i agree
The age distribution of a population is, the number of individuals of each age in the population.
The age distribution of a population is, the number of individuals of each age in the population.
No.
Sampling distribution is the probability distribution of a given sample statistic. For example, the sample mean. We could take many samples of size k and look at the mean of each of those. The means would form a distribution and that distribution has a mean, a variance and standard deviation. Now the population only has one mean, so we can't do this. Population distribution can refer to how some quality of the population is distributed among the population.
Population growth refers to the increase in the number of individuals in a population over time, often measured as a percentage. Population density refers to the number of individuals living in a given area, usually expressed as individuals per square kilometer. Both factors are important for understanding demographic trends and the distribution of resources.
Age distribution of population refers to the percentage of people in different age groups within a given population. This information provides insights into the demographic structure of a society, such as the proportion of children, working-age adults, and elderly individuals. It is used to understand trends in population growth, age-related policies, and potential social and economic impacts.
The mean of the sampling distribution is the population mean.
64.
Mean means Average of a particular distribution Mean means Average of a particular distribution
The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.
no
True.