The
three questions below will guide the selection of appropriate methods
to create data visualizations to show smoking cessation expenditures
and to assess associations between smoking cessation program expenditures,
smoking-related health care expenditures, and demographic factors.
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What is the response
(Y) of interest and how is it measured? The primary responses of interest
are total health care expenditures and cessation expenditures. Both
of these are continuous variables.
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Are predictor variables
mentioned in the problem statement? If so, how many and what are
their measurement levels? Associations between smoking-related expenditures
and the demographic factors land area, median household income, state
gross domestic product, and tobacco production are of interest. These
are all continuous variables.
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What are you being asked
to deliver? A data description, an interval estimate, an answer to
a question, or a predictive model? We are being asked to give a data
description of smoking prevention programs and to assess the association
with health care expenditures and demographic factors. Geospatial
data is easily assimilated when plotted on maps and allows the audience
to identify regional or geographic trends. Correlation analysis can
be conducted to quantify the association between pairs of continuous
variables.