chw11.2. analyzing covid outbreaks with pca understanding the progression of the ongoing covid-19 pandemic has required coordinated efforts of public health organizations at every level around the globe. in this exercise, we will apply principal component analysis (pca) to compare the coronavirus outbreak in israel to those in thirteen other countries. weekly new case counts per capita for the thirteen other countries are provided as rows of covid global. these other countries are: australia,brazil,canada,china,ethiopia,germany,india,mexico,nigeria,russia,south africa,united kingdom,united states (names given in order by names global). each row of covid global represents the time-series of new cases per capita for a given country in weekly increments. the first entry of each row represents the new cases per capita reported over january 22-28, 2020, while the final entry represents those reported over april 13-20, 2022. first, conduct pca on the data provided in covid global. to this end, you will need to compute the average outbreak time-series, shift the data according to this average, then apply singular value decomposition to the shifted data. store the matrix of right singular vectors from the svd of the shifted data as v. remember to compute the reduced svd by passing the argument full matrices