By definition, the 1st 6-tile is the point below which 1/6 of the population falls (irrespective of which distribution is involved). The 2nd 6-tile is the point below which 2/6 of the population falls.
This is 100 * 1/3 ~ 33.3% of the population.
The first is the concentration of sperm cells in semen - the higher the concentration better the possibility of conception. The second is the percentage of normal sperm cells - the lesser the number of abnormal sperm cells, the better the possibility of conception.
No, a distribution can have infinitely many moments: the first is the mean, the second variance. Then there are skewness (3), kurtosis (4), hyperskewness (5), hyperflatness (6) and so on.If mk represents the kth moment, thenmk = E[(X - m1)k] where E is the expected value.It is, therefore, perfectly possible for m1 and m2 to be the same but for the distribution to differ at the higher moments.
According to the links, Karl Pearson was first to formally introduce the gamma distribution. However, the symbol gamma for the gamma function, as a part of calculus, originated far earlier, by Legrenge (1752 to 1853). The beta and gamma functions are related. Please review the related links, particularly the second one from Wikipedia.
A probability sampling method is any method of sampling that utilizes some form of random selection. See: http://www.socialresearchmethods.net/kb/sampprob.php The simple random sample is an assumption when the chi-square distribution is used as the sampling distribution of the calculated variance (s^2). The second assumption is that the particular variable is normally distributed. It may not be in the sample, but it is assumed that the variable is normally distributed in the population. For a very good discussion of the chi-square test, see: http://en.wikipedia.org/wiki/Pearson%27s_chi-square_test
Not necessarily. It might mean that the experiment has a highly stable outcome. You need to evaluate if that is true or if the experiment is flawed. It comes down to theoretical expectations versus experimental outcomes - you should know a priori (before the fact) what to expect, so you can know if the results are good. For instance... If you were measuring the radioactivity of a sample with a relatively low count rate using a detector that recorded counts in each second, you would expect a poissen distribution. If you were measuring the same sample with a detector that counted for 1 minute, you would expect a more gaussian distribution. If, on the other hand, you were measuring the wavelength of a red laser, you would expect that every single observation would give you the same results, within an extremely tight distribution.
Assuming a normal distribution, the proportion falling between the mean (of 8) and 7 with standard deviation 2 is: z = (7 - 8) / 2 = -0.5 → 0.1915 (from normal distribution tables) → less than 7 is 0.5 - 0.1915 = 0.3085 = 0.3085 x 100 % = 30.85 % (Note: the 0.5 in the second sum is because half (0.5) of a normal distribution is less than the mean, not because 7 is half a standard deviation away from the mean, and the tables give the proportion of the normal distribution between the mean and the number of standard deviations from the mean.)
The normal distribution is a bell shaped curve. Properly normalized, the area under the curve is 1.0. Start by drawing axes. The Y axis is probability, peaking at 0.4, crossing the X axis at the mean, and the X axis is standard deviation. Draw points (-3, 0.01), (-2, 0.05), (-1, 0.25), (0, 0.4), (+1, 0.25), (+2, 0.05), (+3, 0.01). These are all approximations. Connect the dots, understanding that the curve is asymptotic to the X axis.For a better picture, as well as an explanation, please see the related link below. This picture also shows you the percentage each area, grouped by standard deviation, or sigma, is. The normal distribution is the second picture on the right. Scroll up to see the picture, call "Normal Distribution".
The second step in making an income distribution table is to rank individuals or households from lowest to highest income. This step helps organize the data and prepare for further analysis of income distribution in the population.
The answer depends on the level at which the student is expected to be. A 15-year old should know the probability of getting heads on the toss of a coin but even a mathematics graduate - who did not specialise in probability - would be expected to be able to prove the mathematical relationship between the Normal distribution and the F-distribution. If asked, most student would not even know what the second part of the sentence meant.
It is difficult to determine the percentage of Americans who have second mortgages on their homes as this number is constantly changing. Many Americans have taken out second mortgages.
There are 4 normal forms in databases. First normal form, second, third and fourth normal forms are there.
45%
its normal
weewwqew
A percentage is a form of ratio. A ratio requires two numbers. Unless you have the second number, a percentage cannot be determined.
A percentage.
4.3%