Inference statistics is used to model the data in the data, calculate its probability and infer the matrix. This inference can be used as an answer to the true/false question (hypothesis test).
Estimation of digital features (estimation), prediction of future observation, prediction of correlation (correlation) or modeling of relationship (regression). Other modeling techniques include analysis of variance (ANOVA), time series and data mining.
For practical reasons, we choose to study a subset of the matrix instead of every piece of data in the matrix. This subset is called a sample. The samples collected by experienced design experiments are called data.
Data is the object of statistical analysis and is used for two related purposes: description and inference. Descriptive statistics deal with narrative questions: can data be effectively summarized, whether in mathematics or in pictures, to represent the properties of matrices? Basic mathematical descriptions include mean and standard deviation. The image summary contains a variety of tables and graphics.