mirror of
https://github.com/ArthurDanjou/ArtStudies.git
synced 2026-01-23 19:51:48 +01:00
Add TP1 GLM & DM Monte Carlo
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M1/General Linear Models/TP1/.RData
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M1/General Linear Models/TP1/.RData
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M1/General Linear Models/TP1/.Rhistory
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M1/General Linear Models/TP1/.Rhistory
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library(rmarkdown)
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health <- read.table("health.txt", header = TRUE, sep = " ", dec = ".")
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paged_table(health)
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library(dplyr)
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names(health)
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library(dplyr)
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names(health)
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Health<-health[,2:4]
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names(Ozone)
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library(dplyr)
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names(health)
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Health<-health[,2:4]
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names(Health)
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library(dplyr)
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names(health)
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Health<-health[,2:5]
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names(Health)
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library(ggplot2)
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library(plotly)
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p1<-ggplot(health) + aes(x = T12, y = maxO3) + geom_point(col = 'red', size = 0.5) + geom_smooth(method = "lm", se = FALSE)
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ggplotly(p1)
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library(ggplot2)
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library(plotly)
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p1<-ggplot(health) + aes(x = age, y = y) + geom_point(col = 'red', size = 0.5) + geom_smooth(method = "lm", se = FALSE)
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ggplotly(p1)
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library(rmarkdown)
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health <- read.table("health.txt", header = TRUE, sep = " ", dec = ".")
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paged_table(health)
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library(rmarkdown)
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health <- read.table("health.txt", header = TRUE, sep = " ", dec = ".")
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paged_table(health)
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library(dplyr)
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library(corrplot)
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install.packages("corrplot")
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(Health)
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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GGally
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install.packages("GGally")
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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library(GGally)
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ggpairs(Ozone)
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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library(GGally)
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ggpairs(health)
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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library(GGally)
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ggpairs(health)
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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Health <- health[2:5]
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library(dplyr)
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library(corrplot)
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correlation_matrix<-cor(Health)
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corrplot(correlation_matrix, order = 'hclust',addrect = 3)
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model <- lm(y ~ ., data = Health)
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coefficients(model)
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summary(model)
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library(ggfortify)
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install.packages("ggfortify")
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library(ggfortify)
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autoplot(mod,1)
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library(ggfortify)
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autoplot(model,1)
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library(ggfortify)
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autoplot(model,1)
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autoplot(model,3)
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library(ggfortify)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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install.packages("car")
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library(ggfortify)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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library(ggfortify)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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autoplot(model,2)
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model <- lm(y ~ age, data = Health)
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coefficients(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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autoplot(model,2)
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model <- lm(y ~ ., data = Health)
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coefficients(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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autoplot(model,2)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,3)
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set.seed(0)
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durbinWatsonTest(model)
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autoplot(model,2)
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model <- lm(y ~ ., data = Health)
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coefficients(model)
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summary(model)
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model <- lm(y ~ age, data = Health)
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coefficients(model)
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summary(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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model <- lm(y ~ ., data = Health)
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coefficients(model)
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summary(model)
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library(ggfortify)
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library(car)
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autoplot(model,1)
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library(GGally)
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ggpairs(model)
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library(GGally)
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ggpairs(health)
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library(GGally)
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ggpairs(Health)
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library(GGally)
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ggpairs(Health, mapping = F)
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library(GGally)
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ggpairs(Health, progress = F)
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model2 <- lm(y ~ age**2 + tri + chol, data = Health)
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model2 <- lm(y ~ age**2 + tri + chol, data = Health)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,3)
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set.seed(0)
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durbinWatsonTest(model2)
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autoplot(model2,2)
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model2 <- lm(y ~ sqrt(age) + tri + chol, data = Health)
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summary(model2)
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coefficients(model2)
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model2 <- lm(y ~ sqrt(age) + tri + chol, data = Health)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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Health2 <- Health
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Health2[1] <- Health2[1]^2
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model2 <- lm(y ~ ., data = Health)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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View(Health)
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View(Health)
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View(Health2)
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View(Health2)
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Health2 <- Health
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View(Health2)
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View(Health2)
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Health2 <- Health
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Health2[2] <- Health2[2]^2
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model2 <- lm(y ~ ., data = Health)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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View(Health2)
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View(Health2)
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autoplot(model2,3)
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Health2 <- Health
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Health2[2] <- Health2[2]^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,3)
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set.seed(0)
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durbinWatsonTest(model2)
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autoplot(model2,2)
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Health2 <- Health
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Health2[2] <- sqrt(Health2[2])
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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Health2 <- Health
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Health2[2] <- Health2[2]^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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Health2 <- Health
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Health2[2] <- Health2[2]^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,3)
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Health2 <- Health
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Health2[6] <- Health2[2]^2
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View(Health2)
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View(Health2)
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Health2 <- c(Health, c())
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Health2[6] <- Health2[2]^2
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Health2 <- c(Health, c())
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Health2[5] <- Health2[2]^2
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View(Health2)
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View(Health2)
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Health2 <- Health
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Health2 <- append(Health2, Health[2]^2)
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model2 <- lm(y ~ ., data = Health2)
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View(Health2)
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Health2 <- Health
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Health2 <- append(Health2, Health[2]^2)
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model2 <- lm(y ~ ., data = Health2)
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Health2 <- Health
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# Add to Health a new column with the square of the age named age_sq
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Health2$age_sq <- Health2$age^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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View(Health2)
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Health2 <- Health
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the age named age_sq
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,3)
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autoplot(model2,2)
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View(Health2)
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View(Health2)
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Health2 <- Health
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Health2$age_sq <- sqrt(Health2$age)
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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View(Health2)
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View(Health2)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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Health2 <- Health2[1:99,]
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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Health2 <- Health2[1:5,24]
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model2 <- lm(y ~ ., data = Health2)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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Health2 <- Health2[1:5,]
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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View(Health2)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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Health2 <- Health2[1:24,]
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,3)
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set.seed(0)
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durbinWatsonTest(model2)
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autoplot(model2,2)
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Health2 <- Health
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Health2$age_sq <- Health2$age^2
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Health2 <- Health2[1:24,]
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model2 <- lm(y ~ ., data = Health2)
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summary(model2)
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coefficients(model2)
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@@ -23,16 +23,12 @@ summary(model)
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```{r}
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library(ggfortify)
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library(car)
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autoplot(model,1)
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autoplot(model,1:3)
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```
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The points are not well distributed around 0 -> [P1] is not verified
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```{r}
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autoplot(model,3)
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```
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The points are not well distributed around 1 -> [P2] is not verified
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The QQPlot is align with the line y = x, so it is globally gaussian -> [P4] is verified
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```{r}
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set.seed(0)
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@@ -41,12 +37,6 @@ durbinWatsonTest(model)
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The p-value is 0.58 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified
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```{r}
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autoplot(model,2)
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```
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The QQPlot is align with the line y = x, so it is globally gaussian -> [P4] is verified
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```{r}
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library(GGally)
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ggpairs(Health, progress = F)
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@@ -68,16 +58,12 @@ coefficients(model2)
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```{r}
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library(ggfortify)
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library(car)
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autoplot(model2,1)
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autoplot(model2,1:4)
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```
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The points are well distributed around 0 -> [P1] is verified
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```{r}
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autoplot(model2,3)
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```
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The points are not well distributed around 1 -> [P2] is verified
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The QQPlot is align with the line y = x, so it is gaussian -> [P4] is verified
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```{r}
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set.seed(0)
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@@ -86,8 +72,3 @@ durbinWatsonTest(model2)
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The p-value is 0.294 > 0.05 -> We do not reject H0 so the residuals are not autocorrelated -> [P3] is verified
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```{r}
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autoplot(model2,2)
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```
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The QQPlot is align with the line y = x, so it is gaussian -> [P4] is verified
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Reference in New Issue
Block a user