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•A multivariate technique used for
studying the interdepedent relationship between two or more
categorical variables (i.e., nominal or ordinallevel)
•We usually seek to investigate
the influence of one variable (Independent variable) on another variable
(Dependent variable)
•consider the joint
distribution of sample elements across variables
•It is the most used multivariate
data analysis technique
•TwoWay Cross Tabulations

•It
tests the degree to which the two variables in a crosstabulation analysis are
independent of one another.
•Ho:
variables are independent .
•Ha:
variables are interdependent.
•Pearson chisquare (χ2) test of independence

•A commonly used technique used to
determine whether two groups differ on some characteristic assessed on a
continuous measure
•Examples
•Satisfaction ratings, females
versus males
•Age in years, customers versus
noncustomers
•Number of units purchased,
households with children versus households without children
 •Independent Samples ttest for
 Means

•A technique for comparing two
means when scores for both variables are provided by the same sample
•Examples
•Attitude toward the brand
measures before
and after reviewing
a 30second ad
•Price perceptions for a local
store versus a competitor (i.e., applying the same measure to different
objects)
•Paired Sample ttest for Means

•A statistical technique used with
a continuous dependent (outcome) variable and one or more categorical
independent variables
Advantages of Using Over a Series of ttests to Examine Differences Across Groups
•Applicable to more than two
group.
•Applicable to more than one
categorical independent variable
•Analysis of Variance (ANOVA)

•A statistical technique used with
a continuous dependent (outcome) variable and one independent variable
•Oneway ANOVA

A statistical technique used with
a continuous dependent (outcome) variable and two or more independent
variables; interaction
•Twoway ANOVA

•A statistic that indicates the
degree of linear association between two continuous variables
•The correlation coefficient can
range from 1 (inverse relationship) to +1 (direct relationship)
•Note: Correlation ≠
Causation; Correlation = Relationship
•Examples
•Relationship
between advertising expenditure and sales
•Relationship
between the number of hours studied and exam grade
 •Pearson ProductMoment
 Correlation Coefficient

a statistical procedure foranalyzing the relationship between 1 IV and 1 DV. (also called Bivariate regression)
A statistical technique used to
derive an equation that relates a single continuous dependent variable to a single independent variable
Simple Regression

a statistical procedure for
analyzing the relationship >=2 IVs, 1
DV.
A statistical technique used to
derive an equation that relates a single continuous dependent variable to two
or more independent
variables
Multiple regression

•R2 is the measure of the strength of
the linear relationship between X (IV) and Y (DV).
•R2 measures
the percent of the total variation in Y that is “explained” by the variation in
X.
Simple Regression  Coefficient of determination (R2)

