library(tidyverse)
library(Stat2Data)
data("SpeciesArea")

Create a scatter plot of Area on the x-axis and Species on the y-axis

ggplot(SpeciesArea, aes(x = Area, y = Species)) + 
  geom_point() + 
  labs(title = "Scatter plot of Area vs Species")

Create a scatter plot of Area on the x-axis and log(Species) on the y-axis

ggplot(SpeciesArea, aes(x = Area, y = log(Species))) + 
  geom_point() + 
  labs(title = "Relationship between Area and Log of Species")

Create a scatter plot of log(Area) on the x-axis and log(Species) on the y-axis

ggplot(SpeciesArea, aes(x = log(Area), y = log(Species))) + 
  geom_point() + 
  labs(title = "Relationship between log (Area) and log(Species)")

Fit a simple linear regression of logSpecies ~ logArea

model <- lm(log(Species) ~ log(Area), data = SpeciesArea)

Examine the residuals vs.ย fits plot for the model logArea ~ logSpecies

SpeciesArea %>%
  mutate(y_hat = predict(model),
         residual = log(Species) - y_hat) %>%
  ggplot(aes(x = y_hat, y = residual)) + 
  geom_point() + 
  geom_hline(yintercept = 0) +
  labs(title = "Residuals versus fits plot",
       x = "predicted log of Species")