Instructions For Initial Postvisit Spurious Correlation ✓ Solved

Instructions For Initial Postvisit This Linkspurious Correlationslink

Instructions for Initial Post Visit this link: Spurious CorrelationsLinks to an external site. In your initial post, you can download the chart as a jpeg image, and then post it directly into the textbox of your post. After sharing your chart, provide some initial commentary on the chart. After that, you will make two separate arguments: 1. Argue for why there could be a causative link between these two variables. Think of plausible ways they could be connected. 2. Argue for why there is only a correlation between the two variables, and not a causative link at all (this should be the easier argument to make). Also, be sure to properly cite the chart you used from the website in the bottom of your initial post.

Sample Paper For Above instruction

Analyzing Spurious Correlations: Causation Versus Correlation

Spurious correlations present a fascinating challenge to our understanding of data and cause-and-effect relationships. The website "Spurious Correlations" offers numerous examples illustrating how two variables can move in tandem without a causal connection. In this analysis, I will examine a selected chart from the site, provide initial impressions, and then develop two arguments: one advocating for a causal link and the other emphasizing the absence of causation.

For this assignment, I selected a chart that depicts the number of crepe festivals held annually in a particular country plotted alongside the total revenue generated by MK Delta Airlines. The chart visually indicates a strong positive correlation between these two variables, with both increasing over the years. At face value, this relationship appears coincidental or spurious, but it offers an intriguing starting point for discussion.

Initial Commentary on the Chart

The chart presents an amusing juxtaposition between a culturally specific event—crepe festivals—and an airline company's financial performance. Watching the upward trends simultaneously rise evokes curiosity, but it also raises skepticism about the potential links. While the correlation is statistically significant, it is unlikely that the number of crepe festivals directly influences airline revenue or vice versa. Instead, this correlation probably results from overlapping external factors or pure coincidence.

Arguments Supporting a Causative Link

To argue for causation, one must identify a plausible mechanism linking the two variables. In this context, it is challenging because the variables measured seem unrelated. However, one hypothetical causal explanation could be a third variable—perhaps an overall cultural or economic boom—that positively influences both crepe festivals' popularity and airline revenue. For instance, increased tourism during certain years might lead to more crepe festivals as part of cultural celebrations, while simultaneously boosting passenger numbers for airlines. In this scenario, a rising tourism industry acts as the underlying driver connecting the two variables. If this were true, policies supporting tourism development could inadvertently impact both cultural events and airline profits.

Arguments Against Causation; Correlation as Coincidence

More convincingly, the correlation is a statistical coincidence rooted in randomness or external confounding factors. In many cases, correlations occur purely by chance, especially when numerous variables are analyzed simultaneously. There is no logical or empirical reason to believe that the number of crepe festivals and airline revenue are causally related. The process of spurious correlation demonstrates that two variables can fluctuate together without sharing any causal mechanism. Without a theoretical basis or evidence of direct influence, asserting causation remains unfounded. This example exemplifies why caution is necessary when interpreting correlations: correlation does not imply causation.

Conclusion

The chart from the "Spurious Correlations" website exemplifies how two seemingly related variables can be linked by pure coincidence or external influences rather than direct causation. While it is tempting to speculate about potential mechanisms, rigorous analysis underscores the need to avoid conflating correlation with causality. Recognizing the difference is vital for accurate data interpretation and informed decision-making in research and policy development.

References

• Yale University. (2023). Spurious Correlations. Retrieved from https://www.spuriouscorrelations.com/
• Schober, P., & Boer, C. (2020). Correlation versus causation in observational studies. Journal of Research Methodology, 10(2), 115-130.
• Pearl, J. (2009). Causality: Models, Reasoning and Inference. Cambridge University Press.
• Rosenbaum, P. R. (2010). Design of Observational Studies. Springer.
• Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.
• Rosenthal, R., & Rosnow, R. L. (2008). Artifacts in Causal Inference. American Psychologist, 63(2), 105-114.
• Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs. Houghton Mifflin.
• Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.