Spurious correlation #3,350 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Cape Coral, Florida and the second variable is Kerosene used in United States. The chart goes from 1989 to 2022, 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 Air pollution in Cape Coral, Florida and the second variable is Kerosene used in United States. The chart goes from 1989 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As air quality improved in Cape Coral, residents no longer needed to kerosene-d their lungs, leading to a decrease in demand for kerosene. This air-pollution solution had a fl-earthing impact on kerosene usage nationwide, proving that cleaner air really is the fuel deal!

^{Show image generation prompt} Model: dalle-3
Prompt: [ A photo-realistic image ] A serene, coastal landscape in Cape Coral, Florida unfolds, featuring a deeply vivid blue sky. In the background, the clouds disperse as air pollution subsides, revealing the sun's rays stretching across the horizon. Seagulls, free from their gas masks, soar gracefully through the clean, clear air. Meanwhile, in a contrasting scene depicting the national ripple effect, barrels of unused kerosene lay dormant as the decrease in demand resonates across the United States. The juxtaposition of the pristine coastal setting and the economic shift captures the sense of environmental rejuvenation and broader societal changes. 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 pollution in Cape Coral, Florida and Kerosene used in United States. 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: Voxel art A photo-realistic image An image that looks like it is a scene from A Disney Movie Hyper-realistic Data cyber art
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 pollution in Cape Coral, Florida and Kerosene used in United States, in the following situation: Because as air pollution decreased in Cape Coral, Florida, the local seagulls no longer needed to wear gas masks, resulting in a surplus of kerosene previously used for them. This surplus caused a decrease in national demand for kerosene, impacting overall usage in the United States.. Keep the description simple.
ChatGPT: In Cape Coral, Florida, the sky clears up as air pollution decreases. Seagulls happily soar through the clean air without their gas masks. Meanwhile, across the United States, barrels of kerosene sit unused, as the decrease in demand ripples through the country.

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 Cape Coral, Florida, the sky clears up as air pollution decreases. Seagulls happily soar through the clean air without their gas masks. Meanwhile, across the United States, barrels of kerosene sit unused, as the decrease in demand ripples through the country. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] A serene, coastal landscape in Cape Coral, Florida unfolds, featuring a deeply vivid blue sky. In the background, the clouds disperse as air pollution subsides, revealing the sun's rays stretching across the horizon. Seagulls, free from their gas masks, soar gracefully through the clean, clear air. Meanwhile, in a contrasting scene depicting the national ripple effect, barrels of unused kerosene lay dormant as the decrease in demand resonates across the United States. The juxtaposition of the pristine coastal setting and the economic shift captures the sense of environmental rejuvenation and broader societal changes.

*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 a decrease in Air pollution in Cape Coral, Florida caused Kerosene used in United States to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States

Journal of Environmental Quirks & Quandaries

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 Air pollution in Cape Coral, Florida and Kerosene used in United States. 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States"

[[ABSTRACT]]
This research paper delves into the often overlooked and underappreciated bond between air pollution in Cape Coral, Florida, and the utilization of kerosene in the United States. We utilized data from the Environmental Protection Agency and the Energy Information Administration to rigorously investigate this peculiar but enticing relationship. Our analysis uncovered a striking correlation coefficient of 0.7800135 with a p-value less than 0.01, spanning the time period from 1989 to 2022. Our findings shed light on the intricate dynamics between these seemingly disparate phenomena and present compelling evidence of their coalescence. As we unravel the sooty love affair between air pollution and kerosene use, we invite the scholarly community to join us in embracing the delightful complexity of this unexpected connection.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Air pollution is a matter of grave concern due to its detrimental effects on human health and the environment. The combustion of fuel, whether it be for transportation, heating, or industrial purposes, is a significant contributor to the emission of pollutants into the atmosphere. Among the various sources of fuel, kerosene stands out as a versatile and commonly used product, known for its use in lighting, heating, and cooking. The United States has been a major consumer of kerosene, with its consumption patterns exhibiting intriguing temporal and regional variations.
Cape Coral, Florida, known for its picturesque scenery and vibrant community, has also been a site of interest in the realm of air quality, with studies documenting the presence of particulate matter and other pollutants in its atmospheric milieu. The confluence of these two seemingly dissociated phenomena has piqued the curiosity of researchers seeking to unravel the underlying dynamics at play.
In this study, our aim is to delve into the curious entanglement between air pollution in Cape Coral, Florida, and the consumption of kerosene in the United States. While this association may appear enigmatic at first glance, our efforts to dissect the esoteric connection have resulted in the unearthing of compelling insights. We believe that our findings will not only broaden understanding in the field of environmental science but will also engender a newfound appreciation for the idiosyncratic relationships that underpin the world of atmospheric phenomena and fuel consumption.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Numerous studies have explored the association between air pollution and various sources of fuel consumption, shedding light on the complex interplay between these phenomena. Smith et al. (2015) demonstrated a positive correlation between particulate matter concentration and diesel fuel combustion in urban areas, highlighting the impact of vehicular emissions on air quality. Doe and Jones (2018) further expanded on this line of inquiry, uncovering a significant relationship between sulfur dioxide levels and coal-fired power plants in suburban regions.

Moving beyond the realm of scientific literature, "The Big Smoke: Our Shared Atmosphere" by Clean Air Council presents a comprehensive overview of air pollution across different geographical locations and the diverse sources contributing to its prevalence. In a similar vein, "Choking the Sky: A Global Perspective on Atmospheric Degradation" by Environmental Watchdog explores the multifaceted dimensions of air pollution and its implications for public health and ecosystems.

In a departure from non-fiction works, the fictional novel "A Whiff of Smoke and Mystery" by Jane Puffington offers a whimsical narrative that intertwines the mysteries of air pollution with a gripping tale of intrigue and romance. Meanwhile, "The Foggy Affair" by Arthur Combustible presents a thrilling account of clandestine kerosene dealings amidst a backdrop of murky atmospheric conditions, drawing readers into the enigmatic world of fuel-based subterfuge.

Drawing inspiration from the world of board games, the classic strategy game "Smoke and Mirrors" offers a playful yet thought-provoking exploration of environmental challenges, encouraging players to navigate the intricate web of factors influencing air quality and fuel usage. Likewise, "Fumes and Fortunes" provides an engaging simulation of the financial and environmental trade-offs inherent in kerosene production and consumption, adding a touch of levity to the otherwise weighty discourse on fuel-related dynamics.

In unraveling the intertwined narrative of air pollution in Cape Coral, Florida, and kerosene consumption in the United States, the extant literature sets the stage for a deeper understanding of the serendipitous connections that permeate the domain of atmospheric phenomena and fuel utilization.

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 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 pollution in Cape Coral, Florida and Kerosene used in United States. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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 1989 to 2022

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]]
"The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States"

[[ABSTRACT]]
This research paper delves into the often overlooked and underappreciated bond between air pollution in Cape Coral, Florida, and the utilization of kerosene in the United States. We utilized data from the Environmental Protection Agency and the Energy Information Administration to rigorously investigate this peculiar but enticing relationship. Our analysis uncovered a striking correlation coefficient of 0.7800135 with a p-value less than 0.01, spanning the time period from 1989 to 2022. Our findings shed light on the intricate dynamics between these seemingly disparate phenomena and present compelling evidence of their coalescence. As we unravel the sooty love affair between air pollution and kerosene use, we invite the scholarly community to join us in embracing the delightful complexity of this unexpected connection.

[[INTRODUCTION]]
Air pollution is a matter of grave concern due to its detrimental effects on human health and the environment. The combustion of fuel, whether it be for transportation, heating, or industrial purposes, is a significant contributor to the emission of pollutants into the atmosphere. Among the various sources of fuel, kerosene stands out as a versatile and commonly used product, known for its use in lighting, heating, and cooking. The United States has been a major consumer of kerosene, with its consumption patterns exhibiting intriguing temporal and regional variations.
Cape Coral, Florida, known for its picturesque scenery and vibrant community, has also been a site of interest in the realm of air quality, with studies documenting the presence of particulate matter and other pollutants in its atmospheric milieu. The confluence of these two seemingly dissociated phenomena has piqued the curiosity of researchers seeking to unravel the underlying dynamics at play.
In this study, our aim is to delve into the curious entanglement between air pollution in Cape Coral, Florida, and the consumption of kerosene in the United States. While this association may appear enigmatic at first glance, our efforts to dissect the esoteric connection have resulted in the unearthing of compelling insights. We believe that our findings will not only broaden understanding in the field of environmental science but will also engender a newfound appreciation for the idiosyncratic relationships that underpin the world of atmospheric phenomena and fuel consumption.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Study Design:
A retrospective, nation-wide ecological study design was employed to investigate the relationship between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The study period encompassed data from 1989 to 2022 to capture potential temporal trends and fluctuations in the variables of interest. This protracted timespan was selected to ensure comprehensive coverage of the dynamics under scrutiny and to minimize the risk of overlooking any potentially tantalizing insights.

Data Collection:
The primary sources of data for this inquiry were the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA). These institutions provided an extensive repository of information, allowing for a thorough and meticulous exploration of the interplay between air quality in Cape Coral and kerosene utilization in the United States. The data extraction process involved deftly navigating through the labyrinthine corridors of these online repositories, sifting through a multitude of spreadsheets and reports, and occasionally engaging in a riveting game of digital hide-and-seek with elusive datasets.

Exposure Assessment:
The assessment of kerosene consumption, a key exposure variable in this study, was conducted with due diligence and scholarly finesse. The Energy Information Administration's datasets were scoured, yielding copious amounts of pertinent information on kerosene usage patterns. This exercise entailed parsing through voluminous spreadsheets with unwavering resolve and conducting meticulous calculations to unravel the enigmatic tapestry of kerosene consumption trends across the United States.

Air Pollution Measurement:
To evaluate air pollution in Cape Coral, Florida, air quality data from the EPA's monitoring stations were obtained and meticulously scrutinized. The atmospheric concentrations of various pollutants, including particulate matter, nitrogen oxides, and volatile organic compounds, were examined with an eagle-eyed precision characteristic of seasoned researchers. This rigorous examination involved interpreting intricate charts and graphs, deciphering cryptic data points, and decoding the atmospheric symphony of pollutants with the flair of a virtuoso maestro.

Statistical Analysis:
The quantitative association between air pollution in Cape Coral and kerosene consumption in the United States was elucidated through Pearson correlation, regression analyses, and time series modeling. These analytical methods were chosen for their prowess in unearthing hidden relationships and unveiling the cloaked entanglements between variables. The crux of the statistical analysis was to disentangle the web of complex interactions and quantify the strength and direction of the sooty liaison between air pollution and kerosene usage.

Quality Control:
To ensure the robustness and integrity of the findings, stringent quality control measures were implemented throughout the study. Data validation procedures, peer scrutiny, and meticulous documentation practices were upheld with steadfast determination, safeguarding the veracity of the results against any potential mischief or shenanigans lurking within the datasets.

In summary, the methodological approach encompassed a judicious amalgamation of data wrangling, statistical wizardry, and scholarly perspicacity, thereby affording an incisive examination of the enigmatic connection between air pollution in Cape Coral, Florida, and kerosene consumption in the United States.

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 Air pollution in Cape Coral, Florida and Kerosene used in United States. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
"The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States"

[[ABSTRACT]]
This research paper delves into the often overlooked and underappreciated bond between air pollution in Cape Coral, Florida, and the utilization of kerosene in the United States. We utilized data from the Environmental Protection Agency and the Energy Information Administration to rigorously investigate this peculiar but enticing relationship. Our analysis uncovered a striking correlation coefficient of 0.7800135 with a p-value less than 0.01, spanning the time period from 1989 to 2022. Our findings shed light on the intricate dynamics between these seemingly disparate phenomena and present compelling evidence of their coalescence. As we unravel the sooty love affair between air pollution and kerosene use, we invite the scholarly community to join us in embracing the delightful complexity of this unexpected connection.

[[LITERATURE REVIEW]]
Numerous studies have explored the association between air pollution and various sources of fuel consumption, shedding light on the complex interplay between these phenomena. Smith et al. (2015) demonstrated a positive correlation between particulate matter concentration and diesel fuel combustion in urban areas, highlighting the impact of vehicular emissions on air quality. Doe and Jones (2018) further expanded on this line of inquiry, uncovering a significant relationship between sulfur dioxide levels and coal-fired power plants in suburban regions.
Moving beyond the realm of scientific literature, "The Big Smoke: Our Shared Atmosphere" by Clean Air Council presents a comprehensive overview of air pollution across different geographical locations and the diverse sources contributing to its prevalence. In a similar vein, "Choking the Sky: A Global Perspective on Atmospheric Degradation" by Environmental Watchdog explores the multifaceted dimensions of air pollution and its implications for public health and ecosystems.
In a departure from non-fiction works, the fictional novel "A Whiff of Smoke and Mystery" by Jane Puffington offers a whimsical narrative that intertwines the mysteries of air pollution with a gripping tale of intrigue and romance. Meanwhile, "The Foggy Affair" by Arthur Combustible presents a thrilling account of clandestine kerosene dealings amidst a backdrop of murky atmospheric conditions, drawing readers into the enigmatic world of fuel-based subterfuge.
Drawing inspiration from the world of board games, the classic strategy game "Smoke and Mirrors" offers a playful yet thought-provoking exploration of environmental challenges, encouraging players to navigate the intricate web of factors influencing air quality and fuel usage. Likewise, "Fumes and Fortunes" provides an engaging simulation of the financial and environmental trade-offs inherent in kerosene production and consumption, adding a touch of levity to the otherwise weighty discourse on fuel-related dynamics.
In unraveling the intertwined narrative of air pollution in Cape Coral, Florida, and kerosene consumption in the United States, the extant literature sets the stage for a deeper understanding of the serendipitous connections that permeate the domain of atmospheric phenomena and fuel utilization.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a noteworthy correlation between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The correlation coefficient of 0.7800135 indicates a strong positive relationship between these two seemingly unrelated factors. In other words, as the kerosene use in the United States waxed and waned, so too did the air pollution levels in the picturesque Cape Coral, Florida. If only relationships in the real world were as predictable as this correlation!
Furthermore, the r-squared value of 0.6084210 suggests that approximately 60.8% of the variability in air pollution in Cape Coral can be explained by fluctuations in kerosene consumption in the United States. It seems that the "sooty love affair" between these two variables is quite resilient to external influences, much like a stubborn love-hate relationship that defies conventional explanation.
The p-value of less than 0.01 provides strong evidence against the null hypothesis of no relationship between air pollution and kerosene consumption. It appears that the fumes of kerosene have indeed found their way into the heart of Cape Coral's atmosphere, leaving their unmistakable mark on the air quality of this scenic locale.
In Fig. 1, the scatterplot vividly illustrates the robust and unmistakable correlation between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The data points are tightly clustered around the upward-sloping trend line, emphasizing the inseparable nature of this sooty association. It seems that these two variables are entwined in a dance as old as time, pirouetting in a harmonious yet perplexing rhythm.
These findings not only shed light on the complex dynamics governing atmospheric pollution and fuel utilization but also beckon the scholarly community to appreciate the whimsical intricacies that underlie our understanding of environmental phenomena. As we delve deeper into the sooty love affair between air pollution and kerosene consumption, we are reminded of the vast and often quirky interconnections that weave the fabric of our world.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings provide empirical support for the prior research that has explored the intricate relationship between air pollution and fuel consumption. The correlation coefficient of 0.7800135 reaffirms the notion that the sooty love affair between air pollution in Cape Coral, Florida, and kerosene consumption in the United States is indeed more than a mere flight of fancy. This robust correlation aligns with Smith et al.'s (2015) work on vehicular emissions and particulate matter concentration, illustrating the pervasive influence of fuel combustion on air quality. The remarkable agreement between our results and those of Doe and Jones (2018) regarding sulfur dioxide levels and coal-fired power plants underscores the far-reaching impact of fuel-related activities on atmospheric conditions. The persistent allure of kerosene appears to extend beyond the confines of our study, leaving an indelible imprint on air pollution in Cape Coral and beyond.

Our analysis further accentuates the interconnected nature of these seemingly disparate variables. The r-squared value of 0.6084210 emphasizes the substantial explanatory power of kerosene consumption in elucidating variations in air pollution levels. This echoes the multifaceted dynamics highlighted by Clean Air Council's publication, "The Big Smoke: Our Shared Atmosphere," underscoring the global ramifications of fuel-related contributions to atmospheric degradation. The inextricable link between kerosene use and air pollution mirrors the entwined narrative woven by Arthur Combustible in "The Foggy Affair," where clandestine kerosene dealings unfold against a backdrop of murky atmospheric conditions. The captivating allure of fuel-based subterfuge is captured in our findings, underscoring the whimsical parallels that permeate the intersection of fuel utilization and atmospheric phenomena.

The strong evidence against the null hypothesis of no relationship, as indicated by the p-value of less than 0.01, further solidifies the veracity of our results. The fumes of kerosene have unequivocally permeated the heart of Cape Coral's atmosphere, akin to the gripping narrative of intrigue and romance in Jane Puffington's "A Whiff of Smoke and Mystery." The scatterplot, akin to the playful yet thought-provoking exploration of environmental challenges in the board game "Smoke and Mirrors," vividly illustrates the unmistakable correlation between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. These visual and literary parallels serve as a poignant reminder of the offbeat interconnectedness underlying our understanding of environmental phenomena.

In unraveling the sooty love affair between air pollution and kerosene consumption, our research not only underscores the steadfast correlation between these variables but also beckons the scholarly community to embrace the delightful complexity and serendipitous connections that permeate our understanding of atmospheric and fuel-related dynamics. As we continue to navigate the intricate web of factors influencing air quality and fuel usage, we are reminded that, much like in the fictional novel "A Whiff of Smoke and Mystery," the veils of atmospheric intricacy may yield surprising twists and turns, inviting us to partake in a scholarly journey that is as captivating as it is informative.

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 Air pollution in Cape Coral, Florida and Kerosene used in United States. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
"The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States"

[[ABSTRACT]]
This research paper delves into the often overlooked and underappreciated bond between air pollution in Cape Coral, Florida, and the utilization of kerosene in the United States. We utilized data from the Environmental Protection Agency and the Energy Information Administration to rigorously investigate this peculiar but enticing relationship. Our analysis uncovered a striking correlation coefficient of 0.7800135 with a p-value less than 0.01, spanning the time period from 1989 to 2022. Our findings shed light on the intricate dynamics between these seemingly disparate phenomena and present compelling evidence of their coalescence. As we unravel the sooty love affair between air pollution and kerosene use, we invite the scholarly community to join us in embracing the delightful complexity of this unexpected connection.

[[INTRDUCTION]]
Air pollution is a matter of grave concern due to its detrimental effects on human health and the environment. The combustion of fuel, whether it be for transportation, heating, or industrial purposes, is a significant contributor to the emission of pollutants into the atmosphere. Among the various sources of fuel, kerosene stands out as a versatile and commonly used product, known for its use in lighting, heating, and cooking. The United States has been a major consumer of kerosene, with its consumption patterns exhibiting intriguing temporal and regional variations.
Cape Coral, Florida, known for its picturesque scenery and vibrant community, has also been a site of interest in the realm of air quality, with studies documenting the presence of particulate matter and other pollutants in its atmospheric milieu. The confluence of these two seemingly dissociated phenomena has piqued the curiosity of researchers seeking to unravel the underlying dynamics at play.
In this study, our aim is to delve into the curious entanglement between air pollution in Cape Coral, Florida, and the consumption of kerosene in the United States. While this association may appear enigmatic at first glance, our efforts to dissect the esoteric connection have resulted in the unearthing of compelling insights. We believe that our findings will not only broaden understanding in the field of environmental science but will also engender a newfound appreciation for the idiosyncratic relationships that underpin the world of atmospheric phenomena and fuel consumption.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a noteworthy correlation between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The correlation coefficient of 0.7800135 indicates a strong positive relationship between these two seemingly unrelated factors. In other words, as the kerosene use in the United States waxed and waned, so too did the air pollution levels in the picturesque Cape Coral, Florida. If only relationships in the real world were as predictable as this correlation!
Furthermore, the r-squared value of 0.6084210 suggests that approximately 60.8% of the variability in air pollution in Cape Coral can be explained by fluctuations in kerosene consumption in the United States. It seems that the "sooty love affair" between these two variables is quite resilient to external influences, much like a stubborn love-hate relationship that defies conventional explanation.
The p-value of less than 0.01 provides strong evidence against the null hypothesis of no relationship between air pollution and kerosene consumption. It appears that the fumes of kerosene have indeed found their way into the heart of Cape Coral's atmosphere, leaving their unmistakable mark on the air quality of this scenic locale.
In Fig. 1, the scatterplot vividly illustrates the robust and unmistakable correlation between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The data points are tightly clustered around the upward-sloping trend line, emphasizing the inseparable nature of this sooty association. It seems that these two variables are entwined in a dance as old as time, pirouetting in a harmonious yet perplexing rhythm.
These findings not only shed light on the complex dynamics governing atmospheric pollution and fuel utilization but also beckon the scholarly community to appreciate the whimsical intricacies that underlie our understanding of environmental phenomena. As we delve deeper into the sooty love affair between air pollution and kerosene consumption, we are reminded of the vast and often quirky interconnections that weave the fabric of our world.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has unfurled an enchanting tale of the "sooty love affair" between air pollution in Cape Coral, Florida, and kerosene consumption in the United States. The robust correlation coefficient of 0.7800135 and the r-squared value of 0.6084210 attest to the enduring dance of these two variables, akin to an unpredictable tango between unlikely partners. Our findings speak volumes about the peculiar coalescence of atmospheric pollutants and fuel utilization, painting a picture as vivid and captivating as a Floridian sunset.

The p-value of less than 0.01 acts as a resolute advocate, vehemently arguing against the notion of an absence of connection between air pollution and kerosene consumption. It appears that the fumes of kerosene have indeed left an indelible mark on the atmospheric canvas of Cape Coral, imparting a certain smoky charm to the region's air quality.

As we unravel the complexities of this unanticipated connection, we are reminded that the world of environmental phenomena is not just a series of dry statistical analyses, but a rich tapestry of peculiar interconnections, as delightfully convoluted as a Floridian election result. We invite the scholarly community to partake in this journey of discovery, embracing the whimsical idiosyncrasies that underlie our understanding of atmospheric dynamics and fuel usage.

In light of our findings, we assert with confidence that further research into the sooty love affair between air pollution in Cape Coral, Florida, and kerosene consumption in the United States is unnecessary. The tale has been told, the plot unraveled, and the characters laid bare. It is time now to turn our gaze to other enigmatic relationships, leaving this "sooty love affair" to bask in the spotlight of its newfound revelation.

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 The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Quirks & Quandaries

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 The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States. Respond only with the name of a fictional organization.

ChatGPT:

The Marine Wildlife Conservation and Lemonade Stand Association

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 The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States with an abstract of This research paper delves into the often overlooked and underappreciated bond between air pollution in Cape Coral, Florida, and the utilization of kerosene in the United States. We utilized data from the Environmental Protection Agency and the Energy Information Administration to rigorously investigate this peculiar but enticing relationship. Our analysis uncovered a striking correlation coefficient of 0.7800135 with a p-value less than 0.01, spanning the time period from 1989 to 2022. Our findings shed light on the intricate dynamics between these seemingly disparate phenomena and present compelling evidence of their coalescence. As we unravel the sooty love affair between air pollution and kerosene use, we invite the scholarly community to join us in embracing the delightful complexity of this unexpected connection.

ChatGPT:

air pollution, kerosene consumption, Cape Coral Florida, United States, Environmental Protection Agency, Energy Information Administration, correlation coefficient, p-value, 1989-2022, sooty love affair

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

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Data details

Air pollution in Cape Coral, Florida
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Cape Coral-Fort Myers, FL
Source: Environmental Protection Agency
See what else correlates with Air pollution in Cape Coral, Florida

Kerosene used in United States
Detailed data title: Volume of kerosene used consumed in United States in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in United States

Correlation r = 0.7800135 (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 = 0.6084210 (Coefficient of determination)
This means 60.8% of the change in the one variable (i.e., Kerosene used in United States) is predictable based on the change in the other (i.e., Air pollution in Cape Coral, Florida) over the 34 years from 1989 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.4E-8.^Show 0.0000000538388949702433350000
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 0.78 in 5.4E-6% of random cases. Said differently, if you correlated 18,573,932 random variables^Note You don't actually need 18 million 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 33 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 33 because we have two variables measured over a period of 34 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.

[ 0.6, 0.88 ] 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.

	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Cape Coral, Florida (Bad air quality days)	2.85714	0.84507	0.273973	2.47253	0.821918	2.1978	0.821918	0.819672	1.36986	4.16667	3.0137	3.27869	0.821918	0	2.19178	1.93906	1.0989	1.91781	0.547945	0.819672	0	0.273973	0	0	0	0	0	0.273224	0	0.273973	0.273973	0	0	0
Kerosene used in United States (Million Barrels/Day)	84.1425	42.5671	46.3699	41.4235	49.6466	49.0329	54.063	61.7896	66.0301	78.0986	73.1123	67.3279	72.2904	43.3479	54.6274	64.2951	69.8083	53.6826	32.1391	14.2286	17.5474	19.9292	12.2408	5.27591	5.19713	8.99604	6.38585	8.67062	5.17747	5.41261	6.77142	7.49362	5.94343	5.25028

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.

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([2.85714,0.84507,0.273973,2.47253,0.821918,2.1978,0.821918,0.819672,1.36986,4.16667,3.0137,3.27869,0.821918,0,2.19178,1.93906,1.0989,1.91781,0.547945,0.819672,0,0.273973,0,0,0,0,0,0.273224,0,0.273973,0.273973,0,0,0,])
array_2 = np.array([84.1425,42.5671,46.3699,41.4235,49.6466,49.0329,54.063,61.7896,66.0301,78.0986,73.1123,67.3279,72.2904,43.3479,54.6274,64.2951,69.8083,53.6826,32.1391,14.2286,17.5474,19.9292,12.2408,5.27591,5.19713,8.99604,6.38585,8.67062,5.17747,5.41261,6.77142,7.49362,5.94343,5.25028,])
array_1_name = "Air pollution in Cape Coral, Florida"
array_2_name = "Kerosene used in United States"

# 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 Air pollution in Cape Coral, Florida
Line chart for only Kerosene used in United States
AI-generated correlation image
The spurious research paper: The Sooty Love Affair: Exploring the Relationship Between Air Pollution in Cape Coral, Florida, and Kerosene Consumption in the United States

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Correlation ID: 3350 · Black Variable ID: 20411 · Red Variable ID: 25053


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