Estres academico_tesis_2020

#la matriz

tabla1 <- matrix(c(71, 185, 288, 1131), ncol = 2)

#nombres

colnames(tabla1) <- c("No estres", "Estres")
rownames(tabla1) <- c("Hombre", "Mujer")

#tabla

tabla1

       No estres Estres
Hombre        71    288
Mujer        185   1131

#chi2

chisq.test(tabla1)

    Pearson's Chi-squared test with Yates' continuity correction

data:  tabla1
X-squared = 6.6911, df = 1, p-value = 0.00969

#ANOVa para diferencias entre cursos

df <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSwAgtf1NUgTx02XMSFe0ddcsTcIKQJ4exo5JuE-JIarEQMj5X9ffybk_fJf_r3Mg/pub?gid=1106261659&single=true&output=csv")

#cargo package

library("tidyverse")

#gather

df %>% 
gather(key = "Estres", value = "N", No:Si)

#aov

aov <- aov(df1$N~df1$Nivel.academico)

#summary aov

summary(aov)

                    Df Sum Sq Mean Sq F value Pr(>F)
df1$Nivel.academico  5  43422    8684   0.392  0.838
Residuals            6 132847   22141               

df1 %>% 
  ggplot(aes(x=Nivel.academico, y=N, fill=Estres)) + 
  geom_col()+
  theme_classic()