### Central tendency

#### Mean

The average value of a data set. For instance, the average of data set
S=\{3,4,5,4,6,50\} is
\mu=12.

#### Median

The value that divides the data set in two sets. For instance, the average of data set
S=\{3,4,5,4,6,50\} is
m=4.5.

### Dispersion

#### Standard deviation

A measure that is used to quantify the amount of variation of a data set. It is like a variation mean. For
instance, the standard deviation of the data set
S=\{3,4,5,4,6,50\} is
\sigma=18.64.

#### Skew

Asymmetry measure. Reveals if a distribution is symmetric or is angled for the left or right or a normal
distribution. A positive skew value represents concentration in the left side and the negative one, the
concentration in the right side.
For instance, the dataset
S=\{0,4,1,1,2,2,3,5,4,4,5,2,6,3,7,5,8,7,9,2,1,0,7,1,1,8,1,2,8,1,3,5,1,4,4\}
skew is 0.498. This distribution has a concentration in the left side (see the chart below).

#### Kurtosis

Flatness measure of the distribution. Reveals how flat a distribution is. A value equals to zero represents the
same flatness of the normal distribution, a positive value represents a higher concentration in a small region,
and a negative value represents a flattened distribution.
For instance, the dataset
S=\{0,4,1,1,2,2,3,5,4,4,5,2,6,3,7,5,8,7,9,2,1,0,7,1,1,8,1,2,8,1,3,5,1,4,4\}
kurtosis is -0.89. This distribution is slightly flattened (see the chart below).

\

#### Coefficient of variation

The ratio between standard deviation to the mean. It is a standardized measure of dispersion of a frequency
distribution. Its value is a real between 0 and 1 and is given by
CV=\frac{\sigma}{\mu}.

For instance, the let the datasets
S_1=\{0,4,1,1,2,2,3,5,4,4,5,2,6,3,7,5,8,7,9,2,1,0,7,1,1,8,1,2,8,1,3,5,1,4,4\}
and
S_2=\{1,2,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9\}.
The dataset S_1 has a higher level of variability than the
S_2. Its revealed by their coefficient of variation:
CV(S_1)=0.71 and CV(S_2)=0.10.

#### Entropy

Shannon entropy, or Shannon index. It is a measure of the diversity of dataset.

For instance, the let the datasets
S_1=\{0,4,1,1,2,2,3,5,4,4,5,2,6,3,7,5,8,7,9,2,1,0,7,1,1,8,1,2,8,1,3,5,1,4,4\}
and
S_2=\{1,2,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9\}.
The dataset S_1 is more diverse than the
S_2. Its revealed by their entropy:
H(S_1)=3.52 and H(S_2)=0.63.

### Correlation

Any of a broad class of statistical relationships involving dependence (not necessarily causal). It is
calculated with Pearson product-moment correlation coefficient. If this coefficient is near zero, the
correlation is weak or inexistent, but if near 1 or -1, its strong.

Correlation can be graphically represented with a scatter plot.

For instance, the tables and charts belows represents two bi-dimensional datasets. Each element in the tables
has two properties, V1 and V2. The correlation of these values is stronger in the second dataset than the
first. Their correlation coefficients are -0.17 and 0.74, respectively.