I have been experimenting more and more with ChatGPT on a regular basis. I’m interested and curious how this will help. I had a friend at one of the large FANG companies note that they found ChatGPT to be very helpful in their job, with assistance in various things. Their quote:
”…I used it to collaborate, having a design discussion, writing requirements, comments and document my code (!!), improve my writing and find the gap and provide suggestions…”
I thought that was interesting, so I sent in this prompt: write a one page post in the style of http://www.voiceofthedba.com that summarizes this article https://www.madeiradata.com/post/home-experiments-and-stacked-bar-graphs
I often find links that catch my eye, read them, and save them in a note for a future editorial. This time I asked ChatGPT.
This is part of a series of experiments with the ChatGPT and other AI systems.
The Results
What I got from ChatGPT is below. I read it and then wrote my own editorial, which was called The Best Graph. Mine is quite different, and I think, it’s better. I’m not sure the response below summarizes the post correctly, I don’t know it’s in my style (maybe parts), and ultimately, it’s a summary, not a commentary. Maybe that’s on me. I didn’t specify an editorial or opinion.
Read them both and let me know which one you like more, which you think works better.
The ChatGPT Response
In a recent blog post on Madeira Data Solutions, author Lisa B. shares her findings on the use of stacked bar graphs in visualizing data from home experiments. While stacked bar graphs are commonly used in business and marketing contexts, Lisa explores their potential usefulness in a personal setting.
Lisa begins by describing her home experiments, which involve testing the efficacy of various cleaning products. She notes that while she initially used line graphs to track the results of her tests, she found that stacked bar graphs were more effective in illustrating the relative performance of each product.
Lisa goes on to provide a detailed explanation of stacked bar graphs and their components, including the use of color-coding and labels to differentiate between different categories and subcategories. She also notes that stacked bar graphs are useful in highlighting changes over time and in comparing data across different groups.
One of the key takeaways from Lisa’s post is the importance of choosing the right type of graph for a given dataset. While line graphs may be appropriate for tracking changes in a single variable over time, stacked bar graphs may be more useful when comparing multiple variables or subcategories. By selecting the appropriate type of graph, researchers can more effectively communicate their findings and insights to others.
Overall, Lisa’s post provides a useful overview of stacked bar graphs and their potential applications in a personal, as well as professional, context. Whether you’re conducting home experiments or analyzing business data, it’s important to choose the right type of graph to effectively convey your findings.