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    # Start coding here...
    Run error
    library(tidyverse)
    library(modelr)
    library(broom)
    
    # Load the data
    data("swiss")
    # Inspect the data
    sample_n(swiss, 3)
    
    summary(swiss)
    #Using Linear Model
    Model0 <- lm(Fertility ~1, data = swiss)
    Model1 <- lm(Fertility ~., data = swiss)
    Model2 <- lm(Fertility ~. -Examination, data = swiss)
    summary(Model0)
    summary(Model1)
    summary(Model2)
    
    
    cbind(AIC(Model0),AIC(Model1),AIC(Model2))
    cbind(BIC(Model0),BIC(Model0),BIC(Model2))
    
    library(modelr)
    data.frame(
      R2 = rsquare(Model1, data = swiss),
      RMSE = rmse(Model1, data = swiss),
      MAE = mae(Model1, data = swiss)
    )
    
    
    library(caret)
    predictions <- Model1 %>% predict(swiss)
    data.frame(
      R2 = R2(predictions, swiss$Fertility),
      RMSE = RMSE(predictions, swiss$Fertility),
      MAE = MAE(predictions, swiss$Fertility)
    )
    
    library(broom)
    rbind(glance(Model0),glance(Model1),glance(Model2))
    Run error
    install.packages("Rtools")
    Run error
    library(d3heatmap)
    library(mplot)
    library(pairsD3)
    library(Hmisc)
    library(mfp)
    library(dplyr)
    loc <- "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt"
    dat <- as.matrix(read.table(loc,header=T,sep="\t"))
    dim(dat)
    data("diabetes", package = "mplot")
    # help('diabetes', package='mplot')
    str(diabetes)  # structure of the diabetes
    summary(diabetes) #Descriptive Statistics
    pairs(diabetes)  # traditional pairs plot
    round(cor(diabetes),4)
    boxplot(diabetes)  # always a good idea to check for gross outliers
    pairsD3::shinypairs(diabetes)  # interactive pairs plot of the data set
    d3heatmap::d3heatmap(cor(diabetes))
    Hmisc::describe(diabetes, digits = 1)  # summary of the diabetes data
    #Example Linear Model
    Model0 = lm(y ~ 1, data = diabetes)  # Null model
    summary(Model0)
    Model1 = lm(y ~ ., data = diabetes)  # Full model
    summary(Model1)
    cbind(AIC(Model0),AIC(Model1))
    cbind(BIC(Model0),BIC(Model1))
    #backward selecton using BIC and AIC
    step.back.bic = step(Model1, direction = "backward", trace = FALSE, k = log(442))
    summary(step.back.bic)
    step.back.aic = step(Model1, direction = "backward", trace = FALSE, k = 2)
    summary(step.back.aic)
    #Example Generalised Linear Model (GLM)
    Model00 = glm(y ~ 1, data = diabetes)  # Null model
    Model11 = glm(y ~ ., data = diabetes)  # Full model BY default Gaussian
    summary(Model11)
    summary(Model00)
    Model22 <- glm(y ~ ., data = diabetes, family = "gaussian")
    summary(Model22)
    step.back.aic = step(Model22, direction = "backward", trace = FALSE, k = 2)
    summary(step.back.aic)
    anova(Model22, test = "Chisq")