The vast amount of numbers on a large number of variables need to be properly organized to extract information from them. Broadly speaking there are two methods to summarize data: visual summarization and numerical summarization. Both have their advantages and disadvantages and applied jointly they will get the maximum information from raw data.

Summary statistics are numbers computed from the sample that present a summary of the attributes.

Measures of Location

They are single numbers representing a set of observations. Measures of location also include measures of central tendency. Measures of central tendency can also be taken as the most representative values of the set of observations. The most common measures of location are the Mean, the Median, the Mode, and the Quartiles.

Similarly, Deciles and Percentiles are defined as division points that divide the rank-ordered data into 10 and 100 equal segments. 

Measures of Spread

Measures of location are not enough to capture all aspects of the attributes. Measures of dispersion are necessary to understand the variability of the data. The most common measure of dispersion is the Variance, the Standard Deviation, the Interquartile Range and Range.

Measures of Skewness

In addition to the measures of location and dispersion, the arrangement of data or the shape of the data distribution is also of considerable interest. The most 'well-behaved' distribution is a symmetric distribution where the mean and the median are coincident. The symmetry is lost if there exists a tail in either direction. Skewness measures whether or not a distribution has a single long tail.

Skewness is measured as:

\( \dfrac{\sqrt{n} \left( \Sigma \left(x_{i} - \bar{x} \right)^{3} \right)}{\left(\Sigma \left(x_{i} - \bar{x} \right)^{2}\right)^{\frac{3}{2}}} \)

The figure below gives examples of symmetric and skewed distributions. Note that these diagrams are generated from theoretical distributions and in practice one is likely to see only approximations.

All the above summary statistics are applicable only for univariate data where information on a single attribute is of interest. Correlation describes the degree of the linear relationship between two attributes, X and Y.

With X taking the values x(1), … , x(n) and Y taking the values y(1), … , y(n), the sample correlation coefficient is defined as:

\(\rho (X,Y)=\dfrac{\sum_{i=1}^{n}\left ( x(i)-\bar{x} \right )\left ( y(i)-\bar{y} \right )}{\left( \sum_{i=1}^{n}\left ( x(i)-\bar{x} \right )^2\sum_{i=1}^{n}\left ( y(i)-\bar{y} \right )^2\right)^\frac{1}{2}}\)

The correlation coefficient is always between -1 (perfect negative linear relationship) and +1 (perfect positive linear relationship). If the correlation coefficient is 0, then there is no linear relationship between X and Y.

In the figure below a set of representative plots are shown for various values of the population correlation coefficient ρ ranging from - 1 to + 1. At the two extreme values, the relation is a perfectly straight line. As the value of ρ approaches 0, the elliptical shape becomes round and then it moves again towards an elliptical shape with the principal axis in the opposite direction.