AP Statistics Section 4.3 Establishing Causation. When we study the relationship between two variables, we often hope to show that changes in the explanatory variable cause changes in the response variable. .
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When we study the relationship between two variables, we often hope to show that changes in the explanatory variable cause changes in the response variable.
In the following diagrams, variables X and Y show a strong association (dashed line). This association may be the result of any of several causal relationships (solid arrow).
Changes in x cause changes in y.
Changes in both X and Y are caused by changes in a lurking variable Z.
The effect (if any) of X on Y is confounded with the effect of the lurking variable Z.
For example, experiments have shown that large amounts of saccharin in the diet cause bladder tumors in rats.BUT, humans are not rats!!!!!!!
At least most aren’t.
We cannot experiment with humans, but studies with humans who consume different amounts of saccharin show little association between saccharin and bladder tumors.
In the following examples, state whether the relationship between the two variables involves causation, common response or confounding. When applicable, identify possible lurking variables.
1. There is a negative correlation between the number of flu cases reported each week throughout the year and the amount of ice cream sold in that particular week.
Temperature during the week affects both the number of flu cases and the amount of ice cream sold
Many colleges offer versions of courses that are also taught in the classroom. It often happens that students who enroll in the online version do better than the classroom students on the course exams. Does this show that online instruction is more effective than classroom teaching?
Older people with jobs more likely to take online courses and more likely to take their studies seriously. People with greater computer knowledge more likely to take online courses and perhaps this causes an increase in exam scores