library(readxl)
world_sem_país <- read_excel("C:/Users/User/Desktop/world_sem_país.xlsx",
col_types = c("text", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
#View(world_sem_país)Interações e Dinâmicas de Populações e Continentes - Um panorama global
Resumo
O resumo é uma síntese concisa do artigo, apresentando de forma clara e objetiva o objetivo, metodologia, principais resultados e conclusões da pesquisa. O tamanho do resumo pode variar de acordo com as normas da revista ou evento científico em que será submetido. Neste caso, o resumo deve ter entre 150 a 250 palavras.
Palavras-chave: palavra 1; palavra 2; palavra 3
1. Introdução
O presente estudo se baseia em um banco de dados que reúne informações globais, como indicadores socioeconômicos, ambientais, educacionais e demográficos. Com 196 países representados, as correlações realizadas a partir desse conjunto de dados proporcionam uma perspectiva global completa sobre diversos aspectos das nações. Essa amplitude de informações possibilita análises minuciosas e comparações detalhadas entre países, permitindo uma compreensão mais profunda das disparidades e semelhanças que moldam o cenário internacional. Além do mais, traçar o perfil de cada país é importante pois permite que pesquisadores, governos e empresas obtenham parâmetros valiosos sobre questões globais, identifiquem tendências, desafios e oportunidades, além de promoverem decisões importantes em âmbitos internacionais.
1.1. Objetivo
O objetivo deste trabalho é explorar quais tendências socioeconômicas, ambientais e políticas podem ser observadas no planeta Terra. Para isso, foi realizada uma análise estatística bivariada proveniente do agrupamento das informações contidas no banco de dados utilizado em cinco duplas de variáveis. As duplas de variáveis foram compostas tanto de variáveis qualitativas quanto de quantitativas. As correlações foram escolhidas visando a melhor representação de relações econômicas, sociais e sustentáveis/ambientais internacionais, a fim de criar um panorama geral de como cada país se comporta em determinados indicadores.
1.1.1. Objetivos específicos
- Analisar a influência do PIB nas emissões de CO2;
- Analisar como o valor do salário mínimo de um país influencia em sua taxa de natalidade;
- Analisar como a quantidade de emissão de CO2 de um país influencia na extensão de sua área florestal;
- Quantificar qual a taxa de desemprego nos diferentes continentes;
- Quantificar qual a densidade nos diferentes continentes.
2. Metodologia
Descreva os dados utilizados, informando a composição da amostra, incluindo critérios de inclusão e exclusão (se for o caso), tamanho da amostra, características das observações, entre outros aspectos.
Descreva também as variáveis e a fonte dos dados utilizados. Cite todas as variáveis informando as categorias quando se tratar de variável qualitativa.
Em seguida, mencione os gráficos criados, as medidas calculadas, os testes de hipóteses usados e o modelo de regressão. Inclusive, mencione sobre a avaliação da normalidade dos dados. Não se esqueça de citar o software (e versão) utilizado e o nível de significância adotado.
3. Resultados e Discussão
Tabela 1
library(gtsummary)
tbl_summary(world_sem_país)O software utilizado para criação de gráficos, análise e manejo de variáveis e criação de tabelas foi o R version 4.3.1. O banco de dados utilizado foi criado por Nidula Elgiriyewithana, em julho de 2023, e publicado no site Kaggle. O banco inicial era composto por 36 variáveis distintas que abordavam diversos aspectos de todos os países do mundo. Com base na análise das variáveis, foram selecionadas aquelas que exibem certa correlação, ou a ausência dela, que instiga análises globais por meio da comparação de aspectos semelhantes, mas em diferentes países. Isso é feito levando em consideração as distintas realidades sociais e econômicas de cada nação, bem como suas respectivas consequências.
Países de maior PIB emitem mais CO2?
library(readxl)
world_data_2023 <- read_excel("~/Custom Office Templates/world-data-2023.xltx",
col_types = c("text", "text", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
#View(world_data_2023)world_data_2023$`PIB ($)`<-cut(world_data_2023$`PIB ($)`,c(0, 14083906357,95503088538,21427700000000))
#world_data_2023$`PIB ($)`
world_data_2023$`PIB ($)`
levels(world_data_2023$`PIB ($)`)<-c("Até 15 bi.", "De 15 bi. a 96 bi.", "Maior que 96 bi.")world_data_2023$`Emissão de Co2 (toneladas)`<-cut(world_data_2023$`Emissão de Co2 (toneladas)`,c(0,2065,34693,9893038))
#world_data_2023$`Emissão de Co2`
world_data_2023$`Emissão de Co2 (toneladas)`
levels(world_data_2023$`Emissão de Co2 (toneladas)`)<-c("Até 2100 ton.", "De 2100 ton. a 35000 ton.", "Maior que 35000 ton.")library(summarytools)
ctable(world_data_2023$`PIB ($)`, world_data_2023$`Emissão de Co2 (toneladas)`)Cross-Tabulation, Row Proportions
`PIB ($)` * `Emissão de Co2 (toneladas)`
Data Frame: world_data_2023
-------------------- ---------------------------- --------------- --------------------------- ---------------------- ------------ --------------
Emissão de Co2 (toneladas) Até 2100 ton. De 2100 ton. a 35000 ton. Maior que 35000 ton. <NA> Total
PIB ($)
Até 15 bi. 45 (69.2%) 15 (23.1%) 0 ( 0.0%) 5 ( 7.7%) 65 (100.0%)
De 15 bi. a 96 bi. 14 (21.5%) 45 (69.2%) 6 ( 9.2%) 0 ( 0.0%) 65 (100.0%)
Maior que 96 bi. 6 ( 9.5%) 5 ( 7.9%) 52 (82.5%) 0 ( 0.0%) 63 (100.0%)
<NA> 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 3 (100.0%) 3 (100.0%)
Total 65 (33.2%) 65 (33.2%) 58 (29.6%) 8 ( 4.1%) 196 (100.0%)
-------------------- ---------------------------- --------------- --------------------------- ---------------------- ------------ --------------library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
world_data_2023 %>%
filter(!is.na(`Emissão de Co2 (toneladas)`)) %>%
filter(!is.na(`PIB ($)`)) %>%
ggplot() +
aes(x = `PIB ($)`, fill = `Emissão de Co2 (toneladas)`) +
geom_bar(position = "fill") +
scale_fill_manual(values = c(`Até 2100 ton.` = "#BDC5D6", `De 2100 ton. a 35000 ton.` = "#5F7CAD",
`Maior que 35000 ton.` = "#03346B")) +
labs(y = " ", title = "Relação entre o PIB e as emissões de CO2 dos países",
subtitle = "Uma visão global") +
theme_light() +
theme(plot.title = element_text(size = 14L, face = "bold",
hjust = 0.5), plot.subtitle = element_text(size = 12L, hjust = 0.5))
No gráfico de barras empilhadas entre as variáveis qualitativas PIB e emissões de CO2 dos países, é possível observar na primeira coluna, representada pelos menores PIB´s (até 15 bilhões), que 69.2% são que países possuem uma baixa emissão de CO2 ( até 2.100 toneladas) e outra parte inferior de 23.1% com uma emissão de 2.100 toneladas á 35.000 toneladas. Na segunda barra, representada por PIB´s de 15 bilhões a 96 bilhões, é possível notar que 69.2% das nações com esse PIB é representada pelo valor médio de 2.100 a 35.000 toneladas de CO2, outra parte de 21.5% por até 2.100 toneladas e uma parte bem diminuta de 9.2% com valores de emissão maiores que 35.000 toneladas. Por fim, a última barra representa os países com PIB maior que 96 bilhões, onde se encontra 82.5% da emissão maior que 35.000 toneladas e outras duas partes sendo 9.5% referente a emissão de 2100 toneladas e 7.9% de 2.100 toneladas á 35.000 toneladas.
O valor do salário mínimo de um país influencia em sua taxa de natalidade?
library(dplyr)
library(ggplot2)
library(summarytools)
ctable(world_data_2023$`Salário Mínimo($)`, world_data_2023$`Taxa de natalidade`)Cross-Tabulation, Row Proportions
`Salário Mínimo($)` * `Taxa de natalidade`
Data Frame: world_data_2023
------------------- -------------------- ------------ ------------ ---------- ------------ ---------- ------------ ------------ ------------ ------------ ------------ ---------- ------------ ---------- ----------- ------------ ------------ ----------- ------------ ---------- ------------ ------------ ----------- ------------ ------------ ----------- ------------ ------------ ---------- ------------ ---------- ---------- ------------ ------------ ----------- ------------ ------------ ------------ ---------- ----------- ------------ ----------- ----------- ------------ ------------ ----------- ------------ ------------ ----------- ----------- ------------ ---------- ----------- ----------- ------------ ------------ ----------- ---------- ----------- ----------- ---------- ------------ ----------- ------------ ------------ ---------- ------------ ------------ ----------- ------------ ------------ ----------- ------------ ------------ ------------ ----------- ----------- ------------ ------------ ------------ ---------- ------------ ------------ ----------- ----------- ------------ ------------ ----------- ------------ ----------- ------------ ---------- ----------- ------------ ----------- ------------ ------------ ----------- ---------- ------------ ------------ ----------- ----------- ---------- ---------- ----------- ------------ ------------ ----------- ------------ ----------- ---------- ----------- ------------ ----------- ----------- ---------- ----------- ----------- ----------- ---------- ------------ ---------- ----------- ----------- ------------ ----------- ----------- ------------ ---------- ---------- ---------- ----------- ------------ ---------- ----------- ----------- ---------- ----------- ------------ ------------ ----------- ------------ ----------- ------------ ------------ ----------- ------------ ----------- ---------- ------------ ------------ ----------- ----------- ----------- ----------- ---------- ----------- ----------- ----------- ----------- ----------- ------------ ---------- ----------- ------------ ----------- ---------- ----------- ----------- ----------- --------------
Taxa de natalidade 5.90000 6.40000 6.80000 7.20000 7.30000 7.40000 7.90000 8.10000 8.11000 8.50000 8.60000 8.70000 8.80000 8.90000 9.00000 9.20000 9.40000 9.50000 9.54000 9.60000 9.70000 9.90000 10.00000 10.10000 10.17000 10.20000 10.30000 10.33000 10.34000 10.40000 10.46000 10.60000 10.65000 10.70000 10.90000 11.00000 11.30000 11.40000 11.50000 11.60000 11.73000 11.78000 11.98000 12.00000 12.43000 12.50000 12.60000 12.94000 13.47000 13.86000 13.89000 13.92000 13.94000 13.97000 13.99000 14.00000 14.20000 14.24000 14.88000 14.90000 15.33000 15.83000 16.03000 16.10000 16.47000 16.75000 17.02000 17.10000 17.26000 17.55000 17.56000 17.60000 17.80000 17.86000 17.88000 17.95000 18.07000 18.18000 18.25000 18.54000 18.78000 18.83000 18.94000 18.98000 19.19000 19.49000 19.51000 19.72000 19.89000 19.97000 20.51000 20.55000 20.57000 20.64000 20.79000 20.80000 21.28000 21.47000 21.60000 21.75000 21.77000 21.98000 22.46000 22.82000 23.30000 23.55000 23.69000 23.83000 24.13000 24.28000 24.30000 24.35000 24.38000 24.56000 24.82000 26.38000 26.81000 27.07000 27.10000 27.89000 28.25000 28.64000 28.75000 29.03000 29.08000 29.41000 29.42000 29.60000 30.30000 30.45000 30.68000 30.76000 31.61000 31.70000 31.88000 32.18000 32.34000 32.44000 32.49000 32.66000 32.86000 33.04000 33.11000 33.24000 33.41000 33.69000 34.12000 34.52000 35.01000 35.13000 35.35000 35.39000 35.74000 36.19000 36.22000 36.36000 36.70000 37.52000 37.91000 37.93000 38.14000 38.54000 39.01000 40.73000 41.18000 41.54000 41.75000 42.17000 46.08000 <NA> Total
Salário Mínimo($)
0.01 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 2 (100.0%)
0.05 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 2 (100.0%)
0.09 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 1 (50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 2 (100.0%)
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1.23 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (50.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 1 (50.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 2 (100.0%)
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3.11 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 1 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
3.13 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 1 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
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3.78 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
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9.99 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
10.13 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
10.29 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 1 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
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11.49 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
11.72 1 (100.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
13.05 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
13.59 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 (0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (100.0%)
<NA> 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 1 ( 2.2%) 1 ( 2.2%) 1 ( 2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 1 (2.2%) 1 (2.2%) 1 ( 2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 2 ( 4.3%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 ( 2.2%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 1 (2.2%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 1 (2.2%) 0 ( 0.0%) 0 ( 0.0%) 8 (17.4%) 46 (100.0%)
Total 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 2 ( 1.0%) 2 ( 1.0%) 2 ( 1.0%) 3 ( 1.5%) 2 ( 1.0%) 1 ( 0.5%) 2 ( 1.0%) 2 ( 1.0%) 1 (0.5%) 1 ( 0.5%) 1 (0.5%) 1 (0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 3 ( 1.5%) 1 ( 0.5%) 1 ( 0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 2 ( 1.0%) 2 ( 1.0%) 2 ( 1.0%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 2 ( 1.0%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 (0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 ( 0.5%) 1 (0.5%) 1 ( 0.5%) 1 ( 0.5%) 8 ( 4.1%) 196 (100.0%)
------------------- -------------------- ------------ ------------ ---------- ------------ ---------- ------------ ------------ ------------ ------------ ------------ ---------- ------------ ---------- ----------- ------------ ------------ ----------- ------------ ---------- ------------ ------------ ----------- ------------ ------------ ----------- ------------ ------------ ---------- ------------ ---------- ---------- ------------ ------------ ----------- ------------ ------------ ------------ ---------- ----------- ------------ ----------- ----------- ------------ ------------ ----------- ------------ ------------ ----------- ----------- ------------ ---------- ----------- ----------- ------------ ------------ ----------- ---------- ----------- ----------- ---------- ------------ ----------- ------------ ------------ ---------- ------------ ------------ ----------- ------------ ------------ ----------- ------------ ------------ ------------ ----------- ----------- ------------ ------------ ------------ ---------- ------------ ------------ ----------- ----------- ------------ ------------ ----------- ------------ ----------- ------------ ---------- ----------- ------------ ----------- ------------ ------------ ----------- ---------- ------------ ------------ ----------- ----------- ---------- ---------- ----------- ------------ ------------ ----------- ------------ ----------- ---------- ----------- ------------ ----------- ----------- ---------- ----------- ----------- ----------- ---------- ------------ ---------- ----------- ----------- ------------ ----------- ----------- ------------ ---------- ---------- ---------- ----------- ------------ ---------- ----------- ----------- ---------- ----------- ------------ ------------ ----------- ------------ ----------- ------------ ------------ ----------- ------------ ----------- ---------- ------------ ------------ ----------- ----------- ----------- ----------- ---------- ----------- ----------- ----------- ----------- ----------- ------------ ---------- ----------- ------------ ----------- ---------- ----------- ----------- ----------- --------------library(ggplot2)
ggplot(world_data_2023) +
aes(x =`Salário Mínimo($)`, y =`Taxa de natalidade`) +
geom_point(shape = "circle",
size = 1.3, colour = "#062F57") +
geom_smooth(span = 0.8) +
labs(x = "Nível do salário mínimo em moeda local",
y ="Taxa de Natalidade: Número de nascimentos por 1.000 habitantes por ano") +
theme_light()`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 46 rows containing non-finite values (`stat_smooth()`).Warning: Removed 46 rows containing missing values (`geom_point()`).
Uma análise das variáveis salário mínimo e taxa de natalidade revelou uma correlação entre essas duas variáveis. O objetivo era entender como elas se relacionam e responder a perguntas sobre os locais com salários mínimos mais elevados e suas taxas de natalidade, bem como se o salário mínimo afeta a média de filhos. Para isso, foi realizada uma análise de correlação por meio de um gráfico de dispersão de pontos e uma tabela de dados.
Observando o gráfico, identificou-se que a distribuição de pontos entre as variáveis assemelha-se a uma curva de tendência decrescente. Isso sugere que um aumento no salário mínimo está associado a uma redução na taxa de natalidade. Portanto, países com salários mínimos mais elevados tendem a ter uma taxa de natalidade mais baixa em comparação com países que têm salários mínimos mais baixos.
A quantidade de emissão de CO2 de um país influencia na extensão de sua área florestal?
library(readxl)
world_data_2023 <- read_excel("~/Custom Office Templates/world-data-2023.xltx",
col_types = c("text", "text", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"))
#View(world_data_2023)world_data_2023$`Emissão de Co2 (toneladas)`<-cut(world_data_2023$`Emissão de Co2 (toneladas)`,c(0,2065,34693,9893038))
#world_data_2023$`Emissão de Co2`
world_data_2023$`Emissão de Co2 (toneladas)`
levels(world_data_2023$`Emissão de Co2 (toneladas)`)<-c("Até 2100 ton.", "De 2100 ton. a 35000 ton.", "Maior que 35000 ton.")world_data_2023$`Área florestal (%)`<- as.numeric(world_data_2023$`Área florestal (%)`)
world_data_2023$`Área florestal (%)`<-cut(world_data_2023$`Área florestal (%)`,c(0,31.8,98.3))
#world_data_2023$`Área florestal (%)`
levels(world_data_2023$`Área florestal (%)`)<-c("Até 32%", "De 32% a 99%")library(dplyr)
library(ggplot2)
library(summarytools)
ctable(world_data_2023$`Emissão de Co2 (toneladas)`, world_data_2023$`Área florestal (%)`)Cross-Tabulation, Row Proportions
`Emissão de Co2 (toneladas)` * `Área florestal (%)`
Data Frame: world_data_2023
---------------------------- -------------------- ------------ -------------- ------------- --------------
Área florestal (%) Até 32% De 32% a 99% <NA> Total
Emissão de Co2 (toneladas)
Até 2100 ton. 31 (47.7%) 33 (50.8%) 1 ( 1.5%) 65 (100.0%)
De 2100 ton. a 35000 ton. 30 (46.2%) 35 (53.8%) 0 ( 0.0%) 65 (100.0%)
Maior que 35000 ton. 31 (53.4%) 25 (43.1%) 2 ( 3.4%) 58 (100.0%)
<NA> 0 ( 0.0%) 0 ( 0.0%) 8 (100.0%) 8 (100.0%)
Total 92 (46.9%) 93 (47.4%) 11 ( 5.6%) 196 (100.0%)
---------------------------- -------------------- ------------ -------------- ------------- --------------world_data_2023 %>%
filter(!is.na(`Emissão de Co2 (toneladas)`)) %>%
filter(!is.na(`Área florestal (%)`)) %>%
ggplot() +
aes(x = `Emissão de Co2 (toneladas)`, fill = `Área florestal (%)`) +
geom_bar(position = "fill") +
scale_fill_manual(values = c(`Até 32%` = "#ACC9E2", `De 32% a 99%` = "#19498B")) +
labs(x = "Emissões de dióxido de carbono (CO2) em toneladas",
y = " ", title = "Relação entre a área florestal e as emissões de CO2 dos países", subtitle = "Uma visão global") +
theme_light() +
theme(plot.title = element_text(size = 14L, face = "bold", hjust = 0.5), plot.subtitle = element_text(size = 12L,
hjust = 0.5))
O objetivo dessa correlação é ver se a taxa de emissão de dióxido de carbono vai influenciar no tamanho da área florestal. A pergunta que utilizamos para fazermos essa correlação foi: A quantidade de emissão de CO2 de um país influencia na extensão de sua área florestal? Para responder a essa pergunta, utilizamos uma variável quantitativa (Emissão de CO2) e uma qualitativa (Área florestal). E para que tal análise fosse feita, elaboramos um gráfico de boxplot e uma tabela descritiva. A partir de uma análise descritiva do gráfico acima, é possível observar que as áreas que emitem uma quantidade de dióxido de carbono maior que 35.000 toneladas possuem sua maior parte com uma área florestal, chegando até os 32%, enquanto as áreas que emitem até 35.000 toneladas de dióxido de carbono possuem sua maior parte com uma área florestal que varia entre 32% a 99%.
Observamos mais detalhadamente na tabela que até 2100 toneladas de CO2, a área florestal de até 32% corresponde a 47.7%, enquanto de 32% a 99% corresponde a 50.8%. Já de 2100 até 35000 toneladas de CO2, a área florestal de até 32% corresponde a 46.2%, enquanto de 32% a 99% corresponde a 53.8%. Agora, emissões maiores que 35000 toneladas de CO2, a área florestal de até 32% corresponde a 53.4%, enquanto de 32% a 99% corresponde a 43.1%.
Qual a taxa de desemprego nos diferentes continentes?
world_data_2023$`Taxa de desemprego (%)`<-cut(world_data_2023$`Taxa de desemprego (%)`,c(0,14,29))
#world_data_2023$`Taxa de desemprego (%)`
levels(world_data_2023$`Taxa de desemprego (%)`)<-c("Até 14%", "Maior que 14% e até 30%")
library(dplyr)
library(ggplot2)
library(summarytools)
ctable(world_data_2023$`Taxa de desemprego (%)`, world_data_2023$Continente)
library(dplyr)
library(ggplot2)
world_data_2023 %>%
filter(!is.na(Continente)) %>%
filter(!is.na(`Taxa de desemprego (%)`)) %>%
ggplot() +
aes(x = Continente, fill = `Taxa de desemprego (%)`) +
geom_bar(position = "fill") +
scale_fill_manual(values = c(`Até 14%` = "#93AEE9", `Maior que 14% e até 30%` = "#082264")) +
labs(y = " ",
title = "Porcentagem da força de trabalho que está desempregada por continente") +
theme_minimal() +
theme(plot.title = element_text(size = 14L, face = "bold", hjust = 0.5))O objetivo da seguinte correlação é a obtenção de um panorama global do comportamento da taxa de desemprego em todos os continentes. A pergunta que guiou essa correlação foi: Qual a taxa de desemprego nos diferentes continentes? Para responder a essa pergunta, foram utilizadas duas variáveis qualitativas: Taxa de Desemprego (%) e Continente. Dessa forma, para que a análise da correlação entre essas variáveis fosse feita, um gráfico de barras empilhadas e uma tabela descritiva foram construídos. A partir de uma análise estatística descritiva do gráfico e da tabela acima de maneira conjunta, é possível observar que os continentes América do Sul e Oceania possuem uma taxa de desemprego de no máximo 14%. Além disso, o continente africano possui a maior porcentagem relativa de taxa de desemprego entre 14% e 30% (47,1%, o correspondente a um valor absoluto de 8 países africanos). Adicionalmente, vê-se que o continente Oceania possui a menor porcentagem relativa de países com taxa de desemprego até 14% (5,0%, o correspondente a 8 países deste continente).
Qual a densidade nos diferentes continentes?
library(ggplot2)
library(dplyr)
library(ggplot2)
world_data_2023 %>%
filter(!is.na(Continente)) %>%
filter(!is.na(`Densidade (P/Km2)`)) %>%
ggplot() +
aes(x = Continente, y = `Densidade (P/Km2)`) +
geom_boxplot(fill = "#C5D3ED") +
labs(title = "Densidade dos Continentes") +
theme_minimal() +
theme(plot.title = element_text(size = 14L,
face = "bold", hjust = 0.5))
# Por conta do outlyer visto no continente europeu, fizemos um gráfico com a densidade reduzida para até 10 mil pessoas por quilômetro quadrado para possibilitar a vizualização das medianas:
with(world_data_2023, stby(`Densidade (P/Km2)`, Continente, descr))Descriptive Statistics
Densidade (P/Km2) by Continente
Data Frame: world_data_2023
N: 54
África América do Norte América do Sul Ásia Europa Oceania
----------------- -------- ------------------ ---------------- --------- ---------- ---------
Mean 103.46 189.48 24.36 453.00 752.63 185.00
Std.Dev 135.10 157.10 19.42 1258.55 3912.38 239.37
Min 3.00 4.00 4.00 2.00 1.38 3.00
Q1 25.00 58.00 11.00 60.00 64.00 25.00
Median 54.50 167.00 20.00 115.00 105.00 59.50
Q3 111.00 284.00 26.00 341.00 164.00 329.00
Max 626.00 668.00 71.00 8358.00 26337.00 784.00
MAD 54.86 161.60 8.90 130.47 85.99 72.65
IQR 85.25 216.50 12.00 263.50 100.00 258.50
CV 1.31 0.83 0.80 2.78 5.20 1.29
Skewness 2.30 1.14 1.14 5.35 6.21 1.26
SE.Skewness 0.32 0.48 0.66 0.35 0.35 0.60
Kurtosis 4.93 1.28 0.41 30.24 37.64 0.31
N.Valid 54.00 23.00 11.00 47.00 45.00 14.00
Pct.Valid 100.00 100.00 100.00 100.00 100.00 100.00library(dplyr)
library(ggplot2)
world_data_2023 %>%
filter(`Densidade (P/Km2)` >= 0L & `Densidade (P/Km2)` <= 10000L) %>%
ggplot() +
aes(x = Continente, y = `Densidade (P/Km2)`) +
geom_boxplot(fill = "#E4EDFF") +
labs(title = "Densidade dos continentes", subtitle = "Um recorte: Até 10 mil habitantes por km²") +
theme_light() +
theme(plot.title = element_text(size = 14L, face = "bold", hjust = 0.5), plot.subtitle = element_text(size = 13L,
hjust = 0.5))
Para obter uma avaliação abrangente da densidade demográfica em todo o mundo, utilizamos duas variáveis: Densidade (P/Km2) {quantitativa} e Continente (qualitativa). Com essas variáveis, geramos um gráfico de BoxPlot e uma tabela descritiva. No primeiro gráfico, o limite superior da densidade é definido em 20.000 (P/Km2). Entretanto, como evidenciado no gráfico e na tabela, há um valor atípico no continente europeu que ultrapassa os 20.000 (P/Km2) de densidade. Isso resulta em uma compressão das caixas (boxplots) dos outros continentes, prejudicando a visualização geral do gráfico. Portanto, para melhor visualizar os quartis e a mediana, criamos um segundo gráfico em que o limite de densidade é ligeiramente superior a 8.000. Com esse ajuste, o segundo gráfico proporciona uma visualização mais clara dos dados apresentados no gráfico de boxplot.
######################################MODELO###########################################
Apresente os principais resultados obtidos na pesquisa de forma clara e objetiva.
Comece descrevendo sua amostra a partir dos resultados obtidos com a Tabela 1 criada com o gtsummary. Informe as porcentagem das categorias mais frequentes e a mediana e IQR das variáveis quantitativas. Dê um título para a tabela. O título vem antes da tabela.
Logo após a Tabela 1, crie tópicos para cada pergunta, insira o texto referente à interpretação de cada gráfico e, em seguida, o gráfico correspondente. Lembre-se que nesta etapa não podemos concluir nada. Por isso, descreva com base nas medidas que aprendemos.
Em cada tópico criado, descreva os resultados encontrados com os testes. Citem o nome do teste aplicado e o valor-p encontrado. Se realizarem teste qui-quadrado ou exato de Fisher, falem sobre “associação estatisticamente significativa”. Se realizarem teste t ou ANOVA, falem em “diferença estatisticamente significativa das médias”. Se realizarem teste de Mann-Whitney ou Kruskal-Wallis, falem em “diferença estatisticamente significativa das distribuições”. Se realizarem ANOVA ou Kruskal-Wallis e eles forem significativos, realizar um teste post-hoc (Tukey ou Dunn).
Quanto ao modelo de regressão, inclua a tabela gerada no R e interprete os resultados. Importante: se o modelo for de regressão linear, explicar o significado dos coeficientes e informar o valor-p daqueles que foram significativos. Se o modelo for de regressão logística, informar as razões de chance, sua interpretação e o valor-p daquelas variáveis que foram significativas.
Utilize, pelo menos, dois artigos científicos para comparar com os achados do estudo.
4. Conclusão
A seção de conclusão de um artigo científico é a parte final do trabalho, onde você deve resumir os principais pontos abordados no estudo e apresentar suas considerações finais. O que o seu estudo observou? Pegue os principais resultados e escreva de forma sucinta.
5. Referências
GROSFOGUEL, Ramón. Para descolonizar os estudos de economia política e os estudos pós-coloniais: transmodernidade, pensamento de fronteira e colonialidade global. Revista crítica de ciências sociais, n. 80, p. 115-147, 2008.
HELPMAN, Elhanan; ITSKHOKI, Oleg; REDDING, Stephen. Inequality and unemployment in a global economy. Econometrica, v. 78, n. 4, p. 1239-1283, 2010.
SANTOS, R. I. C. (Setembro de 2022). Qual o papel das florestas para a redução do CO2: aplicação a um grupo de países da União Europeia? U Porto, 51.
SÉRGIO CERETTA, P.; AURÉLIO VIEIRA, M. Co2 Emissions: A Dynamic Structural Analysis: EMISSÕES DE CO2: UMA ANÁLISE ESTRUTURAL DINÂMICA. Brazilian Journal of Management / Revista de Administração da UFSM, v. 14, p. 949–969, 2 dez. 2021.