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Marathon Graphing - posted December 18, 2000

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Marathon Graphing

Introduction

Here are record-breaking times for the men's marathon from all over the world. We've graphed the times and a line that models the data. You can use the line to see trends in the data.

Men's Times
Year Athlete Time
1935Kitei Son2h 26m 42s
1947Yun Bok Suh2h 25m 49s
1952Jim Peters2h 20m 43s
1954Jim Peters2h 17m 40s
1963Buddy Edelen2h 14m 28s
1969Derek Clayton 2h 8m 34s
1981Robert de Castella 2h 8m 18s
1985Carlos Lopes2h 7m 12s
1999Khalid Khannouchi2h 5m 42s

Use the applet to graph the women's times for the marathon so you can answer the questions below.

Women's Times
Year Athlete Time(Approx.
Minutes)
1964Dale Grieg 3h 27m 45s207
1970Caroline Walker3h 2m 53s182
1971Adrienne Beames 2h 46m 30s166
1974Jackie Hansen 2h 43m 55s164
1975Jackie Hansen2h 38m 19s158
1977Christa Vahlensieck2h 34m 47s155
1978Grete Waitz2h 32m 30s152
1980Grete Waitz2h 25m 42s145
1983Joan Benoit2h 22m 43s143
1985Ingrid Kristiansen2h 21m 6s141
1998Tegla Loroupe2h 20m 47s140
1999Tegla Loroupe2h 20m 43s140
Click To Show Applet Window

Questions:


Use the graph you made in the applet to answer the following questions:
  1. Based on the graph, what do you think the women's winning time should have been this year at the Sidney 2000 Olympic Games?

  2. Predict when women will run the marathon as fast as men.

  3. What do you think the women's world record marathon time was in 1926? Use this Web link from the Centre for Innovation in Mathematics Teaching (CIMT) to look up times from 1926 to see how well they fit your prediction.

  4. Using lines to model data can be a powerful tool, but we have to be careful to be aware of their limitations. What real-world limitations do you think a line has in modeling the data?

Comments

Teacher Support Page

Because when this PoW first went live there were technical problems that prevented students from submitting their solutions, we ran it again at the end of December. Of course then it was holiday time for many people, so there were very few submissions.

There were only four submissions to this puzzle, and no one received credit for it. The first three questions asked for predictions that used the line to model the data. Since the data weren't really in a line -- making it a little tricky to find a best-fit line -- we were pretty relaxed about what we'd accept. Two of the four students got these numbers right. A third student got the first two right.

Question 4 asked about real-world limitations in using a line to model data. No one answered this question in a way we thought was reasonable. Here are the answers we thought we'd see:

  1. Based on the graph what do you think the women's winning time should have been this year at the Sidney 2000 Olympic Games?

    MANY possible solutions...
    Fitting a line, the answer should be somewhere in the range of 115 to 130 minutes.

    If the students use a linear regression, they will come up with y = -1.6x +3236.5, and get a time of about 37 minutes!

    If students look at the graph and notice that the times have flattened out considerably in the past few races, they may answer somewhere slightly under 140 minutes.... It seems that we have hit a "wall" in terms of our ability to continue to get faster.

  2. Predict when women will run the marathon as fast as men.

    Using the fitted line, this would happen somewhere between 2005 and 2020. If you look at the flattening-out theory, though, it seems as if it would be further out in the future. I'm not even sure we can predict that, because when I flatten out the women's line, the two lines look close to parallel.

  3. What do you think the women's world record marathon time was in 1926?

    In 1926, the women's record time was 3 hours, 40 minutes, 22 seconds, which is 220.36 seconds. Using a fitted line, an estimate of 280 minutes is not unreasonable.

  4. Using lines to model data can be a powerful tool, but we have to be careful to be aware of their limitations. What real-world limitations do you think a line has in modeling the data?

    There are many. The most glaring is that data can look linear in a small window. But when you look at the big picture, other considerations creep in. People have physical limitations, and at some point, they won't be able to run much faster than the previous records, and a line won't fit the data. ....


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