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Analisa Multivariat [1] September 4, 2009

Posted by desrinda in Analisa Multivariat.
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Course Outline:
• Introduction
• Review of Basic Statistics
• Multiple Regression
• Manova
• Principal Component Analysis
• Factor Analysis
• Discriminant Analysis
• Logit Analysis
• Cluster Analysis
• Conjoint analysis
• Multidimensional scaling
• Correspondence Analysis

Prerequisites:
• Basic Statistics
• Marketing

Course Requirement and Grading:
• Problem sets/quizzes 15%
• Term Paper 25%
• Midterm Exam 30%
• Final Exam 30%

Reading Material:
• Hair, J.F./ B. Black, B. Babin, and R.E. Anderson Multivariate Data Analysis, Sixth Edition, Pearson Education Inc., New Jersey, 2006
• Dillon, W.R., and M. Goldstein. Multivariate Analysis: Methods andApplications, New York: Wiley, 1984.
• Johnson, R.A., and D.W. Wichern, Applied Multivariate Statistical Analysis, Fifth Edition, Prentice Hall, 2002.

Multivariate Data Analysis
= all statistical methods that simultaneously analyze multiple measurements on each indlvidual or object under investigation.

Basic Concepts:
• The Variate
• Measurement scales (Nonmetric, Metric)
• Multivariate Measurement
• Measurement Error
• Types of Techniques

The variate is a linear combination of variables with empirically determined weights. Weights are determined to best achieve the objective of the specific multivariate technique.

In addressing measurement error, researchers evaluate two important characteristics of measurement:
• Validity =the degree to which a measure accurately represents what it is supposed to
• Reliability =the degree to which the observed variable measures the “true” value and is thus error free

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