how varience  and standerd deviation is calculated?
547680
I guess you need this for stock market prices?\n\nLet R_i be the daily price variations in percent.\nM = average of R_i \nVar = average of ( (R_i - M)^2 )\nSD = square root of Var
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.
830991
I'm wondering why you created the data 1-3500?  I would think you would use the set of data available and figure the standard deviation and average.  With that information, you would be able to predict tomorrows value at a certain confidence level.  I forget is that the p test or the t test.  its been a while since I took statistics.
813036
Just enter "statistical anomaly" in the search bar on the Yahoo home page for a real answer.
242699
Eh... a little bit imprecise but let's see if I can help:\n\nIf you are just talking about how multiplying the random variable "X" with a constant "a" affects the variance, that's easy enough: Var(aX) = a^2 * Var(X)\n\nIf you are asking about how to compute the expected Var(aX) given a probability distribution function then you need two things: \n\n1) Introduce a new random variable Y = aX and write up the probability distribution function for that\n\n2) Using the new probability distribution function, find the probability density function (continuous distributions) OR probability mass function (discrete distributions).\n\n3) Compute the variance using the functions you got in 2). \n\nFor discrete distributions, the variance is then given by\n\nVar(Y) = sum[from i=1 to N] (P(y_i) * (y_i - E(Y))^2)\n\nwhere P is the probability mass function. \n\nFor continuous distributions: \n\nVar(Y) = integral[from i=1 to N] P(y) * (y - E(Y))^2 dy\n\nwhere P is the probability distribution function.
139457
The approximate variance of the product of two random numbers (Var_1x2) is a function of their means (Mean_1,Mean_2), their variances (Var_1,Var_2) and Covariance (Cov_12) :\n\nVar_1x2=\nVar_1*(Mean_2)^2 + \nVar_2*(Mean_1)^2 +\n2*Mean_1*Mean_2*Cov_12
659445
well first, you need the actual value, then use the %error formula\n\n(actual-experimental) / actual X 100% = (just change the sign if it is negative)\n\nyou should probably use the averages of the 2 methods you used, but without the actual value, i can't get you the answer
869354
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.
919781
No.  The formula is much more complex.  For a two-asset portfolio, the variance of the portfolio, vp, will be a function of the proportions invested in the assets (x1 and x2), their return variances (v1 and v2), and the covariance between their returns (c12):\n\nvp = ((x1^2)*v1) + (x2^2)*v2) + 2*x1*x2*c12 \n\nWith 20 assets, the formula will be even more confusing (about 20 times so).  \n\nYou need to employ an alternative strategy.  Build a time series of your portfolio value, compute periodic changes in that value, and calculate standard deviation of that change.
1047890
class C\n{\npublic:\nenum { NUM_VALUES = 10 };\n\nprivate:\ndouble m_values[NUM_VALUES];\n\npublic:\n// Sets the number stored at index i in m_values to v\nvoid SetValue(int i, double v) throw( std::exception )\n{\nif ( i < 0 || i >= NUM_VALUES ) throw std::exception("Index out of range");\nm_values[i] = v;\n}\n\n// Gets the number stored at index i in m_values\ndouble GetValue(int i) const throw( std::exception )\n{\nif ( i < 0 || i >= NUM_VALUES ) throw std::exception("Index out of range");\nreturn m_values[i];\n}\n\n    // Returns the mean of the numbers stored in m_values\n    double GetMean(void) const \n    {\n        double sum = 0;\n        for ( int i = 0; i < NUM_VALUES; i ++ ) sum += m_values[i];\n        return sum / NUM_VALUES;\n    }\n\n    // Returns the sample standard deviation of the numbers stored in m_values\n    double GetStandardDeviation(void) const \n    {\n        double mean = GetMean();\n        double sum = 0;\n        for ( int i = 0; i < NUM_VALUES; i ++ ) sum += (m_values[i]-mean)*(m_values[i]-mean);\n        return sqrt(sum/(NUM_VALUES-1));\n    }\n};
400910
"...the variance of a random variable is a measure of its statistical dispersion, indicating how far from the expected value its values typically are."\nhttp://en.wikipedia.org/wiki/Variance\n\nHere are some descriptions of how to calculate it:\nhttp://davidmlane.com/hyperstat/A16252.html\n\nCheers!
967643
z score= (n-mean)/standard deviation\nstandard deviation = square root of variance\n\nz score = (50-24)/ 5\n            = 26/5\n            = 5.2\n\nThe z score of the data is 5.2, meaning n is 5.2 standard deviations from the mean.
905827
If a random variable has a standard deviation of s and you take N samples, then the standard deviation of the sample mean is s/sqrt(N).\n\nIn your case you assume that the standard deviation of the random variable is close to 50 because you have such a huge number of samples. Now if you turn around and only look at 30 samples then the standard deviation will be\n50/sqrt(30).
437434
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.
695289
standard deviation of intercept is a quantity related to the precision of the estimated intercept of the fitted line. It can be calculated by the formula:\n\nS_a = (S_b)*(sqrt( (sum(x_j)^2)/m )) \n\nWhere, S_b is standard deviation of slope\n\nS_b = sqrt( ((m*(s_y)^^2) / [m*(sum(x_j)^2) -(sum x_j)^2] )\n\ns_y is the residual standard deviation.\n\na and b are estimated parameters of the line y = a + bx +err.\n\n[Here, by S_b, I mean S with suffix b, Sorry but its a bit difficult to type mathematics with only text.]\nI hope , I answered your question.
937106
Standard deviation tells us how far from the mean the data points tend to be.\n\nFor example, if the mean is 20 and SD is 5. Then all the numbers used to calculate the average (20) can be 5 standard deviation away from it. So the numbers can be from anywhere of 15-25.
1001878
The formula for Standard Deviation:\n\nSigma = sqrt(E((X-E(X))^2) = sqrt(E(X^2) - (E(X))^2)