Population variation refers to how the demographic characteristics of a certain population vary over time. Some examples of population variables include birth rate, mortality rate, and average age. These population characteristics can be considered as distributions of data points. For example, a birth rate that is high can mean that there are more children born than when it is low, while a low mortality rate means that people are not as likely to die as they might otherwise have.

There are many different ways that a person can interpret the distribution of data points over the different characteristics. Many of the distributions look like curves. Some examples of distributions that curve include the normal curve and exponential curve. A curve that is linear can appear as an arrow. A distribution that does not curve can appear as a straight line.

One of the most important questions to ask oneself is this: what shape would I choose to curve? A distribution that is not very flat will appear difficult to interpret. A distribution that curves too much will probably take up too much space and not fit very well. This can result in a plot that looks messy. In addition, a curve that looks too straight and smooth will require a lot of practice to see the data in its true colors.

What are the implications of these curves? The main implications of a curve are that one can see the effects of population change on a given characteristic. For example, if there is a rapid change in birth rate, it will likely mean that there are more young adults and less old people, which mean the population will change slowly. However, if there is a decrease in birth rate but an increase in the old people, it will mean that there are fewer elderly people and younger people.

The next thing that we need to consider when evaluating a distribution is what we expect to find when we plot the distribution. A plot that is flat but has some variations will show us that a decrease in some of the characteristic and an increase in another characteristic. It is possible that the effect of the curve will depend on the characteristics.

We may want to take the distribution of a curve and break it into two categories. If there are more people in the younger age category, then we may see a decrease in the number of elderly. On the other hand, if there is an increase in the elderly population, then we may see an increase in the number of the younger. When this occurs, we will see that the distribution may be unstable. By plotting these two distributions, we will be able to see if the distribution is stable or if there is some variation that we cannot explain.

When we plot the distribution, we will be able to see whether the distribution we are looking at is symmetrical. Symmetrical distributions are considered normal distributions. They will tend to follow the same patterns no matter how they are broken down. If the distribution is not symmetrical, it is called non-symmetrical, meaning there will be variations in the distribution of the data. In addition, if the distribution is symmetric, we can use it to calculate the probability of observing that characteristic under different assumptions.