$\frac{2}{3}$

Question $4\mathrm{.}4$: Recast the data along the principal components' axes. What are the main characteristics of each quadrant? $($Hint: You may recall the mineral waters example that you analyzed in the lecture as in the diagram below.$)(dot\frac{s}{4})$

$\backslash$table$[[$PC$2],[\backslash$table$[[$Low concentration of Dry$],[$Residue$,$ Potassium,$],[$Fluoride$,$ Bicarbonates,$],[$Sodium$]],],[\backslash$table$[[$High concentration of$],[$Calcium$,$ Sulphates,$],[$Magnesium$,$ and high price$]],\backslash$table$[[$High concentration of all$],[$components and high price$]]],[\backslash$table$[[$Low concentration of all$],[$components and low price$]],\backslash$table$[[$Sodium$],[$Low concentration of$],[$Calcium$,$ Sulphates,$],[$Magnesium$,$ and low price$],[$Sluoride$,$ Bicarbonates,$],[$Residue$,$ Potassium, Dry$]]]]$

Step $5$: Recast the neighborhoods along the principal components' axes.

Go to the "graph$\_$individus" tab in the pca$\_$macro.xlsm file and click on the $"$tiquettes$"$ button.

You can now find a plot presenting the $50$ different NY neighborhoods using the values from the two first principal components. Hence, this plot represents a two$-$dimensional approximation of the original multidimensional dataset.

Question $5\mathrm{.}1$: What does this plot explain us about "West Village", "Bedford Park" and "Greenpoint"? $(../3)$