4.
Analysis of variance (ANOVA) can be used to see whether there are any differences across
the categories of the non‐metric variables with respect to any of the metric variables.
5.
Correlation analysis measures the degree to which there is a linear association between
two interval or ratio scaled variables.
6.
Multiple regression can be used to explain the variation in dependent variables (outcome
or effect variables) using other metric variables as independent variables (predictors),
and/or test for differences across groups (using dummy variables).
7.
Cluster analysis to identify key market segments
3. Key Descriptive/Summary Statistics
First, briefly describe the data based on the information you have. For
EACH
of your variables,
provide appropriate descriptive statistics. These could include frequency bar-charts/histograms or
descriptive tables.
•
You only need to produce a
bar chart/histogram
for each variable (i.e., the SPSS
frequency table is NOT required)
•
You can also choose descriptive tables for metric variables.
•
For the
re-coded
variables in these RQs, provide this information for the re-coded
version (i.e., not the source variable from which the re-coded variable is derived)