Atividade 7 - Luiz Carlos.rmd
Luiz
19/11/2020
# Carregar a base de dados
load("~/Base_de_dados-master/diamante.RData")
# VARIAVEL CORTE
prop.table(table(diamante$corte))##
## Justo Bom Muito Bom Premium Ideal
## 0.02984798 0.09095291 0.22398962 0.25567297 0.39953652summary(diamante$corte)## Justo Bom Muito Bom Premium Ideal
## 1610 4906 12082 13791 21551library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondiamante %>%
pull(quilate) %>%
table() %>%
prop.table()## .
## 0.2 0.21 0.22 0.23 0.24 0.25
## 2.224694e-04 1.668521e-04 9.269559e-05 5.431961e-03 4.708936e-03 3.930293e-03
## 0.26 0.27 0.28 0.29 0.3 0.31
## 4.690397e-03 4.319614e-03 3.670745e-03 2.410085e-03 4.827586e-02 4.169448e-02
## 0.32 0.33 0.34 0.35 0.36 0.37
## 3.411198e-02 2.204301e-02 1.687060e-02 1.236559e-02 1.060438e-02 7.304412e-03
## 0.38 0.39 0.4 0.41 0.42 0.43
## 1.242121e-02 7.378569e-03 2.408231e-02 2.562106e-02 1.308862e-02 9.047089e-03
## 0.44 0.45 0.46 0.47 0.48 0.49
## 3.930293e-03 2.039303e-03 3.299963e-03 1.835373e-03 1.167964e-03 8.342603e-04
## 0.5 0.51 0.52 0.53 0.54 0.55
## 2.332221e-02 2.089359e-02 1.514646e-02 1.314423e-02 1.158695e-02 9.195402e-03
## 0.56 0.57 0.58 0.59 0.6 0.61
## 9.121246e-03 7.971821e-03 5.747126e-03 5.228031e-03 4.226919e-03 3.781980e-03
## 0.62 0.63 0.64 0.65 0.66 0.67
## 2.502781e-03 1.890990e-03 1.483129e-03 1.205043e-03 8.898776e-04 8.898776e-04
## 0.68 0.69 0.7 0.71 0.72 0.73
## 4.634779e-04 4.820171e-04 3.672599e-02 2.398962e-02 1.416389e-02 9.121246e-03
## 0.74 0.75 0.76 0.77 0.78 0.79
## 5.969596e-03 4.616240e-03 4.653319e-03 4.653319e-03 3.466815e-03 2.873563e-03
## 0.8 0.81 0.82 0.83 0.84 0.85
## 5.265109e-03 3.707824e-03 2.595476e-03 2.428624e-03 1.186504e-03 1.149425e-03
## 0.86 0.87 0.88 0.89 0.9 0.91
## 6.303300e-04 5.747126e-04 4.263997e-04 3.893215e-04 2.753059e-02 1.056730e-02
## 0.92 0.93 0.94 0.95 0.96 0.97
## 4.189841e-03 2.632555e-03 1.093808e-03 1.205043e-03 1.909529e-03 1.093808e-03
## 0.98 0.99 1 1.01 1.02 1.03
## 5.747126e-04 4.263997e-04 2.888395e-02 4.156470e-02 1.637004e-02 9.695958e-03
## 1.04 1.05 1.06 1.07 1.08 1.09
## 8.806081e-03 6.692621e-03 6.915091e-03 6.340378e-03 4.560623e-03 5.320727e-03
## 1.1 1.11 1.12 1.13 1.14 1.15
## 5.153875e-03 5.710048e-03 4.653319e-03 4.560623e-03 3.837597e-03 2.762329e-03
## 1.16 1.17 1.18 1.19 1.2 1.21
## 3.188728e-03 2.039303e-03 2.280311e-03 2.335929e-03 1.195773e-02 8.769003e-03
## 1.22 1.23 1.24 1.25 1.26 1.27
## 5.561735e-03 5.172414e-03 4.375232e-03 3.466815e-03 2.706711e-03 2.484242e-03
## 1.28 1.29 1.3 1.31 1.32 1.33
## 1.965146e-03 1.872451e-03 2.261772e-03 2.465703e-03 1.649981e-03 1.612903e-03
## 1.34 1.35 1.36 1.37 1.38 1.39
## 1.260660e-03 1.427512e-03 9.269559e-04 8.527994e-04 4.820171e-04 6.674082e-04
## 1.4 1.41 1.42 1.43 1.44 1.45
## 9.269559e-04 7.415647e-04 4.634779e-04 3.522432e-04 3.337041e-04 2.780868e-04
## 1.46 1.47 1.48 1.49 1.5 1.51
## 3.337041e-04 3.893215e-04 1.297738e-04 2.039303e-04 1.470152e-02 1.496107e-02
## 1.52 1.53 1.54 1.55 1.56 1.57
## 7.063404e-03 4.078606e-03 3.225806e-03 2.298851e-03 2.020764e-03 1.965146e-03
## 1.58 1.59 1.6 1.61 1.62 1.63
## 1.649981e-03 1.649981e-03 1.761216e-03 1.186504e-03 1.130886e-03 9.269559e-04
## 1.64 1.65 1.66 1.67 1.68 1.69
## 7.971821e-04 5.932518e-04 5.561735e-04 4.634779e-04 3.522432e-04 4.449388e-04
## 1.7 1.71 1.72 1.73 1.74 1.75
## 3.985910e-03 2.206155e-03 1.056730e-03 9.640341e-04 7.415647e-04 9.269559e-04
## 1.76 1.77 1.78 1.79 1.8 1.81
## 5.190953e-04 3.151650e-04 2.224694e-04 2.780868e-04 3.893215e-04 1.668521e-04
## 1.82 1.83 1.84 1.85 1.86 1.87
## 2.410085e-04 3.337041e-04 7.415647e-05 5.561735e-05 1.668521e-04 1.297738e-04
## 1.88 1.89 1.9 1.91 1.92 1.93
## 7.415647e-05 7.415647e-05 1.297738e-04 2.224694e-04 3.707824e-05 1.112347e-04
## 1.94 1.95 1.96 1.97 1.98 1.99
## 5.561735e-05 5.561735e-05 7.415647e-05 7.415647e-05 9.269559e-05 5.561735e-05
## 2 2.01 2.02 2.03 2.04 2.05
## 4.912866e-03 8.157212e-03 3.281424e-03 2.261772e-03 1.594364e-03 1.242121e-03
## 2.06 2.07 2.08 2.09 2.1 2.11
## 1.112347e-03 9.269559e-04 7.601038e-04 8.342603e-04 9.640341e-04 7.971821e-04
## 2.12 2.13 2.14 2.15 2.16 2.17
## 4.634779e-04 3.893215e-04 8.898776e-04 4.078606e-04 4.634779e-04 3.337041e-04
## 2.18 2.19 2.2 2.21 2.22 2.23
## 5.747126e-04 4.078606e-04 5.932518e-04 4.263997e-04 5.005562e-04 2.410085e-04
## 2.24 2.25 2.26 2.27 2.28 2.29
## 2.966259e-04 3.337041e-04 2.780868e-04 2.224694e-04 3.707824e-04 3.151650e-04
## 2.3 2.31 2.32 2.33 2.34 2.35
## 3.893215e-04 2.410085e-04 2.966259e-04 1.668521e-04 9.269559e-05 1.297738e-04
## 2.36 2.37 2.38 2.39 2.4 2.41
## 1.483129e-04 1.112347e-04 1.483129e-04 1.297738e-04 2.410085e-04 9.269559e-05
## 2.42 2.43 2.44 2.45 2.46 2.47
## 1.483129e-04 1.112347e-04 7.415647e-05 7.415647e-05 5.561735e-05 5.561735e-05
## 2.48 2.49 2.5 2.51 2.52 2.53
## 1.668521e-04 5.561735e-05 3.151650e-04 3.151650e-04 1.668521e-04 1.483129e-04
## 2.54 2.55 2.56 2.57 2.58 2.59
## 1.668521e-04 5.561735e-05 5.561735e-05 5.561735e-05 5.561735e-05 1.853912e-05
## 2.6 2.61 2.63 2.64 2.65 2.66
## 5.561735e-05 5.561735e-05 5.561735e-05 1.853912e-05 1.853912e-05 5.561735e-05
## 2.67 2.68 2.7 2.71 2.72 2.74
## 1.853912e-05 3.707824e-05 1.853912e-05 1.853912e-05 5.561735e-05 5.561735e-05
## 2.75 2.77 2.8 3 3.01 3.02
## 3.707824e-05 1.853912e-05 3.707824e-05 1.483129e-04 2.595476e-04 1.853912e-05
## 3.04 3.05 3.11 3.22 3.24 3.4
## 3.707824e-05 1.853912e-05 1.853912e-05 1.853912e-05 1.853912e-05 1.853912e-05
## 3.5 3.51 3.65 3.67 4 4.01
## 1.853912e-05 1.853912e-05 1.853912e-05 1.853912e-05 1.853912e-05 3.707824e-05
## 4.13 4.5 5.01
## 1.853912e-05 1.853912e-05 1.853912e-05# Média, mediana e desvio padrão da variável "PREÇO"
diamante %>%
group_by(preco) %>%
summarize(media=mean(preco),mediana=median(preco),desvio_padrao=sd(preco),tamanho=n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 11,602 x 5
## preco media mediana desvio_padrao tamanho
## <int> <dbl> <dbl> <dbl> <int>
## 1 326 326 326 0 2
## 2 327 327 327 NA 1
## 3 334 334 334 NA 1
## 4 335 335 335 NA 1
## 5 336 336 336 0 2
## 6 337 337 337 0 2
## 7 338 338 338 NA 1
## 8 339 339 339 NA 1
## 9 340 340 340 NA 1
## 10 342 342 342 NA 1
## # ... with 11,592 more rows# ESTATISTICA DAS DUAS VARIÁVEIS
tabela_diamante1<-diamante%>%
group_by(corte)%>%
summarize(media=mean (preco), mediana=median(preco), desvio_padrao=sd(preco))%>%
arrange(desc(media))## `summarise()` ungrouping output (override with `.groups` argument)# BOXPLOT
par(cex=0.5)
boxplot(diamante$preco~diamante$corte,col="darkblue")
Conclusão A média de quilates do corte Justo é a maior entra as outras classificações, com o valor de “1,05” e o corte “justo” e “premium” possuem o mesmo desvio padrão de “0,52”. É possível observar através do gráfico que o tipo de corte “muito bom” é o mais simétrico. O tipo de corte “justo” possui o maior outlier e o tipo de corte “Premium” possui a maior quantidade de quilates.