Los paquetes

library("tidyverse")

There were 38 warnings (use warnings() to see them)

Cargo datos para crear mi df

summary(df2)

      Edad              Sexo          c                e                o               ceod       
 Min.   :1.000   Femenino :58   Min.   : 0.000   Min.   :0.0000   Min.   :0.0000   Min.   : 0.000  
 1st Qu.:2.000   Masculino:55   1st Qu.: 0.000   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.: 0.000  
 Median :3.000                  Median : 0.000   Median :0.0000   Median :0.0000   Median : 1.000  
 Mean   :3.248                  Mean   : 1.965   Mean   :0.0354   Mean   :0.3805   Mean   : 2.381  
 3rd Qu.:4.000                  3rd Qu.: 4.000   3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.: 5.000  
 Max.   :5.000                  Max.   :12.000   Max.   :1.0000   Max.   :6.0000   Max.   :13.000

Agrupo por sexo y calculo promedios, SD y mediana para ceod

Agrupo por sexo y calculo promedios, SD y mediana para el componente c del ceod

Agrupo por edad y calculo promedios, SD y mediana para ceod

Agrupo por edad y calculo promedios, SD y mediana para el componente c del ceod

Visualizo mis datos en graficos

grafico para edad y componente c del ceod

Ahora mis analisis estadisticos:Existe diferencia entre sexo y ceod?

t.test(df2$ceod~df2$Sexo)


    Welch Two Sample t-test

data:  df2$ceod by df2$Sexo
t = -1.2654, df = 102.87, p-value = 0.2086
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.91623  0.42344
sample estimates:
 mean in group Femenino mean in group Masculino 
               2.017241                2.763636

Existe diferencia entre sexo y componente c del copd?

t.test(df2$c~df2$Sexo)


    Welch Two Sample t-test

data:  df2$c by df2$Sexo
t = -1.5269, df = 100.83, p-value = 0.1299
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.7874456  0.2325867
sample estimates:
 mean in group Femenino mean in group Masculino 
               1.586207                2.363636

Existe diferencia entre ceod y edad??

anova

Call:
   aov(formula = df2$ceod ~ df2$Edad)

Terms:
                df2$Edad Residuals
Sum of Squares  379.7070  712.9302
Deg. of Freedom        1       111

Residual standard error: 2.534323
Estimated effects may be unbalanced
summary(anova)

             Df Sum Sq Mean Sq F value   Pr(>F)    
df2$Edad      1  379.7   379.7   59.12 6.45e-12 ***
Residuals   111  712.9     6.4                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ajusto modelo para prueba POST-HOC

pairwise.t.test(df2$ceod,df2$Edad)


    Pairwise comparisons using t tests with pooled SD 

data:  df2$ceod and df2$Edad 

  1       2       3       4      
2 0.87831 -       -       -      
3 0.32321 0.05806 -       -      
4 0.15874 0.00269 0.87831 -      
5 0.00108 1.1e-10 0.00061 0.00317

P value adjustment method: holm

ajusto modelo para prueba POST-HOC bonferroni

pairwise.t.test(df2$ceod,df2$Edad, p.adjust = "bonferroni")


    Pairwise comparisons using t tests with pooled SD 

data:  df2$ceod and df2$Edad 

  1       2       3       4      
2 1.00000 -       -       -      
3 1.00000 0.11612 -       -      
4 0.39685 0.00385 1.00000 -      
5 0.00135 1.1e-10 0.00068 0.00528

P value adjustment method: bonferroni
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