File Name: variance and coeffecient of variance of .zip
The variance of a discrete random variable is the sum of the square of all the values the variable can take times the probability of that value occurring minus the sum of all the values the variable can take times the probability of that value occurring squared as shown in the formula below:. The coefficient of variation of a random variable can be defined as the standard deviation divided by the mean or expected value of X, as shown in the formula below:. The variance of X is sometimes often referred to as the second moment of X about the mean. The third moment of X is referred to as the skewness, and the fourth moment is called kurtosis. In general, the mth moment of X can be calculated from the following formula:.
In probability theory and statistics , the coefficient of variation CV , also known as relative standard deviation RSD , is a standardized measure of dispersion of a probability distribution or frequency distribution. The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay.
The coefficient of variation should be computed only for data measured on a ratio scale , that is, scales that have a meaningful zero and hence allow relative comparison of two measurements i. The coefficient of variation may not have any meaning for data on an interval scale. On the other hand, Kelvin temperature has a meaningful zero, the complete absence of thermal energy, and thus is a ratio scale.
In plain language, it is meaningful to say that 20 Kelvin is twice as hot as 10 Kelvin, but only in this scale with a true absolute zero. While a standard deviation SD can be measured in Kelvin, Celsius, or Fahrenheit, the value computed is only applicable to that scale. Only the Kelvin scale can be used to compute a valid coefficient of variability. Measurements that are log-normally distributed exhibit stationary CV; in contrast, SD varies depending upon the expected value of measurements.
In most cases, a CV is computed for a single independent variable e. However, data that are linear or even logarithmically non-linear and include a continuous range for the independent variable with sparse measurements across each value e. A data set of [, , ] has constant values. Its standard deviation is 0 and average is , giving the coefficient of variation as.
A data set of [90, , ] has more variability. Its sample standard deviation is 10 and its average is , giving the coefficient of variation as. A data set of [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is Comparing coefficients of variation between parameters using relative units can result in differences that may not be real.
If we compare the same set of temperatures in Celsius and Fahrenheit both relative units, where kelvin and Rankine scale are their associated absolute values :. The sample standard deviations are The CV of the first set is For the second set which are the same temperatures it is If, for example, the data sets are temperature readings from two different sensors a Celsius sensor and a Fahrenheit sensor and you want to know which sensor is better by picking the one with the least variance, then you will be misled if you use CV.
The problem here is that you have divided by a relative value rather than an absolute. The sample standard deviations are still The coefficients of variation, however, are now both equal to 5.
Mathematically speaking, the coefficient of variation is not entirely linear. But this estimator, when applied to a small or moderately sized sample, tends to be too low: it is a biased estimator.
For normally distributed data, an unbiased estimator  for a sample of size n is:. In many applications, it can be assumed that data are log-normally distributed evidenced by the presence of skewness in the sampled data. However, "geometric coefficient of variation" has also been defined by Kirkwood  as:. The coefficient of variation is useful because the standard deviation of data must always be understood in the context of the mean of the data.
In contrast, the actual value of the CV is independent of the unit in which the measurement has been taken, so it is a dimensionless number.
For comparison between data sets with different units or widely different means, one should use the coefficient of variation instead of the standard deviation. The coefficient of variation is also common in applied probability fields such as renewal theory , queueing theory , and reliability theory. In these fields, the exponential distribution is often more important than the normal distribution.
The standard deviation of an exponential distribution is equal to its mean, so its coefficient of variation is equal to 1. Some formulas in these fields are expressed using the squared coefficient of variation , often abbreviated SCV. While many natural processes indeed show a correlation between the average value and the amount of variation around it, accurate sensor devices need to be designed in such a way that the coefficient of variation is close to zero, i.
In actuarial science , the CV is known as unitized risk. In Industrial Solids Processing, CV is particularly important to measure the degree of homogeneity of a powder mixture. Comparing the calculated CV to a specification will allow to define if a sufficient degree of mixing has been reached. CV measures are often used as quality controls for quantitative laboratory assays. While intra-assay and inter-assay CVs might be assumed to be calculated by simply averaging CV values across CV values for multiple samples within one assay or by averaging multiple inter-assay CV estimates, it has been suggested that these practices are incorrect and that a more complex computational process is required.
The coefficient of variation fulfills the requirements for a measure of economic inequality. Archaeologists often use CV values to compare the degree of standardisation of ancient artefacts.
This is useful, for instance, in the construction of hypothesis tests or confidence intervals. Statistical inference for the coefficient of variation in normally distributed data is often based on McKay's chi-square approximation for the coefficient of variation      . According to Liu ,  Lehmann See Normalization statistics for further ratios.
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In probability theory and statistics , the coefficient of variation CV , also known as relative standard deviation RSD , is a standardized measure of dispersion of a probability distribution or frequency distribution. The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. The coefficient of variation should be computed only for data measured on a ratio scale , that is, scales that have a meaningful zero and hence allow relative comparison of two measurements i. The coefficient of variation may not have any meaning for data on an interval scale. On the other hand, Kelvin temperature has a meaningful zero, the complete absence of thermal energy, and thus is a ratio scale. In plain language, it is meaningful to say that 20 Kelvin is twice as hot as 10 Kelvin, but only in this scale with a true absolute zero.
It appears that your browser may be outdated and performance may be limited. The My Study builder is best viewed with the latest internet browsers. In order to express the precision, or repeatability, of immunoassay test results, researchers in the social and behavioral sciences typically report two measures of the Coefficient of Variability CV in their publications: the Inter-Assay CV and the Intra-Assay CV. The CV is a dimensionless number defined as the standard deviation of a set of measurements divided by the mean of the set. Since the usage of the term intra-assay CV may vary somewhat between fields of study, some clarification of terminology and methods is in order.
2 requires the precomputed value of before we can compute. For this reason, Eq. 4 is used often in the computations of the mean and variance. However, if you.
The coefficient of variation CV is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. It is generally expressed as a percentage. Without units, it allows for comparison between distributions of values whose scales of measurement are not comparable. When we are presented with estimated values, the CV relates the standard deviation of the estimate to the value of this estimate.
A coefficient of variation CV can be calculated and interpreted in two different settings: analyzing a single variable and interpreting a model. The standard formulation of the CV, the ratio of the standard deviation to the mean, applies in the single variable setting. In the modeling setting, the CV is calculated as the ratio of the root mean squared error RMSE to the mean of the dependent variable. In both settings, the CV is often presented as the given ratio multiplied by
It is helpful instead to have a dimensionless measure of dependency, such as the correlation coefficient does. So now the natural question is "what does that tell us? Well, we'll be exploring the answer to that question in depth on the page titled More on Understanding Rho, but for now let the following interpretation suffice.
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