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Update model-slr.qmd #507

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6 changes: 3 additions & 3 deletions model-slr.qmd
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Expand Up @@ -33,7 +33,7 @@ However, the prediction would be far from perfect, since other factors play a ro
#| fig-cap: |
#| Requests from twelve separate buyers were simultaneously placed with a
#| trading company to purchase Target Corporation stock (ticker TGT, December
#| 28th, 2018), and the total cost of the shares were reported. Because the
#| 28th, 2018), and the total cost of the shares was reported. Because the
#| cost is computed using a linear formula, the linear fit is perfect.
#| fig-alt: |
#| A scatterplot showing a perfect linear relationship between number of
Expand Down Expand Up @@ -212,7 +212,7 @@ ggplot(possum, aes(x = total_l, y = head_l)) +
)
```

We want to describe the relationship between head and total length of possum's with a line.
We want to describe the relationship between head and total length of possums with a line.
In this example, we will use the total length as the predictor variable, $x,$ to predict a possum's head length, $y.$ We could fit the linear relationship by eye, as in @fig-scattHeadLTotalLLine.

\clearpage
Expand Down Expand Up @@ -489,7 +489,7 @@ ggplot(m_head_total_aug, aes(x = .fitted, y = .resid)) +
\clearpage

::: {.workedexample data-latex=""}
One purpose of residual plots is to identify characteristics or patterns still apparent in data after fitting a model.
One purpose of residual plots is to identify characteristics or patterns still apparent in the data after fitting a model.
The figure below shows three scatterplots with linear models in the first row and residual plots in the second row.
Can you identify any patterns in the residuals?

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