Zille Engineer_ADEC Discussion Week 1

Problem 1.7: Fisher’s “iris” dataset

1. There are 150 observations or cases in the dataset
2. There are 4 numerical variables in the dataset:

• Petal Length
• Sepal Length
• Petal width
• Sepal width

All 4 are continuous variables.

3. “Species” is a categorical variable and the values or labels are setosa, versicolor and vriginica.

Data Description

The dataset is summarized below:

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 

library(psych)
pairs.panels(iris[1:4],gap=0)

Using the “pairs.panel” function in library “psych” for this dataset, a high correlation can be observed between:

1. Sepal Length and Petal Length,

2. Sepal Length and Petal Width and

3. Petal Length and Petal Width.

Using a Linear model to run linear regression by species:

plot(iris$Sepal.Width, iris$Sepal.Length, pch=21, bg=c("red","green3","blue")[unclass(iris$Species)], main="Edgar Anderson's Iris Data", xlab="Petal length", ylab="Sepal length")
abline(lm(Sepal.Length ~ Sepal.Width, data=iris)$coefficients, col="black")
abline(lm(Sepal.Length ~ Sepal.Width, data=iris[which(iris$Species=="setosa"),])$coefficients, col="red")
abline(lm(Sepal.Length ~ Sepal.Width, data=iris[which(iris$Species=="versicolor"),])$coefficients, col="green3")
abline(lm(Sepal.Length ~ Sepal.Width, data=iris[which(iris$Species=="virginica"),])$coefficients, col="blue")

Petal length and Sepal length are highly correlated by species.