Statistics

For the following functions, when the input is a collection, it can take the following forms:

  • Multiple arguments, e.g. ["Mean", 1, 2, 3]
  • A list of numbers, e.g. ["Mean", ["List", 1, 2, 3]]
  • A matrix, e.g. ["Mean", ["List", ["List", 1, 2], ["List", 3, 4]]]
  • A range, e.g. ["Mean", ["Range", 1, 10]]
  • A linear space: ["Mean", ["Linspace", 1, 5, 10]]

Functions

["Mean", collection]

\operatorname{mean}(\lbrack3, 5, 7\rbrack)
\[ \operatorname{mean}(\lbrack3, 5, 7\rbrack) \]

Evaluate to the arithmetic mean of a collection of numbers.

The arithmetic mean is the average of the list of numbers. The mean is calculated by dividing the sum of the numbers by the number of numbers in the list.

The formula for the mean of a list of numbers is \( \bar{x} = \frac{1}{n} \sum_{i=1}^n x_i\), where \(n\) is the number of numbers in the list, and \(x_i\) is the \(i\)-th number in the list.

["Mean", ["List", 7, 8, 3.1, 12, 77]]
// 21.02

["Median", collection]

Evaluate to the median of a collection of numbers.

The median is the value separating the higher half from the lower half of a data sample. For a list of numbers sorted in ascending order, the median is the middle value of the list. If the list has an odd number of elements, the median is the middle element. If the list has an even number of elements, the median is the average of the two middle elements.

["Median", ["List", 1, 2, 3, 4, 5]]
// 3

["Mode", collection]

Evaluate to the mode of a collection of numbers.

The mode is the value that appears most often in a list of numbers. A list of numbers can have more than one mode. If there are two modes, the list is called bimodal. For example \( \lbrack 2, 5, 5, 3, 2\rbrack\). If there are three modes, the list is called trimodal. If there are more than three modes, the list is called multimodal.

["Variance", collection]

Evaluate to the variance of a collection of numbers.

The variance is a measure of the amount of variation or dispersion of a set of values. A low variance indicates that the values tend to be close to the mean of the set, while a high variance indicates that the values are spread out over a wider range.

The formula for the variance of a list of numbers is

\[\frac{1}{n} \sum_{i=1}^n(x_i - \mu)^2\]

where \(\mu\) is the mean of the list.

["StandardDeviation", collection]

Evaluate to the standard deviation of a collection of numbers.

The standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

The formula for the standard deviation of a collection of numbers is

\[\sqrt{\frac{1}{n} \sum_{i=1}^n (x_i - \mu)^2}\]

where \(\mu\) is the mean of the list.

["Skewness", collection]

Evaluate to the skewness of a list of numbers.

The skewness is a measure of the asymmetry of the distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or undefined.

The formula for the skewness of a collection of numbers is: \[\frac{1}{n} \sum_{i=1}^n \left(\frac{x_i - \mu}{\sigma}\right)^3\]

where \(\mu\) is the mean of the collection, and \(\sigma\) is the standard deviation of the collection.

["Kurtosis", collection]

Evaluate to the kurtosis of a collection of numbers.

The kurtosis is a measure of the “tailedness” of the distribution of a real-valued random variable. The kurtosis value can be positive or negative, or undefined.

The formula for the kurtosis of a collection of numbers is

\[ \frac{1}{n} \sum_{i=1}^n \left(\frac{x_i - \mu}{\sigma}\right)^4\]

where \(\mu\) is the mean of the list, and \(\sigma\) is the standard deviation of the list.

["Quantile", collection, q:number]

Evaluate to the quantile of a collection of numbers.

The quantile is a value that divides a collection of numbers into equal-sized groups. The quantile is a generalization of the median, which divides a collection of numbers into two equal-sized groups.

So, \(\operatorname{median} = \operatorname{quantile}(0.5)\).

["Quartiles", collection]

Evaluate to the quartiles of a collection of numbers.

The quartiles are the three points that divide a collection of numbers into four equal groups, each group comprising a quarter of the collection.

["InterquartileRange", collection]

Evaluate to the interquartile range (IRQ) of a collection of numbers.

The interquartile range is the difference between the third quartile and the first quartile.

["Sum", collection]

Evaluate to a sum of all the elements in collection. If all the elements are numbers, the result is a number. Otherwise it is a simplified collection.

\sum x_{i}
\[ \sum x_{i} \]
["Sum", ["List", 5, 7, 11]]
// ➔ 23

["Sum", body, bounds]

Return the sum of bodyfor each value in bounds.

\sum{i=1}^{n} f(i)
\[ \sum{i=1}^{n} f(i) \]
["Sum", ["Add", "x", 1], ["Tuple", 1, 10, "x"]]
// ➔ 65

["Product", collection]

Evaluate to a product of all the elements in collection.

If all the elements are numbers, the result is a number. Otherwise it is a simplified collection.

\prod x_{i}
\[ \prod x_{i} \]
["Product", ["List", 5, 7, 11]]
// ➔ 385
 
["Product", ["List", 5, "x", 11]]
// ➔ ["List", 55, "x"]

["Product", body, bounds]

Return the product of bodyfor each value in bounds.

\prod_{i=1}^{n} f(i)
\[ \prod_{i=1}^{n} f(i) \]
["Product", ["Add", "x", 1], ["Tuple", 1, 10, "x"]]
// ➔ 39916800

["Erf", z:complex]

Evaluate to the error function of a complex number.

The error function is an odd function ( \( \operatorname{erf} -z = - \operatorname{erf} z\) ) that is used in statistics to calculate probabilities of normally distributed events.

The formula for the error function of a complex number is:

\[ \operatorname{erf} z = \frac{2}{\sqrt{\pi}} \int_0^z e^{-t^2} dt\]

where \(z\) is a complex number.

["Erfc", z:complex]

Evaluate to the complementary error function of a complex number.

It is defined as \( \operatorname{erfc} z = 1 - \operatorname {erf} z \).

["ErfInv", x:real]

Evaluate to the inverse error function of a real number \( -1 < x < 1 \)

It is defined as \( \operatorname{erf} \left(\operatorname{erf} ^{-1}x\right) = x \).