# LAB 4A: If the Line Fits…

## Lab 4A - If the line fits ...

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

### How to make predictions

• 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.

### Predicting heights

• 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.

• Make a plot of the `height` variable.

If you had to predict the height of someone in the LA 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?

### Predicting heights knowing arm spans

• Create two subsets of our `arm_span` data:

One for `armspan >= 61` and `armspan <= 63`.

A second for `armspan >= 64` and `armspan <= 66`.

• 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?

### Fitting lines

• 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.

• Create a scatterplot for `height` and `armspan`. Then run the following code.

``````add_line()
``````
• On the Plot pane, click two data points to draw a line through.

• NOTE: If your line does not appear or it appears but is above the points you selected, zoom out on your browser (typically 50% if you have a Mac, 80% on Windows). Or if your line appears below the points you selected, zoom in on your browser. Then run the `add_line()` function again and click on two points. Zoom out (or in) until your line appears through the points you selected.

### Predicting with lines

• 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?