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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 armspans 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?