fix images pca II

DS-100 · Apr 19, 2024 · 8b3fbea · 8b3fbea
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diff --git a/pca_1/images/lin_reg.png → pca_2/images/lin_reg.png b/pca_1/images/lin_reg.png → pca_2/images/lin_reg.png
diff --git a/pca_1/images/lin_reg_reverse.png → pca_2/images/lin_reg_reverse.png b/pca_1/images/lin_reg_reverse.png → pca_2/images/lin_reg_reverse.png
diff --git a/pca_1/images/rank1.png → pca_2/images/rank1.png b/pca_1/images/rank1.png → pca_2/images/rank1.png
diff --git a/pca_2/pca_2.qmd b/pca_2/pca_2.qmd
@@ -634,7 +634,7 @@ $$ \text{component score} = \frac{s_i^{2}}{N}$$
 
 $$ \text{total variance} = \text{sum of all the component scores} = \sum\_{i=1}^k \frac{s_i^{2}}{N} $$
 
-$$ \text{variance ratio of principal component i} = \frac{\text{component score i}}{\text{total variance}} = \frac{s*i^{2} / N}{\sum*{i=1}^k s_i^{2} / N}$$
+$$ \text{variance ratio of principal component j} = \frac{\text{component score j}}{\text{total variance}} = \frac{s_j^{2} / N}{\sum_{i=1}^k s_i^{2} / N}$$
 
 In Python, assuming you had a 1D `NumPy` array of singular values `s` returned by `np.linalg.svd`, you could compute the list of variances ratios with `s**2 / sum(s**2)`.