Part 1: 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?
Part 2: 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?
Part 3: lr-plot with filtered tables
9 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")
10 What is the predictor function? 𝑦 = 𝑥 +
11 What is the slope?
12 What is the y-intercept?
13 How does this line of best fit for cats compare to the line of best fit for all animals?
14 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.
These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, 1738598, 2031479, and 1501927). 
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