U3A5

setwd("~/PROBABILIDAD Y ESTADISTICA (R Studio)")
library(pacman)
p_load("base64enc", "htmltools", "mime", "xfun","prettydoc", "readr", "knitr", "DT", "dplyr",  "ggplot2")

semilla <- read_csv("semilla.csv")

## Parsed with column specification:
## cols(
##   Kilogramos = col_double(),
##   tiempo = col_character()
## )

T2010 <- subset(semilla, tiempo == "T2010" )
T2013 <- subset(semilla, tiempo == "T2013" )

• Gráficos de caja y bigote para los subconjuntos 2010 y 2013

boxplot(semilla$Kilogramos ~ semilla$tiempo, col = "Light Green")

boxplot(T2010$Kilogramos ~ T2010$tiempo, col = "Red")

boxplot(T2013$Kilogramos ~ T2013$tiempo, col = "Pink") #2013

• 5 Números de Tukey (Kilogramos)

fivenum(T2010$Kilogramos) #2010

## [1] 3.0 5.0 6.0 7.5 9.0

fivenum(T2013$Kilogramos) #2013

## [1] 3.0 4.0 5.0 7.5 9.0

• Histogramas

hist(T2010$Kilogramos, col = "Purple")

hist(T2013$Kilogramos, col = "Yellow")

* Caja y bigote comparando las desviaciones con gráfico de barra

op <- par(mfrow = c(1,2), cex.axis = 0.7, cex.lab = 0.9)
boxplot(semilla$Kilogramos ~ semilla$tiempo, col = "Green", main = "A")
barplot(tapply(semilla$Kilogramos, list(semilla$tiempo), mean), beside = T, main = "B" , col = "Gold")

* Pruebas de normalidad (las 2), Kolmogorov-Smirnov

ks.test(T2010$Kilogramos, "pnorm", mean = mean(T2010$Kilogramos), sd = sd(T2010$Kilogramos)) #2010

## Warning in ks.test(T2010$Kilogramos, "pnorm", mean = mean(T2010$Kilogramos), :
## ties should not be present for the Kolmogorov-Smirnov test

## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  T2010$Kilogramos
## D = 0.16135, p-value = 0.7991
## alternative hypothesis: two-sided

ks.test(T2013$Kilogramos, "pnorm", mean = mean(T2013$Kilogramos), sd = sd(T2013$Kilogramos)) #2013

## Warning in ks.test(T2013$Kilogramos, "pnorm", mean = mean(T2013$Kilogramos), :
## ties should not be present for the Kolmogorov-Smirnov test

## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  T2013$Kilogramos
## D = 0.17708, p-value = 0.6973
## alternative hypothesis: two-sided

• Prueba de t

t.test(T2010$Kilogramos, T2013$Kilogramos, var.equal = T)

## 
##  Two Sample t-test
## 
## data:  T2010$Kilogramos and T2013$Kilogramos
## t = 0.83205, df = 30, p-value = 0.412
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.8181596  1.9431596
## sample estimates:
## mean of x mean of y 
##    6.1250    5.5625

• Prueba de F

t.test(T2010$Kilogramos, T2013$Kilogramos)

## 
##  Welch Two Sample t-test
## 
## data:  T2010$Kilogramos and T2013$Kilogramos
## t = 0.83205, df = 29.988, p-value = 0.412
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.8181827  1.9431827
## sample estimates:
## mean of x mean of y 
##    6.1250    5.5625