ESPECIALIZAÇÃO CIÊNCIA DE DADOS - T. 01

Projeto Final

Carregando Pacotes

vetor_pacotes=c("readr",
                "ggplot2",
                "plotly",
                "e1071",
                "dplyr",
                "Hmisc",
                "DescTools",
                "esquisse",
                "kableExtra",
                "gridExtra",
                "e1071",
                "devtools"
)
# install.packages(vetor_pacotes)
lapply(vetor_pacotes, 
       require, 
       character.only = TRUE)

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Questão A

QUESTAO A - IMPORTAÇÃO PARA DO R DA BASE MICRODADOS_ENADE_2017.txt

enade2017 = read_csv2("MICRODADOS_ENADE_2017.txt")

Questão B

QUESTAO B - Faca as estatisticas descritivas do seu banco (Resumo geral), avaliando se hávariáveis faltantes ou não, e se existirem, elimine-as

microdados_enade_filtrados= enade2017 %>% dplyr::select(
  CO_GRUPO,
  CO_REGIAO_CURSO,
  NU_IDADE,
  TP_SEXO,
  CO_TURNO_GRADUACAO,
  NT_GER,
  QE_I01,
  QE_I02,
  QE_I08,
  QE_I21,
  QE_I23,
  NT_OBJ_FG, 
  NT_OBJ_CE
) 

#Verificando a quantidade de valores faltantes (NAs) em cada variavel
resumo_nas = microdados_enade_filtrados %>%
  select(everything()) %>%
  summarise_all(list(~ sum(is.na(.))))
#View(resumo_nas)

#Removendo NAS De Todas As Variaveis, que possuem NA
microdados_sem_NA = microdados_enade_filtrados %>% na.omit()

#resumo_nas = microdados_ti_sem_NA %>%
#select(everything()) %>%
#summarise_all(list(~ sum(is.na(.))))
#View(resumo_nas)

#Estatisticas descritivas da variavel NT_GER

#Consideramos 

microdados_sem_NA %>%
  select(NT_GER) %>%
  summarise(
    quantidade = n(),
    media = mean(NT_GER),
    mediana = median(NT_GER),
    Q3 = quantile(NT_GER,.75),
    moda = Mode(NT_GER),
    cv = sd(NT_GER) / media * 100,
    assimetria = skewness(NT_GER),
    curtose = kurtosis(NT_GER)
  ) %>%
  arrange(desc(mediana))

## # A tibble: 1 x 8
##   quantidade media mediana    Q3  moda    cv assimetria curtose
##        <int> <dbl>   <dbl> <dbl> <dbl> <dbl>      <dbl>   <dbl>
## 1     442496  43.6      43  53.2  44.6  32.2      0.162  -0.239

Questão C

QUESTAO C - Utilize a variavel NT_GER para calcular a média geral das notas dos alunos de todos os cursos do ENADE 2017

microdados_sem_NA %>%
  select(NT_GER) %>%
  summarise(
    media = mean(NT_GER),
  ) %>%
  arrange(desc(media))

## # A tibble: 1 x 1
##   media
##   <dbl>
## 1  43.6

Questão D

QUESTAO D - Escolha um curso a sua escolha, calculando a média de notas de alunos do mesmo

microdados_ti = microdados_sem_NA %>% filter(CO_GRUPO==79) 

MediaNota <- microdados_ti %>%
  select(NT_GER) %>%
  summarise(
    media = mean(NT_GER),
  ) %>%
  arrange(desc(media))

MediaNota

## # A tibble: 1 x 1
##   media
##   <dbl>
## 1  33.7

Questão E

QUESTAO E - Calcule o desvio padrão amostral das notas dos alunos do curso escolhido em D

#Amostra
a0= microdados_ti$NT_GER
length(a0)

## [1] 2480

sort(a0)

##    [1]  0.0  0.0  3.2  3.8  5.3  5.9  7.5  7.5  7.5  7.5  7.5  7.5  8.0  8.4
##   [15]  8.6  8.7  9.4  9.4  9.4  9.6 10.0 10.1 10.2 10.7 10.8 10.9 11.0 11.3
##   [29] 11.3 11.3 11.3 11.3 11.3 11.3 11.3 11.3 11.3 11.3 11.3 11.5 11.5 11.6
##   [43] 11.6 11.7 11.8 11.9 12.1 12.3 12.4 12.7 12.7 12.7 13.0 13.0 13.1 13.1
##   [57] 13.1 13.1 13.1 13.1 13.1 13.1 13.1 13.1 13.1 13.1 13.2 13.2 13.3 13.4
##   [71] 13.4 13.5 13.6 13.7 13.7 13.8 13.9 14.1 14.1 14.1 14.1 14.2 14.3 14.4
##   [85] 14.5 14.6 14.6 14.7 14.7 14.7 14.8 14.9 14.9 15.0 15.0 15.0 15.0 15.0
##   [99] 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.1 15.3 15.3 15.4 15.4 15.4
##  [113] 15.4 15.5 15.6 15.6 15.6 15.6 15.7 15.7 15.8 15.8 15.8 16.0 16.1 16.1
##  [127] 16.2 16.2 16.2 16.3 16.4 16.6 16.6 16.6 16.7 16.7 16.7 16.8 16.8 16.9
##  [141] 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9
##  [155] 16.9 16.9 16.9 16.9 17.0 17.0 17.0 17.1 17.1 17.1 17.1 17.1 17.1 17.3
##  [169] 17.4 17.4 17.4 17.5 17.5 17.5 17.5 17.5 17.5 17.6 17.6 17.7 17.8 17.8
##  [183] 17.8 17.9 17.9 17.9 17.9 17.9 18.0 18.0 18.0 18.0 18.0 18.1 18.2 18.2
##  [197] 18.3 18.3 18.3 18.4 18.4 18.4 18.5 18.5 18.6 18.6 18.7 18.8 18.8 18.8
##  [211] 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8
##  [225] 18.8 18.8 18.9 18.9 18.9 18.9 19.0 19.0 19.0 19.0 19.1 19.1 19.1 19.1
##  [239] 19.2 19.2 19.2 19.2 19.2 19.3 19.3 19.3 19.3 19.4 19.4 19.4 19.4 19.4
##  [253] 19.5 19.5 19.6 19.6 19.6 19.6 19.6 19.6 19.7 19.7 19.7 19.7 19.8 19.8
##  [267] 19.8 19.8 19.8 19.9 19.9 19.9 20.0 20.0 20.0 20.0 20.0 20.1 20.1 20.1
##  [281] 20.2 20.2 20.2 20.3 20.3 20.3 20.3 20.4 20.4 20.4 20.4 20.5 20.5 20.5
##  [295] 20.5 20.5 20.5 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6
##  [309] 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6 20.6
##  [323] 20.6 20.6 20.7 20.7 20.7 20.7 20.7 20.7 20.7 20.8 20.8 20.8 20.8 20.8
##  [337] 20.8 20.9 20.9 20.9 20.9 21.0 21.0 21.0 21.0 21.0 21.1 21.1 21.2 21.2
##  [351] 21.2 21.2 21.3 21.3 21.3 21.3 21.3 21.3 21.3 21.4 21.4 21.4 21.4 21.4
##  [365] 21.5 21.5 21.5 21.5 21.5 21.5 21.5 21.6 21.6 21.6 21.6 21.7 21.7 21.7
##  [379] 21.7 21.7 21.8 21.8 21.8 21.8 21.9 21.9 21.9 22.0 22.0 22.0 22.0 22.0
##  [393] 22.0 22.1 22.1 22.1 22.1 22.1 22.1 22.2 22.2 22.2 22.2 22.2 22.2 22.2
##  [407] 22.2 22.3 22.3 22.3 22.3 22.3 22.3 22.3 22.4 22.4 22.4 22.4 22.4 22.4
##  [421] 22.4 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5
##  [435] 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5 22.5
##  [449] 22.6 22.6 22.6 22.6 22.6 22.6 22.6 22.7 22.7 22.7 22.7 22.7 22.7 22.8
##  [463] 22.8 22.9 22.9 22.9 22.9 23.0 23.0 23.0 23.0 23.0 23.0 23.1 23.1 23.1
##  [477] 23.1 23.2 23.2 23.2 23.2 23.3 23.3 23.3 23.3 23.4 23.4 23.4 23.4 23.4
##  [491] 23.4 23.4 23.4 23.5 23.5 23.5 23.6 23.6 23.6 23.6 23.6 23.6 23.7 23.7
##  [505] 23.7 23.7 23.8 23.8 23.8 23.8 23.8 23.9 23.9 23.9 24.0 24.0 24.0 24.1
##  [519] 24.1 24.1 24.1 24.1 24.1 24.1 24.2 24.2 24.2 24.3 24.3 24.3 24.3 24.3
##  [533] 24.3 24.3 24.3 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4
##  [547] 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4
##  [561] 24.4 24.4 24.4 24.4 24.4 24.4 24.5 24.5 24.5 24.5 24.6 24.6 24.6 24.7
##  [575] 24.7 24.7 24.7 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.9 24.9 24.9
##  [589] 24.9 24.9 24.9 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.1
##  [603] 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.2 25.2 25.2 25.2 25.2 25.2
##  [617] 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.4 25.4 25.5 25.5 25.5 25.5
##  [631] 25.5 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.7 25.7 25.7 25.7 25.7
##  [645] 25.7 25.7 25.7 25.8 25.8 25.8 25.8 25.9 25.9 25.9 25.9 25.9 25.9 25.9
##  [659] 25.9 25.9 25.9 25.9 25.9 26.0 26.0 26.0 26.1 26.1 26.1 26.1 26.1 26.1
##  [673] 26.1 26.2 26.2 26.2 26.2 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
##  [687] 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.4 26.4 26.4 26.4 26.4 26.4
##  [701] 26.4 26.5 26.5 26.5 26.5 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6
##  [715] 26.6 26.6 26.7 26.7 26.7 26.8 26.8 26.8 26.8 26.8 26.9 26.9 26.9 26.9
##  [729] 26.9 26.9 26.9 26.9 26.9 26.9 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0
##  [743] 27.0 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.2
##  [757] 27.2 27.3 27.3 27.3 27.3 27.3 27.3 27.4 27.4 27.4 27.4 27.4 27.4 27.4
##  [771] 27.5 27.5 27.5 27.5 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.7 27.7 27.7
##  [785] 27.7 27.8 27.8 27.8 27.8 27.8 27.8 27.9 27.9 27.9 27.9 27.9 27.9 28.0
##  [799] 28.0 28.0 28.0 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1
##  [813] 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1
##  [827] 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.2 28.2
##  [841] 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.4 28.4 28.4 28.4
##  [855] 28.4 28.4 28.4 28.4 28.4 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.6 28.6
##  [869] 28.6 28.6 28.6 28.7 28.7 28.7 28.7 28.7 28.7 28.7 28.7 28.7 28.7 28.8
##  [883] 28.8 28.8 28.8 28.8 28.8 28.8 28.8 28.8 28.9 28.9 28.9 28.9 28.9 28.9
##  [897] 28.9 28.9 28.9 28.9 28.9 29.0 29.0 29.0 29.0 29.0 29.0 29.0 29.0 29.1
##  [911] 29.1 29.1 29.1 29.1 29.1 29.1 29.1 29.1 29.2 29.2 29.2 29.2 29.2 29.2
##  [925] 29.2 29.2 29.2 29.2 29.3 29.3 29.3 29.3 29.3 29.3 29.3 29.3 29.3 29.3
##  [939] 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.4 29.5 29.5 29.5 29.5
##  [953] 29.5 29.5 29.6 29.6 29.6 29.6 29.6 29.6 29.6 29.6 29.7 29.7 29.7 29.7
##  [967] 29.7 29.7 29.7 29.7 29.7 29.7 29.7 29.7 29.7 29.7 29.8 29.8 29.8 29.8
##  [981] 29.9 29.9 29.9 29.9 29.9 29.9 29.9 30.0 30.0 30.0 30.0 30.0 30.0 30.0
##  [995] 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0 30.0
## [1009] 30.0 30.0 30.0 30.0 30.0 30.1 30.1 30.1 30.1 30.1 30.2 30.2 30.2 30.2
## [1023] 30.2 30.2 30.2 30.2 30.2 30.3 30.3 30.3 30.3 30.3 30.3 30.4 30.4 30.4
## [1037] 30.4 30.4 30.5 30.5 30.5 30.5 30.5 30.5 30.5 30.6 30.6 30.6 30.6 30.6
## [1051] 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.7 30.8 30.8
## [1065] 30.8 30.8 30.8 30.9 30.9 30.9 30.9 30.9 30.9 30.9 30.9 31.0 31.0 31.0
## [1079] 31.0 31.1 31.1 31.1 31.1 31.2 31.2 31.2 31.2 31.2 31.2 31.2 31.2 31.2
## [1093] 31.2 31.3 31.3 31.3 31.3 31.3 31.3 31.3 31.4 31.4 31.4 31.4 31.5 31.5
## [1107] 31.5 31.5 31.5 31.6 31.6 31.6 31.6 31.6 31.6 31.6 31.7 31.7 31.7 31.7
## [1121] 31.7 31.7 31.7 31.8 31.8 31.8 31.8 31.8 31.8 31.8 31.8 31.8 31.9 31.9
## [1135] 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9 31.9
## [1149] 31.9 31.9 31.9 31.9 31.9 31.9 32.0 32.0 32.0 32.0 32.0 32.1 32.1 32.1
## [1163] 32.1 32.1 32.2 32.2 32.2 32.2 32.2 32.2 32.2 32.3 32.3 32.3 32.3 32.3
## [1177] 32.3 32.3 32.3 32.3 32.4 32.4 32.4 32.4 32.4 32.4 32.4 32.4 32.5 32.5
## [1191] 32.5 32.5 32.5 32.5 32.5 32.5 32.5 32.5 32.6 32.6 32.6 32.6 32.6 32.6
## [1205] 32.6 32.6 32.6 32.7 32.7 32.7 32.7 32.7 32.7 32.7 32.8 32.8 32.8 32.8
## [1219] 32.8 32.8 32.8 32.8 32.8 32.8 32.9 32.9 32.9 33.0 33.0 33.0 33.0 33.0
## [1233] 33.1 33.1 33.1 33.1 33.1 33.2 33.2 33.2 33.2 33.2 33.2 33.3 33.3 33.3
## [1247] 33.3 33.3 33.3 33.3 33.3 33.3 33.3 33.4 33.4 33.4 33.4 33.4 33.4 33.4
## [1261] 33.4 33.4 33.4 33.5 33.5 33.5 33.5 33.5 33.5 33.5 33.6 33.6 33.6 33.6
## [1275] 33.6 33.6 33.7 33.7 33.7 33.7 33.7 33.7 33.7 33.7 33.7 33.7 33.8 33.8
## [1289] 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8 33.8
## [1303] 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 33.9 34.0 34.0 34.0 34.0
## [1317] 34.0 34.0 34.0 34.0 34.1 34.1 34.1 34.1 34.1 34.1 34.2 34.2 34.2 34.3
## [1331] 34.3 34.3 34.3 34.3 34.3 34.4 34.4 34.4 34.4 34.4 34.4 34.4 34.4 34.4
## [1345] 34.4 34.4 34.4 34.4 34.4 34.4 34.5 34.5 34.5 34.5 34.5 34.5 34.5 34.6
## [1359] 34.6 34.6 34.6 34.6 34.6 34.6 34.6 34.7 34.7 34.7 34.8 34.8 34.8 34.8
## [1373] 34.8 34.8 34.9 34.9 34.9 34.9 34.9 34.9 34.9 34.9 34.9 35.0 35.0 35.0
## [1387] 35.1 35.1 35.1 35.1 35.1 35.1 35.1 35.1 35.1 35.1 35.1 35.2 35.2 35.2
## [1401] 35.3 35.3 35.3 35.3 35.3 35.3 35.3 35.3 35.3 35.4 35.4 35.4 35.4 35.4
## [1415] 35.4 35.4 35.4 35.4 35.4 35.5 35.5 35.5 35.5 35.5 35.5 35.5 35.6 35.6
## [1429] 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6 35.6
## [1443] 35.6 35.6 35.6 35.6 35.6 35.6 35.7 35.7 35.7 35.7 35.7 35.7 35.7 35.8
## [1457] 35.8 35.8 35.8 35.8 35.8 35.8 35.9 35.9 35.9 35.9 35.9 35.9 35.9 36.0
## [1471] 36.0 36.0 36.0 36.0 36.0 36.0 36.1 36.1 36.1 36.1 36.1 36.1 36.1 36.1
## [1485] 36.2 36.2 36.2 36.2 36.2 36.3 36.3 36.3 36.3 36.3 36.3 36.3 36.3 36.3
## [1499] 36.3 36.4 36.4 36.4 36.4 36.4 36.4 36.4 36.4 36.4 36.5 36.5 36.6 36.6
## [1513] 36.6 36.6 36.6 36.7 36.7 36.7 36.7 36.7 36.7 36.7 36.7 36.7 36.7 36.8
## [1527] 36.8 36.8 36.8 36.8 36.8 36.9 36.9 36.9 36.9 36.9 36.9 36.9 37.0 37.0
## [1541] 37.0 37.0 37.0 37.0 37.0 37.0 37.1 37.1 37.1 37.1 37.1 37.1 37.1 37.1
## [1555] 37.1 37.2 37.2 37.2 37.2 37.3 37.3 37.3 37.3 37.3 37.3 37.3 37.3 37.4
## [1569] 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.4 37.5 37.5 37.5 37.5 37.5
## [1583] 37.5 37.5 37.5 37.5 37.5 37.5 37.5 37.5 37.6 37.6 37.6 37.6 37.6 37.6
## [1597] 37.6 37.6 37.6 37.6 37.6 37.6 37.6 37.6 37.7 37.7 37.7 37.7 37.7 37.7
## [1611] 37.7 37.7 37.8 37.8 37.8 37.9 37.9 37.9 37.9 37.9 37.9 37.9 37.9 38.0
## [1625] 38.0 38.0 38.0 38.1 38.1 38.1 38.1 38.1 38.2 38.2 38.2 38.2 38.2 38.2
## [1639] 38.2 38.3 38.3 38.3 38.3 38.3 38.4 38.4 38.4 38.4 38.4 38.4 38.4 38.5
## [1653] 38.5 38.5 38.5 38.5 38.5 38.5 38.6 38.6 38.6 38.6 38.6 38.6 38.7 38.7
## [1667] 38.7 38.8 38.8 38.8 38.8 38.8 38.8 38.8 38.8 38.8 38.9 38.9 38.9 38.9
## [1681] 38.9 38.9 38.9 38.9 39.0 39.0 39.0 39.0 39.0 39.0 39.0 39.0 39.0 39.0
## [1695] 39.0 39.1 39.1 39.1 39.1 39.1 39.1 39.1 39.2 39.2 39.2 39.2 39.2 39.2
## [1709] 39.2 39.2 39.3 39.3 39.3 39.4 39.4 39.4 39.4 39.4 39.4 39.4 39.4 39.4
## [1723] 39.4 39.4 39.4 39.5 39.5 39.5 39.5 39.5 39.6 39.6 39.6 39.6 39.6 39.7
## [1737] 39.7 39.7 39.7 39.7 39.7 39.7 39.7 39.7 39.7 39.7 39.8 39.8 39.8 39.8
## [1751] 39.8 39.8 39.8 39.8 39.9 39.9 39.9 39.9 39.9 39.9 39.9 39.9 40.0 40.0
## [1765] 40.0 40.0 40.0 40.0 40.1 40.1 40.1 40.1 40.1 40.1 40.1 40.1 40.1 40.2
## [1779] 40.2 40.2 40.2 40.2 40.2 40.2 40.3 40.3 40.3 40.3 40.3 40.3 40.3 40.4
## [1793] 40.4 40.4 40.4 40.4 40.5 40.5 40.5 40.5 40.5 40.5 40.5 40.5 40.5 40.6
## [1807] 40.6 40.6 40.6 40.6 40.6 40.7 40.7 40.7 40.7 40.7 40.7 40.8 40.8 40.8
## [1821] 40.8 40.8 40.8 40.9 40.9 40.9 40.9 40.9 40.9 40.9 40.9 40.9 40.9 40.9
## [1835] 41.0 41.0 41.0 41.0 41.0 41.0 41.1 41.1 41.1 41.1 41.1 41.1 41.1 41.2
## [1849] 41.2 41.2 41.3 41.3 41.3 41.3 41.3 41.3 41.3 41.3 41.3 41.3 41.3 41.3
## [1863] 41.3 41.4 41.4 41.4 41.4 41.4 41.4 41.4 41.5 41.5 41.6 41.6 41.6 41.6
## [1877] 41.6 41.7 41.7 41.7 41.7 41.8 41.8 41.8 41.9 41.9 41.9 41.9 41.9 42.0
## [1891] 42.0 42.0 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.1 42.2 42.2 42.2
## [1905] 42.2 42.2 42.3 42.3 42.3 42.3 42.3 42.3 42.4 42.4 42.4 42.4 42.4 42.4
## [1919] 42.4 42.5 42.5 42.5 42.5 42.5 42.6 42.6 42.6 42.7 42.7 42.7 42.7 42.7
## [1933] 42.7 42.7 42.7 42.7 42.7 42.8 42.8 42.8 42.8 42.8 42.8 42.8 42.8 42.8
## [1947] 42.8 42.9 42.9 42.9 42.9 42.9 42.9 42.9 42.9 43.0 43.0 43.0 43.0 43.0
## [1961] 43.0 43.0 43.0 43.0 43.0 43.0 43.1 43.1 43.1 43.1 43.1 43.1 43.1 43.1
## [1975] 43.1 43.1 43.1 43.1 43.1 43.2 43.2 43.2 43.2 43.2 43.2 43.2 43.3 43.3
## [1989] 43.3 43.3 43.3 43.3 43.4 43.4 43.4 43.4 43.4 43.5 43.5 43.5 43.5 43.5
## [2003] 43.6 43.6 43.6 43.6 43.6 43.6 43.6 43.6 43.6 43.7 43.7 43.7 43.7 43.8
## [2017] 43.8 43.8 43.8 43.9 43.9 43.9 43.9 43.9 43.9 44.0 44.0 44.0 44.0 44.0
## [2031] 44.0 44.1 44.1 44.1 44.2 44.2 44.2 44.3 44.3 44.3 44.3 44.4 44.4 44.4
## [2045] 44.5 44.5 44.5 44.5 44.6 44.6 44.6 44.6 44.6 44.6 44.7 44.8 44.8 44.8
## [2059] 44.8 44.8 44.8 44.9 44.9 44.9 45.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0
## [2073] 45.0 45.0 45.0 45.0 45.0 45.0 45.1 45.1 45.1 45.2 45.2 45.2 45.3 45.3
## [2087] 45.3 45.3 45.4 45.4 45.4 45.5 45.5 45.5 45.5 45.5 45.5 45.5 45.6 45.6
## [2101] 45.6 45.6 45.6 45.6 45.7 45.7 45.7 45.7 45.7 45.8 45.8 45.8 45.8 45.8
## [2115] 45.8 45.8 45.8 45.8 45.9 45.9 45.9 45.9 45.9 45.9 45.9 46.0 46.0 46.0
## [2129] 46.0 46.0 46.0 46.1 46.1 46.1 46.1 46.1 46.1 46.2 46.2 46.3 46.3 46.4
## [2143] 46.4 46.4 46.5 46.5 46.5 46.5 46.5 46.5 46.6 46.6 46.6 46.6 46.6 46.7
## [2157] 46.7 46.7 46.8 46.8 46.8 46.9 46.9 46.9 46.9 46.9 46.9 46.9 47.0 47.1
## [2171] 47.1 47.1 47.2 47.2 47.2 47.3 47.3 47.3 47.3 47.4 47.4 47.4 47.5 47.5
## [2185] 47.5 47.5 47.5 47.5 47.6 47.7 47.7 47.7 47.7 47.7 47.7 47.8 47.8 47.8
## [2199] 47.8 48.0 48.0 48.0 48.0 48.1 48.1 48.1 48.1 48.1 48.2 48.2 48.2 48.2
## [2213] 48.3 48.3 48.3 48.4 48.5 48.5 48.5 48.6 48.6 48.6 48.6 48.6 48.6 48.6
## [2227] 48.7 48.7 48.8 48.8 48.9 48.9 49.0 49.1 49.1 49.1 49.3 49.3 49.4 49.4
## [2241] 49.5 49.5 49.5 49.6 49.6 49.7 49.7 49.7 49.8 49.8 49.8 49.8 49.8 49.8
## [2255] 49.8 49.8 49.9 49.9 49.9 49.9 50.0 50.0 50.0 50.0 50.1 50.1 50.2 50.2
## [2269] 50.2 50.3 50.3 50.4 50.4 50.5 50.5 50.5 50.6 50.6 50.6 50.6 50.6 50.6
## [2283] 50.6 50.7 50.8 50.8 50.8 50.9 50.9 50.9 51.0 51.1 51.1 51.2 51.2 51.2
## [2297] 51.3 51.3 51.3 51.3 51.4 51.5 51.5 51.7 51.8 51.8 51.8 51.8 51.8 51.8
## [2311] 51.8 51.8 51.9 52.0 52.0 52.1 52.1 52.1 52.1 52.3 52.4 52.4 52.4 52.4
## [2325] 52.5 52.5 52.5 52.5 52.5 52.5 52.6 52.7 52.7 52.8 52.8 52.9 52.9 53.0
## [2339] 53.0 53.0 53.1 53.2 53.2 53.3 53.3 53.3 53.3 53.3 53.4 53.4 53.5 53.6
## [2353] 53.6 53.7 53.8 53.9 53.9 53.9 54.0 54.0 54.0 54.1 54.2 54.2 54.3 54.4
## [2367] 54.5 54.5 54.5 54.6 54.6 54.7 54.7 54.7 54.8 54.8 54.8 54.8 54.9 54.9
## [2381] 55.0 55.0 55.3 55.6 55.6 55.7 55.7 55.7 55.8 55.8 55.9 56.0 56.0 56.1
## [2395] 56.2 56.2 56.2 56.3 56.3 56.4 56.5 56.6 56.6 56.8 56.8 56.9 56.9 57.0
## [2409] 57.2 57.3 57.3 57.3 57.3 57.7 57.7 57.8 57.9 58.0 58.0 58.1 58.4 58.5
## [2423] 58.6 59.2 59.3 59.4 59.5 59.5 59.6 59.6 59.8 59.8 59.9 60.0 60.5 60.7
## [2437] 60.7 60.9 61.0 61.0 61.3 61.6 61.8 62.2 62.2 62.3 62.4 63.2 63.2 63.4
## [2451] 63.5 63.5 63.5 63.8 63.9 63.9 64.0 64.1 65.0 65.1 65.4 65.6 65.7 66.1
## [2465] 66.2 66.4 66.4 67.3 67.3 67.4 67.7 68.1 68.7 68.9 69.0 69.0 69.3 69.4
## [2479] 70.0 76.4

#Amplitude amostral
h0 = diff(range(a0))

#Variancia amostral
var0 = var(a0)

#Desvio padrao amostral
sd0=sd(a0)
sqrt(var0)

## [1] 11.58592

Questão F

QUESTAO F - Calcule o intervalo de confiança(IC) ao nível de confiança de 95% para a média

populacional das notas do curso escolhido em D) e o interprete corretamente.

head(microdados_ti)

## # A tibble: 6 x 13
##   CO_GRUPO CO_REGIAO_CURSO NU_IDADE TP_SEXO CO_TURNO_GRADUACAO NT_GER QE_I01
##      <dbl>           <dbl>    <dbl> <chr>                <dbl>  <dbl> <chr> 
## 1       79               3       23 M                        4   20.6 A     
## 2       79               3       29 M                        4   32.3 A     
## 3       79               3       35 M                        4   29.7 A     
## 4       79               3       40 M                        4   24.3 B     
## 5       79               3       22 M                        4   28.8 A     
## 6       79               3       24 M                        4   17.6 A     
## # ... with 6 more variables: QE_I02 <chr>, QE_I08 <chr>, QE_I21 <chr>,
## #   QE_I23 <chr>, NT_OBJ_FG <dbl>, NT_OBJ_CE <dbl>

NT_GER.IC = microdados_ti$NT_GER
a = mean(NT_GER.IC,na.rm=TRUE) #Média da amostra
#var(NT_GER.IC,na.rm=TRUE)
#sqrt(var(NT_GER.IC,na.rm=TRUE))
s = sd(NT_GER.IC,na.rm=TRUE) #Desvio Padrão da amostra
n = length(NT_GER.IC) #Tamanho da amostra

error <- qnorm(0.975)*s/sqrt(n)
left <- a-error
right <- a+error

Resposta: Temos 95% de certeza de que a nossa estimativa média está entre 33.28893 e 34.20091

Questão G

QUESTAO G - Qual a distribuição será aplicada para encontrar os quantis

das notas do curso escolhido em D) e diga o porquê da escolha da mesma.

Verificando tipo de variável da amostra: Contínua (números que admitem vírgula)

summary(NT_GER.IC)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   25.30   33.20   33.74   41.30   76.40

#Distribuição normal utilizando qq-plot e Histograma 
#Para análise (Caracteristicas apresentadas representam a distribuição normal)

qqnorm(NT_GER.IC)
qqline(NT_GER.IC)

hist(NT_GER.IC)

Questão H

QUESTAO H - Segundo o IC calculado em F),

você diria que a nota dos alunos do curso escolhido em D)

foi atípica ou não?

CO_GRUPO==79 (D) O intervalor de confiança calculado em 95% são: 33.28893 e 34.20091 (F) 33,74 A média (F) 2480 é o Tamanho da amostra 11.58592 é o Desvio Padrão

Resposta: Não, não foi atípica. O intervalo diz que os valores plausíveis para a média real estão entre 33.28 e 34.20. Como este intervalo contém a nota no valor de 33,74. Nosso resultado parece plausível dentro do esperado e dentro do intervalo desejavel.

Questão I

QUESTAO I - Faça um teste de hipótese bilateral ao nível de confiança de 95%

para média das notas do curso escolhido, verifique se a razão para dizer que

a média das notas do curso escolhido é significativamente diferente da média geral

das notas do Enade, ao nível de confiança de 95%.(Valor 2,5 Pontos)

# Curso Escolhido
a0= microdados_ti$NT_GER
length(a0)

## [1] 2480

n <- length(microdados_ti$NT_GER) 
n

## [1] 2480

t.m <- mean(microdados_ti$NT_GER)
t.m

## [1] 33.74492

t.v <- var(microdados_ti$NT_GER)
t.v

## [1] 134.2336

t.ic <- t.m + qt(c(0.025, 0.975), df = n - 1) * sqrt(t.v/length(microdados_ti$NT_GER))
# Curso Escolhido
t.ic

## [1] 33.28871 34.20113

# Média Geral
a0= microdados_sem_NA$NT_GER
length(a0)

## [1] 442496

n <- length(microdados_sem_NA$NT_GER) 
n

## [1] 442496

t.m <- mean(microdados_sem_NA$NT_GER)
t.m

## [1] 43.61828

t.v <- var(microdados_sem_NA$NT_GER)
t.v

## [1] 197.7714

t.icGeral <- t.m + qt(c(0.025, 0.975), df = n - 1) * sqrt(t.v/length(microdados_sem_NA$NT_GER))

#########********************
# Média Geral
t.icGeral

## [1] 43.57684 43.65971

# Curso Escolhido
t.ic

## [1] 33.28871 34.20113

SIm é significativamente diferente a média geral da média do cusro escolhido. Considerar, conjuntamente, o estimador e a precisão com que se estima o parâmetro. A forma usual de se fazer isso é através dos chamados Intervalos de Confança (I.C.). É importante observar que o nível de confança se aplica ao processo de construção de intervalos, e não a um intervalo, e não a um intervalo específico.