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A classic example of a relationship between two variables in a career is the relationship between hours of work and annual wages. In this case, the coupling is strong, with a correlation coefficient of 1. This follows from the fact that, as a rule, the employee set at a flat rate of pay per hour. The correlation coefficient of 1 means that the relationship is direct and functional. Less strict relationship can be observed between indicators such as income and life expectancy. It is well known that in developed countries with higher income, people are living longer than in developing countries. This is not a functional relationship and cannot be it, because life expectancy is influenced by many other factors (environment, crime, social policy, etc.). However, in this case, the association will be positive (direct), because when income increases, longevity increases too, and vice versa.
An example of association, which is not casual, can be the relationship between a car's fuel consumption and its cost. The attributed variable could be a engine volume. Cars with larger engine volume usually have higher fuel consumption. Also cars with larger engine volume are usually more expensive. In this case, the association is positive.
Another example of such association could be positive correlation between the number of churches and police stations in certain city. The attributed variable is a city population. In cities with higher population there are more churches and more police stations.
A positive correlation exists even between the amount of ice cream sold and the number of rape cases (both increasing with increasing temperature).
Examples of negative correlation are the number of children of fluoride intake and the amount of their teeth affected by caries, the number of hours that a student devotes occupations, and the number of failures in examinations.
