1 Exploração

1.1 Padrão de dados

## Rows: 44
## Columns: 31
## $ MAMO_SAWIBR__Score              <dbl> 71, 100, 82, 71, 29, 82, 29, 71, 100, ~
## $ MAMO_PSWE__Score                <dbl> 93, 100, 100, 93, 45, 100, 66, 93, 100~
## $ MAMO_SEWE__Score                <dbl> 100, 100, 66, 100, 24, 66, 59, 100, 10~
## $ MAMO_PHWECH__Score              <dbl> 50, 24, 24, 50, 100, 24, 20, 50, 24, 2~
## $ MAMO_ADEFOFRA__Score            <dbl> NA, 0, NA, NA, 90, NA, 20, NA, 0, 20, ~
## $ MAMO_SAWISU__Score              <dbl> 100, 100, 100, 100, 38, 100, 100, 100,~
## $ MAMO_SAWIMETE__Score            <dbl> 100, 100, 100, 100, 100, 100, 100, 100~
## $ MAMO_SAWIOFST__Score            <dbl> 100, 100, 100, 100, 100, 100, 100, 100~
## $ RE_PSWE__Score                  <dbl> 93, 100, 100, 93, 100, 100, 71, 93, 10~
## $ RE_SEWE__Score                  <dbl> 100, 100, 59, 100, 31, 59, 59, 100, 10~
## $ RE_SAWIBR__Score                <dbl> 82, 82, 100, 82, 24, 100, 82, 82, 82, ~
## $ RE_SAWIBR__Score.1              <dbl> 75, 71, 75, 75, 78, 75, 75, 75, 71, 75~
## $ RE_PHWECH__Score                <dbl> 40, 24, 24, 40, 92, 24, 14, 40, 24, 14~
## $ RE_PHWEAB__Score                <dbl> NA, 28, 54, NA, 100, 54, 13, NA, 28, 1~
## $ RE_PHWEAB__Score.1              <dbl> NA, 23, 54, NA, 81, 54, 34, NA, 23, 34~
## $ RE_SAWIBA__Score                <dbl> 23, NA, 31, 23, NA, 31, NA, 0, NA, NA,~
## $ RE_PHWEBAANSH__Score            <dbl> 54, NA, 54, 54, 55, 54, 46, 54, NA, 46~
## $ RE_ADEFOFRA__Score              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ RE_PAEXSAWIIN__Score            <dbl> 100, 100, 61, 100, 62, 61, 100, 100, 1~
## $ RE_SAWISU__Score                <dbl> 100, 100, 100, 100, 100, 100, 100, 100~
## $ RE_SAWIMETE__Score              <dbl> 100, 100, 100, 100, 100, 100, 100, 100~
## $ RE_SAWIOFST__Score              <dbl> 100, 100, 100, 100, 100, 100, 100, 100~
## $ SF36.VeBrdoQudeQudeVi_1__score  <dbl> 3.4, 5.0, 3.4, 3.4, 2.0, 3.4, 3.4, 3.4~
## $ SF36.VeBrdoQudeQudeVi_2__score  <dbl> 2, NA, 1, 2, 4, 1, 1, 2, NA, 1, NA, NA~
## $ SF36.VeBrdoQudeQudeVi_6__score  <dbl> 3, 5, 5, 3, 2, 5, 3, 3, 5, 3, 3, 3, 3,~
## $ SF36.VeBrdoQudeQudeVi_7__score  <dbl> 2.0, 4.2, 3.1, 2.0, 2.0, 3.1, 3.1, 2.0~
## $ SF36.VeBrdoQudeQudeVi_8__score  <dbl> 3, 5, 2, 3, 2, 2, 3, 3, 5, 3, 3, 4, 2,~
## $ SF36.VeBrdoQudeQudeVi_9__score  <dbl> 43, 46, 37, 43, 31, 37, 41, 43, 46, 41~
## $ SF36.VeBrdoQudeQudeVi_10__score <dbl> 5, 5, 3, 5, 4, 3, 3, 5, 5, 3, 3, 4, 3,~
## $ SF36.VeBrdoQudeQudeVi_11__score <dbl> 4, 16, 4, 4, 1, 4, 4, 4, 16, 4, 11, 16~
## $ Score                           <dbl> 0.7171429, 0.9600000, 0.6750000, 0.717~

1.2 Visualização de dados

Unique (#) Missing (%) Mean SD Min Median Max
MAMO_SAWIBR__Score 12 0 64.0 26.7 12.0 64.0 100.0
MAMO_PSWE__Score 22 0 74.2 21.6 29.0 74.0 100.0
MAMO_SEWE__Score 20 0 65.4 26.2 0.0 66.0 100.0
MAMO_PHWECH__Score 22 0 48.7 23.2 20.0 50.0 100.0
MAMO_ADEFOFRA__Score 4 89 26.0 37.1 0.0 20.0 90.0
MAMO_SAWISU__Score 7 0 95.1 13.3 38.0 100.0 100.0
MAMO_SAWIMETE__Score 4 0 98.3 4.9 80.0 100.0 100.0
MAMO_SAWIOFST__Score 3 0 99.2 3.5 82.0 100.0 100.0
RE_PSWE__Score 20 0 77.1 21.6 29.0 77.0 100.0
RE_SEWE__Score 20 0 64.4 26.5 0.0 60.5 100.0
RE_SAWIBR__Score 12 0 70.1 23.7 23.0 71.0 100.0
RE_SAWIBR__Score.1 23 0 68.2 22.3 13.0 71.0 100.0
RE_PHWECH__Score 18 5 50.5 26.5 14.0 45.0 100.0
RE_PHWEAB__Score 7 80 34.7 30.4 0.0 28.0 100.0
RE_PHWEAB__Score.1 6 82 37.9 24.7 0.0 34.0 81.0
RE_SAWIBA__Score 5 86 25.3 14.6 0.0 27.0 44.0
RE_PHWEBAANSH__Score 5 80 50.9 5.1 41.0 54.0 55.0
RE_PAEXSAWIIN__Score 11 0 85.9 17.0 40.0 91.0 100.0
RE_SAWISU__Score 3 0 98.1 5.1 78.0 100.0 100.0
RE_SAWIMETE__Score 2 0 98.6 4.4 85.0 100.0 100.0
RE_SAWIOFST__Score 2 0 99.5 1.8 93.0 100.0 100.0
SF36.VeBrdoQudeQudeVi_1__score 4 0 3.8 0.8 2.0 3.4 5.0
SF36.VeBrdoQudeQudeVi_2__score 4 82 1.8 1.0 1.0 1.5 4.0
SF36.VeBrdoQudeQudeVi_6__score 5 0 3.9 1.2 1.0 4.5 5.0
SF36.VeBrdoQudeQudeVi_7__score 7 0 3.8 1.5 1.0 3.1 6.0
SF36.VeBrdoQudeQudeVi_8__score 6 0 3.8 1.4 1.0 3.0 6.0
SF36.VeBrdoQudeQudeVi_9__score 22 0 38.4 7.5 21.0 40.5 49.0
SF36.VeBrdoQudeQudeVi_10__score 5 0 4.0 1.1 1.0 4.0 5.0
SF36.VeBrdoQudeQudeVi_11__score 11 0 12.5 5.1 1.0 15.0 19.0
Score 38 0 34.3 56.7 0.5 0.8 152.9

1.3 Teste de Normalidade

1.4 Medidas de Centralidade

1.5 Mann-Whitney (wilcox.test)

✅ Se p-value < 0.05, há uma diferença estatisticamente significativa entre os grupos. ✅ Se p-value ≥ 0.05, não há evidências suficientes para afirmar que há diferença entre eles.

1.6 Kruskal-Wallis

✅ Ele compara cada par de grupos e mostra os p-valores. ✅ Usa ajuste de Bonferroni para evitar erros estatísticos.

Interpretação: Se um par de grupos tem p < 0.05, há diferença significativa entre eles.

Se nenhum par tem p < 0.05, a diferença vem da variabilidade dos dados.

1.7 Teste pós-hoc para Kruskal-Wallis

✅ Ele compara cada par de grupos e mostra os p-valores. ✅ Usa ajuste de Bonferroni para evitar erros estatísticos.

Interpretação: Se um par de grupos tem p < 0.05, há diferença significativa entre eles.

Se nenhum par tem p < 0.05, a diferença vem da variabilidade dos dados.

2 Dicionário de dados