Iris Data Set

data(iris)
str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

library(psych)

describe(iris)

##              vars   n mean   sd median trimmed  mad min max range  skew
## Sepal.Length    1 150 5.84 0.83   5.80    5.81 1.04 4.3 7.9   3.6  0.31
## Sepal.Width     2 150 3.06 0.44   3.00    3.04 0.44 2.0 4.4   2.4  0.31
## Petal.Length    3 150 3.76 1.77   4.35    3.76 1.85 1.0 6.9   5.9 -0.27
## Petal.Width     4 150 1.20 0.76   1.30    1.18 1.04 0.1 2.5   2.4 -0.10
## Species*        5 150 2.00 0.82   2.00    2.00 1.48 1.0 3.0   2.0  0.00
##              kurtosis   se
## Sepal.Length    -0.61 0.07
## Sepal.Width      0.14 0.04
## Petal.Length    -1.42 0.14
## Petal.Width     -1.36 0.06
## Species*        -1.52 0.07

by(iris, iris$Species, summary)

## iris$Species: setosa
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.300   Min.   :1.000   Min.   :0.100  
##  1st Qu.:4.800   1st Qu.:3.200   1st Qu.:1.400   1st Qu.:0.200  
##  Median :5.000   Median :3.400   Median :1.500   Median :0.200  
##  Mean   :5.006   Mean   :3.428   Mean   :1.462   Mean   :0.246  
##  3rd Qu.:5.200   3rd Qu.:3.675   3rd Qu.:1.575   3rd Qu.:0.300  
##  Max.   :5.800   Max.   :4.400   Max.   :1.900   Max.   :0.600  
##        Species  
##  setosa    :50  
##  versicolor: 0  
##  virginica : 0  
##                 
##                 
##                 
## ------------------------------------------------------------ 
## iris$Species: versicolor
##   Sepal.Length    Sepal.Width     Petal.Length   Petal.Width          Species  
##  Min.   :4.900   Min.   :2.000   Min.   :3.00   Min.   :1.000   setosa    : 0  
##  1st Qu.:5.600   1st Qu.:2.525   1st Qu.:4.00   1st Qu.:1.200   versicolor:50  
##  Median :5.900   Median :2.800   Median :4.35   Median :1.300   virginica : 0  
##  Mean   :5.936   Mean   :2.770   Mean   :4.26   Mean   :1.326                  
##  3rd Qu.:6.300   3rd Qu.:3.000   3rd Qu.:4.60   3rd Qu.:1.500                  
##  Max.   :7.000   Max.   :3.400   Max.   :5.10   Max.   :1.800                  
## ------------------------------------------------------------ 
## iris$Species: virginica
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.900   Min.   :2.200   Min.   :4.500   Min.   :1.400  
##  1st Qu.:6.225   1st Qu.:2.800   1st Qu.:5.100   1st Qu.:1.800  
##  Median :6.500   Median :3.000   Median :5.550   Median :2.000  
##  Mean   :6.588   Mean   :2.974   Mean   :5.552   Mean   :2.026  
##  3rd Qu.:6.900   3rd Qu.:3.175   3rd Qu.:5.875   3rd Qu.:2.300  
##  Max.   :7.900   Max.   :3.800   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    : 0  
##  versicolor: 0  
##  virginica :50  
##                 
##                 
##

plot(x = iris$Sepal.Width, y = iris$Sepal.Length,
     xlab = 'Sepal Width', 
     ylab = 'Sepal Length',
     col = iris$Species,
     main = 'Iris Sepal Width by Length',
     xlim = c(0,5),
     ylim = c(0,10))

legend("topright",
       legend = unique(iris$Species),
       col = c('black', 'red', 'green'),
       pch = 1)

plot(x = iris$Petal.Width, 
     y = iris$Petal.Length,
     xlab = 'Petal Width',
     ylab = 'Petal Length',
     xlim = c(0,4),
     col = unique(iris$Species))

legend("topleft",
       legend = unique(iris$Species),
       col = unique(iris$Species),
       pch = 1)

Questions:

There are 150 total cases in the data. However, there is 50 cases for each specific species of iris flower; which there were 3 of.
5 numeric variables in the data

Discrete: ‘Observation Number’: measuring occurrences and has counted and finite value.

Continuous: Sepal.Width, Sepal.Length, Petal.Width, Petal.Length: these are continuous because these variables are on a continuous scale and have decimal places.

There is one categorical variable present in the dataset; Species. The Species variable has 3 categories (setosa, versicolor, virginica).

boxplot(iris, col = c('blue', 'green', 'red', 'yellow', 'purple'),
        main = 'Iris Data Boxplot')

BOD Data Set

data(BOD)
plot(y = BOD$demand, x = BOD$Time, type = 'l',
     xlab = 'Time', ylab = 'Oxygen Demand')

describe(BOD)

##        vars n  mean   sd median trimmed  mad min  max range  skew kurtosis   se
## Time      1 6  3.67 2.16    3.5    3.67 2.22 1.0  7.0   6.0  0.26    -1.58 0.88
## demand    2 6 14.83 4.63   15.8   14.83 5.34 8.3 19.8  11.5 -0.29    -1.86 1.89

The dataset BOD shows Biochemical Oxygen Demand with ‘Time’ and ‘Oxygen Demand’ as the variables. This is a time series because it is tracking one entity (biochemical oxygen demand) over time.

plot.ts(BOD)

Discussion2

Ryan OHara

2023-09-11

Iris Data Set

BOD Data Set