Until recently, Karl Pearson Correlation analysis was one of the most popular methods to measure linear association between two or more than two variables in a data set. For example, establishing the Karl Pearson Correlation between X variable and Y variable, where both variables belong to a single data set. Canonical Correlation Analysis (CCA), on the other hand, helps measure the correlation among variables which are in different datasets.

A decision tree is a graphical representation of possible solutions to a problem based on given conditions. It is called a tree because diagrammatically it starts with a single box (target variable) and ends up in numerous branches and roots (numerous solutions).

Thus to assess the model, a common practice in data science is to iterate over various models and select the most appropriate model. In other words it is important to test the same model with different values of parameters.This is called the cross validation method.

Outliers are those data points which are distant from the other observations in the data set. They can be either because of the variability in the data set or due to measurement errors.

In statistics, Generalized Least Squares (GLS) is one of the most popular methods for estimating unknown coefficients of a linear regression model when the independent variable is correlating with the residuals.

Linear discriminant model is a multivariate model. It is used for modeling the differences in groups. In this model a categorical variable can be predicted through continuous or binary dependent variable.

While many statistical methods in machine learning are used either to predict or analyse trends in the data, cluster analysis is used for organizing the data. It is a process of grouping observations of similar kinds within a large population.

Machine learning involves solutions to predict scenarios based on past data. Logistic regression offers probability functions based on inputs and their corresponding output.