set.seed(2022)
fumagina =round(runif(n = 80 ,
                      min = 0.35,
                      max = 1),0) 

table(fumagina)[1]/sum(table(fumagina))*100
##    0 
## 22.5
pr2 = prop.test(x = table(fumagina)[1],
                n = 80,
                p = 0.05,alternative = "g")
## Warning in prop.test(x = table(fumagina)[1], n = 80, p = 0.05, alternative =
## "g"): Chi-squared approximation may be incorrect
ifelse(pr2$p.value<0.05,
       "Rechazo Ho",
       "No rechazo Ho")
## [1] "Rechazo Ho"
set.seed(132)
brix = c(rnorm(60, 16, 1.5), 
         rnorm(60, 18, 1.5))
var = gl(2, 60, 120, c('VR1', 'VR2'))

data = data.frame(brix, var)
head(data)
##       brix var
## 1 16.71117 VR1
## 2 15.16758 VR1
## 3 15.98506 VR1
## 4 17.60049 VR1
## 5 14.93166 VR1
## 6 15.51900 VR1
boxplot(data$brix~data$var)

library(vioplot)
## Warning: package 'vioplot' was built under R version 4.2.2
## Loading required package: sm
## Warning: package 'sm' was built under R version 4.2.2
## Package 'sm', version 2.2-5.7: type help(sm) for summary information
## Loading required package: zoo
## Warning: package 'zoo' was built under R version 4.2.2
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
vioplot(data$brix~data$var,)

stripchart(data$brix~data$var, 
           method = "jitter", 
           col = "blue",
           vertical = TRUE, 
           pch = 19, add = TRUE)

Prueba de hipotesis H0:mu var1 = mu var2 Hi: mu var1 != mu var2

varianza de cada grupo (variedad)

tapply(data$brix, data$var, var)
##      VR1      VR2 
## 1.731974 2.325301

Prueba de igualdad de varianzas

Ho: var 1 = var 2 Ha: var 1 != var 2

var.test(data$brix~data$var) # No rechazo
## 
##  F test to compare two variances
## 
## data:  data$brix by data$var
## F = 0.74484, num df = 59, denom df = 59, p-value = 0.2608
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.4449103 1.2469582
## sample estimates:
## ratio of variances 
##          0.7448386
# como el p valor de la prueba de varianza es mayor al 0.05 no se rechaza Ho de la prueba de varianzas

Prueba de medias H0: med var1 = med var2 H1: med var2 != med var2

tt_1 = t.test(data$brix~data$var, 
              alternative = 't', 
              paired = F,
              var.equal = T)
tt_1
## 
##  Two Sample t-test
## 
## data:  data$brix by data$var
## t = -7.4058, df = 118, p-value = 2.119e-11
## alternative hypothesis: true difference in means between group VR1 and group VR2 is not equal to 0
## 95 percent confidence interval:
##  -2.440759 -1.410856
## sample estimates:
## mean in group VR1 mean in group VR2 
##          16.09341          18.01922

Comparar 2 Promedios pareados Cocción de Agave Contenido de azúcares antes y después de la cocción

set.seed(2025)

az_a=rnorm(120,8,0.5)
az_d=rnorm(120,7.7,0.4)

par(mfrow = c(1, 2))
boxplot(az_a, main = 'Contenido azucar antes', 
        ylim = c(6,10))
boxplot(az_d, main = 'Contenido azucar despues',
        ylim = c(6,10))

Pueba de hipótesis por varianza entre tratamientos H0: mu az antes = mu az después Hi: mu az antes != mu az después Pureba t para 2 muestras pareadas

ttest_2 = t.test(az_a, az_d, 
       alternative = 'g',
       paired = T)
ttest_2
## 
##  Paired t-test
## 
## data:  az_a and az_d
## t = 5.297, df = 119, p-value = 2.737e-07
## alternative hypothesis: true mean difference is greater than 0
## 95 percent confidence interval:
##  0.2001709       Inf
## sample estimates:
## mean difference 
##        0.291353
#P-valor < 0.05 rechazo H0
#si hay merma en el contenido de azúcar, este sí disminuye después de la cocción

Prueba para 2 Proporciones diferentes
pp: Porcentaje de parasitismo d: Dosis H0: pp d menor = pp dmayor H1: pp d menor != pp dmayor

set.seed(535) # establecen variables 
huevos_q1 = rbinom(n = 80, 
                    size = 12, 
                    prob = 0.5)
pars_1 = round(huevos_q1**(1/3))

porc_par_1 = round(100*pars_1/huevos_q1, 2)

huevos_q2 = rbinom(n = 81, 
                   size = 11, 
                   prob = 0.6)
pars_2 = round(huevos_q2**(1/2))

porc_par_2 = round(100*pars_2/huevos_q2, 2)

hist(porc_par_2)

Histograma para la porción paracitada de huevos 2

hist(porc_par_1)

hist(porc_par_2)

Datos para la prueba

#Total parasitados de la dosis menor
x1 <- sum(pars_1)
#huevos totales, dosis menor 
n1 = sum(huevos_q1)

# total parasitados de la dosis mayor
x2 = sum(pars_2)
# huevos totales dosis mayor
n2 = sum(huevos_q2)

n1
## [1] 480
n2
## [1] 546

Prueba

prop_test_2 = prop.test(x = c(x1, x2),
                      n = c(n1, n2),
                      alternative = "l")
prop_test_2
## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c(x1, x2) out of c(n1, n2)
## X-squared = 3.7842, df = 1, p-value = 0.02587
## alternative hypothesis: less
## 95 percent confidence interval:
##  -1.00000000 -0.00920476
## sample estimates:
##    prop 1    prop 2 
## 0.3208333 0.3809524
#pvalor < 0.05 rechazo H0, dosis menor tiene menor parasitismo que dosis mayor