how to compare variance of two samples?
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F Test to Compare Two Variances If the variances are equal, the ratio of the variances will equal 1. For example, if you had two data sets with a sample 1 (variance of 10) and a sample 2 (variance of 10), the ratio would be 10/10 = 1.
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To figure out the variance, divide the sum, 82.5, by N-1, which is the sample size (in this case 10) minus 1. The result is a variance of 82.5/9 = 9.17. Standard deviation is the square root of the variance so that the standard deviation would be about 3.03.
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['Calculate the mean (simple average of the numbers).', 'For each number: subtract the mean. Square the result.', 'Add up all of the squared results.', 'Divide this sum by one less than the number of data points (N - 1). ... ', 'Take the square root of this value to obtain the sample standard deviation.']
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['First make sure the both weights have the same units.', 'Take your experimental yield and divide it by the theoretical yield.', 'Multiply this value by 100 to find the percent yield.']
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['Covariance measures the total variation of two random variables from their expected values. ... ', 'Obtain the data.', 'Calculate the mean (average) prices for each asset.', 'For each security, find the difference between each value and mean price.', 'Multiply the results obtained in the previous step.']
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Calculating Standard Error of the Mean First, take the square of the difference between each data point and the sample mean, finding the sum of those values. Then, divide that sum by the sample size minus one, which is the variance. Finally, take the square root of the variance to get the SD.
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Calculating Standard Error of the Mean First, take the square of the difference between each data point and the sample mean, finding the sum of those values. Then, divide that sum by the sample size minus one, which is the variance.
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A one sample t-test is used to compare the mean of a sample to a known value (often 0, but not always). A two sample t test is used to compare the means of two different samples.
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['Direct material yield variance = (Standard output × Standard cost) – (Actual output × Standard cost) ... ', '*34,100 × (1,000/1,100) ... ', 'Direct material yield variance = (Actual quantity used × Standard cost) – (Standard quantity allowed for actual output × Standard cost) ... ', '*$41,800/1,100 tons = $38.00.']
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['The mean value is calculated by adding all the data points and dividing by the number of data points.', 'The variance for each data point is calculated, first by subtracting the value of the data point from the mean. ... ', 'The square root of the variance—result from no.']
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Sample variance and Standard Deviation using R var(y) instructs R to calculate the sample variance of Y. In other words it uses n-1 'degrees of freedom', where n is the number of observations in Y. sd(y) instructs R to return the sample standard deviation of y, using n-1 degrees of freedom. sd(y) = sqrt(var(y)).
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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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Two unpaired t tests Use the unequal variance t test, also called the Welch t test. It assues that both groups of data are sampled from Gaussian populations, but does not assume those two populations have the same standard deviation.
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Variance. The variance is the average of the squared differences from the mean. To figure out the variance, first calculate the difference between each point and the mean; then, square and average the results. ... Standard deviation is the square root of the variance so that the standard deviation would be about 3.03.
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The unequal variance t test reports a confidence interval for the difference between two means that is usable even if the standard deviations differ. Both t tests report both a P value and confidence interval. The calculations differ in two ways: Calculation of the standard error of the difference between means.
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['Determine the confidence level and find the appropriate z*-value. Refer to the above table.', 'Find the sample mean. for the sample size (n). ... ', 'Multiply z* times. and divide that by the square root of n. ... ', 'Take.']
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The simplest way to measure the proportion of variance explained in an analysis of variance is to divide the sum of squares between groups by the sum of squares total. This ratio represents the proportion of variance explained.