INSTRUCTIONS

You will need to use $R$ to answer this question.

You must submit a single pdf file containing your complete solutions including the $R$ code and output.

Solutions need not be typed $($although they can be$)$ but handwritten solutions will need to be scanned

to a pdf file which must be readable.

Question $1$

You will work with the NHANES dataset.

Task $1$: Load the NHANES package and the NHANES dataset in your workspace. $(1)$

Task $2$: Remove the duplicate cases from the dataframe and store the new dataframe in another object. I

am calling it df here. $(1)$

Use the code below for this task:

#Extract the unique IDs

df NHANES$[!$duplicated$($NHANES$ID$),]$

Task $3$: Inspect the HHIncome variable!

How would you classify the variable

$($i$)$ based on its characteristic $-$ Quantitative or Qualitative?

$($ii$)$ based on how $R$ interprets it$?$

$($iii$)$ based on scales of measurement? $(3)$

How many categories are there in the variable? $(1)$

Calculate the frequency table and report the category with highest frequency $($modal category$).(2)$

To display the variable, we can use a Barplot. In R$,$ the barplot$()$ function can be used to create it$.$

Use the code below to create the plot. $(2)$HHI$\_$bp barplot$($freq$\_$table, horiz $=$ TRUE, yaxt$=\text{'}$n$\text{'})$axis$(2,$ at$=$HHI$\_$bp$,$ labels$=$levels$($df$HHIncome$),$ las $=1)$Provide a suitable title using the main parameter and labels for x$-$axis and y$-$axis using xlab and ylab

parameter. $(3)$

Task $4$: Inspect the BMI variable!

How would you classify the variable

$($i$)$ based on its characteristic $-$ Quantitative or Qualitative?

$($ii$)$ based on how $R$ interprets it$?$

$($iii$)$ based on scales of measurement? $(3)$

Construct three histograms with $10,50$ and $100$ breaks. Make the y$-$axis of these histograms density.

You will need to use the parameter probability. Provide a suitable title for each plot $($write how

many breaks$)$ and label the x$-$axis. $(5)$

Comment on the shape of the histogram. $(2)$

Compute the sample mean and the sample standard deviation. Watchout for the missing values!

$R$ uses NA to code the missing observations. $(2)$