Sample Operators
Sample operators
The statistical sample operators discussed in this section can help you to analyze the results of an experiment. The following operators are available in AIMMS:
the
Mean
operator,the
GeometricMean
operator,the
HarmonicMean
operator,the
RootMeanSquare
operator,the
Median
operator,the
SampleDeviation
operator,the
PopulationDeviation
operator,the
Skewness
operator,the
Kurtosis
operator,the
Correlation
operator, andthe
RankCorrelation
operator.
Associated units
The results of the Skewness
, Kurtosis
, Correlation
and
RankCorrelation
operator are unitless. The results of the other
sample operators listed above should have the same unit of measurement
as the expression on which the statistical computation is performed.
Whenever your model contains one or more QUANTITY
declarations,
AIMMS will perform a unit consistency check on arguments of the
statistical operators and their result.
Mean
The following mean computation methods are supported: (arithmetic) mean or average, geometric mean, harmonic mean and root mean square (RMS). The first method is well known and has the property that it is an unbiased estimate of the expectation of a distribution. The geometric mean is defined as the \(N\)th root of the product of \(N\) values. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. The root mean square is defined as the square root of the arithmetic mean of the squares. It is mostly used for averaging the measurements of a physical process.
Operator 

Formula 
\(\frac{1}{n} \sum_{i=1}^{n} x_i\) 
Operator 

Formula 
\(\sqrt[{\displaystyle n}]{\prod_{i=1}^{n} x_i}\) 
Operator 

Formula 
\(\frac{{\displaystyle n}}{{\displaystyle \sum_{i=1}^{n} \frac{1}{x_i}}}\) 
Operator 

Formula 
\(\sqrt{\frac{1}{n} \sum_{i=1}^{n} x_{i}^{2}}\) 
Median
The median is the middle value of a sorted group of values. In case of an odd number of values the median is equal to the middle value. If the number of values is even, the median is the mean of the two middle values.
Operator 

Formula 
\({median} = \left\{ \begin{array}{ll} x_{\frac{N + 1}{2}} & \; \; \mbox{if} \; N \; \mbox{is odd} \\ \frac{1}{2} \left( x_{\frac{N}{2}} + x_{\frac{N + 2}{2}} \right)& \;\; \mbox{if} \; N \; \mbox{is even} \end{array} \right\}\) 
Standard deviation
The standard deviation is a measure of dispersion about the mean. It is defined as the root mean square of the distance of a set of values from the mean. There are two kinds of standard deviation: the standard deviation of a sample of a population, also known as \(\sigma_{n1}\) or \(s\), and the standard deviation of a population, which is denoted by \(\sigma_n\). The relation between these two standard deviations is that the first kind is an unbiased estimate of the second kind. This implies that for large \(n\) \(\sigma_{n1} \approx \sigma_n\). The standard deviation of an sample of a population can be computed by means of
Operator 

Formula 
\(\sqrt{ \frac{1}{n1} \left( \sum_{i=1}^{n} x_{i}^{2}  \frac{1}{n} {\left( \sum_{i=1}^{n} x_i \right)}^2 \right)}\) 
whereas the standard deviation of a population can be determined by
Operator 

Formula 
\(\sqrt{ \frac{1}{n} \left( \sum_{i=1}^{n} x_{i}^{2}  \frac{1}{n} {\left( \sum_{i=1}^{n} x_i \right)}^2 \right)}\) 
Skewness
The skewness is a measure of the symmetry of a distribution. Two kinds of skewness are distinguished: positive and negative. A positive skewness means that the tail of the distribution curve on the right side of the central maximum is longer than the tail on the left side (skewed “to the right”). A distribution is said to have a negative skewness if the tail on the left side of the central maximum is longer than the tail on the right side (skewed “to the left”).
In general one can say that a skewness value greater than \(1\) of less than \(1\) indicates a highly skewed distribution. Whenever the value is between \(0.5\) and \(1\) or \(0.5\) and \(1\), the distribution is considered to be moderately skewed. A value between \(0.5\) and \(0.5\) indicates that the distribution is fairly symmetrical.
Operator 

Formula 
\(\frac{{\displaystyle \sum_{i=1}^{n} (x_i  \mu)^3}} {{\displaystyle \sigma_{n1}^3}}\) 
where \(\mu\) denotes the mean and \(\sigma_{n1}\) denotes the standard deviation.
Kurtosis
The kurtosis coefficient is a measure for the peakedness of a distribution. If a distribution is fairly peaked, it will have a high kurtosis coefficient. On the other hand, a low kurtosis coefficient indicates that a distribution has a flat peak. It is common practice to use the kurtosis coefficient of the standard Normal distribution, equal to 3, as a standard of reference. Distributions which have a kurtosis coefficient less than 3 are considered to be platykurtic (meaning flat), whereas distributions with a kurtosis coefficient greater than 3 are leptokurtic (meaning peaked). Be aware that in literature also an alternative definition of kurtosis is used in which 3 is subtracted from the formula used here.
Operator 

Formula 
\(\frac{{\displaystyle \sum_{i=1}^{n} (x_i  \mu)^4}} {{\displaystyle \sigma_{n1}^4}}\) 
where \(\mu\) denotes the mean and \(\sigma_{n1}\) denotes the standard deviation.
Correlation coefficient
The correlation coefficient is a measurement for the relationship between two variables. Two variables are positive correlated with each other when the correlation coefficient lies between 0 and 1. If the correlation coefficient lies between \(1\) and 0, the variables are negative correlated. In case the correlation coefficient is 0, the variables are considered to be unrelated to one another.
Positive correlation means that if one variable increases, the other variable increases also. Negative correlation means that if one variable increases, the other variable decreases.
Operator 

Formula 
\(\frac{ { \displaystyle n \sum_{i=1}^{n} x_i y_i  \sum_{i=1}^{n} x_i \sum_{i=1}^{n} y_i } } { { \displaystyle \sqrt{ \left( n \sum_{i=1}^{n} x_{i}^{2}  {\left( \sum_{i=1}^{n} x_i \right)}^2 \right) \left( n \sum_{i=1}^{n} y_{i}^{2}  {\left( \sum_{i=1}^{n} y_i \right)}^2 \right)} } }\) 
Rank correlation
If one wants to determine the relationship between two variables, but their distributions are not equal or the precision of the data is not trusted, one can use the rank correlation coefficient to determine their relationship. In order to compute the rank correlation coefficient the data is ranked by their value using the numbers \(1,2,\ldots,n\). These rank numbers are used to compute the rank correlation coefficient.
Operator 

Formula 
\(1  \frac{{\displaystyle 6 \sum_{i=1}^{n} {\left( \mbox{Rank}(x_i)  \mbox{Rank}(y_i) \right)}^2 }} {{\displaystyle n(n^2  1)}}\) 