In this example, a logistic regression is performed on a data set containing bank marketing information to predict whether or not a customer subscribed for a term deposit.

In this example, a principal component analysis is used as a dimension reduction technique to determine the principal components of a data set containing bank marketing information. These principal components are then used in a logistic regression to predict whether or not a customer subscribed for a term deposit.

In this example, clustering is used to separate a data set containing bank marketing information into two classes. The clustering results are then examined to see if they accurately reflect the underlying pattern in the data set, which in this example is whether or not a customer subscribed for a term deposit.

In this example, a least squares regression is performed on a data set containing the returns of a number of international stock exchanges and is used to show the linear relationship between the Istanbul Stock Exchange and the other exchanges.

In this example, a weighted least squares regression is applied to a data set containing weighted census data to show the relationship between both the age and education level of a worker and that person's income.