library(readxl)
library(gmodels)
library(sjPlot)
library(psych)
library(stats)
cursorcp<- read_excel("/Volumes/DANI/INVEST_RCP/CURSO/BD_CURSO_2023.xlsx")Datos sociodemográficos
attach(cursorcp)
describe(edad)describe(peso)describe(talla)describe(imc)attach(cursorcp)## The following objects are masked from cursorcp (pos = 3):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, zaCrossTable(sexo)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | hombre | mujer |
## |-----------|-----------|
## | 6 | 30 |
## | 0.167 | 0.833 |
## |-----------|-----------|
##
##
##
## CrossTable(titul)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | enf | pod |
## |-----------|-----------|
## | 27 | 9 |
## | 0.750 | 0.250 |
## |-----------|-----------|
##
##
##
## EVALUACIÓN OBJETIVA
CrossTable(aprox) ##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 33 | 3 |
## | 0.917 | 0.083 |
## |-----------|-----------|
##
##
##
## CrossTable(comp_cons)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## CrossTable(preg)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 2 | 34 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
## CrossTable(za)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(abre_va)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(frentem)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 7 | 29 |
## | 0.194 | 0.806 |
## |-----------|-----------|
##
##
##
## table(comp_resp )## comp_resp
## si
## 36CrossTable(`mant_f-m`)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 14 | 22 |
## | 0.389 | 0.611 |
## |-----------|-----------|
##
##
##
## CrossTable(vos)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 9 | 27 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
## CrossTable(val_10s)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 6 | 30 |
## | 0.167 | 0.833 |
## |-----------|-----------|
##
##
##
## CrossTable(llama_112)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 6 | 30 |
## | 0.167 | 0.833 |
## |-----------|-----------|
##
##
##
## CrossTable(pide_desa)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 12 | 24 |
## | 0.333 | 0.667 |
## |-----------|-----------|
##
##
##
## table(r_30_2)## r_30_2
## si
## 36CrossTable(emp_comp)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## table(col_man)## col_man
## si
## 36table(col_vert)## col_vert
## si
## 36CrossTable(comp_desd)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 2 | 34 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
## CrossTable(ritmo)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 2 | 34 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
## CrossTable(abre_va_2)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 7 | 29 |
## | 0.194 | 0.806 |
## |-----------|-----------|
##
##
##
## table(pinza)## pinza
## si
## 36table(insuf)## insuf
## si
## 36CrossTable(enc_desa)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## CrossTable(sig_ins)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(coloca_par)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(real_com)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 22 | 14 |
## | 0.611 | 0.389 |
## |-----------|-----------|
##
##
##
## CrossTable(min_inte)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 17 | 19 |
## | 0.472 | 0.528 |
## |-----------|-----------|
##
##
##
## CrossTable(cont_rcp)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 14 | 21 |
## | 0.400 | 0.600 |
## |-----------|-----------|
##
##
##
## DIFERENCIAS POR SEXO
attach(cursorcp)## The following objects are masked from cursorcp (pos = 3):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 4):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, zalibrary(sjPlot)
sjt.xtab(cursorcp$aprox,cursorcp$sexo, show.row.prc = T)| aprox | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
5 15.2 % |
28 84.8 % |
33 100 % |
| si |
1 33.3 % |
2 66.7 % |
3 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.135 · Fisher’s p=0.431 |
sjt.xtab(comp_cons, sexo, show.row.prc = T)| comp_cons | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 100 % |
0 0 % |
1 100 % |
| si |
5 14.3 % |
30 85.7 % |
35 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.823 · df=1 · φ=0.378 · Fisher’s p=0.167 |
sjt.xtab(preg, sexo, show.row.prc = T) #p=0,024| preg | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
2 100 % |
0 0 % |
2 100 % |
| si |
4 11.8 % |
30 88.2 % |
34 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=5.188 · df=1 · φ=0.542 · Fisher’s p=0.024 |
sjt.xtab(za, sexo, show.row.prc = T)| za | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
2 66.7 % |
1 33.3 % |
3 100 % |
| si |
4 12.1 % |
29 87.9 % |
33 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=2.618 · df=1 · φ=0.405 · Fisher’s p=0.066 |
sjt.xtab(abre_va, sexo, show.row.prc = T)| abre_va | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 33.3 % |
2 66.7 % |
3 100 % |
| si |
5 15.2 % |
28 84.8 % |
33 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.135 · Fisher’s p=0.431 |
sjt.xtab(frentem, sexo, show.row.prc = T)| frentem | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 14.3 % |
6 85.7 % |
7 100 % |
| si |
5 17.2 % |
24 82.8 % |
29 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.031 · Fisher’s p=1.000 |
table(cursorcp$comp_resp, cursorcp$sexo )##
## hombre mujer
## si 6 30sjt.xtab(`mant_f-m`, sexo, show.row.prc = T)| mant_f-m | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
4 28.6 % |
10 71.4 % |
14 100 % |
| si |
2 9.1 % |
20 90.9 % |
22 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=1.145 · df=1 · φ=0.255 · Fisher’s p=0.181 |
sjt.xtab(vos, sexo, show.row.prc = T)| vos | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 11.1 % |
8 88.9 % |
9 100 % |
| si |
5 18.5 % |
22 81.5 % |
27 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.086 · Fisher’s p=1.000 |
sjt.xtab(val_10s, sexo, show.row.prc = T)| val_10s | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 16.7 % |
5 83.3 % |
6 100 % |
| si |
5 16.7 % |
25 83.3 % |
30 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.000 · Fisher’s p=1.000 |
sjt.xtab(llama_112, sexo, show.row.prc = T)| llama_112 | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
0 0 % |
6 100 % |
6 100 % |
| si |
6 20 % |
24 80 % |
30 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.360 · df=1 · φ=0.200 · Fisher’s p=0.561 |
sjt.xtab(pide_desa, sexo, show.row.prc = T)| pide_desa | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 8.3 % |
11 91.7 % |
12 100 % |
| si |
5 20.8 % |
19 79.2 % |
24 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.225 · df=1 · φ=0.158 · Fisher’s p=0.640 |
table(r_30_2, sexo)## sexo
## r_30_2 hombre mujer
## si 6 30sjt.xtab(emp_comp, sexo, show.row.prc = T)| emp_comp | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 100 % |
0 0 % |
1 100 % |
| si |
5 14.3 % |
30 85.7 % |
35 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.823 · df=1 · φ=0.378 · Fisher’s p=0.167 |
table(col_man, sexo)## sexo
## col_man hombre mujer
## si 6 30table(col_vert, sexo)## sexo
## col_vert hombre mujer
## si 6 30sjt.xtab(comp_desd, sexo, show.row.prc = T)| comp_desd | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
0 0 % |
2 100 % |
2 100 % |
| si |
6 17.6 % |
28 82.4 % |
34 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.108 · Fisher’s p=1.000 |
sjt.xtab(ritmo, sexo, show.row.prc = T)| ritmo | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
0 0 % |
2 100 % |
2 100 % |
| si |
6 17.6 % |
28 82.4 % |
34 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.108 · Fisher’s p=1.000 |
sjt.xtab(abre_va_2, sexo, show.row.prc = T)| abre_va_2 | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
2 28.6 % |
5 71.4 % |
7 100 % |
| si |
4 13.8 % |
25 86.2 % |
29 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.142 · df=1 · φ=0.157 · Fisher’s p=0.573 |
table(pinza, sexo)## sexo
## pinza hombre mujer
## si 6 30table(insuf, sexo)## sexo
## insuf hombre mujer
## si 6 30sjt.xtab(enc_desa, sexo, show.row.prc = T)| enc_desa | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
0 0 % |
1 100 % |
1 100 % |
| si |
6 17.1 % |
29 82.9 % |
35 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.076 · Fisher’s p=1.000 |
sjt.xtab(sig_ins, sexo, show.row.prc = T)| sig_ins | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
0 0 % |
3 100 % |
3 100 % |
| si |
6 18.2 % |
27 81.8 % |
33 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.135 · Fisher’s p=1.000 |
sjt.xtab(coloca_par, sexo, show.row.prc = T)| coloca_par | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
1 33.3 % |
2 66.7 % |
3 100 % |
| si |
5 15.2 % |
28 84.8 % |
33 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.135 · Fisher’s p=0.431 |
sjt.xtab(real_com, sexo, show.row.prc = T)| real_com | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
4 18.2 % |
18 81.8 % |
22 100 % |
| si |
2 14.3 % |
12 85.7 % |
14 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.051 · Fisher’s p=1.000 |
sjt.xtab(min_inte, sexo, show.row.prc = T)| min_inte | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| no |
4 23.5 % |
13 76.5 % |
17 100 % |
| si |
2 10.5 % |
17 89.5 % |
19 100 % |
| Total |
6 16.7 % |
30 83.3 % |
36 100 % |
χ2=0.357 · df=1 · φ=0.174 · Fisher’s p=0.391 |
sjt.xtab(cont_rcp, sexo, show.row.prc = T)| cont_rcp | sexo | Total | |
|---|---|---|---|
| hombre | mujer | ||
| 0 |
4 28.6 % |
10 71.4 % |
14 100 % |
| 1 |
2 9.5 % |
19 90.5 % |
21 100 % |
| Total |
6 17.1 % |
29 82.9 % |
35 100 % |
χ2=1.014 · df=1 · φ=0.248 · Fisher’s p=0.191 |
DIFERENCIAS POR TITULACION
sjt.xtab(aprox, titul, show.row.prc = T)| aprox | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
26 78.8 % |
7 21.2 % |
33 100 % |
| si |
1 33.3 % |
2 66.7 % |
3 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=1.091 · df=1 · φ=0.290 · Fisher’s p=0.148 |
sjt.xtab(comp_cons, titul, show.row.prc = T)| comp_cons | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
0 0 % |
1 100 % |
1 100 % |
| si |
27 77.1 % |
8 22.9 % |
35 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.343 · df=1 · φ=0.293 · Fisher’s p=0.250 |
sjt.xtab(preg, titul, show.row.prc = T)| preg | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
1 50 % |
1 50 % |
2 100 % |
| si |
26 76.5 % |
8 23.5 % |
34 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.140 · Fisher’s p=0.443 |
sjt.xtab(za, titul, show.row.prc = T)| za | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
2 66.7 % |
1 33.3 % |
3 100 % |
| si |
25 75.8 % |
8 24.2 % |
33 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.058 · Fisher’s p=1.000 |
sjt.xtab(abre_va, titul, show.row.prc = T)| abre_va | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
2 66.7 % |
1 33.3 % |
3 100 % |
| si |
25 75.8 % |
8 24.2 % |
33 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.058 · Fisher’s p=1.000 |
sjt.xtab(frentem, titul, show.row.prc = T)| frentem | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
4 57.1 % |
3 42.9 % |
7 100 % |
| si |
23 79.3 % |
6 20.7 % |
29 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.532 · df=1 · φ=0.203 · Fisher’s p=0.333 |
table(comp_resp, titul )## titul
## comp_resp enf pod
## si 27 9sjt.xtab(`mant_f-m`, titul, show.row.prc = T)| mant_f-m | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
9 64.3 % |
5 35.7 % |
14 100 % |
| si |
18 81.8 % |
4 18.2 % |
22 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.623 · df=1 · φ=0.197 · Fisher’s p=0.267 |
sjt.xtab(vos, titul, show.row.prc = T)| vos | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
6 66.7 % |
3 33.3 % |
9 100 % |
| si |
21 77.8 % |
6 22.2 % |
27 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.049 · df=1 · φ=0.111 · Fisher’s p=0.660 |
sjt.xtab(val_10s, titul, show.row.prc = T)| val_10s | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
4 66.7 % |
2 33.3 % |
6 100 % |
| si |
23 76.7 % |
7 23.3 % |
30 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.086 · Fisher’s p=0.627 |
sjt.xtab(llama_112, titul, show.row.prc = T)| llama_112 | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
5 83.3 % |
1 16.7 % |
6 100 % |
| si |
22 73.3 % |
8 26.7 % |
30 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.086 · Fisher’s p=1.000 |
sjt.xtab(pide_desa, titul, show.row.prc = T)| pide_desa | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
10 83.3 % |
2 16.7 % |
12 100 % |
| si |
17 70.8 % |
7 29.2 % |
24 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.167 · df=1 · φ=0.136 · Fisher’s p=0.685 |
table(r_30_2, titul)## titul
## r_30_2 enf pod
## si 27 9sjt.xtab(emp_comp, titul, show.row.prc = T)| emp_comp | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
0 0 % |
1 100 % |
1 100 % |
| si |
27 77.1 % |
8 22.9 % |
35 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.343 · df=1 · φ=0.293 · Fisher’s p=0.250 |
table(col_man, titul)## titul
## col_man enf pod
## si 27 9table(col_vert, titul)## titul
## col_vert enf pod
## si 27 9sjt.xtab(comp_desd, titul, show.row.prc = T)| comp_desd | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
2 100 % |
0 0 % |
2 100 % |
| si |
25 73.5 % |
9 26.5 % |
34 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.140 · Fisher’s p=1.000 |
sjt.xtab(ritmo, titul, show.row.prc = T)| ritmo | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
1 50 % |
1 50 % |
2 100 % |
| si |
26 76.5 % |
8 23.5 % |
34 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.140 · Fisher’s p=0.443 |
sjt.xtab(abre_va_2, titul, show.row.prc = T)| abre_va_2 | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
5 71.4 % |
2 28.6 % |
7 100 % |
| si |
22 75.9 % |
7 24.1 % |
29 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.041 · Fisher’s p=1.000 |
table(pinza, titul)## titul
## pinza enf pod
## si 27 9table(insuf, titul)## titul
## insuf enf pod
## si 27 9sjt.xtab(enc_desa, titul, show.row.prc = T)| enc_desa | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
0 0 % |
1 100 % |
1 100 % |
| si |
27 77.1 % |
8 22.9 % |
35 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.343 · df=1 · φ=0.293 · Fisher’s p=0.250 |
sjt.xtab(sig_ins, titul, show.row.prc = T)| sig_ins | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
2 66.7 % |
1 33.3 % |
3 100 % |
| si |
25 75.8 % |
8 24.2 % |
33 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.058 · Fisher’s p=1.000 |
sjt.xtab(coloca_par, titul, show.row.prc = T)| coloca_par | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
2 66.7 % |
1 33.3 % |
3 100 % |
| si |
25 75.8 % |
8 24.2 % |
33 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.000 · df=1 · φ=0.058 · Fisher’s p=1.000 |
sjt.xtab(real_com, titul, show.row.prc = T)| real_com | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
15 68.2 % |
7 31.8 % |
22 100 % |
| si |
12 85.7 % |
2 14.3 % |
14 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=0.623 · df=1 · φ=0.197 · Fisher’s p=0.432 |
sjt.xtab(min_inte, titul, show.row.prc = T)| min_inte | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| no |
10 58.8 % |
7 41.2 % |
17 100 % |
| si |
17 89.5 % |
2 10.5 % |
19 100 % |
| Total |
27 75 % |
9 25 % |
36 100 % |
χ2=3.009 · df=1 · φ=0.353 · Fisher’s p=0.055 |
sjt.xtab(cont_rcp, titul, show.row.prc = T)| cont_rcp | titul | Total | |
|---|---|---|---|
| enf | pod | ||
| 0 |
11 78.6 % |
3 21.4 % |
14 100 % |
| 1 |
15 71.4 % |
6 28.6 % |
21 100 % |
| Total |
26 74.3 % |
9 25.7 % |
35 100 % |
χ2=0.006 · df=1 · φ=0.080 · Fisher’s p=0.712 |
EVALUACIÓN OBJETIVA
library(psych)
describe(ptotal)describe(pcomp)describe(num_comp)describe(ratio)describe(prof_med)describe(lib_comp)describe(prof_com)describe(frec_comp_m)describe(posic)describe(pvent)describe(num_vent)describe(vol_vent_m)describe(tiemp_ins)describe(frac_comp)describe(num_pausas)describe(pausa_med)describe(paus_larg)DIFERENCIAS POR SEXO
attach(cursorcp)## The following objects are masked from cursorcp (pos = 3):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 4):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 5):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, zalibrary(psych)
describeBy(ptotal, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 74.17 27.01 87.5 74.17 7.41 25 93 68 -0.88 -1.07 11.03
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 74.27 17.1 76 75.04 20.02 45 99 54 -0.35 -1.31 3.12by(ptotal, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.76426, p-value = 0.02737
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.91943, p-value = 0.02593bartlett.test(ptotal, sexo)##
## Bartlett test of homogeneity of variances
##
## data: ptotal and sexo
## Bartlett's K-squared = 2.042, df = 1, p-value = 0.153wilcox.test(ptotal~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: ptotal by sexo
## W = 99, p-value = 0.7181
## alternative hypothesis: true location shift is not equal to 0describeBy(pcomp, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 72 33.02 87.5 72 10.38 11 95 84 -0.92 -0.96 13.48
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 73.77 20.8 75.5 75.75 22.98 29 99 70 -0.63 -0.81 3.8by(pcomp, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.77107, p-value = 0.03178
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.913, p-value = 0.01773bartlett.test(pcomp, sexo)##
## Bartlett test of homogeneity of variances
##
## data: pcomp and sexo
## Bartlett's K-squared = 2.0966, df = 1, p-value = 0.1476wilcox.test(pcomp~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: pcomp by sexo
## W = 94, p-value = 0.8818
## alternative hypothesis: true location shift is not equal to 0describeBy(num_comp, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 179 25.02 173.5 179 5.19 153 227 74 0.98 -0.54 10.21
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 172.07 15.61 166 171.75 16.31 143 201 58 0.23 -1.05 2.85by(num_comp, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.79265, p-value = 0.05042
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95712, p-value = 0.261bartlett.test(num_comp, sexo)##
## Bartlett test of homogeneity of variances
##
## data: num_comp and sexo
## Bartlett's K-squared = 2.1934, df = 1, p-value = 0.1386t.test(num_comp~sexo)##
## Welch Two Sample t-test
##
## data: num_comp by sexo
## t = 0.65382, df = 5.8024, p-value = 0.5383
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## -19.23030 33.09697
## sample estimates:
## mean in group hombre mean in group mujer
## 179.0000 172.0667describeBy(ratio, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 1.01 0.15 1.04 1.01 0.19 0.82 1.17 0.35 -0.19 -1.99 0.06
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 0.79 0.25 0.82 0.79 0.21 0.3 1.44 1.14 0.07 0.13 0.05by(ratio, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.88015, p-value = 0.2697
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96987, p-value = 0.5356bartlett.test(ratio, sexo)##
## Bartlett test of homogeneity of variances
##
## data: ratio and sexo
## Bartlett's K-squared = 1.4907, df = 1, p-value = 0.2221t.test(ratio~sexo) #p=0.012##
## Welch Two Sample t-test
##
## data: ratio by sexo
## t = 2.9534, df = 11.129, p-value = 0.01298
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## 0.05849798 0.39883535
## sample estimates:
## mean in group hombre mean in group mujer
## 1.0150000 0.7863333describeBy(prof_med, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 48.33 4.89 49.5 48.33 2.22 39 53 14 -0.98 -0.62 1.99
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 49.1 6.6 50 49.21 8.9 37 59 22 -0.1 -1.3 1.2by(prof_med, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.82663, p-value = 0.1006
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95017, p-value = 0.1708bartlett.test(prof_med, sexo)##
## Bartlett test of homogeneity of variances
##
## data: prof_med and sexo
## Bartlett's K-squared = 0.62551, df = 1, p-value = 0.429t.test(prof_med~sexo)##
## Welch Two Sample t-test
##
## data: prof_med by sexo
## t = -0.32903, df = 9.1054, p-value = 0.7496
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## -6.028334 4.495000
## sample estimates:
## mean in group hombre mean in group mujer
## 48.33333 49.10000describeBy(lib_comp, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 36 29.69 27 36 17.79 4 90 86 0.77 -0.94 12.12
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 86.63 21.04 95.5 91.83 6.67 12 100 88 -2.24 4.53 3.84by(lib_comp, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.88333, p-value = 0.2847
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.65367, p-value = 3.53e-07bartlett.test(lib_comp, sexo)##
## Bartlett test of homogeneity of variances
##
## data: lib_comp and sexo
## Bartlett's K-squared = 1.1068, df = 1, p-value = 0.2928wilcox.test(lib_comp~sexo) #p=0.002## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: lib_comp by sexo
## W = 17, p-value = 0.002037
## alternative hypothesis: true location shift is not equal to 0describeBy(prof_com, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 53.83 32.1 49.5 53.83 29.65 2 90 88 -0.3 -1.43 13.11
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 53.17 33.24 64 54.46 40.03 0 98 98 -0.36 -1.39 6.07by(prof_com, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.9087, p-value = 0.4279
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.90054, p-value = 0.008657bartlett.test(prof_com, sexo)##
## Bartlett test of homogeneity of variances
##
## data: prof_com and sexo
## Bartlett's K-squared = 0.0094566, df = 1, p-value = 0.9225wilcox.test(prof_com~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: prof_com by sexo
## W = 91, p-value = 0.9831
## alternative hypothesis: true location shift is not equal to 0describeBy(frec_comp_m, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 117.5 15.41 114.5 117.5 11.86 101 145 44 0.69 -1.05 6.29
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 116.8 11.05 114 115.42 10.38 103 148 45 0.97 0.39 2.02by(frec_comp_m, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.90986, p-value = 0.4355
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.91239, p-value = 0.0171bartlett.test(frec_comp_m, sexo)##
## Bartlett test of homogeneity of variances
##
## data: frec_comp_m and sexo
## Bartlett's K-squared = 1.0299, df = 1, p-value = 0.3102wilcox.test(frec_comp_m~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: frec_comp_m by sexo
## W = 86, p-value = 0.8817
## alternative hypothesis: true location shift is not equal to 0describeBy(posic, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 99.83 0.41 100 99.83 0 99 100 1 -1.36 -0.08 0.17
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 99.67 1.21 100 100 0 94 100 6 -3.74 13.67 0.22by(posic, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.49609, p-value = 2.073e-05
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.31251, p-value = 9.004e-11bartlett.test(posic, sexo)##
## Bartlett test of homogeneity of variances
##
## data: posic and sexo
## Bartlett's K-squared = 5.7461, df = 1, p-value = 0.01653wilcox.test(posic~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: posic by sexo
## W = 85, p-value = 0.7263
## alternative hypothesis: true location shift is not equal to 0describeBy(pvent, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 80.67 13.84 79 80.67 16.31 65 99 34 0.12 -2.03 5.65
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 76.7 26.67 87 81.75 17.79 0 99 99 -1.51 1.86 4.87by(pvent, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.90588, p-value = 0.4098
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.79011, p-value = 4.352e-05bartlett.test(pvent, sexo)##
## Bartlett test of homogeneity of variances
##
## data: pvent and sexo
## Bartlett's K-squared = 2.5237, df = 1, p-value = 0.1121wilcox.test(pvent~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: pvent by sexo
## W = 87, p-value = 0.915
## alternative hypothesis: true location shift is not equal to 0describeBy(num_vent, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 8.83 1.94 8.5 8.83 2.22 7 12 5 0.47 -1.53 0.79
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 8.03 3.02 8.5 8.38 2.22 0 12 12 -1.08 0.86 0.55by(num_vent, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.91238, p-value = 0.4522
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.87082, p-value = 0.001747bartlett.test(num_vent, sexo)##
## Bartlett test of homogeneity of variances
##
## data: num_vent and sexo
## Bartlett's K-squared = 1.2702, df = 1, p-value = 0.2597wilcox.test(num_vent~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: num_vent by sexo
## W = 97.5, p-value = 0.7611
## alternative hypothesis: true location shift is not equal to 0describeBy(vol_vent_m, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 438.17 78.33 473.5 438.17 42.25 338 509 171 -0.45 -1.96 31.98
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 30 433.8 163.63 449.5 448.29 152.71 0 693 693 -0.89 0.96
## se
## X1 29.87by(vol_vent_m, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.78806, p-value = 0.04577
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92098, p-value = 0.02844bartlett.test(vol_vent_m, sexo)##
## Bartlett test of homogeneity of variances
##
## data: vol_vent_m and sexo
## Bartlett's K-squared = 3.0662, df = 1, p-value = 0.07994wilcox.test(vol_vent_m~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: vol_vent_m by sexo
## W = 91.5, p-value = 0.9661
## alternative hypothesis: true location shift is not equal to 0describeBy(tiemp_ins, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 0.38 0.08 0.4 0.38 0.07 0.3 0.5 0.2 0.17 -1.54 0.03
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 3.48 17.1 0.4 0.38 0.15 0 94 94 4.94 23.19 3.12by(tiemp_ins, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.86626, p-value = 0.2117
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.18563, p-value = 8.624e-12bartlett.test(tiemp_ins, sexo)##
## Bartlett test of homogeneity of variances
##
## data: tiemp_ins and sexo
## Bartlett's K-squared = 45.721, df = 1, p-value = 1.364e-11wilcox.test(tiemp_ins~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: tiemp_ins by sexo
## W = 90.5, p-value = 1
## alternative hypothesis: true location shift is not equal to 0describeBy(frac_comp, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 76 4.73 76 76 5.19 70 83 13 0.14 -1.66 1.93
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 74.47 4.22 75 74.5 2.97 65 84 19 -0.15 -0.11 0.77by(frac_comp, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.9844, p-value = 0.9712
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96391, p-value = 0.3883bartlett.test(frac_comp, sexo)##
## Bartlett test of homogeneity of variances
##
## data: frac_comp and sexo
## Bartlett's K-squared = 0.11269, df = 1, p-value = 0.7371t.test(frac_comp~sexo)##
## Welch Two Sample t-test
##
## data: frac_comp by sexo
## t = 0.73723, df = 6.6839, p-value = 0.4861
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## -3.432297 6.498964
## sample estimates:
## mean in group hombre mean in group mujer
## 76.00000 74.46667describeBy(num_pausas, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 5.17 0.41 5 5.17 0 5 6 1 1.36 -0.08 0.17
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 4.9 0.71 5 4.88 0.74 4 6 2 0.13 -1.09 0.13by(num_pausas, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.49609, p-value = 2.073e-05
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.80703, p-value = 8.829e-05bartlett.test(num_pausas, sexo)##
## Bartlett test of homogeneity of variances
##
## data: num_pausas and sexo
## Bartlett's K-squared = 1.8984, df = 1, p-value = 0.1683wilcox.test(num_pausas~sexo)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: num_pausas by sexo
## W = 109.5, p-value = 0.3691
## alternative hypothesis: true location shift is not equal to 0describeBy(pausa_med, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 4.83 0.75 5 4.83 0.74 4 6 2 0.17 -1.54 0.31
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 5.67 1.09 6 5.71 1.48 4 7 3 -0.11 -1.39 0.2by(pausa_med, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.86626, p-value = 0.2117
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.86, p-value = 0.001011bartlett.test(pausa_med, sexo)##
## Bartlett test of homogeneity of variances
##
## data: pausa_med and sexo
## Bartlett's K-squared = 0.93157, df = 1, p-value = 0.3345t.test(pausa_med~sexo) #p=0.046##
## Welch Two Sample t-test
##
## data: pausa_med by sexo
## t = -2.274, df = 9.8081, p-value = 0.04674
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## -1.65202270 -0.01464396
## sample estimates:
## mean in group hombre mean in group mujer
## 4.833333 5.666667describeBy(paus_larg, sexo)##
## Descriptive statistics by group
## group: hombre
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6 6.17 0.98 6.5 6.17 0.74 5 7 2 -0.25 -2.08 0.4
## ------------------------------------------------------------
## group: mujer
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 30 6.87 1.48 7 6.83 1.48 4 10 6 0.04 -1 0.27by(paus_larg, sexo, shapiro.test)## sexo: hombre
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.77516, p-value = 0.03473
##
## ------------------------------------------------------------
## sexo: mujer
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92824, p-value = 0.0441bartlett.test(paus_larg, sexo)##
## Bartlett test of homogeneity of variances
##
## data: paus_larg and sexo
## Bartlett's K-squared = 1.0978, df = 1, p-value = 0.2948t.test(paus_larg~sexo)##
## Welch Two Sample t-test
##
## data: paus_larg by sexo
## t = -1.4469, df = 10.193, p-value = 0.178
## alternative hypothesis: true difference in means between group hombre and group mujer is not equal to 0
## 95 percent confidence interval:
## -1.7752141 0.3752141
## sample estimates:
## mean in group hombre mean in group mujer
## 6.166667 6.866667DIFERENCIAS POR TITULACIÓN
describeBy(ptotal, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 72.85 18.65 75 74.26 20.76 25 95 70 -0.68 -0.49 3.59
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 78.44 18.97 87 78.44 14.83 47 99 52 -0.62 -1.36 6.32by(ptotal, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.9185, p-value = 0.03633
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.86635, p-value = 0.1123bartlett.test(ptotal, titul)##
## Bartlett test of homogeneity of variances
##
## data: ptotal and titul
## Bartlett's K-squared = 0.0035942, df = 1, p-value = 0.9522t.test(ptotal~titul)##
## Welch Two Sample t-test
##
## data: ptotal by titul
## t = -0.76907, df = 13.547, p-value = 0.455
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -21.23819 10.05300
## sample estimates:
## mean in group enf mean in group pod
## 72.85185 78.44444describeBy(pcomp, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 73.04 22.7 77 75.52 23.72 11 98 87 -0.93 0.12 4.37
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 74.78 23.97 85 74.78 19.27 32 99 67 -0.73 -1.17 7.99by(pcomp, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.89505, p-value = 0.01032
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.85432, p-value = 0.08312bartlett.test(pcomp, titul)##
## Bartlett test of homogeneity of variances
##
## data: pcomp and titul
## Bartlett's K-squared = 0.035365, df = 1, p-value = 0.8508t.test(pcomp~titul)##
## Welch Two Sample t-test
##
## data: pcomp by titul
## t = -0.19118, df = 13.137, p-value = 0.8513
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -21.39094 17.90946
## sample estimates:
## mean in group enf mean in group pod
## 73.03704 74.77778describeBy(num_comp, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 172.52 17.9 166 171.17 16.31 143 227 84 0.93 1.08 3.45
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 175.33 16 172 175.33 17.79 153 201 48 0.23 -1.48 5.33by(num_comp, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.93973, p-value = 0.12
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96714, p-value = 0.8692bartlett.test(num_comp, titul)##
## Bartlett test of homogeneity of variances
##
## data: num_comp and titul
## Bartlett's K-squared = 0.14191, df = 1, p-value = 0.7064t.test(num_comp~titul)##
## Welch Two Sample t-test
##
## data: num_comp by titul
## t = -0.44332, df = 15.253, p-value = 0.6638
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -16.32866 10.69903
## sample estimates:
## mean in group enf mean in group pod
## 172.5185 175.3333describeBy(ratio, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 0.77 0.24 0.82 0.78 0.19 0.3 1.17 0.87 -0.37 -0.68 0.05
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 0.98 0.24 0.96 0.98 0.16 0.61 1.44 0.83 0.37 -0.68 0.08by(ratio, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.95231, p-value = 0.2438
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96702, p-value = 0.8681bartlett.test(ratio, titul)##
## Bartlett test of homogeneity of variances
##
## data: ratio and titul
## Bartlett's K-squared = 0.002783, df = 1, p-value = 0.9579t.test(ratio~titul) #p=0.046##
## Welch Two Sample t-test
##
## data: ratio by titul
## t = -2.1936, df = 13.571, p-value = 0.04622
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -0.399061222 -0.003901741
## sample estimates:
## mean in group enf mean in group pod
## 0.7740741 0.9755556describeBy(prof_med, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 48.96 6.92 50 49.04 8.9 37 59 22 -0.12 -1.38 1.33
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 49 4.18 50 49 4.45 41 55 14 -0.42 -0.89 1.39by(prof_med, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.94019, p-value = 0.1232
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96068, p-value = 0.8054bartlett.test(prof_med, titul)##
## Bartlett test of homogeneity of variances
##
## data: prof_med and titul
## Bartlett's K-squared = 2.4456, df = 1, p-value = 0.1179t.test(prof_med~titul)##
## Welch Two Sample t-test
##
## data: prof_med by titul
## t = -0.019208, df = 23.287, p-value = 0.9848
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -4.023070 3.948996
## sample estimates:
## mean in group enf mean in group pod
## 48.96296 49.00000describeBy(lib_comp, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 78.74 29.71 95 83.04 7.41 4 100 96 -1.33 0.31 5.72
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 76.56 29.73 90 76.56 13.34 22 100 78 -0.99 -0.91 9.91by(lib_comp, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.72909, p-value = 1.011e-05
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.73824, p-value = 0.003959bartlett.test(lib_comp, titul)##
## Bartlett test of homogeneity of variances
##
## data: lib_comp and titul
## Bartlett's K-squared = 2.4568e-06, df = 1, p-value = 0.9987wilcox.test(lib_comp~titul) ## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: lib_comp by titul
## W = 135, p-value = 0.634
## alternative hypothesis: true location shift is not equal to 0describeBy(prof_com, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 52.67 34.39 63 53.57 37.06 0 98 98 -0.41 -1.42 6.62
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 55.11 28.28 53 55.11 32.62 15 98 83 0.06 -1.58 9.43by(prof_com, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.87856, p-value = 0.004472
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.97308, p-value = 0.9197bartlett.test(prof_com, titul)##
## Bartlett test of homogeneity of variances
##
## data: prof_com and titul
## Bartlett's K-squared = 0.41755, df = 1, p-value = 0.5182wilcox.test(prof_com~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: prof_com by titul
## W = 119.5, p-value = 0.9563
## alternative hypothesis: true location shift is not equal to 0describeBy(frec_comp_m, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 115.7 10.11 113 114.7 8.9 103 145 42 1.01 0.55 1.95
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 120.56 15.45 119 120.56 11.86 101 148 47 0.47 -1.2 5.15by(frec_comp_m, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.90916, p-value = 0.0218
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.94332, p-value = 0.6172bartlett.test(frec_comp_m, titul)##
## Bartlett test of homogeneity of variances
##
## data: frec_comp_m and titul
## Bartlett's K-squared = 2.3945, df = 1, p-value = 0.1218wilcox.test(frec_comp_m~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: frec_comp_m by titul
## W = 103.5, p-value = 0.522
## alternative hypothesis: true location shift is not equal to 0describeBy(posic, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 99.7 1.17 100 99.96 0 94 100 6 -4.29 17.89 0.23
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 99.67 1 100 99.67 0 97 100 3 -2.07 2.63 0.33by(posic, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.27784, p-value = 1.733e-10
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.38984, p-value = 3.217e-07bartlett.test(posic, titul)##
## Bartlett test of homogeneity of variances
##
## data: posic and titul
## Bartlett's K-squared = 0.27441, df = 1, p-value = 0.6004wilcox.test(posic~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: posic by titul
## W = 122, p-value = 1
## alternative hypothesis: true location shift is not equal to 0describeBy(pvent, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 73.48 27.17 73 77.65 29.65 0 99 99 -1.28 1.29 5.23
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 89 10.25 92 89 7.41 66 99 33 -1.09 0.07 3.42by(pvent, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.82334, p-value = 0.0003571
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.83299, p-value = 0.04823bartlett.test(pvent, titul)##
## Bartlett test of homogeneity of variances
##
## data: pvent and titul
## Bartlett's K-squared = 7.5998, df = 1, p-value = 0.005838wilcox.test(pvent~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: pvent by titul
## W = 88, p-value = 0.2252
## alternative hypothesis: true location shift is not equal to 0describeBy(num_vent, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 7.59 2.95 8 7.87 2.97 0 12 12 -0.98 0.84 0.57
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 9.89 1.76 10 9.89 0 6 12 6 -0.82 0.09 0.59by(num_vent, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.89338, p-value = 0.009469
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.81537, p-value = 0.03054bartlett.test(num_vent, titul)##
## Bartlett test of homogeneity of variances
##
## data: num_vent and titul
## Bartlett's K-squared = 2.5476, df = 1, p-value = 0.1105wilcox.test(num_vent~titul) #p=0,023## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: num_vent by titul
## W = 60.5, p-value = 0.02373
## alternative hypothesis: true location shift is not equal to 0describeBy(vol_vent_m, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 27 424.74 163.28 450 442.09 143.81 0 654 654 -1 0.94
## se
## X1 31.42
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 463.89 113.85 450 463.89 99.33 334 693 359 0.65 -0.72 37.95by(vol_vent_m, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.90575, p-value = 0.01815
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92097, p-value = 0.4004bartlett.test(vol_vent_m, titul)##
## Bartlett test of homogeneity of variances
##
## data: vol_vent_m and titul
## Bartlett's K-squared = 1.3292, df = 1, p-value = 0.249wilcox.test(vol_vent_m~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: vol_vent_m by titul
## W = 112, p-value = 0.7423
## alternative hypothesis: true location shift is not equal to 0describeBy(tiemp_ins, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 3.83 18.02 0.4 0.38 0.15 0 94 94 4.63 20.22 3.47
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 0.38 0.07 0.4 0.38 0 0.3 0.5 0.2 0.18 -1.12 0.02by(tiemp_ins, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.19918, p-value = 4.327e-11
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.81259, p-value = 0.02841bartlett.test(tiemp_ins, titul)##
## Bartlett test of homogeneity of variances
##
## data: tiemp_ins and titul
## Bartlett's K-squared = 77.031, df = 1, p-value < 2.2e-16wilcox.test(tiemp_ins~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: tiemp_ins by titul
## W = 125.5, p-value = 0.8948
## alternative hypothesis: true location shift is not equal to 0describeBy(frac_comp, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 75.26 4.16 76 75.3 2.97 65 84 19 -0.29 0.28 0.8
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 73.11 4.46 72 73.11 4.45 68 82 14 0.55 -0.8 1.49by(frac_comp, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.96071, p-value = 0.3836
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.92325, p-value = 0.4198bartlett.test(frac_comp, titul)##
## Bartlett test of homogeneity of variances
##
## data: frac_comp and titul
## Bartlett's K-squared = 0.05831, df = 1, p-value = 0.8092t.test(frac_comp~titul)##
## Welch Two Sample t-test
##
## data: frac_comp by titul
## t = 1.2732, df = 12.976, p-value = 0.2253
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -1.497514 5.793810
## sample estimates:
## mean in group enf mean in group pod
## 75.25926 73.11111describeBy(num_pausas, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 4.85 0.66 5 4.83 0 4 6 2 0.15 -0.85 0.13
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 5.22 0.67 5 5.22 0 4 6 2 -0.18 -1.12 0.22by(num_pausas, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.79263, p-value = 0.0001023
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.81259, p-value = 0.02841bartlett.test(num_pausas, titul)##
## Bartlett test of homogeneity of variances
##
## data: num_pausas and titul
## Bartlett's K-squared = 0.00048861, df = 1, p-value = 0.9824wilcox.test(num_pausas~titul)## Warning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot
## compute exact p-value with ties##
## Wilcoxon rank sum test with continuity correction
##
## data: num_pausas by titul
## W = 86.5, p-value = 0.1604
## alternative hypothesis: true location shift is not equal to 0describeBy(pausa_med, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 5.59 1.19 5 5.61 1.48 4 7 3 -0.01 -1.59 0.23
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 5.33 0.71 5 5.33 1.48 4 6 2 -0.42 -1.22 0.24by(pausa_med, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.83398, p-value = 0.000564
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.8054, p-value = 0.02353bartlett.test(pausa_med, titul)##
## Bartlett test of homogeneity of variances
##
## data: pausa_med and titul
## Bartlett's K-squared = 2.5615, df = 1, p-value = 0.1095t.test(pausa_med~titul) #p=0.046##
## Welch Two Sample t-test
##
## data: pausa_med by titul
## t = 0.79045, df = 23.623, p-value = 0.4371
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -0.4182469 0.9367654
## sample estimates:
## mean in group enf mean in group pod
## 5.592593 5.333333describeBy(paus_larg, titul)##
## Descriptive statistics by group
## group: enf
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 27 6.7 1.44 6 6.61 1.48 5 10 5 0.35 -1.01 0.28
## ------------------------------------------------------------
## group: pod
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 9 6.89 1.45 7 6.89 1.48 4 9 5 -0.48 -0.66 0.48by(paus_larg, titul, shapiro.test)## titul: enf
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.8867, p-value = 0.006724
##
## ------------------------------------------------------------
## titul: pod
##
## Shapiro-Wilk normality test
##
## data: dd[x, ]
## W = 0.94179, p-value = 0.6009bartlett.test(paus_larg, titul)##
## Bartlett test of homogeneity of variances
##
## data: paus_larg and titul
## Bartlett's K-squared = 0.0015837, df = 1, p-value = 0.9683t.test(paus_larg~titul)##
## Welch Two Sample t-test
##
## data: paus_larg by titul
## t = -0.33209, df = 13.615, p-value = 0.7449
## alternative hypothesis: true difference in means between group enf and group pod is not equal to 0
## 95 percent confidence interval:
## -1.384385 1.014015
## sample estimates:
## mean in group enf mean in group pod
## 6.703704 6.888889EVALUACIÓN CONOCIMIENTOS
attach(cursorcp) ## The following objects are masked from cursorcp (pos = 3):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 4):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 5):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, za## The following objects are masked from cursorcp (pos = 6):
##
## abre_va, abre_va_2, aprox, col_man, col_vert, coloca_par,
## comp_cons, comp_desd, comp_resp, cont_rcp, curso, edad, emp_comp,
## enc_desa, fenac, ferec, frac_comp, frec_comp_m, frentem, id, imc,
## insuf, lib_comp, llama_112, mant_f-m, min_inte, num_comp,
## num_pausas, num_vent, paus_larg, pausa_med, pcomp, peso, pide_desa,
## pinza, posic, post1, post10, post2, post3, post4, post5, post6,
## post7, post8, post9, pre1, pre10, pre2, pre3, pre4, pre5, pre6,
## pre7, pre8, pre9, preg, prof_com, prof_med, ptotal, pvent, r_30_2,
## ratio, real_com, ritmo, sexo, sig_ins, talla, tiemp_ins, tiempo,
## titul, val_10s, vol_vent_m, vos, zalibrary(gmodels)
library(psych)
CrossTable(pre1)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 2 | 34 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
## CrossTable(pre2)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## CrossTable(pre3)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(pre4)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 18 | 18 |
## | 0.500 | 0.500 |
## |-----------|-----------|
##
##
##
## CrossTable(pre5)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 4 | 32 |
## | 0.111 | 0.889 |
## |-----------|-----------|
##
##
##
## CrossTable(pre6)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 15 | 21 |
## | 0.417 | 0.583 |
## |-----------|-----------|
##
##
##
## CrossTable(pre7)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 13 | 23 |
## | 0.361 | 0.639 |
## |-----------|-----------|
##
##
##
## CrossTable(pre8)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## CrossTable(pre9)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 8 | 28 |
## | 0.222 | 0.778 |
## |-----------|-----------|
##
##
##
## CrossTable(pre10)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 22 | 14 |
## | 0.611 | 0.389 |
## |-----------|-----------|
##
##
##
## CrossTable(post1)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(post2)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 2 | 34 |
## | 0.056 | 0.944 |
## |-----------|-----------|
##
##
##
## CrossTable(post3)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 9 | 27 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
## table(post4)## post4
## si
## 36table(post5)## post5
## si
## 36CrossTable(post6)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 9 | 27 |
## | 0.250 | 0.750 |
## |-----------|-----------|
##
##
##
## CrossTable(post7)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 3 | 33 |
## | 0.083 | 0.917 |
## |-----------|-----------|
##
##
##
## CrossTable(post8)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 1 | 35 |
## | 0.028 | 0.972 |
## |-----------|-----------|
##
##
##
## table(post9)## post9
## si
## 36CrossTable(post10)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | no | si |
## |-----------|-----------|
## | 15 | 21 |
## | 0.417 | 0.583 |
## |-----------|-----------|
##
##
##
## cursorcp$pre1r<-ifelse(cursorcp$pre1=="si", 1,0)
cursorcp$pre2r<-ifelse(cursorcp$pre2=="si", 1,0)
cursorcp$pre3r<-ifelse(cursorcp$pre3=="si", 1,0)
cursorcp$pre4r<-ifelse(cursorcp$pre4=="si", 1,0)
cursorcp$pre5r<-ifelse(cursorcp$pre5=="si", 1,0)
cursorcp$pre6r<-ifelse(cursorcp$pre6=="si", 1,0)
cursorcp$pre7r<-ifelse(cursorcp$pre7=="si", 1,0)
cursorcp$pre8r<-ifelse(cursorcp$pre8=="si", 1,0)
cursorcp$pre9r<-ifelse(cursorcp$pre9=="si", 1,0)
cursorcp$pre10r<-ifelse(cursorcp$pre10=="si", 1,0)
cursorcp$prenota<-(cursorcp$pre1r+cursorcp$pre2r+cursorcp$pre3r+cursorcp$pre4r+cursorcp$pre5r+cursorcp$pre6r+cursorcp$pre7r+cursorcp$pre8r+cursorcp$pre9r+cursorcp$pre10r)
describe(cursorcp$prenota)cursorcp$post1r<-ifelse(cursorcp$post1=="si", 1,0)
cursorcp$post2r<-ifelse(cursorcp$post2=="si", 1,0)
cursorcp$post3r<-ifelse(cursorcp$post3=="si", 1,0)
cursorcp$post4r<-ifelse(cursorcp$post4=="si", 1,0)
cursorcp$post5r<-ifelse(cursorcp$post5=="si", 1,0)
cursorcp$post6r<-ifelse(cursorcp$post6=="si", 1,0)
cursorcp$post7r<-ifelse(cursorcp$post7=="si", 1,0)
cursorcp$post8r<-ifelse(cursorcp$post8=="si", 1,0)
cursorcp$post9r<-ifelse(cursorcp$post9=="si", 1,0)
cursorcp$post10r<-ifelse(cursorcp$post10=="si", 1,0)
cursorcp$postnota<-(cursorcp$post1r+cursorcp$post2r+cursorcp$post3r+cursorcp$post4r+cursorcp$post5r+cursorcp$post6r+cursorcp$post7r+cursorcp$post8r+cursorcp$post9r+cursorcp$post10r)
describe(cursorcp$postnota)wilcox.test(cursorcp$prenota, cursorcp$postnota, paired = T) #p<0.001## Warning in wilcox.test.default(cursorcp$prenota, cursorcp$postnota, paired = T):
## cannot compute exact p-value with ties## Warning in wilcox.test.default(cursorcp$prenota, cursorcp$postnota, paired = T):
## cannot compute exact p-value with zeroes##
## Wilcoxon signed rank test with continuity correction
##
## data: cursorcp$prenota and cursorcp$postnota
## V = 66.5, p-value = 0.0003008
## alternative hypothesis: true location shift is not equal to 0