Tarea mg_1

Cargar mi df

str(df)

'data.frame':   200 obs. of  10 variables:
 $ id      : int  1 2 3 4 5 6 7 8 9 10 ...
 $ estvital: int  1 0 0 0 0 0 0 0 0 0 ...
 $ sexo    : int  1 1 1 1 1 0 1 1 0 1 ...
 $ edad    : int  19 17 20 20 16 18 18 20 24 18 ...
 $ servuci : int  0 0 0 0 0 1 0 1 1 0 ...
 $ infecing: int  0 0 0 0 0 0 0 0 0 0 ...
 $ psistoli: int  140 136 164 104 100 146 156 120 140 112 ...
 $ pdiastol: int  104 77 91 67 75 107 70 80 90 59 ...
 $ pulsoing: int  76 78 108 83 140 112 99 68 86 76 ...
 $ tipoadm : int  1 1 1 1 1 1 0 1 1 1 ...

Niveles

levels(df$servuci)[levels(df$servuci) == "0"] <- "Cirugia"
levels(df$servuci)[levels(df$servuci) == "1"] <- "Medicina"

levels(df$infecing)[levels(df$infecing) == "0"] <- "No"
levels(df$infecing)[levels(df$infecing) == "1"] <- "Si"

levels(df$tipoadm)[levels(df$tipoadm) == "0"] <- "Electivo"
levels(df$tipoadm)[levels(df$tipoadm) == "1"] <- "Urgencia"

levels(df$estvital)[levels(df$estvital) == "0"] <- "Vivo"
levels(df$estvital)[levels(df$estvital) == "1"] <- "Muerto"

Pruebas estadisticas

chisq.test(df$sexo,df$estvital)

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

data:  df$sexo and df$estvital
X-squared = 0.011938, df = 1, p-value = 0.913