how to calculate correlation with variance?
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The strength of the relationship between X and Y is sometimes expressed by squaring the correlation coefficient and multiplying by 100. The resulting statistic is known as variance explained (or R2). Example: a correlation of 0.5 means 0.52x100 = 25% of the variance in Y is "explained" or predicted by the X variable.
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['Calculate the deviation of each measurement. ... ', 'Calculate the squares of each of the deviations (i.e. di2). ... ', 'Add together all the squares of the deviations i.e.', 'Divide by the total number of measurements (i.e. n) to obtain the mean of all the square deviations.']
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What of the variance of the sum of two random variables? If you work through the algebra, you'll find that Var[X+Y] = Var[X] + Var[Y]+ 2∙(E[XY] - E[X]∙E[Y]) . Var[X+Y] = Var[X] + Var[Y] + 2∙Cov[X,Y] . Note that the covariance of a random variable with itself is just the variance of that random variable.
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Calculating Stock Correlation To find the correlation between two stocks, you'll start by finding the average price for each one. Choose a time period, then add up each stock's daily price for that time period and divide by the number of days in the period.
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Step 3: Square each deviation to make it positive. Step 4: Add the squared deviations together. Step 5: Divide the sum by one less than the number of data points in the sample. The result is called the variance.
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['Direct materials quantity variance = SP x (SQ – AQ) ... ', 'Direct materials price variance = (SP – AP) x AQ = ($10.35 – $9.90) x 30,000 = $13,500 favorable.']
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To calculate standard deviation, add up all the data points and divide by the number of data points, calculate the variance for each data point and then find the square root of the variance.
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['Find the difference each number is away from the mean, and then square that difference. For example, subtract 38 from 21 and then square the result. ... ', 'Add together the two values you calculated in the previous step. For the example, add 289 and 289 to get a result of 578.', 'This is the variance between the two numbers. Tip.']
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['Calculate the average (mean) price for the number of periods or observations.', "Determine each period's deviation (close less average price).", "Square each period's deviation.", 'Sum the squared deviations.', 'Divide this sum by the number of observations.']
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mx and my are the means of x and y variables. the p-value (significance level) of the correlation can be determined : by using the correlation coefficient table for the degrees of freedom : df=n−2. or by calculating the t value : t=r√1−r2√n−2.
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Follow these steps: Work out the mean (the simple average of the numbers.) Then, for each number, subtract the mean and square the result (the squared difference). Finally, work out the average of those squared differences. If the coefficient of variance is 16 and mean is 25, how do I find the variance?
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The procedure for calculating the variance and standard deviation for ungrouped data is as follows. First sum up all the values of the variable X, divide this by n and obtain the mean, that is, ¯X = ΣX/n. Next subtract each individual value of X from the mean to obtain the differences about the mean.
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The variance is the average of the squared deviations from the mean, i.e., var = mean(abs(x - x. mean())**2) . The mean is normally calculated as x. sum() / N , where N = len(x) .
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['Step 1: Calculate Actual Cost. Actual Cost = Actual Quantity x Actual Price. ... ', 'Step 2: Find the Standard Cost of Actual Quantity. Standard Cost = Actual Quantity x Standard Price. ... ', 'Step 3: Calculate the Variance.']
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You calculate the percent variance by subtracting the benchmark number from the new number and then dividing that result by the benchmark number. In this example, the calculation looks like this: (150-120)/120 = 25%. The Percent variance tells you that you sold 25 percent more widgets than yesterday.
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['The mean of the distribution is equal to n * k / N .', 'The variance is n * k * ( N - k ) * ( N - n ) / [ N2 * ( N - 1 ) ] .']
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Divide the sum by sx ∗ sy. Divide the result by n – 1, where n is the number of (x, y) pairs. (It's the same as multiplying by 1 over n – 1.) This gives you the correlation, r.