog   
  -2.1873963    1.2384067 
 ( 0.1501789) ( 0.1061925)

ks.test(a,y="plnorm",-2.1873963,1.2384067) # para o teste de aderência

    Exact one-sample Kolmogorov-Smirnov test

data:  a
D = 0.13322, p-value = 0.1634
alternative hypothesis: two-sided

curve(dlnorm(x,-2.1873963,1.2384067),
  from = min(a),to=max(a),lwd=4,col="blue",xlab = "R$",
  ylab = "Densidade",main="Curva Lognormal")

fitdistr(a,"exponential")

     rate   
  5.2871427 
 (0.6411602)

ks.test(a,y="pexp",5.2871427) # para o teste de aderência

    Exact one-sample Kolmogorov-Smirnov test

data:  a
D = 0.082866, p-value = 0.7073
alternative hypothesis: two-sided

curve(dlnorm(x,5.2871427),
      from = min(a),to=max(a),lwd=4,col="blue",xlab = "R$",
      ylab = "Densidade",main="Curva Exponencial")