Population distribution reflects a region's economic activities and resources by indicating where people are drawn for jobs, services, and amenities. For instance, urban areas typically show higher population densities due to industrialization and access to services, while rural areas may have sparse populations reliant on agriculture. Additionally, regions rich in Natural Resources may attract people for mining or agriculture, influencing settlement patterns. Overall, the distribution highlights the interplay between economic opportunities and geographic factors.
It doesn't but a mp represents 18000 people
example from your business or industry that seems to reflect the normal distribution
Z is the standard normal distribution. T is the standard normal distribution revised to reflect the results of sampling. This is the first step in targeted sales developed through distribution trends.
they often do not represent an accurate cross section of the total population.
Employment, profits, and business are low reflect a bust phase in a capitalist economy.
Employment, profits, and business are low reflect a bust phase in a capitalist economy.
Not necessarily.
In ecosystems, normal distribution refers to the way certain biological traits or variables, such as species abundance or individual size, are distributed in a population. This distribution typically forms a bell-shaped curve, indicating that most individuals exhibit average traits, while fewer individuals show extreme traits. This pattern can reflect ecological processes like resource availability, reproductive success, and environmental conditions, helping ecologists understand population dynamics and predict how ecosystems respond to changes.
Population growth in industrialized countries tends to reflect the economy, war and post war factors and immigration. In non-industrialized nations, the population is dependent on factors that are often out of the control of the country such as famine, natural disasters, tribal conflicts, drought and pandemic illnesses.
Standard error (which is the standard deviation of the distribution of sample means), defined as σ/√n, n being the sample size, decreases as the sample size n increases. And vice-versa, as the sample size gets smaller, standard error goes up. The law of large numbers applies here, the larger the sample is, the better it will reflect that particular population.
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