Spurious correlation #1,279 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Dallas and the second variable is Wind power generated in Puerto Rico. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Dallas and the second variable is Wind power generated in Puerto Rico. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the name Dallas gained popularity, more and more people were drawn to the idea of the Wild West. This led to a surge in demand for cowboy hats, which are notorious for their ability to catch the wind. As the cowboy hat industry boomed, there was a sudden excess of wind in Puerto Rico, leading to a boost in wind power generation. (In reality, these two variables are unlikely to have any direct causal relationship.)

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image showing a futuristic market street in Dallas, Texas, where colorful holographic wind chimes and spinning pinwheels catch the sun's rays, emanating a high-tech glow. Contrast this with a view of Puerto Rico's coast, where a sleek, high-tech wind farm with rows of advanced, precision-engineered turbines harness the ocean breeze, with the turbines' blades creating a mesmerizing visual pattern as they generate clean energy with futuristic efficiency. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: Because as the name Dallas became more popular, people started associating it with the Wild West, prompting an increase in demand for wind-themed decorations, leading to an uptick in wind power generated in Puerto Rico.. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Cubism Voxel art Brutalism High-tech Futuristic
ChatGPT: High-tech

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. Because as the name Dallas became more popular, people started associating it with the Wild West, prompting an increase in demand for wind-themed decorations, leading to an uptick in wind power generated in Puerto Rico.
ChatGPT: In a bustling market street in Dallas, Texas, colorful wind chimes and spinning pinwheels catch the sunlight, as the sound of wind-themed decorations fills the air. Meanwhile, on the shores of Puerto Rico, rows of wind turbines stand tall, their blades spinning steadily in the ocean breeze, generating clean energy for the island.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ High-tech ] and depicting this scene: In a bustling market street in Dallas, Texas, colorful wind chimes and spinning pinwheels catch the sunlight, as the sound of wind-themed decorations fills the air. Meanwhile, on the shores of Puerto Rico, rows of wind turbines stand tall, their blades spinning steadily in the ocean breeze, generating clean energy for the island. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image showing a futuristic market street in Dallas, Texas, where colorful holographic wind chimes and spinning pinwheels catch the sun's rays, emanating a high-tech glow. Contrast this with a view of Puerto Rico's coast, where a sleek, high-tech wind farm with rows of advanced, precision-engineered turbines harness the ocean breeze, with the turbines' blades creating a mesmerizing visual pattern as they generate clean energy with futuristic efficiency.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Popularity of the first name Dallas caused Wind power generated in Puerto Rico to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Blowin' in the Names: The Dallas Wind Power Connection

The Journal of Renewable Energy Innovations

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Popularity of the first name Dallas and Wind power generated in Puerto Rico. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
Blowin' in the Names: The Dallas Wind Power Connection

[[ABSTRACT]]
The relationship between the popularity of the first name Dallas and wind power generated in Puerto Rico is explored in this study. Using data from the US Social Security Administration and the Energy Information Administration, we conducted an analysis to investigate this peculiar association. Our findings revealed a surprising correlation coefficient of 0.9284064 and p < 0.01 for the years 2010 to 2021.

In our research, we uncovered an unanticipated link between the frequency of the name Dallas and the wind power output in Puerto Rico. It seems that the "winds of change" may indeed be directed by the naming trends in the United States, as suggested by our data. One might say that this correlation blew our minds!

This study contributes to the interdisciplinary exploration of quirky connections and provokes further inquiries into the unforeseen influences on renewable energy sources. With these findings, we present a new perspective on the interplay between personal nomenclature and environmental factors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between human names and various socio-environmental phenomena has been a subject of intrigue and curiosity for researchers across diverse fields. While most studies have focused on the cultural, societal, or psychological implications of personal nomenclature, the potential connection between the popularity of a specific first name and environmental forces, such as wind power generation, has received comparatively scant attention. The aim of this paper is to address this gap by delving into the unexpected correlation between the prevalence of the name Dallas and the production of wind power in Puerto Rico.
It's quite "windsome" to think how something as seemingly unrelated as naming trends could play a role in the generation of renewable energy. After all, few things can quite "blow" one's mind like the unexpected intertwining of seemingly disparate factors.
Our investigation leverages data from multiple sources, including the US Social Security Administration and the Energy Information Administration, to explore this peculiar association. The analysis uncovers a surprising correlation coefficient, prompting further examination into the potential mechanisms underlying this connection. It appears that the winds of change may indeed be influenced by the winds of nomenclature, turning a breeze of a hypothesis into a gust of substantial findings.
This study not only adds an unexpected twist to the discourse on environmental influences but also provides a breath of fresh air in the sometimes staid world of interdisciplinary research. As we unravel the intertwined nature of personal names and environmental factors, it becomes evident that there is much to be gained from peeking beyond the obvious horizons of scientific inquiry.
In the following sections, we expound upon the methods employed, the intricacies of the data analysis, and the implications of our findings. The winds of curiosity blow us forward as we embark on this journey of unexpected connections and meteorological mysteries.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that personal names have been studied in various contexts, including their societal, cultural, and psychological implications. However, the potential connection between the frequency of a specific first name and environmental phenomena has been a relatively unexplored area of research. This study seeks to address this gap by examining the unexpected correlation between the prevalence of the name Dallas and wind power generation in Puerto Rico.

In "Doe and Jones," the authors mention the relevance of interdisciplinary investigations in uncovering surprising associations within complex systems. Our study contributes to this approach by delving into the unforeseen relationship between the popularity of a particular name and the production of renewable energy from wind sources.

On the more serious end of the spectrum, "The History of Wind Power" by John Devlin provides a comprehensive overview of the evolution of wind power technology and its impact on energy production. Similarly, "Names and Society" by Gary Armstrong delves into the cultural and societal significance of personal names, offering a detailed examination of naming trends and their implications.

Moving toward more fictional realms, the novel "Gone with the Wind" by Margaret Mitchell takes readers on a gripping journey through the windswept landscapes of the American South. While the book may not provide direct insights into our research topic, it certainly captures the essence of wind-related intrigue. Additionally, "The Name of the Wind" by Patrick Rothfuss, though a work of fantasy, sparks the imagination with its whimsical exploration of the power of names and their potential influence on the forces of nature.

In the realm of children's entertainment, the animated series "Avatar: The Last Airbender" involves characters who manipulate the elements, including air and wind, through their names. Similarly, "The Magic School Bus" features educational adventures that touch upon various scientific concepts, potentially including wind energy and its enigmatic connection to personal names.

As we venture into the unexpected convergence of personal nomenclature and renewable energy, it is essential to acknowledge the diverse sources that have shaped our understanding of this peculiar association. The following sections delve into the methodology, analysis, and implications of our research, as we unravel the enthralling mystery of the Dallas wind power connection.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Popularity of the first name Dallas and Wind power generated in Puerto Rico. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Blowin' in the Names: The Dallas Wind Power Connection

[[ABSTRACT]]
The relationship between the popularity of the first name Dallas and wind power generated in Puerto Rico is explored in this study. Using data from the US Social Security Administration and the Energy Information Administration, we conducted an analysis to investigate this peculiar association. Our findings revealed a surprising correlation coefficient of 0.9284064 and p < 0.01 for the years 2010 to 2021.

In our research, we uncovered an unanticipated link between the frequency of the name Dallas and the wind power output in Puerto Rico. It seems that the "winds of change" may indeed be directed by the naming trends in the United States, as suggested by our data. One might say that this correlation blew our minds!

This study contributes to the interdisciplinary exploration of quirky connections and provokes further inquiries into the unforeseen influences on renewable energy sources. With these findings, we present a new perspective on the interplay between personal nomenclature and environmental factors.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that personal names have been studied in various contexts, including their societal, cultural, and psychological implications. However, the potential connection between the frequency of a specific first name and environmental phenomena has been a relatively unexplored area of research. This study seeks to address this gap by examining the unexpected correlation between the prevalence of the name Dallas and wind power generation in Puerto Rico.
In "Doe and Jones," the authors mention the relevance of interdisciplinary investigations in uncovering surprising associations within complex systems. Our study contributes to this approach by delving into the unforeseen relationship between the popularity of a particular name and the production of renewable energy from wind sources.
On the more serious end of the spectrum, "The History of Wind Power" by John Devlin provides a comprehensive overview of the evolution of wind power technology and its impact on energy production. Similarly, "Names and Society" by Gary Armstrong delves into the cultural and societal significance of personal names, offering a detailed examination of naming trends and their implications.
Moving toward more fictional realms, the novel "Gone with the Wind" by Margaret Mitchell takes readers on a gripping journey through the windswept landscapes of the American South. While the book may not provide direct insights into our research topic, it certainly captures the essence of wind-related intrigue. Additionally, "The Name of the Wind" by Patrick Rothfuss, though a work of fantasy, sparks the imagination with its whimsical exploration of the power of names and their potential influence on the forces of nature.
In the realm of children's entertainment, the animated series "Avatar: The Last Airbender" involves characters who manipulate the elements, including air and wind, through their names. Similarly, "The Magic School Bus" features educational adventures that touch upon various scientific concepts, potentially including wind energy and its enigmatic connection to personal names.
As we venture into the unexpected convergence of personal nomenclature and renewable energy, it is essential to acknowledge the diverse sources that have shaped our understanding of this peculiar association. The following sections delve into the methodology, analysis, and implications of our research, as we unravel the enthralling mystery of the Dallas wind power connection.

[[RESULTS]]
The analysis of the data revealed a striking correlation coefficient of 0.9284064 between the popularity of the first name Dallas and the wind power generated in Puerto Rico for the years 2010 to 2021. The high correlation suggests a strong relationship between these seemingly unrelated variables. It seems that the winds of change may indeed be accompanied by the naming trends of the United States, making a "Dallas wind" a more literal concept than previously thought.
The r-squared value of 0.8619384 further indicates that approximately 86.2% of the variance in wind power generation in Puerto Rico can be explained by the prevalence of the name Dallas. This substantial explanatory power underscores the robustness of the relationship uncovered in our analysis. One might say that the name Dallas is blowing a strong wind of influence on the energy landscape of Puerto Rico.
The significance level of p < 0.01 suggests that the observed correlation is unlikely to have occurred by chance, providing further support for the validity of the identified association. It appears that the winds of nomenclature carry a weighty impact, creating a breeze of statistical significance that cannot be ignored.
In Fig. 1, the scatterplot visually demonstrates the strong positive correlation between the popularity of the first name Dallas and the wind power generated in Puerto Rico. The data points tightly cluster around a clear upward trend, emphasizing the compelling nature of the relationship. The visualization serves as a powerful testament to the unexpected intertwining of personal nomenclature and environmental factors, as well as a testament to the power of puns in academic writing.
These findings offer an intriguing perspective on the potential influences on wind power generation, urging further exploration into the underlying mechanisms and implications of this connection. The winds of investigation continue to blow us into uncharted territory, proving that even the most unlikely associations can reveal meaningful insights.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our analysis provide compelling evidence of a substantial and unexpected correlation between the popularity of the first name Dallas and the wind power generated in Puerto Rico. This surprising association aligns with prior research that has explored unforeseen connections within complex systems. As wind power technology has evolved over time, it appears that the windswept landscapes of Puerto Rico may be influenced by a force as simple as personal nomenclature.

Our findings substantiate the hypothesis posed in our literature review, further cementing the unanticipated link between the prevalence of the name Dallas and wind power production. As an unforeseen influence on renewable energy sources, this correlation challenges traditional understandings of environmental factors and invites a whimsical perspective on the interplay between personal names and natural phenomena. It seems that in the realm of wind power, the name Dallas holds considerable weight, or rather, gust.

The high correlation coefficient and r-squared value bolster the statistical robustness of this connection, reinforcing the substantial explanatory power of the frequency of the name Dallas in predicting wind power generation in Puerto Rico. Indeed, the winds of nomenclature appear to blow with a force that cannot be denied, much like a particularly strong gust of wind. One could say that the influence of the name Dallas on wind power output is as clear as a breezy day.

The significance level of p < 0.01 further substantiates the validity of this unexpected association, emphasizing that the observed correlation is highly unlikely to have occurred by chance. The winds of statistical significance do not waver, lending further credence to the impactful influence of personal names on environmental factors. It seems that the winds of chance and choice have aligned to create an intriguing breeze of correlation.

The visualization of the strong positive correlation in the scatterplot not only serves as a powerful testament to the unexpected intertwining of personal nomenclature and environmental factors but also highlights the potential for visual puns in academic writing. After all, who would have thought that a scatterplot could blow us away with its visual representation of a "Dallas wind"? It appears that even in the realm of statistical analysis, a well-placed pun can create quite the gust of amusement.

In conclusion, our study opens up a new frontier in the exploration of personal naming trends and their unanticipated impacts. The winds of investigation continue to blow us into uncharted territory, proving that even the most unlikely associations can reveal meaningful insights. Our research invites further inquiry into the intricate dynamics of the Dallas wind power connection, as we continue to navigate the winds of whimsy and statistical significance in our scholarly pursuits.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Popularity of the first name Dallas and Wind power generated in Puerto Rico. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Blowin' in the Names: The Dallas Wind Power Connection

[[ABSTRACT]]
The relationship between the popularity of the first name Dallas and wind power generated in Puerto Rico is explored in this study. Using data from the US Social Security Administration and the Energy Information Administration, we conducted an analysis to investigate this peculiar association. Our findings revealed a surprising correlation coefficient of 0.9284064 and p < 0.01 for the years 2010 to 2021.

In our research, we uncovered an unanticipated link between the frequency of the name Dallas and the wind power output in Puerto Rico. It seems that the "winds of change" may indeed be directed by the naming trends in the United States, as suggested by our data. One might say that this correlation blew our minds!

This study contributes to the interdisciplinary exploration of quirky connections and provokes further inquiries into the unforeseen influences on renewable energy sources. With these findings, we present a new perspective on the interplay between personal nomenclature and environmental factors.

[[INTRDUCTION]]
The relationship between human names and various socio-environmental phenomena has been a subject of intrigue and curiosity for researchers across diverse fields. While most studies have focused on the cultural, societal, or psychological implications of personal nomenclature, the potential connection between the popularity of a specific first name and environmental forces, such as wind power generation, has received comparatively scant attention. The aim of this paper is to address this gap by delving into the unexpected correlation between the prevalence of the name Dallas and the production of wind power in Puerto Rico.
It's quite "windsome" to think how something as seemingly unrelated as naming trends could play a role in the generation of renewable energy. After all, few things can quite "blow" one's mind like the unexpected intertwining of seemingly disparate factors.
Our investigation leverages data from multiple sources, including the US Social Security Administration and the Energy Information Administration, to explore this peculiar association. The analysis uncovers a surprising correlation coefficient, prompting further examination into the potential mechanisms underlying this connection. It appears that the winds of change may indeed be influenced by the winds of nomenclature, turning a breeze of a hypothesis into a gust of substantial findings.
This study not only adds an unexpected twist to the discourse on environmental influences but also provides a breath of fresh air in the sometimes staid world of interdisciplinary research. As we unravel the intertwined nature of personal names and environmental factors, it becomes evident that there is much to be gained from peeking beyond the obvious horizons of scientific inquiry.
In the following sections, we expound upon the methods employed, the intricacies of the data analysis, and the implications of our findings. The winds of curiosity blow us forward as we embark on this journey of unexpected connections and meteorological mysteries.

[[RESULTS]]
The analysis of the data revealed a striking correlation coefficient of 0.9284064 between the popularity of the first name Dallas and the wind power generated in Puerto Rico for the years 2010 to 2021. The high correlation suggests a strong relationship between these seemingly unrelated variables. It seems that the winds of change may indeed be accompanied by the naming trends of the United States, making a "Dallas wind" a more literal concept than previously thought.
The r-squared value of 0.8619384 further indicates that approximately 86.2% of the variance in wind power generation in Puerto Rico can be explained by the prevalence of the name Dallas. This substantial explanatory power underscores the robustness of the relationship uncovered in our analysis. One might say that the name Dallas is blowing a strong wind of influence on the energy landscape of Puerto Rico.
The significance level of p < 0.01 suggests that the observed correlation is unlikely to have occurred by chance, providing further support for the validity of the identified association. It appears that the winds of nomenclature carry a weighty impact, creating a breeze of statistical significance that cannot be ignored.
In Fig. 1, the scatterplot visually demonstrates the strong positive correlation between the popularity of the first name Dallas and the wind power generated in Puerto Rico. The data points tightly cluster around a clear upward trend, emphasizing the compelling nature of the relationship. The visualization serves as a powerful testament to the unexpected intertwining of personal nomenclature and environmental factors, as well as a testament to the power of puns in academic writing.
These findings offer an intriguing perspective on the potential influences on wind power generation, urging further exploration into the underlying mechanisms and implications of this connection. The winds of investigation continue to blow us into uncharted territory, proving that even the most unlikely associations can reveal meaningful insights.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unearthed a remarkable correlation between the popularity of the first name Dallas and the wind power generated in Puerto Rico. It appears that the winds of nomenclature have not blown this association out of proportion, as evidenced by the striking correlation coefficient and r-squared value. One could even say that the name Dallas has become a powerful force of its own, akin to the winds it influences - quite the "gust-y" situation we have here!

These findings challenge conventional assumptions about the factors influencing renewable energy sources and propel us into a whirlwind of whimsical ponderings. While it may seem like a "breezy" notion at first, the data speaks for itself, carrying implications that extend beyond statistical significance and into the realms of unexpected interconnectedness. It's as if the winds of change have blown through the corridors of conventional scientific inquiry, leaving a trail of "wind"er and astonishment in their wake.

The implications of our research extend beyond the theoretical realm, opening up avenues of exploration into the potential mechanisms underlying this unexpected connection. While it might seem like a "blow-hard" theory to some, our data provide a gust of credibility to the concept that personal nomenclature may indeed exert influence on environmental phenomena, perhaps in ways we are only beginning to grasp.

As for the future of research in this area, it seems that the wind has settled. Our findings have yielded substantial evidence of the correlation between the prevalence of the name Dallas and wind power generation in Puerto Rico. It might be safe to say that no further research is needed in this "breezy" corner of interdisciplinary inquiry - we have ridden this wind to its whimsical end.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Blowin' in the Names: The Dallas Wind Power Connection. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Renewable Energy Innovations

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Blowin' in the Names: The Dallas Wind Power Connection. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Alternative Energy Research and Development (SAERD)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Blowin' in the Names: The Dallas Wind Power Connection with an abstract of The relationship between the popularity of the first name Dallas and wind power generated in Puerto Rico is explored in this study. Using data from the US Social Security Administration and the Energy Information Administration, we conducted an analysis to investigate this peculiar association. Our findings revealed a surprising correlation coefficient of 0.9284064 and p < 0.01 for the years 2010 to 2021.
In our research, we uncovered an unanticipated link between the frequency of the name Dallas and the wind power output in Puerto Rico. It seems that the "winds of change" may indeed be directed by the naming trends in the United States, as suggested by our data. One might say that this correlation blew our minds!
This study contributes to the interdisciplinary exploration of quirky connections and provokes further inquiries into the unforeseen influences on renewable energy sources. With these findings, we present a new perspective on the interplay between personal nomenclature and environmental factors.

ChatGPT:

Dallas, wind power, Puerto Rico, correlation, popularity of names, US Social Security Administration, Energy Information Administration, correlation coefficient, renewable energy, naming trends, unexpected connections, interdisciplinary, environmental factors

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

Popularity of the first name Dallas
Detailed data title: Babies of all sexes born in the US named Dallas
Source: US Social Security Administration
See what else correlates with Popularity of the first name Dallas

Wind power generated in Puerto Rico
Detailed data title: Total wind power generated in Puerto Rico in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in Puerto Rico

Correlation r = 0.9284064 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.8619384 (Coefficient of determination)
This means 86.2% of the change in the one variable (i.e., Wind power generated in Puerto Rico) is predictable based on the change in the other (i.e., Popularity of the first name Dallas) over the 12 years from 2010 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.3E-5.^Show 0.0000131245107603583820000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.93 in 0.0013% of random cases. Said differently, if you correlated 76,193 random variables^Note You don't actually need 76 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.76, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Popularity of the first name Dallas (Babies born)	1224	1197	1272	1624	1889	1969	1705	1921	1700	1735	1719	1748
Wind power generated in Puerto Rico (Billion kWh)	0.001	0.001	0.015	0.173	0.218	0.239	0.193	0.149	0.108	0.159	0.143	0.162554

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([1224,1197,1272,1624,1889,1969,1705,1921,1700,1735,1719,1748,])
array_2 = np.array([0.001,0.001,0.015,0.173,0.218,0.239,0.193,0.149,0.108,0.159,0.143,0.162554,])
array_1_name = "Popularity of the first name Dallas"
array_2_name = "Wind power generated in Puerto Rico"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Popularity of the first name Dallas
Line chart for only Wind power generated in Puerto Rico
The spurious research paper: Blowin' in the Names: The Dallas Wind Power Connection

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Correlation ID: 1279 · Black Variable ID: 2339 · Red Variable ID: 23910


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