## age

You should already have plotted `lr-plot(animals-table, "name", "age", "weeks")`

in the Animals Starter File.

1 What is the predictor function? *y* = *x* +

2 What is the slope?

3 What is the y-intercept?

4 How long would our line of best fit predict it would take for a 5 year-old animal to be adopted?

5 What if they were a newborn, or just 0 years old?

6 Does it make sense to find the adoption time for a newborn using this predictor function? Why or why not?

## weight

*Make another lr-plot, but this time use the animals' weight as our explanatory variable instead of their age.*

7 How long would our line of best fit predict it would take for an animal weighing 21 pounds to be adopted?

8 What if they weighed 0.1 pounds?

## cats

*Make another lr-plot, comparing the age v. weeks columns for *

**only the cats**using the following code:

```
fun is-cat(r): r["species"] == "cat" end
lr-plot(filter(animals-table, is-cat), "name", "age", "weeks")
```

9 What is the predictor function? *y* = *x* +

10 What is the slope?

11 What is the y-intercept?

12 How does this line of best fit for *cats* compare to the line of best fit for *all animals*?

13 How long would our line of best fit predict it would take for a 5 year-old cat to be adopted?

★ Make another `lr-plot`

, comparing the `age`

v. `weeks`

columns for *only the dogs*.

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