chapter 20 m303 final

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1. •A multivariate technique used for
studying the interdepedent relationship between two or more
categorical variables (i.e., nominal- or ordinal-level)

•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
•Two-Way Cross Tabulations
2. •It
tests the degree to which the two variables in a cross-tabulation analysis are
independent of one another.

•Ho:
variables are independent .

•Ha:
variables are  interdependent.
•Pearson chi-square (χ2) test of independence
3. •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
non-customers

•Number of units purchased,
households with children versus households without children
• •Independent Samples t-test for
• Means
4. •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

•Price perceptions for a local
store versus a competitor (i.e., applying the same measure to different
objects)
•Paired Sample t-test for Means
5. •A statistical technique used with
a continuous dependent (outcome) variable and one or more categorical
independent variables

Advantages of Using Over a Series of t-tests to Examine Differences Across Groups

•Applicable to more than two
group.

•Applicable to more than one
categorical independent variable
•Analysis of Variance (ANOVA)
6. •A statistical technique used with
a continuous dependent (outcome) variable and one independent variable
•One-way ANOVA
7. A statistical technique used with
a continuous dependent (outcome) variable and two or more independent
variables; interaction
•Two-way ANOVA
8. •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 Product-Moment
• Correlation Coefficient
9. 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
10. 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
11. •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)
 Author: jodiesc08 ID: 214988 Card Set: chapter 20 m303 final Updated: 2013-04-22 04:21:06 Tags: m303 final Folders: Description: m303 final Show Answers: