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Analisa Multivariat [4]
*October 17, 2009*

*Posted by desrinda in Analisa Multivariat.*

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**DATA EXAMINATION**

Graphical Examination

– Histogram and The Normal Curve

– Stem and Leaf Diagram

– Scatter Plot

Missing data:

– Systematic (affected by researcher or respondent)

– Random (occurs naturally)

Researcher’s concern = to identify the patterns and relationships underlying the missing data.

Outlier = distinctively different data (can be identified by box plot), probably caused by:

– Procedural error

– Extraordinary event

Multivariate testing assumptions:

– Normality (e.g. normal probability plot)

– Linearity

– Homoscedascity

– Non correlated errors

Data can be transformed, dummy variable can be created. Multicollinearity (http://www.nd.edu/~rwilliam/stats2/l11.pdf) should be avoided.

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Analisa Multivariat [3]
*October 9, 2009*

*Posted by desrinda in Analisa Multivariat.*

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The third class of this term has been for us to refresh our memory (*if there was any, hahaha*…) about basic definitions on multivariate analysis previously learnt in **mathematics** (matrix algebra – vector, matrix, distance, inverse, singularity) and **statistics** (variance, covariance, corelation, t-test, hotelling).

http://www.edmeasurement.net/matrix/notes/Definitions.pdf

More about hotelling: http://knowledgeforge.net/opentextbook/svn/multivariatestatistics/interactivenotes/STAT3401Week6Acrotex.pdf

Wilks Lambda: http://gauss.usouthal.edu/ajmms-imst07/status/306/manu306.pdf

Likelihood ratio test (in MS Excel = OLS formula): http://arxiv.org/PS_cache/math/pdf/0610/0610835v1.pdf

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Analisa Multivariat [2]
*September 11, 2009*

*Posted by desrinda in Analisa Multivariat.*

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Types of multivariate techniques:

1. Dependence Techniques

2. Interdependence Techniques

**DEPENDENCE TECHNIQUES**

*= a variable or set of variables is identified as the dependent variable to be predicted or explained by other variables known as independent variables
*• Multiple Regression

• Multiple Discriminant Analysis

• Logit/Logistic Regression

• Multivariate Analysis of Variance (MANOVA) and Covariance

• Conjoint Analysis

• Canonical Correlation

• Structural Equations Modeling (SEM)

**INTERDEPENDENCE TECHNIQUES**

*= involve the simultaneous analysis of all variables in the set, without distinction between dependent variables and independent variables
*• Principal Components and Common Factor Analysis

• Cluster Analysis

• Multidimensional Scaling (perceptual mapping)

• Correspondence Analysis

**SELECTING A MULTIVARIATE TECHNIQUE**

1. What type of relationship is being examined – dependence or interdependence?

2. Dependence relationship: How many variables are being predicted?

3. Interdependence relationship: Are you examining relationships between variables, respondents, or objects?

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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