Gender and Athletic Competition Records

The following article appeared in newspapers in 1992, just before the 1992 Summer Olympic games. You will be assigned one timed race or measured event in which both men and women compete. You will use scatterplots, correlation, and least squares regression to determine if and when men and women will achieve equal performance in your race or event.

"Women May Outrun Men, Researchers Suggest"

Women runners might start beating men in world-class competitions within a few generations, researchers said Thursday.

An analysis of world records for a variety of distances found that women have been improving about twice as quickly as men. And if that continues, the top female and male runners might start performing equally well between the years 2015 and 2055 in the 200-, 400-, 800-, and 1,500-meter events, according to a study. The findings were reported in a letter in the journal Nature.

"None of the current women's world-record holders at these events could even meet the men's qualifying standard to compete in the 1992 Olympic games," researchers Brian Wipp and Susan Ward wrote.

"However, it is the rates of improvement that are so strikingly different - the gap is progressively closing." But other researchers said they doubted the projections because they believed women's rate of improvement would slow.

Source: Journal staff and wire services, Milwaukee Journal, January 1992

Tasks:

  1. Construct a scatterplot of times or measurements in your event against year for the men's data. Construct a separate scatterplot for the women's data. Construct another scatterplot in which the men's and women's scatterplots are overlaid.

  2. Calculate the correlation between men's times or measurments in your event the year. Do the same for the women's times or measurments. Which gender's times or measurements in your event exhibit a stronger correlation with year?

  3. Fit a least squares regression line to the data for men's times or measurments in your event. Draw the line on the men's scatterplot.

  4. Fit a line to the data for women's times or measurements. Again, draw the line on the scatterplot.

  5. Construct a scatterplot which displays the original data for both men and women as well as the regression lines for men and women on the same plot.

  6. Use the regression lines to compute the predictions and residuals for both men's and women's times or measurements in all of the years from 2000 through 2010. Report the original observed values, the predictions, and the residuals in a table.

  7. Construct a scatterplot of the residuals against year for the men's data. Construct same kind of residual scatterplot for the women's data. Are the patterns in the two residuals plots similar? Do the patterns suggest that lines are appropriate models for the data? Give reasons to support your answers.

  8. Use the two regression lines you found to predict in what year men and women will run the same times or achieve the same distance in your event. Do you think it is realistic to expect that this will happen? Why or why not?

  9. Write a letter to the editor of the Milwaukee Journal (the newspaper in which the article at the beginning of this handout appeared) explaining your conclusion about if and when you predict that men and women will run the same times or achieve the same distance in your event, as the article states might happen.

    Note: You should not actually mail your letter to the Milwaukee Journal! But please write it as if you were going to mail it! Use language that an editor would understand, but refer to the statistical results you found to support your position.

The data for the Olympic 200 meter run are contained in the accompanying table.

Times for the Olympic 200 Meter Run

To illustrate the findings reported in the preceding article, you can analyze the winning times, in seconds, for the Olympic 200-meter run.

Year Males Time                       Females Time
1900 Walter Tewksbury, US 22.2s
1904 Archie Hahn, US 21.6
1908 Robert Kerr, Canada 22.6
1912 Ralph Craig, US 21.7
1920 Allan Woodring, US 22
1924 Jackson Scholz, US 21.6
1928 Percy Williams, Canada 21.6
1932 Eddie Tolan, US 21.2
1936 Jesse Owens, US 20.7
1948 Mel Patton, US 21.1 F. Blankers-Koen, Netherlands 24.4s
1952 Andrew Stanfield, US 20.7 Marjorie Jackson, Australia 23.7
1956 Bobby Morrow, US 20.6 Betty Cuthbert, Australia 23.4
1960 Livio Berruti, Italy 20.5 Wilma Rudolph, US 24.0
1964 Harry Car, US 20.3 Edith McGuire, US 23.0
1968 Tommie Smith, US 19.83 Irena Szewinska, Poland 22.5
1972 Valeri Borzov, USSR 20.00 Renate Stecher, E. Germany 22.40
1976 Donald Quarrie, Jamaica 20.23 Barbel Eckert, E. Germany 22.37
1980 Pletro Mennes, Italy 20.19 Barbel Wockel, E. Germany 22.03
1984 Carl Lewis, US 19.80 Valerie Brisco-Hooks, US 21.81
1988 Joe Deloach, US 19.75 Florence Griffith-Joyner, US 21.34
1992 Mike Marsh, US 20.01 Gwen Torrence, US 21.81
1996 Michael Johnson, US 19.32 Marie-Jose Perec, France 22.12
2000 Konstantinos Kenteris, Greece 20.09 Marion Jones, US 21.84
2004 Shawn Crawford, US 19.79 Veronica Campbell, Jamaica 22.05

Here is a list of the races and events that are available in our class. Please be sure to circle or mark the race or event you have been assigned.

Track

100 meters
200 meters (*)
400 meters
800 meters
1500 meters
5000 meters
10,000 meters
Marathon
400 meter hurdles
4 x 100 meter relay
4 x 400 meter relay
20 kilometer walk

Field

High jump
Pole vault
Long jump
Shot
Discus
Javelin
Hammer

Speed Skating

500 meters
1000 meters
1500 meters
5000 meters

Swimming

50 meter freestyle
100 meter freestyle
200 meter freestyle
400 meter freestyle
100 meter backstroke
200 meter backstroke
100 meter breaststroke
200 meter breaststroke
100 meter butterfly
200 meter butterfly
200 meter individual medley
400 meter individual medley
400 meter freestyle relay
800 meter freestyle relay
400 meter medley relay
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