factor analysis

On 25th January we had our last workshop on Business intelligence. We dealt with two topics discriminant analysis and factor analysis.
Discriminant analysis is a technique for classifying a set of observations into predefined classes. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. the technique constructs a set of linear functions of the predictors, known as discriminant functions, such that
L = b1x1 + b2x2 + ? + bnxn + c , where the b's are discriminant coefficients, the x's are the input variables or predictors and c is a constant.
These discriminant functions are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the ith discriminant function has the highest value.

Factor analysis is a data reduction technique that tries to reduce a list of attributes or other measures to their essence; that is, a smaller set of “factors”that capture the paterns seen in the data. Marketers and researchers who study a product, service, or industry professionally sometimes perceive many more distinctions within their category than do their consumers. This can lead to questionnaires containing attribute lists that consumers see as somewhat or largely synonymous. Factor analysis tells you how many different core factors that consumers perceived out of the list of attributes thay rated.
The main benefits of factor analysis are that the analyst can focus their attention on the unique core elements instead of the redundant attributes, and as a data ‘pre-processor’for regression models.

Submitted by,
Pragya Mishra
12153
SIBM Bangalore