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A scatterplot is a graphing of each individual outcome (y-axis, dependent or continuous variable) plotted against the associated independent variable (x-axis, continuous or categorical variable). This shows if a relationship of an independent variable with an outcome variable exists.
Here are the steps in producing the scatterplots for Bangladesh:
BANGLADESH (district level):
INTERPRETATION: As a continuous variable, the prevalence of households using other (poor) water sources are scatterplotted here against malnutrition, which does show the relationship one would expect. There is higher malnutrition (low arm circumference) prevalence in those areas where use of poor sources of water high and vice versa. This variable for poor source of water has also been categorized into high and low prevalence of poor water source (variable called wathcat) and is scatterplotted against malnutrition below. A similar relationship is seen.
INTERPRETATION: Using the categorical variable for water source, you also see a positive slope that indicates that those with access to safe water source (0) have a lower prevalence of low arm circumference than those without access to safe water (1). This gives a very similar result as that with the continuous variable for prevalence of access to water (district level data) in the previous scatterplot. We will use the categorized variable in the regression analysis exercise. Now, take a look at the scatterplot for Low AC and latrine safety.
INTERPRETATION: The variable for access to safe latrine at the district level has been categorized as low (0) and high (1) or bad and good respectively. There is also a clear relationship in the scatterplot between low access to latrine and high prevalence of malnutrition (low arm circumference) in comparison to those areas with better access to safe latrines and a lower prevalence of malnutrition. This is in the expected direction.
KENYA (individual level):
INTERPRETATION: The variable for roofing quality is categorized as bad (grass/ thatch) and good (corrugated iron), and the association with the outcome variable of WAZ score does not appear to have a significant slope. There is a slightly lower waz score associated with those that have grass/ thatch roof than those with corrugated iron, which is in the expected direction.
INTERPRETATION: The variable for education level has been categorized as a dummy variable (dlowedn) and shows an association with waz score when scatterplotted. This supports the expected association, those with low education have lower nutrition status on average; therefore, it is likely a good candidate for running in a regression analysis.
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