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Discriminant analysis classifies the given subjects into two categories like "likely to be successful" and "likely to be failures" based on the data that is given for each subject. The decision maker can select the subjects likely to be successful for say, investment.
Factor analysis converts the given data on various subjects into less number of variables termed as factors. The factors can be used for further data analysis.
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Elementary Concepts
Statistics Glossary
Basic Statistics
ANOVA / MANOVA
Association Rules
Boosting Trees
Canonical Analysis
CHAID Analysis
C & R Trees
Classification Trees
Cluster Analysis
Correspondence Analysis
Data Mining Techniques
Discriminant Analysis
Distribution Fitting
Experimental Design
Factor Analysis
General Discrim. Analysis
General Linear Models
Generalized Additive Mod.
Generalized Linear Mod.
General Regression Mod.
Graphical Techniques
Ind.Components Analysis
Linear Regression
Log-Linear Analysis
MARSplines
Machine Learning
Multidimensional Scaling
Neural Networks
Nonlinear Estimation
Nonparametric Statistics
Partial Least Squares
Power Analysis
Process Analysis
Quality Control Charts
Reliability / Item Analysis
SEPATH (Structural eq.)
Survival Analysis
Text Mining
Time Series / Forecasting
Variance Components
Statistical Advisor
Distribution Tables
References Cited
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