And another one
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@ -683,7 +683,7 @@ Finally, we could spot outliers in our data by examining the residuals of the mo
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```{r echo = FALSE, fig.width = 3, fig.show='hold'}
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```{r echo = FALSE, fig.width = 3, fig.show='hold'}
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diamond_mod <- lm(y ~ x, data = diamonds3)
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diamond_mod <- lm(y ~ x, data = diamonds3)
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resids <- augment(diamond_mod)
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resids <- broom::augment(diamond_mod)
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ggplot(resids) +
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ggplot(resids) +
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geom_histogram(aes(x = .resid), binwidth = 0.1)
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geom_histogram(aes(x = .resid), binwidth = 0.1)
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