| Country | Life Exp. | Per TV | Country | Life Exp. | Per TV |
| Angola | 44 | 200 | Mexico | 72 | 6.6 |
| Australia | 76.5 | 2 | Morocco | 64.5 | 21 |
| Cambodia | 49.5 | 177 | Pakistan | 56.5 | 73 |
| Canada | 76.5 | 1.7 | Russia | 69 | 3.2 |
| China | 70 | 8 | South Africa | 64 | 11 |
| Egypt | 60.5 | 15 | Sri Lanka | 71.5 | 28 |
| France | 78 | 2.6 | Uganda | 51 | 191 |
| Haiti | 53.5 | 234 | United Kingdom | 76 | 3 |
| Iraq | 67 | 18 | United States | 75.5 | 1.3 |
| Japan | 79 | 1.8 | Vietnam | 65 | 29 |
| Madagascar | 52.5 | 92 | Yemen | 50 | 38 |
1. Which of the countries listed above has the fewest people per television set? Which has the most? What are those numbers?
2. Use your calculator to sketch a scatterplot of life expectancy vs. people per television set. Does there appear to be an association between the two variables? If so, describe the association.
3. Calculate the correlation between life expectancy and people per television set.
4. Since the association is so strongly negative, one might conclude that simply sending television sets to the countries with lower life expectancies would cause their inhabitants to live longer. Comment on this argument; do you agree or disagree with it? Explain.
5. If two variables have a correlation close to +1 or -1, indicating a strong linear relationship between them, does it follow that there must be a cause-and-effect relationship between them? Explain.
This example illustrates the very important distinction between association and causation. Two variables may be strongly associated (as measured by correlation) without a cause-and-effect relationship existing between them. Often the explanation is that both variables are related to a third variable not being measured; this variable is often called a lurking or confounding variable.
6. In the case of life expectancy and televisions sets, suggest a lurking variable that is associated with both a country's life expectancy and with the prevalence of television sets in the country.