Hey sociology majors! Can U help me ? If the standard deviation equals O, we may conclude that:?
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d.\n\n\nps i'm not a socioogy major, i'm a  biostats grad student
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the probability of you getting a grade greater than an F+ is approximately 46.2% using Lynax's First Theorem of Stupidest Questions Ever.  applying this to Werniff's Go Study Theorem, you'll get a standard deviation of 148.32 units per volume squared on the Cya Graph.
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If the claim is correct, then the mean of samples of size 25 has a normal distribution with a mean of 10 (the population mean) ounces and a standard deviation of 0.5/sqrt(25) = 0.1.\n\nz = (10.15-10)/0.1 = 1.5\n\nAccording to the standard normal table, the probability is 0.0669.\n\nThis is due to the fact that the population is normal.\n\nedit: You guys, it is not three sd's above.  You have to think in terms of standard error.  It is 1.5 standad errors above.  Do the math.
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Your data set would look like this:\n\n10 10 10 10 10 10 10 10 10 10 20 20 20 20 20 20 20 20 235 235\n\nThe mean is 730/20 = 36.5.\nThe median is (10+20)/2 = 15.\nThe mode is 10.\n\nSo certainly what was stated by the dean isn't a lie.\n\nNow, if I were to calculate, say, the average number of classmates for each person in the statistics classes, there would be 100 9's, 160 19's, and 470 234's.  So the average number of classmates for people taking these classes is approximately 113,920/730 = 156.05.  The median is 234 and the mode is also 234.  So the newspaper isn't really lying either.\n\nI guess you can say that they are looking at this from two different perspecitves, so in that sense, there are two truths.
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I've just answered this in your previous question: "Statistics: Variability & SRS Samples of Population?"\n \nIf you look there, you'll find the formula for the s.d. of p_hat. So that increasind n by multiplying it by "m", reduces (devides) sd (or se) by sqrt(m).\n \nFor example, lets say the sd is 0.2 and the sample size is 100. If you increase the sample to 400 (multiply it by 4), the sd is devided by sqrt(4) = 2, i.e., sd = 0.1.
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Do you have area tables for the standard normal distribution? If so, look in the table for 0.2514 and read of the z-value (approx 0.68). That means 25.14% is 0.68 standard deviations from the mean. If 30 is 0.68 standard deviations from the mean, then 30/0.68 = 1 standard deviation. Do the sum then go and get your A+ grade.\n\nIf you haven't studied the standard normal distribution, then your teacher is asking too much too soon.
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Range: 19\nStdev: 6.27\n\nRange is just the max value minus the min value.  To learn more about calculating standard deviation, visit http://en.wikipedia.org/wiki/Standard_deviation\n\nSo, I saw someone got a different standard deviation than me, so I checked it again on Excel and I still got 6.27.  Instead of dividing by 7, I divided by 6 (n-1).\n\nIf these data are a sample of a larger population, then you divide by 6 (n-1) to estimate the stdev of the population, so the stdev is 6.27.  If you just want to know the stdev of those numbers, then you divide by 7 (n) so the stdev is 5.81.
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The standardized and studentized version of x differ in such a way that the former requires parameters (that is, population mean and population variance) while the latter uses statistic (that is, sample mean and sample variance).\n\n\nstandardized\n\nz test = (x - mu)(sqrt of n)/sigma\nz test = (108 - 100)(sqrt of 4)/16\nz test = 1\n\nstudentized\n\nt test = (x - muo)(sqrt of n)/s\nt test = (108 - 100)(sqrt of 4)/12\nt test = 1.3333\n\n\nlast\nBetween the two curves with different degress of freedom, the one with the greater value approximate the standard noramal curve more closely.\nIn fact, if the degrees of freedom approach infinity, the resulting curve will be the standard normal curve.\n\nTherefore the one with degress of freedom of 20 approximate the standard normal curve more closely than with the value of 12.
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1) X/25\n2) X/25\n\nExplanation:\n1) If you divide a set of values by a constant, then both the mean and the standard deviation are divided by that constant.\nSince your question is about the variance, which is the SQUARE of the standard deviation, the variance is divided by the SQUARE of the constant (5, in this case).\n\n2) When you add a constant to a set of values, the mean is increased by the amount of that constant, but the standard deviation is unchanged. This is true because the group of values has all moved up by a constant amount, but their relation to each other is unchanged, and their variation from each other is unchanged. Since the standard deviation is unchanged, the variance (the square of the standard deviation) is also unchanged.
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Standard deviation (or variance, which is the standard deviation squared).
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Enter the number into MS Excel, then use the standard deviation formula from the "function" option. Your answer will depend on whether those numbers represent the population or just a sample from a population.
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oh puke, what a pain in the rear.  i don't envy you.  like the other dude said use excel, but if you have to do it on paper, here's the formula:\n \nIn other words, the standard deviation of a discrete uniform random variable X can be calculated as follows:\n\n1.  find the mean of the set of numbers\n2.  subtract the mean from the first number\n3.  square the difference between that number and the mean\n4.  repeat steps 2 and 3 for all numbers\n5.  add all of the differences from step 3\n6.  dvide this number by the number of numbers you had in the set\n7.  take the square root of this\n\n\nthat's the standard deviation.
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Standard Deviation is a simple measure of how much a list of numbers varies. It quickly tells you how much deviation there is from the average.\n\nThink of the familiar bell curve, the so-called normal curve. It might show how tall students are in your school\n\nAbout 68% of the students will have heights between the 1 standard deviation mark on either side of the central peak -- 34% on the left, and 34% on the right.\n\n68.26% of the area under the curve is within one standard deviation of the mean.\n\n95.45% of the area is within two standard deviations.\n\n99.73% of the area is within three standard deviations.\n\n99.99% of the area is within four standard deviations.\n\nIt is a quick way to explain a large hunk of data!\n\nSo for instance if one standard deviation in your school height list is 1 inch, then you'll know that 68 out of hundred kids are within 1 inch of the central value.
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You must look at the formula for standard deviation of the sample distribution of (I'm thinking) binomial distributions. The proportion "p" is a parameter of the formula. You should check it out but, if I remember correctly, it's:\ns.d. of the sample distribution = sqrt[p*(p-1)/n]
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If you can't answer this, you have no idea what the empirical rule is.\n\nAbout 68% of all values fall within 1 standard deviation of the mean for the normal distribution.
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standard deviation is one of measure of variability use in statistics which has the same unit as the given data and is equivalent ot the square root of the variance.\n\nIt measures how all the data varies. It indicates how the data in a distribution scatter or spread.\n\nThe lower the standard deviation, the closer are all the data around the mean.\nThat is, the data are not scattered and they are less heterogenuous, less variable.\n\nThe greater its value, the more the spreadness of the data around the mean.\n\nFor example,\nGiven:\nA: 45,46,40,43,48,46\nB:10,45,28,100,158,468\n\nThe standard deviation of the data A is 2.804757863\nThe standard deviation of the data B is 171.9400089\n\n]since the statndard deviation of the data A is less that in B,\nthe data in A are closer to each other, compare to the data B.
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From http://www.robertniles.com/stats/stdev.shtml\n\nThe standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you you have a relatively large standard deviation.\n\n----------\n\nFrom http://en.wikipedia.org/wiki/Standard_deviation\n\nIn probability and statistics, the standard deviation is the most common measure of statistical dispersion. Simply put, standard deviation measures how spread out the values in a data set are. More precisely, it is a measure of the average difference between the values of the data in the set. If the data points are all similar, then the standard deviation will be low (closer to zero). If the data points are highly variable, then the standard variation is high (further from zero).