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OBJECTIVES

APPLICATION OF MULTIPLECORRELATION/REGRESSION

ANALYSIS MULTIPLE CORRELATION

MULTIPLE REGRESSION

TECHNICAL DESCRIPTION

COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSIONANALYSIS

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APPLICATION OF MULTIPLECORRELATION/REGRESSION ANALYSIS

Multiple Correlation

Contd

Statistical Techniques for Measuring theCloseness of the Relationship Between

Variables

It Measures the Degree to which Changes inOne Variable are Associated with Changes inAnother

It can Only Indicate the Degree of Associationor Covariance Between Variables. Covarianceis a Measure of the Extent to which TwoVariables are Related

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APPLICATION OF MULTIPLECORRELATION/REGRESSION ANALYSIS

Multiple Regression

Requires Two Operations

Derive an Equation, Called the RegressionEquation, and a Line Representing the Equationto Describe the Shape of the RelationshipBetween the Variables.

Estimate the Dependent Variable (Y) from the

Independent Variable (X), Based on theRelationship Described by the RegressionEquation.

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COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSION

ANALYSIS

Contd

Technical Description

Graphical Representations

Statistical Significance of R

Interpretation of Multiple CorrelationCoefficients

Interpretation of Multiple RegressionCoefficients

Requirement/Assumptions for MultipleRegression

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COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSION

ANALYSIS

Contd

Missing Values: How to Deal the Issue?

Eliminate a Respondent orCompany Entirely

Provide an Estimated Value

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COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSION

ANALYSIS

Contd

Uniform Ratings

A Related Question is what to do

About Respondents Who Have noMissing Values But Give the SameRating for Nearly All ProductAttributes, Attitudes, or Important

Ratings.

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COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSION

ANALYSIS

Contd

Multi-Collinearity

High correlation exists between two

independent variables

This means the two variablescontribute redundant information tothe multiple regression model

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COMMON PROBLEMS IN MULTIPLECORRELATION/REGRESSION

ANALYSIS

Dummy Variables

Categorical Explanatory Variable with

Two or More Levels Yes or No, On or Off, Male or Female

Code as 0 or 1

Regression Intercepts are Different ifthe Variable is Significant

Assume Equal Slopes for Other Variable