Lab 4A

Directions: Follow along with the slides, completing
the questions in **blue** on your
computer, and answering the questions in **red** in your
journal.

Space, Click, Right Arrow or swipe left to move to
the next slide.

- Anyone can make predictions.
- Data scientists use data to inform their predictions by using the information learned from the sample to make predictions for the whole population.

- In this lab, we’ll learn how to make predictions by finding the
*line of best fit*.- You will also learn how to use the information from one variable to make predictions about another variable.

- Use the
`data()`

function to load the`arm_span`

data. - This data comes from a sample of 90 people in the Los Angeles area.
- The measurements of
`height`

and`armspan`

are in inches. - A person’s
`armspan`

is the maximum distance between their fingertips when they spread their arms out wide.

- The measurements of
- Make a plot of the
`height`

variable.**If you had to predict the height of someone in the Los Angeles area, what single height would you choose and why?****Would you describe this as a***good*guess? What might you try to improve your predictions?

- Create two subsets of our
`arm_span`

data:- One for
`armspan >= 61`

and`armspan <= 63`

. - A second for
`armspan >= 64`

and`armspan <= 66`

.

- One for
- Create a
`histogram`

for the`height`

of people in each subset. **Answer the following based on the data:****What**`height`

would you predict if you knew a person had an`armspan`

around 62 inches?**What**`height`

would you predict if you knew a person had an`armspan`

around 65 inches?**Does knowing someone’s**`armspan`

help you predict their`height`

? Why or why not?

- Notice that there is a trend that people with a larger
`armspan`

also tend to have a larger mean`height`

.- One way of describing this sort of trend is with a line.

- Data scientists often
*fit*lines to their data to make predictions.- What we mean by
*fit*is to come up with a line that’s close to as many of the data points as possible.

- What we mean by
- Create a scatterplot for
`height`

and`armspan`

. Then run the following code.

On the

*Plot*pane, click two data points to draw a line through.NOTE: Watch the following video if you are experiencing difficulties obtaining your line:

**Draw a line that you think is a good***fit*and write down its equation. Using this equation:**Predict how tall a person with a 62-inch**`armspan`

and a person with a 65-inch`armspan`

would be.

- Using a line to make predictions also lets us make predictions for
`armspan`

s that aren’t in our data.**How tall would you predict a person with a 63.5-inch**`armspan`

to be?

**Compare your answers with a neighbor. Did both of you come up with the same equation for a line? If not, can you tell which line fits the data best?**