Download - Multiple Reg Analysis 5
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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