#PACOTES

#install.packages("psych")
#install.packages("chisq.posthoc.test")
#install.packages("summarytools")

library(psych)
## Warning: package 'psych' was built under R version 4.0.5
library(chisq.posthoc.test)

Dados

#dados_videos <- read.csv("dados_aline.csv")
dados_videos <- read.csv("banco_aline_quanti_99.csv")

Análise Descritiva - Geral

describe(dados_videos)
summary(dados_videos)
##  Carimbo.de.data.hora      ID                  q1             q2       
##  Length:99            Length:99          Min.   :2.00   Min.   :1.000  
##  Class :character     Class :character   1st Qu.:3.00   1st Qu.:2.000  
##  Mode  :character     Mode  :character   Median :4.00   Median :2.000  
##                                          Mean   :3.96   Mean   :2.232  
##                                          3rd Qu.:5.00   3rd Qu.:3.000  
##                                          Max.   :6.00   Max.   :4.000  
##        q3              q4               q5               q6         
##  Min.   :1.000   Min.   :  1456   Min.   :  35.0   Min.   :    0.0  
##  1st Qu.:2.000   1st Qu.:  2164   1st Qu.: 285.0   1st Qu.:   54.5  
##  Median :2.000   Median :  5326   Median : 487.0   Median :  156.0  
##  Mean   :2.242   Mean   : 22432   Mean   : 736.3   Mean   : 1100.0  
##  3rd Qu.:3.000   3rd Qu.: 13791   3rd Qu.: 797.0   3rd Qu.:  578.5  
##  Max.   :3.000   Max.   :613011   Max.   :3717.0   Max.   :22000.0  
##        q7                q9             q10             q11       
##  Min.   :   0.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:   1.00   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :   4.00   Median :1.000   Median :2.000   Median :2.000  
##  Mean   :  27.41   Mean   :1.071   Mean   :1.586   Mean   :1.566  
##  3rd Qu.:  11.00   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :1400.00   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q12             q13             q14             q15       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.000   Median :1.000   Median :2.000   Median :1.000  
##  Mean   :1.717   Mean   :1.495   Mean   :1.556   Mean   :1.273  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q16             q17             q18             q19       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :1.000   Median :1.000   Median :2.000   Median :1.000  
##  Mean   :1.444   Mean   :1.101   Mean   :1.667   Mean   :1.424  
##  3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q20             q21             q22             q23       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.000   Median :2.000   Median :2.000   Median :2.000  
##  Mean   :1.737   Mean   :1.636   Mean   :1.616   Mean   :1.586  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q24             q25             q26             q27       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :1.000   Median :2.000   Median :2.000  
##  Mean   :1.566   Mean   :1.333   Mean   :1.717   Mean   :1.899  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q28             q29             q30             q31       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.500   1st Qu.:2.000   1st Qu.:2.000  
##  Median :2.000   Median :2.000   Median :2.000   Median :2.000  
##  Mean   :1.687   Mean   :1.747   Mean   :1.838   Mean   :1.859  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q32             q33             q34             q35       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Median :2.000   Median :1.000   Median :2.000   Median :2.000  
##  Mean   :1.667   Mean   :1.434   Mean   :1.545   Mean   :1.657  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q36             q37             q38             q39       
##  Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:2.000  
##  Median :2.000   Median :1.000   Median :2.000   Median :2.000  
##  Mean   :1.808   Mean   :1.384   Mean   :1.646   Mean   :1.919  
##  3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##       q40             q41       
##  Min.   :1.000   Min.   :1.000  
##  1st Qu.:2.000   1st Qu.:1.000  
##  Median :2.000   Median :2.000  
##  Mean   :1.939   Mean   :1.626  
##  3rd Qu.:2.000   3rd Qu.:2.000  
##  Max.   :2.000   Max.   :2.000

Provisão de Conteúdo

table<-with(dados_videos,table(q9,q3))
table
##    q3
## q9   1  2  3
##   1 17 34 41
##   2  3  1  3
prop.table(table)
##    q3
## q9           1          2          3
##   1 0.17171717 0.34343434 0.41414141
##   2 0.03030303 0.01010101 0.03030303
chisq.test(dados_videos$q9, dados_videos$q3)
## Warning in chisq.test(dados_videos$q9, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q9 and dados_videos$q3
## X-squared = 2.8637, df = 2, p-value = 0.2389
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.008333333
qnorm(sigAdj/2)
## [1] -2.638257

Informações a respeito de experiência pessoal

table<-with(dados_videos,table(q10,q3))
table
##    q3
## q10  1  2  3
##   1 18  5 18
##   2  2 30 26
prop.table(table)
##    q3
## q10          1          2          3
##   1 0.18181818 0.05050505 0.18181818
##   2 0.02020202 0.30303030 0.26262626
chisq.test(dados_videos$q10, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q10 and dados_videos$q3
## X-squared = 30.079, df = 2, p-value = 2.94e-07
chisq.posthoc.test(table)

Menciona quem são acometidos pela DC

table<-with(dados_videos,table(q11,q3))
table
##    q3
## q11  1  2  3
##   1  3 11 29
##   2 17 24 15
prop.table(table)
##    q3
## q11          1          2          3
##   1 0.03030303 0.11111111 0.29292929
##   2 0.17171717 0.24242424 0.15151515
chisq.test(dados_videos$q11, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q11 and dados_videos$q3
## X-squared = 17.681, df = 2, p-value = 0.0001448
chisq.posthoc.test(table)

Menciona quem está em risco

table<-with(dados_videos,table(q12,q3))
table
##    q3
## q12  1  2  3
##   1  3 10 15
##   2 17 25 29
prop.table(table)
##    q3
## q12          1          2          3
##   1 0.03030303 0.10101010 0.15151515
##   2 0.17171717 0.25252525 0.29292929
chisq.test(dados_videos$q12, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q12 and dados_videos$q3
## X-squared = 2.4729, df = 2, p-value = 0.2904
chisq.posthoc.test(table)

Menciona a DC como uma doença autoimune

table<-with(dados_videos,table(q13,q3))
table
##    q3
## q13  1  2  3
##   1 10 16 24
##   2 10 19 20
prop.table(table)
##    q3
## q13         1         2         3
##   1 0.1010101 0.1616162 0.2424242
##   2 0.1010101 0.1919192 0.2020202
chisq.test(dados_videos$q13, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q13 and dados_videos$q3
## X-squared = 0.61074, df = 2, p-value = 0.7369
chisq.posthoc.test(table)

Menciona que a DC é uma doença hereditária

table<-with(dados_videos,table(q14,q3))
table
##    q3
## q14  1  2  3
##   1  8 11 25
##   2 12 24 19
prop.table(table)
##    q3
## q14          1          2          3
##   1 0.08080808 0.11111111 0.25252525
##   2 0.12121212 0.24242424 0.19191919
chisq.test(dados_videos$q14, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q14 and dados_videos$q3
## X-squared = 5.2898, df = 2, p-value = 0.07101
chisq.posthoc.test(table)

Menciona como a DC afeta o corpo

table<-with(dados_videos,table(q15,q3))
table
##    q3
## q15  1  2  3
##   1 13 24 35
##   2  7 11  9
prop.table(table)
##    q3
## q15          1          2          3
##   1 0.13131313 0.24242424 0.35353535
##   2 0.07070707 0.11111111 0.09090909
chisq.test(dados_videos$q15, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q15 and dados_videos$q3
## X-squared = 1.9381, df = 2, p-value = 0.3794
chisq.posthoc.test(table)

Menciona danos de “vilosidades” no intestino delgado

table<-with(dados_videos,table(q16,q3))
table
##    q3
## q16  1  2  3
##   1  5 21 29
##   2 15 14 15
prop.table(table)
##    q3
## q16          1          2          3
##   1 0.05050505 0.21212121 0.29292929
##   2 0.15151515 0.14141414 0.15151515
chisq.test(dados_videos$q16, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q16 and dados_videos$q3
## X-squared = 9.7527, df = 2, p-value = 0.007625
chisq.posthoc.test(table)

#Menciona o glúten como a causa primária de DC

table<-with(dados_videos,table(q17,q3))
table
##    q3
## q17  1  2  3
##   1 17 30 42
##   2  3  5  2
prop.table(table)
##    q3
## q17          1          2          3
##   1 0.17171717 0.30303030 0.42424242
##   2 0.03030303 0.05050505 0.02020202
chisq.test(dados_videos$q17, dados_videos$q3)
## Warning in chisq.test(dados_videos$q17, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q17 and dados_videos$q3
## X-squared = 2.6991, df = 2, p-value = 0.2594
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona a idade que a DC pode se desenvolver

table<-with(dados_videos,table(q18,q3))
table
##    q3
## q18  1  2  3
##   1  3  7 23
##   2 17 28 21
prop.table(table)
##    q3
## q18          1          2          3
##   1 0.03030303 0.07070707 0.23232323
##   2 0.17171717 0.28282828 0.21212121
chisq.test(dados_videos$q18, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q18 and dados_videos$q3
## X-squared = 12.927, df = 2, p-value = 0.001559
chisq.posthoc.test(table)

#Menciona o risco para o desenvolvimento de outras condições crônicas de saúde

table<-with(dados_videos,table(q19,q3))
table
##    q3
## q19  1  2  3
##   1 11 17 29
##   2  9 18 15
prop.table(table)
##    q3
## q19          1          2          3
##   1 0.11111111 0.17171717 0.29292929
##   2 0.09090909 0.18181818 0.15151515
chisq.test(dados_videos$q19, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q19 and dados_videos$q3
## X-squared = 2.467, df = 2, p-value = 0.2913
chisq.posthoc.test(table)

Mencionam a inibição do crescimento em crianças

table<-with(dados_videos,table(q20,q3))
table
##    q3
## q20  1  2  3
##   1  5  9 12
##   2 15 26 32
prop.table(table)
##    q3
## q20          1          2          3
##   1 0.05050505 0.09090909 0.12121212
##   2 0.15151515 0.26262626 0.32323232
chisq.test(dados_videos$q20, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q20 and dados_videos$q3
## X-squared = 0.045081, df = 2, p-value = 0.9777
chisq.posthoc.test(table)

Menciona alterações na rotina e nas relações sociais

table<-with(dados_videos,table(q21,q3))
table
##    q3
## q21  1  2  3
##   1 11  8 17
##   2  9 27 27
prop.table(table)
##    q3
## q21          1          2          3
##   1 0.11111111 0.08080808 0.17171717
##   2 0.09090909 0.27272727 0.27272727
chisq.test(dados_videos$q21, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q21 and dados_videos$q3
## X-squared = 5.8592, df = 2, p-value = 0.05342
chisq.posthoc.test(table)

#Menciona alterações na qualidade de vida

table<-with(dados_videos,table(q22,q3))
table
##    q3
## q22  1  2  3
##   1 11  9 18
##   2  9 26 26
prop.table(table)
##    q3
## q22          1          2          3
##   1 0.11111111 0.09090909 0.18181818
##   2 0.09090909 0.26262626 0.26262626
chisq.test(dados_videos$q22, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q22 and dados_videos$q3
## X-squared = 4.8289, df = 2, p-value = 0.08942
chisq.posthoc.test(table)

Menciona comidas e bebidas que contém glúten

table<-with(dados_videos,table(q23,q3))
table
##    q3
## q23  1  2  3
##   1  4 15 22
##   2 16 20 22
prop.table(table)
##    q3
## q23          1          2          3
##   1 0.04040404 0.15151515 0.22222222
##   2 0.16161616 0.20202020 0.22222222
chisq.test(dados_videos$q23, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q23 and dados_videos$q3
## X-squared = 5.1469, df = 2, p-value = 0.07627
chisq.posthoc.test(table)

Menciona inchaço

table<-with(dados_videos,table(q24,q3))
table
##    q3
## q24  1  2  3
##   1  7 14 22
##   2 13 21 22
prop.table(table)
##    q3
## q24          1          2          3
##   1 0.07070707 0.14141414 0.22222222
##   2 0.13131313 0.21212121 0.22222222
chisq.test(dados_videos$q24, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q24 and dados_videos$q3
## X-squared = 1.5191, df = 2, p-value = 0.4679
chisq.posthoc.test(table)

Menciona diarréia crônica

table<-with(dados_videos,table(q25,q3))
table
##    q3
## q25  1  2  3
##   1 11 20 35
##   2  9 15  9
prop.table(table)
##    q3
## q25          1          2          3
##   1 0.11111111 0.20202020 0.35353535
##   2 0.09090909 0.15151515 0.09090909
chisq.test(dados_videos$q25, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q25 and dados_videos$q3
## X-squared = 5.9377, df = 2, p-value = 0.05136
chisq.posthoc.test(table)

Menciona constipação

table<-with(dados_videos,table(q26,q3))
table
##    q3
## q26  1  2  3
##   1  5  8 15
##   2 15 27 29
prop.table(table)
##    q3
## q26          1          2          3
##   1 0.05050505 0.08080808 0.15151515
##   2 0.15151515 0.27272727 0.29292929
chisq.test(dados_videos$q26, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q26 and dados_videos$q3
## X-squared = 1.346, df = 2, p-value = 0.5102
chisq.posthoc.test(table)

Menciona náuseas

table<-with(dados_videos,table(q27,q3))
table
##    q3
## q27  1  2  3
##   1  1  1  8
##   2 19 34 36
prop.table(table)
##    q3
## q27          1          2          3
##   1 0.01010101 0.01010101 0.08080808
##   2 0.19191919 0.34343434 0.36363636
chisq.test(dados_videos$q27, dados_videos$q3)
## Warning in chisq.test(dados_videos$q27, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q27 and dados_videos$q3
## X-squared = 5.7596, df = 2, p-value = 0.05614
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona dores de estômago

table<-with(dados_videos,table(q28,q3))
table
##    q3
## q28  1  2  3
##   1  8  5 18
##   2 12 30 26
prop.table(table)
##    q3
## q28          1          2          3
##   1 0.08080808 0.05050505 0.18181818
##   2 0.12121212 0.30303030 0.26262626
chisq.test(dados_videos$q28, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q28 and dados_videos$q3
## X-squared = 7.3036, df = 2, p-value = 0.02594
chisq.posthoc.test(table)

Menciona vômitos

table<-with(dados_videos,table(q29,q3))
table
##    q3
## q29  1  2  3
##   1  5  6 14
##   2 15 29 30
prop.table(table)
##    q3
## q29          1          2          3
##   1 0.05050505 0.06060606 0.14141414
##   2 0.15151515 0.29292929 0.30303030
chisq.test(dados_videos$q29, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q29 and dados_videos$q3
## X-squared = 2.225, df = 2, p-value = 0.3287
chisq.posthoc.test(table)

Menciona dermatite herpetiforme

table<-with(dados_videos,table(q30,q3))
table
##    q3
## q30  1  2  3
##   1  3  7  6
##   2 17 28 38
prop.table(table)
##    q3
## q30          1          2          3
##   1 0.03030303 0.07070707 0.06060606
##   2 0.17171717 0.28282828 0.38383838
chisq.test(dados_videos$q30, dados_videos$q3)
## Warning in chisq.test(dados_videos$q30, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q30 and dados_videos$q3
## X-squared = 0.60757, df = 2, p-value = 0.738
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona por quê os sintomas variam entre os indivíduos

table<-with(dados_videos,table(q31,q3))
table
##    q3
## q31  1  2  3
##   1  2  3  9
##   2 18 32 35
prop.table(table)
##    q3
## q31          1          2          3
##   1 0.02020202 0.03030303 0.09090909
##   2 0.18181818 0.32323232 0.35353535
chisq.test(dados_videos$q31, dados_videos$q3)
## Warning in chisq.test(dados_videos$q31, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q31 and dados_videos$q3
## X-squared = 2.6212, df = 2, p-value = 0.2697
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona os sintomas na criança

table<-with(dados_videos,table(q32,q3))
table
##    q3
## q32  1  2  3
##   1  6 11 16
##   2 14 24 28
prop.table(table)
##    q3
## q32          1          2          3
##   1 0.06060606 0.11111111 0.16161616
##   2 0.14141414 0.24242424 0.28282828
chisq.test(dados_videos$q32, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q32 and dados_videos$q3
## X-squared = 0.33896, df = 2, p-value = 0.8441
chisq.posthoc.test(table)

Menciona como é diagnosticada a DC

table<-with(dados_videos,table(q33,q3))
table
##    q3
## q33  1  2  3
##   1 10 17 29
##   2 10 18 15
prop.table(table)
##    q3
## q33         1         2         3
##   1 0.1010101 0.1717172 0.2929293
##   2 0.1010101 0.1818182 0.1515152
chisq.test(dados_videos$q33, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q33 and dados_videos$q3
## X-squared = 2.8248, df = 2, p-value = 0.2436
chisq.posthoc.test(table)

Menciona exames de sangue como forma de diagnóstico

table<-with(dados_videos,table(q34,q3))
table
##    q3
## q34  1  2  3
##   1  6 14 25
##   2 14 21 19
prop.table(table)
##    q3
## q34          1          2          3
##   1 0.06060606 0.14141414 0.25252525
##   2 0.14141414 0.21212121 0.19191919
chisq.test(dados_videos$q34, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q34 and dados_videos$q3
## X-squared = 4.6383, df = 2, p-value = 0.09836
chisq.posthoc.test(table)

Menciona a utilização da endoscopia como diagnóstico

table<-with(dados_videos,table(q35,q3))
table
##    q3
## q35  1  2  3
##   1  6 12 16
##   2 14 23 28
prop.table(table)
##    q3
## q35          1          2          3
##   1 0.06060606 0.12121212 0.16161616
##   2 0.14141414 0.23232323 0.28282828
chisq.test(dados_videos$q35, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q35 and dados_videos$q3
## X-squared = 0.24702, df = 2, p-value = 0.8838
chisq.posthoc.test(table)

Menciona a importância da testagem/rastreamento para outros membros da família

table<-with(dados_videos,table(q36,q3))
table
##    q3
## q36  1  2  3
##   1  4  3 12
##   2 16 32 32
prop.table(table)
##    q3
## q36          1          2          3
##   1 0.04040404 0.03030303 0.12121212
##   2 0.16161616 0.32323232 0.32323232
chisq.test(dados_videos$q36, dados_videos$q3)
## Warning in chisq.test(dados_videos$q36, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q36 and dados_videos$q3
## X-squared = 4.4066, df = 2, p-value = 0.1104
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona tratamento para DC

table<-with(dados_videos,table(q37,q3))
table
##    q3
## q37  1  2  3
##   1  8 22 31
##   2 12 13 13
prop.table(table)
##    q3
## q37          1          2          3
##   1 0.08080808 0.22222222 0.31313131
##   2 0.12121212 0.13131313 0.13131313
chisq.test(dados_videos$q37, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q37 and dados_videos$q3
## X-squared = 5.4274, df = 2, p-value = 0.06629
chisq.posthoc.test(table)

Menciona dieta isenta de glúten, tanto para bebidas como para comidas

table<-with(dados_videos,table(q38,q3))
table
##    q3
## q38  1  2  3
##   1  4 11 20
##   2 16 24 24
prop.table(table)
##    q3
## q38          1          2          3
##   1 0.04040404 0.11111111 0.20202020
##   2 0.16161616 0.24242424 0.24242424
chisq.test(dados_videos$q38, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q38 and dados_videos$q3
## X-squared = 4.2631, df = 2, p-value = 0.1187
chisq.posthoc.test(table)

Menciona medicações isentas de glúten

table<-with(dados_videos,table(q39,q3))
table
##    q3
## q39  1  2  3
##   1  0  4  4
##   2 20 31 40
prop.table(table)
##    q3
## q39          1          2          3
##   1 0.00000000 0.04040404 0.04040404
##   2 0.20202020 0.31313131 0.40404040
chisq.test(dados_videos$q39, dados_videos$q3)
## Warning in chisq.test(dados_videos$q39, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q39 and dados_videos$q3
## X-squared = 2.3468, df = 2, p-value = 0.3093
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona seguimento anual para avaliação e reavaliação das instruções do tratamento

table<-with(dados_videos,table(q40,q3))
table
##    q3
## q40  1  2  3
##   1  0  0  6
##   2 20 35 38
prop.table(table)
##    q3
## q40          1          2          3
##   1 0.00000000 0.00000000 0.06060606
##   2 0.20202020 0.35353535 0.38383838
chisq.test(dados_videos$q40, dados_videos$q3)
## Warning in chisq.test(dados_videos$q40, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q40 and dados_videos$q3
## X-squared = 7.9839, df = 2, p-value = 0.01846
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect

Menciona potenciais complicações se o plano de tratamento não for seguido

table<-with(dados_videos,table(q41,q3))
table
##    q3
## q41  1  2  3
##   1  7  8 22
##   2 13 27 22
prop.table(table)
##    q3
## q41          1          2          3
##   1 0.07070707 0.08080808 0.22222222
##   2 0.13131313 0.27272727 0.22222222
chisq.test(dados_videos$q41, dados_videos$q3)
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q41 and dados_videos$q3
## X-squared = 6.1963, df = 2, p-value = 0.04513
chisq.posthoc.test(table)

#ano de publicação

table<-with(dados_videos,table(q1,q3))
table
##    q3
## q1   1  2  3
##   2  2  4  7
##   3  5  8 13
##   4  6  8  6
##   5  4 14 14
##   6  3  1  4
prop.table(table)
##    q3
## q1           1          2          3
##   2 0.02020202 0.04040404 0.07070707
##   3 0.05050505 0.08080808 0.13131313
##   4 0.06060606 0.08080808 0.06060606
##   5 0.04040404 0.14141414 0.14141414
##   6 0.03030303 0.01010101 0.04040404
chisq.test(dados_videos$q1, dados_videos$q3)
## Warning in chisq.test(dados_videos$q1, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q1 and dados_videos$q3
## X-squared = 6.8474, df = 8, p-value = 0.5532
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.003333333
qnorm(sigAdj/2)
## [1] -2.935199

#sexo

table<-with(dados_videos,table(q2,q3))
table
##    q3
## q2   1  2  3
##   1  1 16  5
##   2 17 11 15
##   3  0  2 21
##   4  2  6  3
prop.table(table)
##    q3
## q2           1          2          3
##   1 0.01010101 0.16161616 0.05050505
##   2 0.17171717 0.11111111 0.15151515
##   3 0.00000000 0.02020202 0.21212121
##   4 0.02020202 0.06060606 0.03030303
chisq.test(dados_videos$q2, dados_videos$q3)
## Warning in chisq.test(dados_videos$q2, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q2 and dados_videos$q3
## X-squared = 46.229, df = 6, p-value = 2.666e-08
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.004166667
qnorm(sigAdj/2)
## [1] -2.86526

#número de visualizações

table<-with(dados_videos,table(q4,q3))
table
##         q3
## q4       1 2 3
##   1456   1 0 0
##   1570   0 0 1
##   1635   0 0 1
##   1725   0 0 1
##   1729   0 1 1
##   1753   0 0 1
##   1763   0 0 1
##   1769   0 0 1
##   1774   0 1 0
##   1786   0 0 1
##   1791   1 0 0
##   1797   0 0 1
##   1808   0 1 0
##   1828   1 0 0
##   1829   1 0 0
##   1852   0 0 1
##   1890   0 0 1
##   1953   0 0 1
##   1984   1 0 0
##   1994   0 1 0
##   2007   1 0 0
##   2018   0 1 0
##   2034   0 0 1
##   2041   0 1 0
##   2287   0 1 0
##   2292   0 1 0
##   2331   0 1 0
##   2340   1 0 0
##   2367   0 0 1
##   2396   0 1 0
##   2567   0 0 1
##   2611   1 0 0
##   2674   0 0 1
##   3046   1 0 0
##   3155   0 0 1
##   3189   0 0 1
##   3229   0 0 1
##   3270   0 1 0
##   3341   0 1 0
##   3488   0 1 0
##   3577   0 0 1
##   3626   0 0 1
##   3703   0 1 0
##   3714   0 0 1
##   3728   0 1 0
##   3860   0 0 1
##   4673   1 0 0
##   5219   1 0 0
##   5326   1 0 0
##   5351   0 0 1
##   5492   0 0 1
##   5781   0 0 1
##   6273   0 0 1
##   6285   0 0 1
##   6725   0 1 0
##   6934   0 0 1
##   7119   0 1 0
##   7184   0 0 1
##   7626   0 0 1
##   7885   0 0 1
##   7970   0 0 1
##   8819   1 0 0
##   9176   0 1 0
##   9299   0 0 1
##   9782   0 1 0
##   9797   0 1 0
##   9836   0 1 0
##   10205  1 0 0
##   10924  0 0 1
##   11144  0 0 1
##   11516  0 1 0
##   12128  0 1 0
##   13498  0 0 1
##   14084  0 0 1
##   15360  0 0 1
##   16716  0 1 0
##   17032  1 0 0
##   17185  0 0 1
##   17750  0 0 1
##   20554  0 0 1
##   21750  1 0 0
##   22101  0 1 0
##   25522  0 0 1
##   25529  1 0 0
##   28737  1 0 0
##   28771  0 0 1
##   32618  0 1 0
##   34722  0 1 0
##   46725  1 0 0
##   61239  0 1 0
##   63895  0 1 0
##   74243  0 1 0
##   78564  0 1 0
##   79413  1 0 0
##   93865  0 1 0
##   138438 0 1 0
##   298713 0 1 0
##   613011 0 1 0
prop.table(table)
##         q3
## q4                1          2          3
##   1456   0.01010101 0.00000000 0.00000000
##   1570   0.00000000 0.00000000 0.01010101
##   1635   0.00000000 0.00000000 0.01010101
##   1725   0.00000000 0.00000000 0.01010101
##   1729   0.00000000 0.01010101 0.01010101
##   1753   0.00000000 0.00000000 0.01010101
##   1763   0.00000000 0.00000000 0.01010101
##   1769   0.00000000 0.00000000 0.01010101
##   1774   0.00000000 0.01010101 0.00000000
##   1786   0.00000000 0.00000000 0.01010101
##   1791   0.01010101 0.00000000 0.00000000
##   1797   0.00000000 0.00000000 0.01010101
##   1808   0.00000000 0.01010101 0.00000000
##   1828   0.01010101 0.00000000 0.00000000
##   1829   0.01010101 0.00000000 0.00000000
##   1852   0.00000000 0.00000000 0.01010101
##   1890   0.00000000 0.00000000 0.01010101
##   1953   0.00000000 0.00000000 0.01010101
##   1984   0.01010101 0.00000000 0.00000000
##   1994   0.00000000 0.01010101 0.00000000
##   2007   0.01010101 0.00000000 0.00000000
##   2018   0.00000000 0.01010101 0.00000000
##   2034   0.00000000 0.00000000 0.01010101
##   2041   0.00000000 0.01010101 0.00000000
##   2287   0.00000000 0.01010101 0.00000000
##   2292   0.00000000 0.01010101 0.00000000
##   2331   0.00000000 0.01010101 0.00000000
##   2340   0.01010101 0.00000000 0.00000000
##   2367   0.00000000 0.00000000 0.01010101
##   2396   0.00000000 0.01010101 0.00000000
##   2567   0.00000000 0.00000000 0.01010101
##   2611   0.01010101 0.00000000 0.00000000
##   2674   0.00000000 0.00000000 0.01010101
##   3046   0.01010101 0.00000000 0.00000000
##   3155   0.00000000 0.00000000 0.01010101
##   3189   0.00000000 0.00000000 0.01010101
##   3229   0.00000000 0.00000000 0.01010101
##   3270   0.00000000 0.01010101 0.00000000
##   3341   0.00000000 0.01010101 0.00000000
##   3488   0.00000000 0.01010101 0.00000000
##   3577   0.00000000 0.00000000 0.01010101
##   3626   0.00000000 0.00000000 0.01010101
##   3703   0.00000000 0.01010101 0.00000000
##   3714   0.00000000 0.00000000 0.01010101
##   3728   0.00000000 0.01010101 0.00000000
##   3860   0.00000000 0.00000000 0.01010101
##   4673   0.01010101 0.00000000 0.00000000
##   5219   0.01010101 0.00000000 0.00000000
##   5326   0.01010101 0.00000000 0.00000000
##   5351   0.00000000 0.00000000 0.01010101
##   5492   0.00000000 0.00000000 0.01010101
##   5781   0.00000000 0.00000000 0.01010101
##   6273   0.00000000 0.00000000 0.01010101
##   6285   0.00000000 0.00000000 0.01010101
##   6725   0.00000000 0.01010101 0.00000000
##   6934   0.00000000 0.00000000 0.01010101
##   7119   0.00000000 0.01010101 0.00000000
##   7184   0.00000000 0.00000000 0.01010101
##   7626   0.00000000 0.00000000 0.01010101
##   7885   0.00000000 0.00000000 0.01010101
##   7970   0.00000000 0.00000000 0.01010101
##   8819   0.01010101 0.00000000 0.00000000
##   9176   0.00000000 0.01010101 0.00000000
##   9299   0.00000000 0.00000000 0.01010101
##   9782   0.00000000 0.01010101 0.00000000
##   9797   0.00000000 0.01010101 0.00000000
##   9836   0.00000000 0.01010101 0.00000000
##   10205  0.01010101 0.00000000 0.00000000
##   10924  0.00000000 0.00000000 0.01010101
##   11144  0.00000000 0.00000000 0.01010101
##   11516  0.00000000 0.01010101 0.00000000
##   12128  0.00000000 0.01010101 0.00000000
##   13498  0.00000000 0.00000000 0.01010101
##   14084  0.00000000 0.00000000 0.01010101
##   15360  0.00000000 0.00000000 0.01010101
##   16716  0.00000000 0.01010101 0.00000000
##   17032  0.01010101 0.00000000 0.00000000
##   17185  0.00000000 0.00000000 0.01010101
##   17750  0.00000000 0.00000000 0.01010101
##   20554  0.00000000 0.00000000 0.01010101
##   21750  0.01010101 0.00000000 0.00000000
##   22101  0.00000000 0.01010101 0.00000000
##   25522  0.00000000 0.00000000 0.01010101
##   25529  0.01010101 0.00000000 0.00000000
##   28737  0.01010101 0.00000000 0.00000000
##   28771  0.00000000 0.00000000 0.01010101
##   32618  0.00000000 0.01010101 0.00000000
##   34722  0.00000000 0.01010101 0.00000000
##   46725  0.01010101 0.00000000 0.00000000
##   61239  0.00000000 0.01010101 0.00000000
##   63895  0.00000000 0.01010101 0.00000000
##   74243  0.00000000 0.01010101 0.00000000
##   78564  0.00000000 0.01010101 0.00000000
##   79413  0.01010101 0.00000000 0.00000000
##   93865  0.00000000 0.01010101 0.00000000
##   138438 0.00000000 0.01010101 0.00000000
##   298713 0.00000000 0.01010101 0.00000000
##   613011 0.00000000 0.01010101 0.00000000
chisq.test(dados_videos$q4, dados_videos$q3)
## Warning in chisq.test(dados_videos$q4, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q4 and dados_videos$q3
## X-squared = 195.46, df = 194, p-value = 0.4571
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.000170068
qnorm(sigAdj/2)
## [1] -3.759772

#duração do vídeo

table<-with(dados_videos,table(q5,q3))
table
##       q3
## q5     1 2 3
##   35   0 0 2
##   51   0 1 0
##   62   0 0 1
##   71   0 1 0
##   76   0 0 1
##   95   0 0 1
##   103  0 1 0
##   108  0 0 1
##   144  0 0 1
##   149  0 1 0
##   156  1 0 0
##   157  1 0 0
##   161  0 1 0
##   163  0 0 1
##   182  0 1 0
##   187  0 1 0
##   206  0 1 0
##   208  0 1 0
##   210  0 0 1
##   219  0 1 0
##   225  0 0 1
##   226  0 1 0
##   257  0 0 1
##   284  0 1 0
##   286  0 1 0
##   303  0 2 0
##   307  1 0 0
##   317  0 0 1
##   319  0 1 0
##   343  0 0 1
##   359  0 0 1
##   367  0 1 0
##   376  0 0 1
##   385  0 1 0
##   388  0 0 1
##   391  1 0 0
##   395  0 0 1
##   397  0 1 0
##   402  1 0 0
##   404  1 0 0
##   414  1 0 0
##   421  0 1 0
##   434  0 0 1
##   451  0 0 1
##   466  0 0 1
##   471  0 0 1
##   479  0 1 0
##   487  1 0 0
##   489  0 1 0
##   493  0 1 0
##   497  1 0 0
##   506  0 0 1
##   512  0 1 0
##   532  0 0 1
##   535  0 0 1
##   553  0 0 1
##   558  0 1 0
##   559  1 0 0
##   562  1 0 0
##   563  0 1 0
##   577  0 0 1
##   586  0 1 0
##   605  0 0 1
##   635  1 0 0
##   636  1 0 0
##   650  0 1 0
##   659  0 0 1
##   662  0 0 1
##   709  1 0 0
##   716  0 1 0
##   794  1 0 0
##   797  2 0 0
##   821  0 0 1
##   852  0 1 0
##   868  1 0 0
##   887  0 0 1
##   888  0 0 1
##   896  0 0 1
##   965  0 0 1
##   1105 1 0 0
##   1131 0 0 1
##   1253 0 0 1
##   1471 0 0 1
##   1472 1 0 0
##   1651 0 0 1
##   1700 0 0 1
##   1920 0 1 0
##   1947 0 1 0
##   2065 0 1 0
##   2856 0 0 1
##   2867 0 1 0
##   2912 0 0 1
##   3238 0 0 1
##   3279 0 0 1
##   3515 0 0 1
##   3717 0 1 0
prop.table(table)
##       q3
## q5              1          2          3
##   35   0.00000000 0.00000000 0.02020202
##   51   0.00000000 0.01010101 0.00000000
##   62   0.00000000 0.00000000 0.01010101
##   71   0.00000000 0.01010101 0.00000000
##   76   0.00000000 0.00000000 0.01010101
##   95   0.00000000 0.00000000 0.01010101
##   103  0.00000000 0.01010101 0.00000000
##   108  0.00000000 0.00000000 0.01010101
##   144  0.00000000 0.00000000 0.01010101
##   149  0.00000000 0.01010101 0.00000000
##   156  0.01010101 0.00000000 0.00000000
##   157  0.01010101 0.00000000 0.00000000
##   161  0.00000000 0.01010101 0.00000000
##   163  0.00000000 0.00000000 0.01010101
##   182  0.00000000 0.01010101 0.00000000
##   187  0.00000000 0.01010101 0.00000000
##   206  0.00000000 0.01010101 0.00000000
##   208  0.00000000 0.01010101 0.00000000
##   210  0.00000000 0.00000000 0.01010101
##   219  0.00000000 0.01010101 0.00000000
##   225  0.00000000 0.00000000 0.01010101
##   226  0.00000000 0.01010101 0.00000000
##   257  0.00000000 0.00000000 0.01010101
##   284  0.00000000 0.01010101 0.00000000
##   286  0.00000000 0.01010101 0.00000000
##   303  0.00000000 0.02020202 0.00000000
##   307  0.01010101 0.00000000 0.00000000
##   317  0.00000000 0.00000000 0.01010101
##   319  0.00000000 0.01010101 0.00000000
##   343  0.00000000 0.00000000 0.01010101
##   359  0.00000000 0.00000000 0.01010101
##   367  0.00000000 0.01010101 0.00000000
##   376  0.00000000 0.00000000 0.01010101
##   385  0.00000000 0.01010101 0.00000000
##   388  0.00000000 0.00000000 0.01010101
##   391  0.01010101 0.00000000 0.00000000
##   395  0.00000000 0.00000000 0.01010101
##   397  0.00000000 0.01010101 0.00000000
##   402  0.01010101 0.00000000 0.00000000
##   404  0.01010101 0.00000000 0.00000000
##   414  0.01010101 0.00000000 0.00000000
##   421  0.00000000 0.01010101 0.00000000
##   434  0.00000000 0.00000000 0.01010101
##   451  0.00000000 0.00000000 0.01010101
##   466  0.00000000 0.00000000 0.01010101
##   471  0.00000000 0.00000000 0.01010101
##   479  0.00000000 0.01010101 0.00000000
##   487  0.01010101 0.00000000 0.00000000
##   489  0.00000000 0.01010101 0.00000000
##   493  0.00000000 0.01010101 0.00000000
##   497  0.01010101 0.00000000 0.00000000
##   506  0.00000000 0.00000000 0.01010101
##   512  0.00000000 0.01010101 0.00000000
##   532  0.00000000 0.00000000 0.01010101
##   535  0.00000000 0.00000000 0.01010101
##   553  0.00000000 0.00000000 0.01010101
##   558  0.00000000 0.01010101 0.00000000
##   559  0.01010101 0.00000000 0.00000000
##   562  0.01010101 0.00000000 0.00000000
##   563  0.00000000 0.01010101 0.00000000
##   577  0.00000000 0.00000000 0.01010101
##   586  0.00000000 0.01010101 0.00000000
##   605  0.00000000 0.00000000 0.01010101
##   635  0.01010101 0.00000000 0.00000000
##   636  0.01010101 0.00000000 0.00000000
##   650  0.00000000 0.01010101 0.00000000
##   659  0.00000000 0.00000000 0.01010101
##   662  0.00000000 0.00000000 0.01010101
##   709  0.01010101 0.00000000 0.00000000
##   716  0.00000000 0.01010101 0.00000000
##   794  0.01010101 0.00000000 0.00000000
##   797  0.02020202 0.00000000 0.00000000
##   821  0.00000000 0.00000000 0.01010101
##   852  0.00000000 0.01010101 0.00000000
##   868  0.01010101 0.00000000 0.00000000
##   887  0.00000000 0.00000000 0.01010101
##   888  0.00000000 0.00000000 0.01010101
##   896  0.00000000 0.00000000 0.01010101
##   965  0.00000000 0.00000000 0.01010101
##   1105 0.01010101 0.00000000 0.00000000
##   1131 0.00000000 0.00000000 0.01010101
##   1253 0.00000000 0.00000000 0.01010101
##   1471 0.00000000 0.00000000 0.01010101
##   1472 0.01010101 0.00000000 0.00000000
##   1651 0.00000000 0.00000000 0.01010101
##   1700 0.00000000 0.00000000 0.01010101
##   1920 0.00000000 0.01010101 0.00000000
##   1947 0.00000000 0.01010101 0.00000000
##   2065 0.00000000 0.01010101 0.00000000
##   2856 0.00000000 0.00000000 0.01010101
##   2867 0.00000000 0.01010101 0.00000000
##   2912 0.00000000 0.00000000 0.01010101
##   3238 0.00000000 0.00000000 0.01010101
##   3279 0.00000000 0.00000000 0.01010101
##   3515 0.00000000 0.00000000 0.01010101
##   3717 0.00000000 0.01010101 0.00000000
chisq.test(dados_videos$q5, dados_videos$q3)
## Warning in chisq.test(dados_videos$q5, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q5 and dados_videos$q3
## X-squared = 198, df = 190, p-value = 0.3304
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.0001736111
qnorm(sigAdj/2)
## [1] -3.754611

#curtidas

table<-with(dados_videos,table(q6,q3))
table
##        q3
## q6      1 2 3
##   0     1 1 2
##   16    0 1 1
##   17    0 0 1
##   22    0 0 1
##   23    0 0 1
##   24    0 0 1
##   31    0 1 0
##   32    0 0 1
##   36    1 0 1
##   39    0 0 1
##   41    0 0 1
##   42    0 0 2
##   43    0 0 1
##   44    1 0 0
##   45    0 0 1
##   50    0 1 0
##   51    0 1 0
##   52    0 0 1
##   54    0 1 0
##   55    0 0 1
##   57    0 1 0
##   62    0 1 0
##   67    0 0 1
##   69    0 0 1
##   70    0 0 1
##   71    1 0 0
##   72    0 0 1
##   77    0 1 1
##   90    0 0 1
##   92    0 0 1
##   98    0 1 0
##   108   0 0 1
##   116   0 1 0
##   118   0 1 0
##   123   2 0 0
##   131   0 1 1
##   134   0 0 1
##   142   0 1 0
##   146   0 0 2
##   156   0 1 0
##   164   0 0 1
##   167   1 0 0
##   173   0 1 0
##   175   0 0 1
##   178   0 1 0
##   228   0 0 1
##   240   1 0 0
##   256   2 2 0
##   266   1 0 1
##   284   0 0 1
##   294   1 0 0
##   302   0 1 0
##   339   0 1 0
##   342   0 0 1
##   387   0 1 0
##   416   0 0 1
##   435   0 0 1
##   490   1 0 0
##   535   1 0 0
##   547   0 1 0
##   610   0 0 1
##   651   0 0 1
##   709   0 0 1
##   716   0 1 0
##   768   0 0 1
##   856   0 0 1
##   862   0 1 0
##   883   0 0 1
##   918   1 0 0
##   984   1 0 0
##   1200  0 0 1
##   1300  1 0 0
##   1500  1 0 0
##   2100  0 1 0
##   2900  1 0 0
##   3200  0 1 0
##   4300  0 1 0
##   5400  1 0 0
##   5600  0 1 0
##   5700  0 1 0
##   6800  0 1 0
##   7200  0 1 0
##   9200  0 1 0
##   12000 0 1 0
##   22000 0 1 0
prop.table(table)
##        q3
## q6               1          2          3
##   0     0.01010101 0.01010101 0.02020202
##   16    0.00000000 0.01010101 0.01010101
##   17    0.00000000 0.00000000 0.01010101
##   22    0.00000000 0.00000000 0.01010101
##   23    0.00000000 0.00000000 0.01010101
##   24    0.00000000 0.00000000 0.01010101
##   31    0.00000000 0.01010101 0.00000000
##   32    0.00000000 0.00000000 0.01010101
##   36    0.01010101 0.00000000 0.01010101
##   39    0.00000000 0.00000000 0.01010101
##   41    0.00000000 0.00000000 0.01010101
##   42    0.00000000 0.00000000 0.02020202
##   43    0.00000000 0.00000000 0.01010101
##   44    0.01010101 0.00000000 0.00000000
##   45    0.00000000 0.00000000 0.01010101
##   50    0.00000000 0.01010101 0.00000000
##   51    0.00000000 0.01010101 0.00000000
##   52    0.00000000 0.00000000 0.01010101
##   54    0.00000000 0.01010101 0.00000000
##   55    0.00000000 0.00000000 0.01010101
##   57    0.00000000 0.01010101 0.00000000
##   62    0.00000000 0.01010101 0.00000000
##   67    0.00000000 0.00000000 0.01010101
##   69    0.00000000 0.00000000 0.01010101
##   70    0.00000000 0.00000000 0.01010101
##   71    0.01010101 0.00000000 0.00000000
##   72    0.00000000 0.00000000 0.01010101
##   77    0.00000000 0.01010101 0.01010101
##   90    0.00000000 0.00000000 0.01010101
##   92    0.00000000 0.00000000 0.01010101
##   98    0.00000000 0.01010101 0.00000000
##   108   0.00000000 0.00000000 0.01010101
##   116   0.00000000 0.01010101 0.00000000
##   118   0.00000000 0.01010101 0.00000000
##   123   0.02020202 0.00000000 0.00000000
##   131   0.00000000 0.01010101 0.01010101
##   134   0.00000000 0.00000000 0.01010101
##   142   0.00000000 0.01010101 0.00000000
##   146   0.00000000 0.00000000 0.02020202
##   156   0.00000000 0.01010101 0.00000000
##   164   0.00000000 0.00000000 0.01010101
##   167   0.01010101 0.00000000 0.00000000
##   173   0.00000000 0.01010101 0.00000000
##   175   0.00000000 0.00000000 0.01010101
##   178   0.00000000 0.01010101 0.00000000
##   228   0.00000000 0.00000000 0.01010101
##   240   0.01010101 0.00000000 0.00000000
##   256   0.02020202 0.02020202 0.00000000
##   266   0.01010101 0.00000000 0.01010101
##   284   0.00000000 0.00000000 0.01010101
##   294   0.01010101 0.00000000 0.00000000
##   302   0.00000000 0.01010101 0.00000000
##   339   0.00000000 0.01010101 0.00000000
##   342   0.00000000 0.00000000 0.01010101
##   387   0.00000000 0.01010101 0.00000000
##   416   0.00000000 0.00000000 0.01010101
##   435   0.00000000 0.00000000 0.01010101
##   490   0.01010101 0.00000000 0.00000000
##   535   0.01010101 0.00000000 0.00000000
##   547   0.00000000 0.01010101 0.00000000
##   610   0.00000000 0.00000000 0.01010101
##   651   0.00000000 0.00000000 0.01010101
##   709   0.00000000 0.00000000 0.01010101
##   716   0.00000000 0.01010101 0.00000000
##   768   0.00000000 0.00000000 0.01010101
##   856   0.00000000 0.00000000 0.01010101
##   862   0.00000000 0.01010101 0.00000000
##   883   0.00000000 0.00000000 0.01010101
##   918   0.01010101 0.00000000 0.00000000
##   984   0.01010101 0.00000000 0.00000000
##   1200  0.00000000 0.00000000 0.01010101
##   1300  0.01010101 0.00000000 0.00000000
##   1500  0.01010101 0.00000000 0.00000000
##   2100  0.00000000 0.01010101 0.00000000
##   2900  0.01010101 0.00000000 0.00000000
##   3200  0.00000000 0.01010101 0.00000000
##   4300  0.00000000 0.01010101 0.00000000
##   5400  0.01010101 0.00000000 0.00000000
##   5600  0.00000000 0.01010101 0.00000000
##   5700  0.00000000 0.01010101 0.00000000
##   6800  0.00000000 0.01010101 0.00000000
##   7200  0.00000000 0.01010101 0.00000000
##   9200  0.00000000 0.01010101 0.00000000
##   12000 0.00000000 0.01010101 0.00000000
##   22000 0.00000000 0.01010101 0.00000000
chisq.test(dados_videos$q6, dados_videos$q3)
## Warning in chisq.test(dados_videos$q6, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q6 and dados_videos$q3
## X-squared = 167.32, df = 168, p-value = 0.5003
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.0001960784
qnorm(sigAdj/2)
## [1] -3.724016

#nao gostgei

table<-with(dados_videos,table(q7,q3))
table
##       q3
## q7      1  2  3
##   0     2  4 12
##   1     3  3  6
##   2     3  3  4
##   3     0  5  3
##   4     0  3  4
##   5     0  1  3
##   6     1  2  0
##   7     1  1  1
##   8     2  0  1
##   9     1  0  2
##   10    0  1  1
##   11    0  0  2
##   13    1  0  1
##   14    0  0  1
##   16    0  0  1
##   17    1  0  0
##   18    1  1  1
##   19    1  0  0
##   23    0  1  0
##   24    0  1  0
##   25    0  0  1
##   30    1  0  0
##   44    0  1  0
##   47    1  0  0
##   60    0  1  0
##   64    1  0  0
##   65    0  1  0
##   80    0  1  0
##   84    0  1  0
##   89    0  1  0
##   98    0  1  0
##   199   0  1  0
##   1400  0  1  0
prop.table(table)
##       q3
## q7              1          2          3
##   0    0.02020202 0.04040404 0.12121212
##   1    0.03030303 0.03030303 0.06060606
##   2    0.03030303 0.03030303 0.04040404
##   3    0.00000000 0.05050505 0.03030303
##   4    0.00000000 0.03030303 0.04040404
##   5    0.00000000 0.01010101 0.03030303
##   6    0.01010101 0.02020202 0.00000000
##   7    0.01010101 0.01010101 0.01010101
##   8    0.02020202 0.00000000 0.01010101
##   9    0.01010101 0.00000000 0.02020202
##   10   0.00000000 0.01010101 0.01010101
##   11   0.00000000 0.00000000 0.02020202
##   13   0.01010101 0.00000000 0.01010101
##   14   0.00000000 0.00000000 0.01010101
##   16   0.00000000 0.00000000 0.01010101
##   17   0.01010101 0.00000000 0.00000000
##   18   0.01010101 0.01010101 0.01010101
##   19   0.01010101 0.00000000 0.00000000
##   23   0.00000000 0.01010101 0.00000000
##   24   0.00000000 0.01010101 0.00000000
##   25   0.00000000 0.00000000 0.01010101
##   30   0.01010101 0.00000000 0.00000000
##   44   0.00000000 0.01010101 0.00000000
##   47   0.01010101 0.00000000 0.00000000
##   60   0.00000000 0.01010101 0.00000000
##   64   0.01010101 0.00000000 0.00000000
##   65   0.00000000 0.01010101 0.00000000
##   80   0.00000000 0.01010101 0.00000000
##   84   0.00000000 0.01010101 0.00000000
##   89   0.00000000 0.01010101 0.00000000
##   98   0.00000000 0.01010101 0.00000000
##   199  0.00000000 0.01010101 0.00000000
##   1400 0.00000000 0.01010101 0.00000000
chisq.test(dados_videos$q7, dados_videos$q3)
## Warning in chisq.test(dados_videos$q7, dados_videos$q3): Chi-squared
## approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  dados_videos$q7 and dados_videos$q3
## X-squared = 69.079, df = 64, p-value = 0.3099
chisq.posthoc.test(table)
## Warning in chisq.test(x, ...): Chi-squared approximation may be incorrect
sig<-.05
sigAdj<-sig/(nrow(table)*ncol(table))
sigAdj
## [1] 0.0005050505
qnorm(sigAdj/2)
## [1] -3.478063