Understanding the relationship between two quantitative variables is often neede
ID: 3020380 • Letter: U
Question
Understanding the relationship between two quantitative variables is often needed in epidemiology and biostatistics. For example, a researcher might need to know the relationship between birth and death rates. Think of an example that’s interesting to you that concerns the relationship between two quantitative variables. Describe the issue and clearly identify the observational units and the two quantitative variables. How could you illustrate this with a scatterplot? Finally, if a scatterplot revealed a strong association, is this sufficient statistical evidence to show cause and effect? Explain your response
Explanation / Answer
A scatterplot is the most useful display technique for comparing two quantitative variables. We plot on the y-axis the variable we consider the response variable and on the x-axis we place the explanatory or predictor variable.
How do we determine which variable is which? In general, the explanatory variable attempts to explain, or predict, the observed outcome. The response variable measures the outcome of a study. One may even consider exploring whether one variable causes the variation in another variable – for example, a popular research study is that taller people are more likely to receive higher salaries. In this case, Height would be the explanatory variable used to explain the variation in the response variable Salaries.
In summarizing the relationship between two quantitative variables, we need to consider:
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