Solution
library("car")
library("MASS")
plants<-read.csv("D:/Work/REGRESSION-ANOVA-IDER/plants.csv", header=TRUE)
head(plants)
## numberofspecies areakm2 elevationm numsoiltype latitude isolation
## 1          75 0.8    340       1 55.3    14.0
## 2          577 429.4    874       4 55.6    5.2
## 3          177 31.1    226       1 60.1 201.6
## 4          1666 22984.8    1343       16 54.3    0.0
## 5          443 74.1    103       3 56.6    14.5
## 6          453 29.0    393       1 56.9    12.3
plants$numberofspecies_log<-log(plants$numberofspecies)
plants$areakm2_log <-log(plants$areakm2)
plants$numsoiltype_log <-log(plants$numsoiltype)
plants$isolation_log <-log(plants$isolation)

# Str of the data
str(plants)
## 'data.frame': 15 obs. of 10 variables:
## $ numberofspecies : int 75 577 177 1666 443 453 189 401 388 444 ...
## $ areakm2          : num 0.8 429.4 31.1 22984.8 74.1 ...
## $ elevationm       : int 340 874 226 1343 103 393 159 101 101 784 ...
## $ numsoiltype    : int 1 4 1 16 3 1 2 1 1 3 ...
## $ latitude       : num 55.3 55.6 60.1 54.3 56.6 56.9 60.6 55.7 56.3 56 ...
## $ isolation       : num 14 5.2 201.6 0 14.5 ...
## $ numberofspecies_log: num 4.32 6.36 5.18 7.42 6.09 ...
## $ areakm2_log    : num -0.223 6.062 3.437 10.043 4.305 ...
## $ numsoiltype_log : num 0 1.39 0 2.77 1.1 ...
## $ isolation_log    : num 2.64 1.65 5.31 -Inf 2.67 ...
# Checking for outliers
boxplot(plants$numberofspecies)

8.2 Lab 2 After working on this lab, you should be able to: Const...