Activity 2.1b introduces one of the most important common shapes of
distributions and one of the common ways this shape is produced. What happens when different people measure the same distance or the same feature of very similar objects? In the activity, you’ll measure a tennis ball with a ruler, but the results you get will reflect what happens even if you use very precise instruments under carefully controlled conditions. For example, a 10-gram platinum weight is used for calibration of scales all across the United States. When scientists at the National Institute of Standards and Technology use an analytical balance for the weight’s weekly weighing, they face a similar challenge due to variability.
Measuring Diameters
What you’ll need: a tennis ball, a ruler with a centimeter scale
1. With your partner, plan a method for measuring the diameter of the tennis ball with the centimeter ruler.
(continued)
2. Using your method, make two measurements of the diameter of your tennis ball to the nearest millimeter.
3. Combine your data with those of the rest of the class and make a dot plot. Speculate first, about the shape you expect for the distribution. 4. Shape. What is the approximate shape of the
plot? Are there clusters and gaps or unusual values (outliers) in the data?
5. Center and spread. Choose two numbers that seem reasonable for completing this sentence:
“Our typical diameter measurement is about —?—, give or take about —?—.” (More than one reasonable set of choices is possible.) 6. Discuss some possible reasons for the variability in the measurements.
How could the variability be reduced? Can the variability be eliminated entirely?
The measurements of the diameter of a tennis ball taken by your class in Activity 2.1b probably were not uniform. More likely, they piled up around some central value, with a few measurements far away on the low side and a few far away on the high side. This common bell shape has an idealized version —the normal distribution—that is especially important in statistics.
Pennies minted in the United States are supposed to weigh 3.110 g, but a tolerance of 0.130 g is allowed in either direction. Display 2.3 shows a plot of the weights of 100 pennies.
Display 2.3 Weights of pennies.[Source: W. J. Youden, Experimentation and
Measurement (National Science Teachers Association, 1985), p. 108.]
The smooth curve superimposed on the graph of the pennies is an example of a normal curve. No real-world example matches the curve perfectly, but many plots of data are approximately normal. The idealized normal shape is perfectly symmetric—the right side is a mirror image of the left side, as in Display 2.4. There is a single peak, or mode, at the line of symmetry, and the curve drops off smoothly on both sides, flattening toward the x-axis but never quite reaching it and stretching infinitely far in both directions. On either side of the mode are inflection points, where the curve changes from concave down to concave up.
© 2008 Key Curriculum Press 2.1 Visualizing Distributions: Shape, Center, and Spread 31
The normal distribution is bell-shaped..
Display 2.4 A normal curve, showing the line of symmetry, mode,
mean, inflection points, and standard deviation (SD).
Use the mean and standard deviation to describe the center and spread of a normal distribution.
You should use the mean (or average) to describe the center of a normal distribution. The mean is the value at the point where the line of symmetry intersects the x-axis. You should use the standard deviation, SD for short, to describe the spread. The SD is the horizontal distance from the mean to an inflection point.
It is difficult to locate inflection points, especially when curves are drawn by hand. A more reliable way to estimate the standard deviation is to use areas. For a normal curve, 68% (roughly) of the total area under the curve is between the vertical lines through the two inflection points. In other words, the interval between one standard deviation on either side of the mean accounts for roughly 68% of the area under the normal curve.
Display 2.5 Distribution of average age for groups of five workers
drawn at random.
Example: Averages of Random Samples
Display 2.5 shows the distribution of average ages computed from 200 sets of five workers chosen at random from the ten hourly workers in Round 2 of the Westvaco case discussed in Chapter 1. Notice that, apart from the bumpiness, the shape is roughly normal. Estimate the mean and standard deviation.
Solution
The curve in the display has center at 47, and the middle 68% of dots fall roughly between 43 and 51. Thus, the estimated mean is 47, and the estimated standard deviation is 4. A typical random sample of five workers has average age 47 years, give or take about 4 years.
[You can graph a normal curve on your calculator by specifying the mean and standard deviation. See Calculator Note 2B.]
In this section, you’ve seen the three most common ways normal distributions arise in practice:
•
through variation in measurements (diameters of tennis balls)•
through natural variation in populations (weights of pennies)•
through variation in averages computed from random samples (average ages) All three scenarios are common, which makes the normal distribution especially important in statistics.Normal Distributions
D3. Determine these summary statistics visually.
a. Estimate the mean and standard deviation of the penny weight data in Display 2.3, and use your estimates to write a summary sentence. b. Estimate the mean and standard deviation of your class data from
Activity 2.1b.