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Update index.Rmd #20

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4 changes: 2 additions & 2 deletions index.Rmd
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Expand Up @@ -222,7 +222,7 @@ As the name implies, the **Spearman rank correlation** is a **Pearson correlatio

$rank(y) = \beta_0 + \beta_1 \cdot rank(x) \qquad \mathcal{H}_0: \beta_1 = 0$

I'll introduce [ranks](#rank) in a minute. For now, notice that the correlation coefficient of the linear model is identical to a "real" Pearson correlation, but p-values are an approximation which is is [appropriate for samples greater than N=10 and almost perfect when N > 20](simulations/simulate_spearman.html).
I'll introduce [ranks](#rank) in a minute. For now, notice that the correlation coefficient of the linear model is identical to a "real" Pearson correlation, but p-values are an approximation which is [appropriate for samples greater than N=10 and almost perfect when N > 20](simulations/simulate_spearman.html).

Such a nice and non-mysterious equivalence that many students are left unaware of! Visualizing them side by side including data labels, we see this rank-transformation in action:

Expand Down Expand Up @@ -521,7 +521,7 @@ a = wilcox.test(y, y2, paired = TRUE)
# Equivalent linear model:
b = lm(signed_rank(y - y2) ~ 1)

# Bonus: identical to one-sample t-test ong signed ranks
# Bonus: identical to one-sample t-test on signed ranks
c = t.test(signed_rank(y - y2))
```

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