## Problem 1

- Load Howell’s craniometric dataset
- Get rid of unwanted data. Select only the following columns: ID, Sex, Population, BNL, MDH, EKB, ZOR, BAA, NBA. Filter the dataset to only include individuals from the following populations: BUSHMAN, PERU, NORSE, ZULU. Use this filtered dataset for all remaining questions in this homework.
- Calculate the variance / covariance matrix for the 6 numeric variables.

## Problem 2

- Compute a Euclidian distance matrix for the 6 numerical variables.
**Save this to a variable, DO NOT PRINT THIS TO THE SCREEN OR INCLUDE IT IN THE OUTPUT**
- Perform a hierarchical cluster analysis using the distance matrix you just computed. Use the
`hclust()`

function for this
- Use the
`plot()`

function to plot the cluster analysis dendrogram.
- Interpret the plot visually. Which which single specimen (identified on the plot by its data-frame row number) is most distinct from all other specimens?

## Problem 3

- Perform a Principal Components Analysis on the data…make sure to use
`scale=TRUE`

to scale your variables!
- Which single variable has the strongest loading on PC1?
- What is the cumulative proportion of variance explained by PC1 and PC2?
- Make a plot of the PC1 scores against PC2 scores. Color code the point based on the population variable.