Part I

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  
##                 
##                 
##

(a) How many cases were included in the data?

  dim(iris)
## [1] 150   5

There were 150 cases across the three species with 50 cases for each species.

(b) How many numerical variables are included in the data? Indicate what they are, and if they are continuous or discrete.

  range(iris$Sepal.Length)
## [1] 4.3 7.9
  range(iris$Sepal.Width)
## [1] 2.0 4.4
  range(iris$Petal.Length)
## [1] 1.0 6.9
  range(iris$Petal.Width)
## [1] 0.1 2.5

There are 600 numeric variables, as there are 150 cases for each of the four variables (sepal or petal, and length or width). These are continuous variables as the context of width and length would allow for any real number value greater than 0.

(c) How many categorical variables are included in the data, and what are they? List the corresponding levels (categories).

  unique(iris$Species)
## [1] setosa     versicolor virginica 
## Levels: setosa versicolor virginica

There are 150 categorical variables, which take 3 unique values (setosa, versicolor, virginica).

Part II

  require(graphics)
  coplot(weight ~ Time | Chick, data = ChickWeight,
       type = "b", show.given = FALSE)

Provided are time series plots for each chick (numbered in dataset) where weight is plotted against time.