Quotes About Distribution

organic food is in higher demand, thanks to the minority rule, distribution costs decrease and the minority rule ends up accelerating in its effect.

wealth is seen as zero-sum.

The distribution was massively skewed, with the bulk of the deaths coming from birth and childhood mortality.

b) The more volatile the something—the more uncertainty—the more the function divorces itself from the something. Let us consider the average number of cars again. The function (travel time) depends more on the volatility around the average. Things degrade if there is unevenness of distribution. For the same average you prefer to have 100,000 cars for both time periods; 80,000 then 120,000, would be even worse than 90,000 and 110,000.

Take a population of 10 people, 9 having a net worth of $ 30,000 and 1 having a net worth of $ 1,000. The average net worth is $ 27,100 and 9 out of 10 people will have above average wealth.

the concept of median used in medical research does not characterize a probability distribution.

If you hear a "prominent" economist using the word equilibrium , or normal distribution , do not argue with him; just ignore him, or try to put a rat down his shirt.

perishable) food costs in America are largely, up to about 80 or 90 percent, determined by distribution and storage, not the cost at the agricultural level.

with huge disparities between efforts and rewards—a few can take a large share of the pie, leaving others out entirely at no fault of their own.

Today, a few take almost everything; the rest, next to nothing.

Who can explain why one species ranges widely and is very numerous, and why another allied species has a narrow range and is rare? Yet these relations are of the highest importance, for they determine the present welfare, and, as I believe, the future success and modification of every inhabitant of this world.

Amazon can deal directly with writers and publish—and distribute—their work to the public, what need is there for the publishing houses? The means of production are in the hands of the masses now, not in the hands of the elites.

we have another statistic that also signals the "middle" of a distribution, albeit differently: the median.

For distributions without serious outliers, the median and the mean will be similar.

The central limit theorem tells us that in repeated samples, the difference between the two means will be distributed roughly as a normal distribution.

The central limit theorem tells us that a large sample will not typically deviate sharply from its underlying population

The central limit theorem tells us that the sample means will be distributed roughly as a normal distribution around the population mean.

characteristic of the median—that it does not weight observations on the basis of how far they lie from the midpoint

All of this will be true no matter what the distribution of the underlying population looks like.

The larger the number of samples, the more closely the distribution will approximate the normal distribution.

If there are 60,000 blue marbles and 40,000 red marbles in a giant urn, then the most likely composition of a sample of 100 marbles drawn randomly from the urn would be 60 blue marbles and 40 red marbles.

some might have 62 blue marbles and 38 red marbles, or 58 blue and 42 red. But the chances of drawing any random sample that deviates hugely from the composition of marbles in the urn are very, very low.

They will still be distributed around the true coefficient for the whole population, but the shape of that distribution will not be our familiar bell-shaped normal curve.

Instead, we have to assume that repeated samples of just 25 will produce more dispersion around the true population coefficient—and therefore a distribution with "fatter tails.