What is the significance of a high variance value in statistics?
116102
Lots of variance means there is a lot of spread in the data. That is, a lot of noise.
387498
That means a survey of a population of similar cases to find out what percentage of cases exhibit one characteristic versus another. It's a statistical kind of attempt to find out what the average is and how much different cases might deviate from the average case. In mathematical statistics they refer to these parameters of the population as the mean and standard deviation.
173519
I guess you are talking about the statistical significance, of the difference of two random variables (or functions of variables).\nThat would be the case, for example if you want to test whethern two means are or not equal.\nThen, the difference of the two means can be tested to be or not zero. For that, you need to know the distribution of the original variables.\nIf the two samples come from the same normal population and are of the same size, then the difference is normal with mean zero and variance = 2*sigma^2/n where n is the sample size.\nThen, if the value (In absolute terms) is greater than X(p) then they are significantly different with probability p.\nX(p) is such P(X>X(p))=1-p.
308472
Find yourself a z-statistic table and look up critical value in the table.  Check your stats book on how to interpret the table.\nhttp://www.math2.org/math/stat/distributions/z-dist.htm
282608
If you can provide more details then I can help you. p-value has to be compared with the level of significance. z-table and t-tables are different and u'll requir specific table for that. are you solving a question of hypothesis testing??\n\nCritical value for 't' requires the degree of freedom.
806818
1. If you know the variances you'd know the mean, because you need to calculate the mean to find the variance. Newer stat texts have finally started to get smart and quit putting stupid topics like this in there!\n\n2. If you suspect that the population variances are equal, you can combine the samples to get one estimate of that variance. That's called 'pooling.' When variances are equal use the formula that combines those variances for the calculation.\n\n3. The p-value comes from this calculation so you don't use it to determine which formula to use. Your background knowledge is what you would use to determine whether variances are likely to be equal.\n\n4. It's never wrong not to pool. Pooling when appropriate does give a test more power, but not pooling is always OK.
975814
Variance = (∑x²)/n - (∑x/n)² \n= (4 + 25 + 64)/3 - [(2 + 5 + 8)/3]²\n= 31 - 25\n= 6\nThus standard deviation = √(variance)\n= √6
184854
Heteroskedastic is an adjective describing a data sample or data-generating process in which the errors are drawn from different distributions for different values of the independent variables.\n\nMost commonly heteroskedasticity takes the form of changes in variance with the magnitude of X. That is, in\n\ny = Xb + e\n\nthat the e's vary in magnitude with the X's. (An example is that variance of income across individuals is systematically higher for higher income individuals.)\n\nIf the errors are drawn from different distributions, or if higher moments of the error distributions vary systematically, these are also forms of heteroskedasticity.
929935
variations matter greatly in interpreting data.  Something might seem like it is returning significantly different results than the control but stat evaluation may show that the variations are minimal at best
991331
If there were extreme data points, for example out of a sample of ten, 5 data points had the value "5," two data points had the value "6," two had the value "4,"  and 1 had the value "10," the median value would be a value between the 5th and 6th data point, the mode would simply be the one with the most frequency, 5. Since the outlier 10 data point skews the mean upward, but the trend is lopsided towards higher numbers, the mean, median, and mode are very close.
9419
A statistical t-test measures the mean difference between two groups.  So it tells you if, on average, one group differs from another group along some dimension.\n\nt-stat is the actual value of the t-test.  It is equal to the mean difference divided by the pooled standard deviation between the two groups.  \n\nt-critical is what you compare the t-stat to in order to decide if it is statistically significant. You usually look this up in a table. So, if the t-stat is bigger than the t-crit, we conclude that there is a difference, on average, between the two groups in the population.
935234
"In probability and statistics, the standard deviation of a probability distribution, random variable, or population or multiset of values is defined as the square root of the variance.\n\nThe standard deviation is measured in the same units as the values of the population. For a population of distances in meters, the standard deviation is also measured in meters, whereas the variance is measured in square meters."\n\n\nIn essence, standard deviation is used for categorizing groups.\nFor example, the average IQ is 100, the standard deviation to the left would be 85, and deviation to the right would be 115. Two left standard deviations would be 60, two right standard deviations would be 130.\nSame applies to SAT scores, statistics, polls, etc.
611725
I thinkit's more the number significance than the amount.\n\n12 is a power number....
121079
Variance is the square of the standard deviation......usually given the symbol (small case) sigma.\n\nVariance ==> σ^2\n\nFind that symbol. It's common and should be readily seen on the keypad.
478561
I don't remember N value, but I know that P value is the probability that something is done by "chance".  In other words, I cannot "prove" that I am a psychic, but I can do things that point farther and farther away from me 'cheating" or "just guessing".  The farther one gets from something being "chance" the smaller the P value is.  In Statistics, P-value is only considered "significant" if it is below .05 (or less than possibly happening one in 20 times).
101856
Standard deviation is a measure of the variability in a sample.  A small standard deviation means that most observations are clost to the mean (average).  The standard deviation is the square root of the variance.  The variance is calculated by taking the difference between each observation and the mean, squaring it, summing for all observations, then dividing by the number of observations less one.\n\nwith m = mean and X1 ... Xn being the observations:\n\nvariance: V = [(X1-m)^2 + (X2-m)^2 ... + (Xn-m)^2] / (n-1)\nstandard deviation: sigma = sqrt(V)
662741
In basic statistical hypothesis testing, the P-value represents the probability of encountering a result more extreme than the value that we've observed in our testing.\n\nAs an example - suppose we had conducted a properly designed experiment, and discovered that the mean score of our sample was 103.5. We would evaluate this finding of 103.5 by comparing it to a known "sampling distribution" - a distribution of sample means of size n that was randomly drawn from a population with (for the sake of this example) a mean of 100 and a standard deviation of 1.75.\n\nWhat we want to do is evaluate the likelihood that a sample of size n - randomly drawn from the population - would have a mean as extreme as 103.5.\n\nWe begin with the assumption that the null hypothesis is true - that our sample belongs to the population - and that the deviation of 3.5 from the population mean of 100 is simply due to random error.\n\nIn this situation, the appropriate statistical tool for evaluating the probability of observing a sample mean as extreme as 103.5 is the t-test. We begin by "standardizing" the raw score of 103.5 by converting it to a deviation score and then dividing that deviation score by the standard deviation of the sampling distribution.\n\nThe formula is simply  (103.5 - 100.0) / 1.75  =  2.0\n\nThis means that our sample mean of 103.5 is 2.0 standard deviation units above the mean of the sampling distribution. Now we need to find out the probability of encountering a sample that is 2 standard deviation units from the mean. \n\nBecause the sampling distribution is assumed to be approximately normally distributed, we can refer to the z-table (reproduced in any statistic text), and discover that only 2.28 percent of all the scores in a normal distribution fall above 2 standard deviation units above the mean. The proper interpretation of this result is that the probability of observing a sample this extreme from a population with a mean of 100 and a standard deviation of 1.75 is 2.28% or less.\n\nP-values are used in conjunction with critical values - pre-determined probabilities that we feel are so unlikely to have occurred by chance, that we are comfortable concluding that results this extreme could not have been selected from the population (thereby rejecting the null hypothesis that the sample was selected from the population).\n\nThe common critical values are .05 and .01, meaning, in each case, that we'll reject the null hypothesis if our finding is so extreme that it would only be expected to happen by chance less than 5 percent of the time - or in the case of .01, less than 1 percent of the time.\n\nReferring back to our finding - we concluded that a result of 103.5 would only happen 2.28 percent of the time or less - if our pre-selected critical value had been .05 (5 percent), we would reject the null hypothesis, and conclude that our sample was not selected from the population. If the critical value had been 1 percent, because our finding happens 2.28 percent of the time, we would fail to reject the null hypothesis.\n\nMan, I hope this made some sense!