Applicable Statistical Approaches for Data Mining

Applicable statistical approaches applicable to DATA
MINING.
Data mining has two ways i.e. statistical analysis and non-statistical
analysis. For extracting knowledge from database’s containing different types of
observations, a variety of statistical methods are applicable in the process. They
include; Logistics regression analysis, classification, correlation analysis,
Discriminate analysis, clustering, Linear discriminate analysis (LDA), Outlier
detection, Factor analysis etc. Few are discussed below;
 Regression analysis – Based on a set of numerical data, by use of
regression, one predicts a range of continuous values. Practically,
use regression to predict the costs of goods and services based on
other variables. A regression model is used across numerous
industries for forecasting financial data, modelling environmental
conditions and analyzing trends.
 Logistics Regression – dependent variables are either binary or
multinomial. One estimates probabilities regarding the
relationship between the independent and dependent variable.
 Linear Regression – uses the best relationship between the
independent and dependent variable to predict the target
variable. In order to achieve the best fit, make sure that all the
distances between the shape and the actual observations at each
other are as small as possible. A good fit can be determined by
determining that no other position would produce fewer errors
given the shape chosen. Types of Linear Regression; Simple and
multiple Linear Regression. By fitting a linear relationship to the
independent variable, the simple linear regression predicts the
dependent variable whereas using multiple independent variables,
multiple linear regression fits the best linear relationship with the
dependent variable.