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post 1:

Before examining the data, I wondered if height and hair length are related. At first, it didn’t seem like they’d be directly related, but societal factors like gender might influence things—taller people (usually men) might tend to have shorter hair. In comparison, shorter people (often women) could have longer hair. To determine if there's a link, I’d check the Pearson correlation coefficient, which indicates the degree to which two variables are linearly related. I guessed there might be a negative correlation—taller people with shorter hair—probably a moderate one, influenced more by social norms than biology. If I found a connection, it doesn’t mean one causes the other; it could simply be that both are influenced by other factors, such as gender or culture, rather than height directly affecting hair length or vice versa.

Based on the data, I calculated the Pearson correlation coefficient using Excel, following the standard steps (Bradburn, 2019). The result was r = -0.72, indicating a strong negative relationship.

This matched my expectations about the negative relationship, including the direction and strength. I thought it might be a bit weaker than it was, probably because I assumed there would be more variation in hair lengths at different heights. However, the data revealed a clearer grouping.

When you make a scatterplot in Excel (Vertex42, 2017), it shows heights on the x-axis and hair lengths on the y-axis. The graph clearly shows a downward trend: taller people tend to have shorter hair, which makes the negative slope of -0.72 easy to see.

This information is based on a small sample of 26 people, making up 52% of the explained variation. It may not apply to larger, more diverse groups. In real-world settings, it could help barbershop owners plan their inventory based on customer height. Sociologists or marketers might also find it helpful in exploring gender roles in grooming.

References

Bradburn, S. (2019, March 12). How to perform a Pearson correlation test in Excel [Video]. YouTube. https://www.youtube.com/watch?v=Ev86DMtLXOk

Grammarly. (2025). Grammarly Handbook. https://www.grammarly.com/handbook/

Vertex42. (2017, August 14). Creating an XY scatter plot in Excel [Video]. YouTube. https://www.youtube.com/watch?v=gBbGBrHTMrM

post 2:

I would say I do think the two characteristics, height and hair length are connected in some type of way. The reason why I think that is because they basely are the same thing but the height is the volume of the hair. Then the length is how long is the hair. Then the only way that I would know if they are related to each other is if they are independent or dependent from each other. I would say that I expect them to correlated in some type of way of knowing what are things that might help or damage the hair. I would say that I expect the correlation to be postitive because of how the two characteristic would help each other out. Also, it being strong because of how it can work out at the end as well. Lastly, there is a correlation and it is based on causation. The reason for it is because of how the hair might grow or not. It would basely be direct into what and where it mainly  suppose to go. I would say after running the data in excel, I have seen the results I expected. Also, what I had anticipated correctly was that they were going to come together. What I didn't  get correct was the they were going to be the same. Although what really caused it not to be correct was the numbers were whole and some were decimal. Then the information is precise of how it is projecting into something being true or not. The way that I can use this in real life is finding how accurate something is. I would say companies, researchers, and scientists would find it useful for things that they do on a daily basis.