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AI explanation
The cleaner air in Tulsa created a positive atmospheric feedback loop, leading to stronger winds in Puerto Rico. As they say, when Tulsa breathes easy, Puerto Rico reaps the breezy benefits! It's a classic case of the butterfly flapping its wings in the city of oil rigs and setting off a chain reaction of clean, green energy production in the land of piña coladas!
Model: dalle-3
Prompt: Certainly! Here is your image generation prompt: [ A photo-realistic image ] Generate a scene that showcases a clear, blue sky over Tulsa, Oklahoma with fresh air and improved air quality. In contrast, depict wind turbines spinning vigorously in Puerto Rico, representing abundant wind power generation. Show a connection between the two locations, indicating that the cleaner air in Tulsa is leading to stronger winds in Puerto Rico, where wind energy production is thriving. 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 variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: An image that looks like it is a scene from 'Star Wars' A photo-realistic image A TIME magazine cover image Symbolism A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s
ChatGPT: A photo-realistic image
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico, in the following situation: The cleaner air in Tulsa created a positive atmospheric feedback loop, leading to stronger winds in Puerto Rico. As they say, when Tulsa breathes easy, Puerto Rico reaps the breezy benefits! It's a classic case of the butterfly flapping its wings in the city of oil rigs and setting off a chain reaction of clean, green energy production in the land of piña coladas!. Keep the description simple.
ChatGPT: In Tulsa, Oklahoma, the air quality index shows a significant improvement with clear blue skies and fresh air. Meanwhile, in Puerto Rico, wind turbines are spinning vigorously, generating abundant wind power. The cleaner air in Tulsa has sparked a chain of breezy benefits, leading to stronger winds in Puerto Rico, where the wind energy production is thriving.
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: [ A photo-realistic image ] and depicting this scene: In Tulsa, Oklahoma, the air quality index shows a significant improvement with clear blue skies and fresh air. Meanwhile, in Puerto Rico, wind turbines are spinning vigorously, generating abundant wind power. The cleaner air in Tulsa has sparked a chain of breezy benefits, leading to stronger winds in Puerto Rico, where the wind energy production is thriving. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Certainly! Here is your image generation prompt: [ A photo-realistic image ] Generate a scene that showcases a clear, blue sky over Tulsa, Oklahoma with fresh air and improved air quality. In contrast, depict wind turbines spinning vigorously in Puerto Rico, representing abundant wind power generation. Show a connection between the two locations, indicating that the cleaner air in Tulsa is leading to stronger winds in Puerto Rico, where wind energy production is thriving.
*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.
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 Air quality in Tulsa, Oklahoma caused Wind power generated in Puerto Rico to increase.
AI academic paper
 |
Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico The Journal of Ecological Engineering and Atmospheric Sciences Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from Environmental Protection Agency and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.8348466 and p < 0.01 for 2010 to 2021.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
"Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Here is the title and abstract of the paper:
[[TITLE]]
"Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
Introduction
The relationship between air quality and wind power generation has long captivated the curious minds of researchers and policymakers alike. On the surface, these two factors may seem as unrelated as a gust of wind in Oklahoma compared to a piña colada-fueled breeze in Puerto Rico. However, beneath their seemingly disparate facades lies a mysterious interconnection waiting to be unraveled. Our study aims to untangle this enigmatic web of influence by scrutinizing the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico, revealing the unseen dance of particles and energy in the atmosphere.
Like a playful zephyr dancing through the air, the winds of change are often unpredictable, yet they carry profound implications for environmental and energy dynamics. As the demand for sustainable energy sources grows, understanding the hidden connections between air quality and wind power becomes increasingly crucial. The allure of wind power lies not only in its renewable nature but also in its potential to breathe new life into the quest for clean and efficient energy solutions. Thus, our investigation seeks to illuminate the subtle ties that bind air quality and wind power generation, shedding light on the whimsical nature of the wind and its far-reaching effects.
In line with our commitment to unraveling this atmospheric enigma, we delved into extensive datasets from the Environmental Protection Agency and the Energy Information Administration, spanning the years 2010 to 2021. The results of our analysis yielded a surprising correlation coefficient of 0.8348466, with a statistically significant p-value of less than 0.01, providing compelling evidence of the unforeseen link between air quality in Tulsa and wind power generation in Puerto Rico. These findings pique our curiosity, urging us to delve deeper into the unseen forces that govern the dynamics of our environment and energy landscape.
While the winds of change often carry with them a sense of mystery and unpredictability, our study marks a breezy stride towards demystifying the intricate relationship between air quality and wind power. As we expand our understanding of the ethereal dance between particles in the air and the harnessing of wind energy, we embark on a journey to uncover the untold stories carried by the wind, transcending geographical boundaries and shaping the very fabric of our environment and energy infrastructure. Thus, we invite readers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
The relationship between air quality and wind power generation has long captivated the curious minds of researchers and policymakers alike. On the surface, these two factors may seem as unrelated as a gust of wind in Oklahoma compared to a piña colada-fueled breeze in Puerto Rico. However, beneath their seemingly disparate facades lies a mysterious interconnection waiting to be unraveled. Our study aims to untangle this enigmatic web of influence by scrutinizing the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico, revealing the unseen dance of particles and energy in the atmosphere.
Like a playful zephyr dancing through the air, the winds of change are often unpredictable, yet they carry profound implications for environmental and energy dynamics. As the demand for sustainable energy sources grows, understanding the hidden connections between air quality and wind power becomes increasingly crucial. The allure of wind power lies not only in its renewable nature but also in its potential to breathe new life into the quest for clean and efficient energy solutions. Thus, our investigation seeks to illuminate the subtle ties that bind air quality and wind power generation, shedding light on the whimsical nature of the wind and its far-reaching effects.
In line with our commitment to unraveling this atmospheric enigma, we delved into extensive datasets from the Environmental Protection Agency and the Energy Information Administration, spanning the years 2010 to 2021. The results of our analysis yielded a surprising correlation coefficient of 0.8348466, with a statistically significant p-value of less than 0.01, providing compelling evidence of the unforeseen link between air quality in Tulsa and wind power generation in Puerto Rico. These findings pique our curiosity, urging us to delve deeper into the unseen forces that govern the dynamics of our environment and energy landscape.
While the winds of change often carry with them a sense of mystery and unpredictability, our study marks a breezy stride towards demystifying the intricate relationship between air quality and wind power. As we expand our understanding of the ethereal dance between particles in the air and the harnessing of wind energy, we embark on a journey to uncover the untold stories carried by the wind, transcending geographical boundaries and shaping the very fabric of our environment and energy infrastructure. Thus, we invite readers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The study of the interplay between air quality and wind power generation has posed a conundrum for researchers, akin to trying to catch the wind in a net made of statistical models. Various scholarly works have endeavored to shed light on this complex relationship, utilizing a diverse array of methodologies ranging from extensive data analysis to meteorological and environmental studies. Smith et al. (2015) conducted a comprehensive analysis of air quality trends in metropolitan areas, while Doe (2018) delved into the intricate dynamics of wind power generation in island regions. These studies, alongside numerous others, have laid the groundwork for unraveling the enigmatic connections between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico.
In "The Wind Business: A Down-to-Earth Guide to Harnessing and Profiting from the Wind," the authors uncover the economic and environmental implications of wind power, providing a comprehensive overview of its potential for sustainable energy generation. Similarly, "Air Pollution and Health" explores the impacts of air quality on human health, highlighting the urgency of addressing pollutants in the atmosphere. These non-fiction works offer valuable insights into the multifaceted nature of the air quality-wind power nexus.
On the fictitious front, "Gone with the Wind" by Margaret Mitchell and "The Airbender Chronicles" by Aang delve into the whimsical and fantastical realms of wind-related tales, weaving narratives of romance, adventure, and elemental mastery. These literary works, while not grounded in empirical research, serve as a testament to the enduring fascination with the power of the wind in shaping human experiences.
Furthermore, the animated series "Avatar: The Last Airbender" and the children's show "The Magic School Bus: Blows Its Top" carry subtle yet pertinent themes related to air quality and wind dynamics, imparting knowledge to viewers in a lighthearted and entertaining manner.
As the research community continues to breeze through the windswept terrain of environmental and energy studies, these diverse sources contribute to the rich tapestry of knowledge surrounding the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. While the subject matter may seem as fleeting as the breeze itself, the findings presented in these works underscore the far-reaching implications of understanding the unseen forces that shape our environmental and energy landscapes.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the methodology 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 methodology section of an academic research paper presenting the findings of the connection between Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration . You used data from 2010 to 2021
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
[[INTRODUCTION]]
Introduction
The relationship between air quality and wind power generation has long captivated the curious minds of researchers and policymakers alike. On the surface, these two factors may seem as unrelated as a gust of wind in Oklahoma compared to a piña colada-fueled breeze in Puerto Rico. However, beneath their seemingly disparate facades lies a mysterious interconnection waiting to be unraveled. Our study aims to untangle this enigmatic web of influence by scrutinizing the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico, revealing the unseen dance of particles and energy in the atmosphere.
Like a playful zephyr dancing through the air, the winds of change are often unpredictable, yet they carry profound implications for environmental and energy dynamics. As the demand for sustainable energy sources grows, understanding the hidden connections between air quality and wind power becomes increasingly crucial. The allure of wind power lies not only in its renewable nature but also in its potential to breathe new life into the quest for clean and efficient energy solutions. Thus, our investigation seeks to illuminate the subtle ties that bind air quality and wind power generation, shedding light on the whimsical nature of the wind and its far-reaching effects.
In line with our commitment to unraveling this atmospheric enigma, we delved into extensive datasets from the Environmental Protection Agency and the Energy Information Administration, spanning the years 2010 to 2021. The results of our analysis yielded a surprising correlation coefficient of 0.8348466, with a statistically significant p-value of less than 0.01, providing compelling evidence of the unforeseen link between air quality in Tulsa and wind power generation in Puerto Rico. These findings pique our curiosity, urging us to delve deeper into the unseen forces that govern the dynamics of our environment and energy landscape.
While the winds of change often carry with them a sense of mystery and unpredictability, our study marks a breezy stride towards demystifying the intricate relationship between air quality and wind power. As we expand our understanding of the ethereal dance between particles in the air and the harnessing of wind energy, we embark on a journey to uncover the untold stories carried by the wind, transcending geographical boundaries and shaping the very fabric of our environment and energy infrastructure. Thus, we invite readers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
METHODOLOGY
In this study, we employed a multidimensional approach to elucidate the relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. Our research endeavor encompassed a comprehensive analysis of diverse data sets sourced primarily from the Environmental Protection Agency and the Energy Information Administration, spanning the time period from 2010 to 2021. The confluence of these datasets allowed for a detailed examination of the intricate interplay between air quality parameters and wind power generation, unveiling the enigmatic connections harbored within the atmospheric currents.
Data Collection and Preprocessing:
To capture the nuances of air quality in Tulsa, Oklahoma, we sourced air quality index (AQI) measurements, particulate matter (PM) concentrations, ozone levels, and other pertinent air quality indicators from the Environmental Protection Agency's comprehensive repository. Similarly, for wind power generation in Puerto Rico, we gathered data on wind speed, wind energy production, and related environmental variables from the Energy Information Administration's thorough archives. The amalgamation of these disparate data sources furnished a rich tapestry of environmental and energy dynamics, laying the groundwork for our subsequent analyses.
Data Analysis and Statistical Modeling:
Upon assembling the requisite datasets, our research team embarked on a rigorous analytical expedition to unravel the subtle connections between air quality in Tulsa and wind power generation in Puerto Rico. Leveraging advanced statistical methodologies, including correlation analysis and regression modeling, we endeavored to discern the underlying patterns and associations latent within the data. The utilization of robust statistical techniques facilitated the identification of potential relationships and associations between air quality parameters and wind power generation, steering our exploration toward revelatory insights.
Correlation Analysis:
The cornerstone of our methodology encompassed an in-depth correlation analysis to ascertain the degree of association between air quality indicators in Tulsa and wind power generation metrics in Puerto Rico. The calculation of correlation coefficients, spearheaded by Pearson's product-moment correlation, elucidated the strength and direction of the relationship between these disparate yet interconnected variables. The unveiling of a substantial correlation coefficient of 0.8348466, coupled with a statistically significant p-value of less than 0.01, underscored the unexpected and compelling linkage between air quality in Tulsa and wind power generation in Puerto Rico.
Time Series Modeling:
In addition to correlation analysis, our analytical framework entailed the employment of time series modeling techniques to capture the temporal dynamics inherent in air quality and wind power generation data. By leveraging autoregressive integrated moving average (ARIMA) models and other time series methodologies, we sought to discern temporal trends, seasonality patterns, and potential cyclical behaviors embedded within the datasets. This multifaceted approach enabled us to glean a comprehensive understanding of the evolving interrelationship between air quality and wind power generation over the specified study period.
Spatial Analysis:
Furthermore, recognizing the spatial dimensions inherent in our investigation, we conducted geospatial analyses to delineate the geographical implications of air quality and wind power generation. Geographic information system (GIS) tools and spatial mapping techniques were harnessed to visualize the spatial distribution of air quality metrics in Tulsa and wind power generation patterns in Puerto Rico. This spatial lens afforded invaluable insights into the geographic dispersion and localized impacts of air quality and wind power, enriching our comprehension of their interconnected dynamics.
Limitations:
While our methodological framework embodies a comprehensive and meticulous approach to interrogating the air quality-wind power nexus, it is essential to acknowledge certain limitations. The reliance on secondary data sources, such as the Environmental Protection Agency and the Energy Information Administration, introduces inherent constraints, including potential data incompleteness, measurement biases, and contextual nuances. Additionally, the complexity of environmental and energy dynamics necessitates a nuanced understanding of confounding variables, temporal lags, and regional disparities that may influence the observed relationships.
In summary, our methodology encapsulates a multidimensional and rigorous expedition into the symbiotic relationship between the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico. By navigating through the labyrinth of environmental and energy data, we endeavored to shed light on the unforeseen connections that enliven our atmosphere and energize our sustainable aspirations. With a fusion of statistical analyses, geographical perspectives, and temporal insights, our methodological approach serves as an empirical compass guiding us toward a deeper comprehension of the ethereal dance between air quality and wind power generation.
-----
Hope you find it amazing!
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration .
For the time period 2010 to 2021, you found a correlation 0.8348466, r-squared of 0.6969689, and p < 0.01.
One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.
Here is the title and abstract of the paper:
[[TITLE]]
"Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
The relationship between air quality and wind power generation has long captivated the curious minds of researchers and policymakers alike. On the surface, these two factors may seem as unrelated as a gust of wind in Oklahoma compared to a piña colada-fueled breeze in Puerto Rico. However, beneath their seemingly disparate facades lies a mysterious interconnection waiting to be unraveled. Our study aims to untangle this enigmatic web of influence by scrutinizing the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico, revealing the unseen dance of particles and energy in the atmosphere.
Like a playful zephyr dancing through the air, the winds of change are often unpredictable, yet they carry profound implications for environmental and energy dynamics. As the demand for sustainable energy sources grows, understanding the hidden connections between air quality and wind power becomes increasingly crucial. The allure of wind power lies not only in its renewable nature but also in its potential to breathe new life into the quest for clean and efficient energy solutions. Thus, our investigation seeks to illuminate the subtle ties that bind air quality and wind power generation, shedding light on the whimsical nature of the wind and its far-reaching effects.
In line with our commitment to unraveling this atmospheric enigma, we delved into extensive datasets from the Environmental Protection Agency and the Energy Information Administration, spanning the years 2010 to 2021. The results of our analysis yielded a surprising correlation coefficient of 0.8348466, with a statistically significant p-value of less than 0.01, providing compelling evidence of the unforeseen link between air quality in Tulsa and wind power generation in Puerto Rico. These findings pique our curiosity, urging us to delve deeper into the unseen forces that govern the dynamics of our environment and energy landscape.
While the winds of change often carry with them a sense of mystery and unpredictability, our study marks a breezy stride towards demystifying the intricate relationship between air quality and wind power. As we expand our understanding of the ethereal dance between particles in the air and the harnessing of wind energy, we embark on a journey to uncover the untold stories carried by the wind, transcending geographical boundaries and shaping the very fabric of our environment and energy infrastructure. Thus, we invite readers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The quantitative analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a remarkable correlation between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico for the years 2010 to 2021. The correlation coefficient, determined to be 0.8348466, suggests a strong positive relationship between these seemingly unrelated variables. This unexpected connection between air quality and wind power generation challenges convention, much like an unexpected gust of wind disrupting a calm day at the beach.
Further analysis through linear regression indicated an r-squared value of 0.6969689, highlighting the degree to which changes in air quality may predict variations in wind power generation. The implications of these findings blow through the research landscape like a refreshing breeze, prompting a reevaluation of the interconnectedness of environmental and energy dynamics.
The results are succinctly depicted in Figure 1, a scatterplot that vividly demonstrates the robust correlation between air quality in Tulsa and wind power generated in Puerto Rico. The data points align with a trajectory that seems to mirror the wind's own capricious path, playfully hinting at the unseen forces at work in our atmospheric and energy systems.
In conclusion, these findings not only underscore the pivotal role of wind power in shaping our energy landscape but also emphasize the intricate relationship between air quality and wind power generation. The unexpected correlation urges us to revisit our presumptions about the complexities of environmental and energy dynamics, reminding us that, much like the wind, there are unforeseen connections waiting to be discovered in the unlikeliest of places.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
[[LITERATURE REVIEW]]
The study of the interplay between air quality and wind power generation has posed a conundrum for researchers, akin to trying to catch the wind in a net made of statistical models. Various scholarly works have endeavored to shed light on this complex relationship, utilizing a diverse array of methodologies ranging from extensive data analysis to meteorological and environmental studies. Smith et al. (2015) conducted a comprehensive analysis of air quality trends in metropolitan areas, while Doe (2018) delved into the intricate dynamics of wind power generation in island regions. These studies, alongside numerous others, have laid the groundwork for unraveling the enigmatic connections between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico.
In "The Wind Business: A Down-to-Earth Guide to Harnessing and Profiting from the Wind," the authors uncover the economic and environmental implications of wind power, providing a comprehensive overview of its potential for sustainable energy generation. Similarly, "Air Pollution and Health" explores the impacts of air quality on human health, highlighting the urgency of addressing pollutants in the atmosphere. These non-fiction works offer valuable insights into the multifaceted nature of the air quality-wind power nexus.
On the fictitious front, "Gone with the Wind" by Margaret Mitchell and "The Airbender Chronicles" by Aang delve into the whimsical and fantastical realms of wind-related tales, weaving narratives of romance, adventure, and elemental mastery. These literary works, while not grounded in empirical research, serve as a testament to the enduring fascination with the power of the wind in shaping human experiences.
Furthermore, the animated series "Avatar: The Last Airbender" and the children's show "The Magic School Bus: Blows Its Top" carry subtle yet pertinent themes related to air quality and wind dynamics, imparting knowledge to viewers in a lighthearted and entertaining manner.
As the research community continues to breeze through the windswept terrain of environmental and energy studies, these diverse sources contribute to the rich tapestry of knowledge surrounding the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. While the subject matter may seem as fleeting as the breeze itself, the findings presented in these works underscore the far-reaching implications of understanding the unseen forces that shape our environmental and energy landscapes.
[[RESULTS]]
The quantitative analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a remarkable correlation between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico for the years 2010 to 2021. The correlation coefficient, determined to be 0.8348466, suggests a strong positive relationship between these seemingly unrelated variables. This unexpected connection between air quality and wind power generation challenges convention, much like an unexpected gust of wind disrupting a calm day at the beach.
Further analysis through linear regression indicated an r-squared value of 0.6969689, highlighting the degree to which changes in air quality may predict variations in wind power generation. The implications of these findings blow through the research landscape like a refreshing breeze, prompting a reevaluation of the interconnectedness of environmental and energy dynamics.
The results are succinctly depicted in Figure 1, a scatterplot that vividly demonstrates the robust correlation between air quality in Tulsa and wind power generated in Puerto Rico. The data points align with a trajectory that seems to mirror the wind's own capricious path, playfully hinting at the unseen forces at work in our atmospheric and energy systems.
In conclusion, these findings not only underscore the pivotal role of wind power in shaping our energy landscape but also emphasize the intricate relationship between air quality and wind power generation. The unexpected correlation urges us to revisit our presumptions about the complexities of environmental and energy dynamics, reminding us that, much like the wind, there are unforeseen connections waiting to be discovered in the unlikeliest of places.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The results of this study provide compelling evidence in support of prior research investigating the curious interplay between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. The correlation coefficient of 0.8348466 observed in our analysis aligns with the findings of Smith et al. (2015) and Doe (2018), shedding light on the surprising connection between these seemingly disparate factors. It appears that the wind does indeed carry more than just energy across the miles; it carries a message of environmental interconnectedness.
The winds of change blowing through our findings also echo the themes found in the fictitious literary works "Gone with the Wind" and "The Airbender Chronicles." While these works may not have been founded on empirical studies, they tantalizingly foreshadow the very real revelations uncovered in our research. Indeed, the wind's whimsical nature seems to elicit more than just a passing breeze of curiosity; it beckons us to delve deeper into the unseen forces shaping our world.
Furthermore, the unexpected correlation coefficient challenges the conventional wisdom much like an unexpected gust of wind disrupts a calm day at the beach. The parallel is uncanny, highlighting the unforeseen connections that exist within our atmospheric and energy systems. This unforeseen connection between air quality in Tulsa and wind power generation adds a layer of complexity to our understanding of environmental and energy dynamics, much like the plot twists in "The Magic School Bus: Blows Its Top" or the unexpected character developments in "Avatar: The Last Airbender."
In light of these wind-swept findings, it becomes evident that our presumptions about the multifaceted nature of the air quality-wind power nexus need to be revisited, much like a sailor plotting a new course to navigate a change in the wind's direction. These findings emphasize the need to recognize the broader implications of understanding the unseen forces that shape our environmental and energy landscapes – after all, it seems that the economic and environmental implications of wind power, as elucidated in "The Wind Business," extend beyond mere profit and environmental sustainability.
In a nutshell, the results of this study blow through the research landscape like a refreshing breeze, prompting a reevaluation of the interconnectedness of environmental and energy dynamics. The correlation coefficient acts as a signpost, pointing to an unexpected correlation that urges us to challenge our preconceptions and embark on a gusty exploration of the unseen threads that tie air quality in Tulsa to wind power generation in Puerto Rico.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Air quality in Tulsa, Oklahoma and Wind power generated in Puerto Rico. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico"
[[ABSTRACT]]
This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
[[INTRDUCTION]]
Introduction
The relationship between air quality and wind power generation has long captivated the curious minds of researchers and policymakers alike. On the surface, these two factors may seem as unrelated as a gust of wind in Oklahoma compared to a piña colada-fueled breeze in Puerto Rico. However, beneath their seemingly disparate facades lies a mysterious interconnection waiting to be unraveled. Our study aims to untangle this enigmatic web of influence by scrutinizing the air quality in Tulsa, Oklahoma, and the wind power generated in Puerto Rico, revealing the unseen dance of particles and energy in the atmosphere.
Like a playful zephyr dancing through the air, the winds of change are often unpredictable, yet they carry profound implications for environmental and energy dynamics. As the demand for sustainable energy sources grows, understanding the hidden connections between air quality and wind power becomes increasingly crucial. The allure of wind power lies not only in its renewable nature but also in its potential to breathe new life into the quest for clean and efficient energy solutions. Thus, our investigation seeks to illuminate the subtle ties that bind air quality and wind power generation, shedding light on the whimsical nature of the wind and its far-reaching effects.
In line with our commitment to unraveling this atmospheric enigma, we delved into extensive datasets from the Environmental Protection Agency and the Energy Information Administration, spanning the years 2010 to 2021. The results of our analysis yielded a surprising correlation coefficient of 0.8348466, with a statistically significant p-value of less than 0.01, providing compelling evidence of the unforeseen link between air quality in Tulsa and wind power generation in Puerto Rico. These findings pique our curiosity, urging us to delve deeper into the unseen forces that govern the dynamics of our environment and energy landscape.
While the winds of change often carry with them a sense of mystery and unpredictability, our study marks a breezy stride towards demystifying the intricate relationship between air quality and wind power. As we expand our understanding of the ethereal dance between particles in the air and the harnessing of wind energy, we embark on a journey to uncover the untold stories carried by the wind, transcending geographical boundaries and shaping the very fabric of our environment and energy infrastructure. Thus, we invite readers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
[[RESULTS]]
The quantitative analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a remarkable correlation between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico for the years 2010 to 2021. The correlation coefficient, determined to be 0.8348466, suggests a strong positive relationship between these seemingly unrelated variables. This unexpected connection between air quality and wind power generation challenges convention, much like an unexpected gust of wind disrupting a calm day at the beach.
Further analysis through linear regression indicated an r-squared value of 0.6969689, highlighting the degree to which changes in air quality may predict variations in wind power generation. The implications of these findings blow through the research landscape like a refreshing breeze, prompting a reevaluation of the interconnectedness of environmental and energy dynamics.
The results are succinctly depicted in Figure 1, a scatterplot that vividly demonstrates the robust correlation between air quality in Tulsa and wind power generated in Puerto Rico. The data points align with a trajectory that seems to mirror the wind's own capricious path, playfully hinting at the unseen forces at work in our atmospheric and energy systems.
In conclusion, these findings not only underscore the pivotal role of wind power in shaping our energy landscape but also emphasize the intricate relationship between air quality and wind power generation. The unexpected correlation urges us to revisit our presumptions about the complexities of environmental and energy dynamics, reminding us that, much like the wind, there are unforeseen connections waiting to be discovered in the unlikeliest of places.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our investigation has unveiled a captivating correlation between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. The robust correlation coefficient of 0.8348466 with a statistically significant p-value has blown through the research landscape, much like an unexpected gust of wind disrupting a calm day at the beach. The wind, it seems, carries more than just energy across the miles - it also carries the subtle influence of air quality.
Our findings, akin to a playful zephyr dancing through the air, highlight the whimsical nature of the wind and its unexpected ties to environmental dynamics. Indeed, the wind's capricious path in Puerto Rico mirrors the unseen forces at work in our atmospheric and energy systems, reminding us that there are unforeseen connections waiting to be discovered in the unlikeliest of places - much like finding a dollar bill in an old pair of jeans.
While our research has been a breezy stride towards understanding the intricate relationship between air quality and wind power, it also serves as a reminder of the need to embrace the unpredictable nature of the wind, much like eagerly awaiting the arrival of an ice cream truck on a hot summer day. With the understanding that the wind carries more than just energy, we invite future researchers to join us in this exhilarating exploration of the unexpected connections that breathe life into our world.
Ultimately, our findings suggest that no more research is needed in this area. The winds of knowledge have blown in our favor, and we can rest assured that this unexpected correlation has been ingeniously unraveled.
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Ecological Engineering and Atmospheric Sciences
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico. Respond only with the name of a fictional organization.
ChatGPT:
The Society for Atmospheric Energy Research and Development
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 Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico with an abstract of This study delves into the intricate relationship between air quality in Tulsa, Oklahoma, and wind power generated in Puerto Rico. By utilizing data from the Environmental Protection Agency and the Energy Information Administration, the research team sought to uncover the subtle interplay between these two seemingly unrelated factors. Surprisingly, the correlation coefficient of 0.8348466 and p < 0.01 for the years 2010 to 2021 emerged, underscoring the unforeseen connection between air quality and wind power generation. The results shed light on the wind's whimsical nature, as it seems to carry more than just energy across the miles. This investigation marks a breezy stride toward understanding the unseen forces that shape our environment and energy dynamics.
ChatGPT:
air quality, wind power, Tulsa Oklahoma, Puerto Rico, correlation coefficient, EPA data, Energy Information Administration, environment, energy dynamics, wind energy, air pollution
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
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Data details
Air quality in Tulsa, OklahomaDetailed data title: Percentage of days with good air quality in Tulsa, OK
Source: Environmental Protection Agency
See what else correlates with Air quality in Tulsa, Oklahoma
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 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 = 0.6969689 (Coefficient of determination)
This means 69.7% 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., Air quality in Tulsa, Oklahoma) over the 12 years from 2010 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00073. 0.0007281895872473775000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.83 in 0.073% of random cases. Said differently, if you correlated 1,373 random variables Which I absolutely did.
with the same 11 degrees of freedom, 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.
[ 0.5, 0.95 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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 | |
| Air quality in Tulsa, Oklahoma (Good air quality) | 40 | 30.6849 | 43.7158 | 54.2466 | 60.5479 | 68.4931 | 63.6612 | 52.6027 | 50.411 | 68.2192 | 73.4973 | 60.274 |
| 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 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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
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
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.
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. 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([40,30.6849,43.7158,54.2466,60.5479,68.4931,63.6612,52.6027,50.411,68.2192,73.4973,60.274,])
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 = "Air quality in Tulsa, Oklahoma"
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. 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.
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Download images for these variables:
- High resolution line chart
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 Air quality in Tulsa, Oklahoma
- Line chart for only Wind power generated in Puerto Rico
- AI-generated correlation image
- The spurious research paper: Blowin' in the Wind: The Air Quality-Wind Power Connection Between Tulsa, Oklahoma and Puerto Rico
Bravo! Your evaluation rocks!
Correlation ID: 5305 · Black Variable ID: 21362 · Red Variable ID: 23910
