what is the significance of standard deviation,confidence limits,Q-test, and polled standard deviation?
957932
Standard deviation tells you the spread of the data, for example the data may be very close together or very spread out.\n\nConfidence limits tell you how accurate a particular model or hypothesis test is\n\nQ-test tells you about extreme values\n\nPolled standard deviation is the standard deviation of the sample of data, not necessarily the same as the actual standard deviation
1038461
check the standard deviation. It represents volatility..
790770
I'm not sure what exactly you want explained about them. A complete explanation requires more than can be provided on Y!A.\n\nBasically, the Confidence Interval is the set of population parameters for which your sample statistic would be a reasonably likely outcome. \n\nIf you knew the population parameter (which you don't) the reasonably likely outcomes would be near the population parameter. How near depends on 1. the level of confidence desired, and 2. the standard error of the samping distribution of the sample statistic.\n\nYou don't know the population parameter, nor the sampling distribution of the sample statistic, but you estimate them from your sample. \n\nThe CI tells you which population parameters are plausible values for the sample statistic you observed.\n\nIf this isn't clear, I don't know how to make it more clear in this format. Please feel free to contact me for clarification.
697476
(a) Adding another observation makes the test more significance, not less significant.    That would make the p-value smaller, and it was already smaller than 1% since you rejected the null originally.  You would still reject the null at the 1% level.\n\n(b) If a test is rejected at the 1% level, the p-value was less than 1%.  Since that is the case, it would also be less than 5%, which means you would reject the null at the 5% level.
529581
Approximately 95% of the data is within 2 standard deviations of the mean.\nCheck out this website\nhttp://en.wikipedia.org/wiki/Confidence_interval\nand look under the section\n"Confidence Intervals in Measurement"
288137
(m)\n\nThe level of significance (alpha) is the probability of a Type I error that an investigator is willing to risk in rejecting a null hypothesis. Generally, it refers to the probability of the event occurring due to chance. If alpha = .01, it is likely that one time out of a hundred the event could occur due to chance. If you lower the significance level from .05 to .01, you decrease the probability of rejecting a true hypothesis but increase the probability of accepting a false hypothesis. A Type II error (beta) occurs when an investigator fails to accept the alternative hypothesis when in fact the alternative hypothesis was true. In other words, the null hypothesis was accepted when it was not true.
399013
As it happens, I just got back from a test on statistics. We covered that very question. \n\nHere goes:\nthe sample size is dependent on how confident you want to be about your results. In the following formula, then Z is the confidence level. Use 1.65 for 90% confidence level, 1.96 for 95%, 2,33 for 98% and 2.58 for 99%. \nYou also need to know the standard deviation, which should be given. and the error allowable, for instance 3%, or 5% or such. That number goes into the formula as E. \n\nso number n equals = (Z * standard deviation/E)^2
1105474
You can get a standard deviation from any of them.  Define "good".\n\nIf you are asking which has the largest SD, it's the second set.  The order (from smallest SD to largest SD) is 3, 1, 2.  Set 3's SD is less than set 1's because if you take a set of numbers and multiply by the same number, it multiplies the SD by that number.  So if you take set 3 and multiply by 7, you get set 1, and its SD will be 7 times that of set 3.  Set 2's SD is larger than set 1 because the numbers in set 2 are more spread out than those of set 1.
585237
Since we are not given any raw data, we must assume normality and use the population standard deviation.\n\nThe 90% CI for the population mean is given by:\n(xbar-z(.05)*sigma/sqrt(n), xbar+z(.05)*sigma/sqrt(n))\n\nHere we are told xbar = 12.4, sigma = 6, and n = 54.  Using a table of normal probabilities, we find z(.05) = 1.645.  Simple calculation shows the CI is:\n(11.0569, 13.7431)\n\nFor part (2), we want our margin or error to be 1 with a 90% confidence level.  Our population standard deviation is still 6 and z(.05) is still 1.645.  Thus, we want:\n1.645*6/sqrt(n) = 1\n9.87/sqrt(n) = 1\nsqrt(n) = 9.87\nn = 97.4169\nRound up to get n = 98.\n\nFor your additional comments:\nIncreasing the confidence level also increases the margin of error--not (a).\nThe sample standard deviation could be higher or lower than the population standard deviation, so it could increase or decrease the margin of error, but there is no guarantee--not (b).\nIncreasing the sample size to 100 makes for a better estimate of the mean and would definitely decrease the margin of error.\n\nThus, (c) is the correct choice.
332290
Generally it is the standard deviation, or range of typical answers.  The mean usually is the center of the bars, but not always.
570140
This will depend on how your experiment is set up - the question you are asking with your experimental design - and how data is collected. In general, the best measure of uncertainty involves repetition, more data points - the more, the better. Lots of reps gives you a more accurate measure of standard deviation. But there are many designs. Before you set up an experiment, talk to a statistician to make sure the data you are collecting satisfies the assumptions of your statistical technique and actually answers the question you are asking.
45470
http://www.math.unb.ca/~knight/BasicStat/u-test.htm\nhttp://www.lifesciences.napier.ac.uk/teaching/MB/MannWhit.html\nhttp://www.texasoft.com/winkmann.html\nhttp://www.statsdirect.com/help/nonparametric_methods/mwt.htm\nhttp://eatworms.swmed.edu/~leon/stats/utest.html
871703
Sorry, if the result can be greater than 1 then you ain't estimating a proportion.  For proportions each unit in the sample gives you either 0 or 1 as a result.\n\nYou need to swith to the general case where you calculate the standard deviation from the sum of squared differences from the mean, etc. PQ/n or whatever cannot be applied.
199982
In statistics, the standard error of a measurement, value or quantity is the standard deviation of the process by which it was generated, after adjusting for sample size. In other words the standard error is the standard deviation of the sample mean.\n\nStandard errors provide simple measures of uncertainty in a value and are often used because:\n\n    * If the standard error of several individual quantities is known then the standard error of some function of the quantities can be easily calculated in many cases;\n    * Where the probability distribution of the value is known, they can be used to calculate an exact confidence interval; and\n    * Where the probability distribution is unknown, relationships like Chebyshev's or the Vysochanskiï-Petunin inequality can be used to calculate a conservative confidence interval.
961030
in Statistics in theory there should zero measurements falling beyond the three standard deviations from the mean on either side. though in practical some values do fall in this range. I don't remember the exact statistical names of these values but i think they are referred to as "outliers".
149140
Don't know Levey Jennings, but in general the variance is the sum of the squared deviations from the mean divided by the number of samples (for large data sets) or the number of samples minus 1 (for smaller data sets).  The standard deviation (SD) is then the square root of the variance.\n\nSo, if "m" is the mean and each sample is x1..n, the variance is:\n\n[(m-x1)^2 + (m-x2)^2 + (m-x3)^2 ... (m-xn)^2]/n\n\nThen, take the square root of the variance to get the standard deviation.
1076565
confidence intervals are about what % confident you are that a given person's score falls between x score and x score on any given day.  formulas: \n\n                                   68% CI=X+/-1(Standard error of measurement)\n                                   95%CI=X+/-1.96(SEM)\n                                   98%CI=X+/-2.58(SEM)\n\nto get the SEM:  SD(standard deviation)times the square root of 1 minus the reliability coefficient(rtt)\n\nI might be wrong about the margin of error, but I think it's the SEM(the index of the amount of error that can be expected in obtained test scores due to the unreliability of the test) \n\nhope this helps--statistics suck!