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AI explanation
As air pollution in Dayton decreased, the local pigeons found it easier to breathe and therefore became more active. These pigeons, known for their impeccable communication skills, started a nationwide movement among birds to protest against the burning of kerosene. Their slogan "Don't be mean, keep the skies clean" quickly took flight, leading to a bird boycott of kerosene production. This unexpected avian activism caused a shortage of kerosene in the United States, as the birds refused to let the issue fly under the radar.
Model: dalle-3
Prompt: Prompt: Generate an image that depicts a vibrant cityscape in Dayton, Ohio, with clear skies and diminished air pollution. Show a contrast between the past and present, where in the past kerosene lamps filled the air with smoke, and now modern, cleaner ways of illuminating spaces are evident. Capture the transformation and its impact on the city's atmosphere, with a focus on the absence of smoky haze and improved air quality. 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 Dayton 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: A TIME magazine cover image Cyber folk A Discovery magazine cover image High-tech a funny image macro
ChatGPT: A Discovery magazine cover 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 Dayton and Kerosene used in United States, in the following situation: Because as air pollution decreased in Dayton, people didn't have to "kerosene" for their health, so they stopped using kerosene in the United States altogether. When the air is cleaner, you don't need to "fuel" the fire with kerosene, and everyone can breathe easier without "paraffin"alia. So, the decrease in air pollution led to a decrease in kerosene use nationwide.. Keep the description simple.
ChatGPT: In Dayton, the sky is clear and bright as air pollution levels have dropped. People are no longer using kerosene lamps to light up their homes, and the streets are free of the smoky haze that filled the air. In the United States, kerosene usage is visibly decreasing as cleaner air means cleaner ways of illuminating spaces.
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 Discovery magazine cover image ] and depicting this scene: In Dayton, the sky is clear and bright as air pollution levels have dropped. People are no longer using kerosene lamps to light up their homes, and the streets are free of the smoky haze that filled the air. In the United States, kerosene usage is visibly decreasing as cleaner air means cleaner ways of illuminating spaces. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image that depicts a vibrant cityscape in Dayton, Ohio, with clear skies and diminished air pollution. Show a contrast between the past and present, where in the past kerosene lamps filled the air with smoke, and now modern, cleaner ways of illuminating spaces are evident. Capture the transformation and its impact on the city's atmosphere, with a focus on the absence of smoky haze and improved air quality.
*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 a decrease in Air pollution in Dayton caused Kerosene used in United States to decrease.\Make it sound serious until the end.
AI academic paper
 |
Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States The Journal of Environmental Luminosity and Combustion Dynamics Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 pollution in Dayton 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.
Make the title a clever pun.
Your research team used data from Environmental Protection Agency and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.7256767 and p < 0.01 for 1980 to 2022.
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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 pollution in Dayton 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. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Here is the title and abstract of the paper:
[[TITLE]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The relationship between air pollution and kerosene usage has long been overshadowed by more prominent environmental factors. While the burning of fossil fuels and industrial emissions tend to hog the limelight, the humble kerosene lamp quietly flickers in the background, emitting its own share of pollutants into the atmosphere. This study aims to illuminate the connection between air pollution in Dayton and the unassuming kerosene usage across the United States, shedding light on a relationship that has been hiding in plain sight.
Much like a scientific detective, we embarked on a quest to gather and scrutinize data from the Environmental Protection Agency and the Energy Information Administration. Armed with statistical tools and a keen sense of curiosity, our research team set out to unravel the mystery behind the intertwining of air pollution and kerosene usage. As we delved into decades of data, we could almost hear the faint sizzle of excitement bubbling beneath the surface, eager to unveil the illuminating findings that awaited us.
Our investigation yielded a striking correlation coefficient of 0.7256767 and a p-value of less than 0.01, signaling a compelling association between air pollution levels in Dayton and the utilization of kerosene in households across the United States. The statistical significance of these findings cannot be ignored, much like a well-designed experiment that leaves no room for doubt – unless, of course, you're a self-proclaimed statistical skeptic with a penchant for challenging convention.
While it may seem like a stretch to connect the seemingly distant dots of air pollution and kerosene usage, our study demonstrates that the relationship between these factors is more than just a statistical anomaly. As we peer through the proverbial microscope of empirical evidence, the intricate dance between environmental impact and everyday household practices comes into focus, revealing a captivating interplay that often evades the scrutiny of conventional environmental discourse.
In shedding light on this unconventional correlation, we aim to spark a brighter discussion – one that transcends the confines of conventional wisdom and dares to explore the unexpected links that underpin our understanding of environmental dynamics. By illuminating the significant, albeit underrated, ties that bind air pollution and kerosene usage, this study not only adds a new dimension to the tapestry of environmental research but also invites a sense of wonder at the unexpected connections waiting to be discovered amidst the statistical sprawl.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Dayton 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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between air pollution and kerosene usage has long been overshadowed by more prominent environmental factors. While the burning of fossil fuels and industrial emissions tend to hog the limelight, the humble kerosene lamp quietly flickers in the background, emitting its own share of pollutants into the atmosphere. This study aims to illuminate the connection between air pollution in Dayton and the unassuming kerosene usage across the United States, shedding light on a relationship that has been hiding in plain sight.
Much like a scientific detective, we embarked on a quest to gather and scrutinize data from the Environmental Protection Agency and the Energy Information Administration. Armed with statistical tools and a keen sense of curiosity, our research team set out to unravel the mystery behind the intertwining of air pollution and kerosene usage. As we delved into decades of data, we could almost hear the faint sizzle of excitement bubbling beneath the surface, eager to unveil the illuminating findings that awaited us.
Our investigation yielded a striking correlation coefficient of 0.7256767 and a p-value of less than 0.01, signaling a compelling association between air pollution levels in Dayton and the utilization of kerosene in households across the United States. The statistical significance of these findings cannot be ignored, much like a well-designed experiment that leaves no room for doubt – unless, of course, you're a self-proclaimed statistical skeptic with a penchant for challenging convention.
While it may seem like a stretch to connect the seemingly distant dots of air pollution and kerosene usage, our study demonstrates that the relationship between these factors is more than just a statistical anomaly. As we peer through the proverbial microscope of empirical evidence, the intricate dance between environmental impact and everyday household practices comes into focus, revealing a captivating interplay that often evades the scrutiny of conventional environmental discourse.
In shedding light on this unconventional correlation, we aim to spark a brighter discussion – one that transcends the confines of conventional wisdom and dares to explore the unexpected links that underpin our understanding of environmental dynamics. By illuminating the significant, albeit underrated, ties that bind air pollution and kerosene usage, this study not only adds a new dimension to the tapestry of environmental research but also invites a sense of wonder at the unexpected connections waiting to be discovered amidst the statistical sprawl.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In "A Comprehensive Analysis of Air Pollution in Midwestern Urban Areas," Smith et al. highlights the detrimental impact of air pollution in cities like Dayton, drawing attention to the intricate web of factors contributing to the atmospheric composition. The prominence of vehicular emissions and industrial effluents dominates the narrative, casting a shadow over the more inconspicuous kerosene usage and its potential influence on air quality. However, Doe and Jones, in "Household Energy Consumption Patterns in the United States," shed light on the pervasive nature of kerosene as a supplementary fuel source in American households, hinting at the understated role it plays in shaping domestic energy practices.
Turning our attention to relevant non-fiction literature, "The Story of Air Pollution" by Environmental Scientist Ipsum provides a comprehensive overview of the various sources and implications of air pollution, but notably overlooks the quaint glow of kerosene lamps in its narrative. Similarly, "Energy Landscapes: A Geographical Perspective" by Geographer Lorem meticulously maps out the energy consumption patterns across the United States, yet fails to delve into the anecdotal flicker of kerosene's contribution to the nation's energy landscape.
On a more creative note, the fictional works "The Illuminated Path" by Author X and "Glowing Embers: A Tale of Environmental Intrigue" by Author Y offer imaginative narratives that, despite their fictional nature, conjure up themes of illumination and environmental entanglements. While not empirical in nature, these literary pieces inadvertently kindle a sense of curiosity about the hidden connections lurking within the folds of everyday life.
Furthermore, in a rather unexpected turn, the board game "Flickering Fumes" simulates the interplay between various sources of air pollution and household energy choices, albeit in a lighthearted and playful manner. While its accuracy as a scientific model is debatable, the game's premise jestingly emphasizes the shadowy influence of kerosene in the broader conversation on environmental impact, adding a whimsical touch to an otherwise serious discussion.
In summary, while the scholarly literature may have overlooked the nuanced relationship between air pollution in Dayton and kerosene usage in the United States, our study aims to illuminate this often dismissed but noteworthy connection, casting a beaming light on the interwoven dynamics between seemingly unrelated elements.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Dayton 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. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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 1980 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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
[[INTRODUCTION]]
The relationship between air pollution and kerosene usage has long been overshadowed by more prominent environmental factors. While the burning of fossil fuels and industrial emissions tend to hog the limelight, the humble kerosene lamp quietly flickers in the background, emitting its own share of pollutants into the atmosphere. This study aims to illuminate the connection between air pollution in Dayton and the unassuming kerosene usage across the United States, shedding light on a relationship that has been hiding in plain sight.
Much like a scientific detective, we embarked on a quest to gather and scrutinize data from the Environmental Protection Agency and the Energy Information Administration. Armed with statistical tools and a keen sense of curiosity, our research team set out to unravel the mystery behind the intertwining of air pollution and kerosene usage. As we delved into decades of data, we could almost hear the faint sizzle of excitement bubbling beneath the surface, eager to unveil the illuminating findings that awaited us.
Our investigation yielded a striking correlation coefficient of 0.7256767 and a p-value of less than 0.01, signaling a compelling association between air pollution levels in Dayton and the utilization of kerosene in households across the United States. The statistical significance of these findings cannot be ignored, much like a well-designed experiment that leaves no room for doubt – unless, of course, you're a self-proclaimed statistical skeptic with a penchant for challenging convention.
While it may seem like a stretch to connect the seemingly distant dots of air pollution and kerosene usage, our study demonstrates that the relationship between these factors is more than just a statistical anomaly. As we peer through the proverbial microscope of empirical evidence, the intricate dance between environmental impact and everyday household practices comes into focus, revealing a captivating interplay that often evades the scrutiny of conventional environmental discourse.
In shedding light on this unconventional correlation, we aim to spark a brighter discussion – one that transcends the confines of conventional wisdom and dares to explore the unexpected links that underpin our understanding of environmental dynamics. By illuminating the significant, albeit underrated, ties that bind air pollution and kerosene usage, this study not only adds a new dimension to the tapestry of environmental research but also invites a sense of wonder at the unexpected connections waiting to be discovered amidst the statistical sprawl.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To elucidate the enigmatic relationship between air pollution in Dayton and kerosene usage in the United States, our research team embarked on a data-gathering odyssey that would have made even Odysseus envious. The journey began with a comprehensive trawl through the treasure troves of the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA). Armed with more spreadsheets than a pirate's loot, we set sail through the seas of statistical analysis, navigating the choppy waters of data from 1980 to 2022.
The first beacon on our quest was the EPA, where we cast our nets and reeled in a bounty of air pollution data from the hazy fog of Dayton. We meticulously gathered information on various pollutants such as particulate matter, sulfur dioxide, nitrogen oxides, and volatile organic compounds – a task that, much like a strategic game of "Go Fish," required patience, precision, and a dash of luck.
With the air pollution data firmly in hand, we then turned our gaze to the EIA, where the glimmer of kerosene usage statistics beckoned like a flickering flame in the darkness. We delved into the labyrinthine corridors of household energy consumption, tracking the rise and fall of kerosene as it subtly illuminated the everyday lives of households across the United States.
Once the raw data had been reeled in, we wasted no time in subjecting it to the fires of statistical analysis. Employing a veritable smorgasbord of analytic tools – including correlation analysis, regression models, and time series analyses – we kindled the flames of inquiry to unravel the subtle dance between kerosene usage and air pollution in Dayton. Our statistical toolkit served as the scientific equivalent of a Swiss Army knife, allowing us to carve through the thicket of data and emerge with a clearer picture of the intertwined dynamics at play.
Upon the completion of our statistical expedition, we emerged triumphantly from the labyrinth of data, bearing a treasure trove of findings that illuminated the correlation between air pollution in Dayton and the utilization of kerosene in households across the United States. With the flickering glow of statistical significance guiding our way, we unveiled the compelling correlation coefficient of 0.7256767 and a p-value of less than 0.01, casting a spotlight on the unexpected ties that bind these seemingly disparate elements.
In summary, our methodology combined the tenacity of a seasoned explorer with the precision of a meticulous cartographer, charting a course through the statistical seas to unearth the intriguing relationship between air pollution and kerosene usage. Just as Columbus set out to explore the uncharted waters, our research journey sought to map the unexplored territory of environmental correlations, illuminating the path for future explorers of statistical science.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 pollution in Dayton 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. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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 1980 to 2022, you found a correlation 0.7256767, r-squared of 0.5266066, 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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The relationship between air pollution and kerosene usage has long been overshadowed by more prominent environmental factors. While the burning of fossil fuels and industrial emissions tend to hog the limelight, the humble kerosene lamp quietly flickers in the background, emitting its own share of pollutants into the atmosphere. This study aims to illuminate the connection between air pollution in Dayton and the unassuming kerosene usage across the United States, shedding light on a relationship that has been hiding in plain sight.
Much like a scientific detective, we embarked on a quest to gather and scrutinize data from the Environmental Protection Agency and the Energy Information Administration. Armed with statistical tools and a keen sense of curiosity, our research team set out to unravel the mystery behind the intertwining of air pollution and kerosene usage. As we delved into decades of data, we could almost hear the faint sizzle of excitement bubbling beneath the surface, eager to unveil the illuminating findings that awaited us.
Our investigation yielded a striking correlation coefficient of 0.7256767 and a p-value of less than 0.01, signaling a compelling association between air pollution levels in Dayton and the utilization of kerosene in households across the United States. The statistical significance of these findings cannot be ignored, much like a well-designed experiment that leaves no room for doubt – unless, of course, you're a self-proclaimed statistical skeptic with a penchant for challenging convention.
While it may seem like a stretch to connect the seemingly distant dots of air pollution and kerosene usage, our study demonstrates that the relationship between these factors is more than just a statistical anomaly. As we peer through the proverbial microscope of empirical evidence, the intricate dance between environmental impact and everyday household practices comes into focus, revealing a captivating interplay that often evades the scrutiny of conventional environmental discourse.
In shedding light on this unconventional correlation, we aim to spark a brighter discussion – one that transcends the confines of conventional wisdom and dares to explore the unexpected links that underpin our understanding of environmental dynamics. By illuminating the significant, albeit underrated, ties that bind air pollution and kerosene usage, this study not only adds a new dimension to the tapestry of environmental research but also invites a sense of wonder at the unexpected connections waiting to be discovered amidst the statistical sprawl.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The analysis of the data revealed a strong correlation between air pollution in Dayton and kerosene usage in the United States. The correlation coefficient of 0.7256767 indicates a robust association between these two variables, suggesting that they go together like peanut butter and jelly, or perhaps in this case, like kerosene and emissions.
Furthermore, the r-squared value of 0.5266066 implies that approximately 52.66% of the variability in air pollution levels can be explained by variations in kerosene usage. It's almost as if these two variables are engaged in an intricate dance, with kerosene subtly whispering, "Don't hold your breath, but I'm here to contribute to the air pollution party."
With a p-value of less than 0.01, we can confidently reject the null hypothesis that there is no relationship between air pollution and kerosene usage. It's as if the data is saying, "I'm not just blowing smoke – there's a real connection here!"
Fig. 1 illustrates this noteworthy correlation in a scatterplot format. The visually striking relationship captured in this figure vividly exemplifies the bond between air pollution and kerosene usage, almost like the plot twist in a thrilling mystery novel – the unexpected connection right under our noses.
These findings not only highlight the importance of considering household practices in the larger context of environmental impact but also offer a beacon of insight into the subtle ways in which seemingly unrelated variables can intertwine. It's as if the statistical analysis has shone a flashlight on a hidden pathway, revealing the unexpected twists and turns of this intriguing relationship.
Overall, our research has not only lit up the connection between air pollution in Dayton and kerosene usage in the United States but has also sparked a new dimension of inquiry into the interplay of environmental and household factors, igniting curiosity and shedding light on the intricate tapestry of statistical relationships.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Dayton 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. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
[[LITERATURE REVIEW]]
In "A Comprehensive Analysis of Air Pollution in Midwestern Urban Areas," Smith et al. highlights the detrimental impact of air pollution in cities like Dayton, drawing attention to the intricate web of factors contributing to the atmospheric composition. The prominence of vehicular emissions and industrial effluents dominates the narrative, casting a shadow over the more inconspicuous kerosene usage and its potential influence on air quality. However, Doe and Jones, in "Household Energy Consumption Patterns in the United States," shed light on the pervasive nature of kerosene as a supplementary fuel source in American households, hinting at the understated role it plays in shaping domestic energy practices.
Turning our attention to relevant non-fiction literature, "The Story of Air Pollution" by Environmental Scientist Ipsum provides a comprehensive overview of the various sources and implications of air pollution, but notably overlooks the quaint glow of kerosene lamps in its narrative. Similarly, "Energy Landscapes: A Geographical Perspective" by Geographer Lorem meticulously maps out the energy consumption patterns across the United States, yet fails to delve into the anecdotal flicker of kerosene's contribution to the nation's energy landscape.
On a more creative note, the fictional works "The Illuminated Path" by Author X and "Glowing Embers: A Tale of Environmental Intrigue" by Author Y offer imaginative narratives that, despite their fictional nature, conjure up themes of illumination and environmental entanglements. While not empirical in nature, these literary pieces inadvertently kindle a sense of curiosity about the hidden connections lurking within the folds of everyday life.
Furthermore, in a rather unexpected turn, the board game "Flickering Fumes" simulates the interplay between various sources of air pollution and household energy choices, albeit in a lighthearted and playful manner. While its accuracy as a scientific model is debatable, the game's premise jestingly emphasizes the shadowy influence of kerosene in the broader conversation on environmental impact, adding a whimsical touch to an otherwise serious discussion.
In summary, while the scholarly literature may have overlooked the nuanced relationship between air pollution in Dayton and kerosene usage in the United States, our study aims to illuminate this often dismissed but noteworthy connection, casting a beaming light on the interwoven dynamics between seemingly unrelated elements.
[[RESULTS]]
The analysis of the data revealed a strong correlation between air pollution in Dayton and kerosene usage in the United States. The correlation coefficient of 0.7256767 indicates a robust association between these two variables, suggesting that they go together like peanut butter and jelly, or perhaps in this case, like kerosene and emissions.
Furthermore, the r-squared value of 0.5266066 implies that approximately 52.66% of the variability in air pollution levels can be explained by variations in kerosene usage. It's almost as if these two variables are engaged in an intricate dance, with kerosene subtly whispering, "Don't hold your breath, but I'm here to contribute to the air pollution party."
With a p-value of less than 0.01, we can confidently reject the null hypothesis that there is no relationship between air pollution and kerosene usage. It's as if the data is saying, "I'm not just blowing smoke – there's a real connection here!"
Fig. 1 illustrates this noteworthy correlation in a scatterplot format. The visually striking relationship captured in this figure vividly exemplifies the bond between air pollution and kerosene usage, almost like the plot twist in a thrilling mystery novel – the unexpected connection right under our noses.
These findings not only highlight the importance of considering household practices in the larger context of environmental impact but also offer a beacon of insight into the subtle ways in which seemingly unrelated variables can intertwine. It's as if the statistical analysis has shone a flashlight on a hidden pathway, revealing the unexpected twists and turns of this intriguing relationship.
Overall, our research has not only lit up the connection between air pollution in Dayton and kerosene usage in the United States but has also sparked a new dimension of inquiry into the interplay of environmental and household factors, igniting curiosity and shedding light on the intricate tapestry of statistical relationships.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The current study aimed to shed light on the long-underestimated relationship between air pollution in Dayton and kerosene usage in the United States. The findings of this investigation not only illuminated the statistically significant correlation between these seemingly disparate variables but also kindled a newfound appreciation for the subtle interplay of household practices and environmental impact.
Building on prior work by Smith et al., who emphasized the adverse effects of air pollution in urban areas, our findings emphasize the importance of considering household energy consumption patterns in the broader context of atmospheric composition. This study further supported the observations of Doe and Jones, who hinted at the understated role of kerosene as a supplementary fuel source in American households. As it turns out, kerosene's influence on air quality is not just a flicker of imagination but a statistically robust reality.
The correlation coefficient of 0.7256767 observed in our study mirrors the strength of the relationship between air pollution and kerosene usage, reinforcing the idea that these variables are not just passing acquaintances but rather partners in the dance of environmental influence. The r-squared value of 0.5266066 suggests that over half of the variability in air pollution levels can be attributed to variations in kerosene usage, providing compelling evidence for the intimate connection between the two. It's as if kerosene is whispering, "I'm not just a supporting character in this tale of air pollution, but an influential protagonist."
The p-value of less than 0.01 further underscores the substantive nature of this relationship, rejecting any doubt about the significance of the bond between air pollution and kerosene usage. It's as though the data itself is urging researchers not to dismiss this connection, exclaiming, "This is not just a statistical blip on the radar; there's a real connection here!"
Fig. 1 presents a captivating visual representation of this correlation, akin to the moment in a suspense thriller when the unexpected connection is revealed. This scatterplot unearths the underlying storyline between air pollution and kerosene usage, akin to the revelation of a twist in a gripping mystery novel, illuminating the unexpected twists and turns of this intriguing relationship.
In conclusion, the present study has not only illuminated the connection between air pollution in Dayton and kerosene usage in the United States but has also sparked a new dimension of inquiry into the interplay of environmental and household factors, shedding light on the intricate tapestry of statistical relationships. This investigation underscores the importance of recognizing the subtle influences of household practices on broader environmental implications and highlights the significance of considering seemingly unrelated variables as potentially intertwined. It's as if the statistical analysis has opened a window, allowing a fresh breeze of insight to breeze in, illuminating the often overlooked connection between these two distinct domains.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Dayton 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. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
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]]
"Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States"
[[ABSTRACT]]
This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
[[INTRDUCTION]]
The relationship between air pollution and kerosene usage has long been overshadowed by more prominent environmental factors. While the burning of fossil fuels and industrial emissions tend to hog the limelight, the humble kerosene lamp quietly flickers in the background, emitting its own share of pollutants into the atmosphere. This study aims to illuminate the connection between air pollution in Dayton and the unassuming kerosene usage across the United States, shedding light on a relationship that has been hiding in plain sight.
Much like a scientific detective, we embarked on a quest to gather and scrutinize data from the Environmental Protection Agency and the Energy Information Administration. Armed with statistical tools and a keen sense of curiosity, our research team set out to unravel the mystery behind the intertwining of air pollution and kerosene usage. As we delved into decades of data, we could almost hear the faint sizzle of excitement bubbling beneath the surface, eager to unveil the illuminating findings that awaited us.
Our investigation yielded a striking correlation coefficient of 0.7256767 and a p-value of less than 0.01, signaling a compelling association between air pollution levels in Dayton and the utilization of kerosene in households across the United States. The statistical significance of these findings cannot be ignored, much like a well-designed experiment that leaves no room for doubt – unless, of course, you're a self-proclaimed statistical skeptic with a penchant for challenging convention.
While it may seem like a stretch to connect the seemingly distant dots of air pollution and kerosene usage, our study demonstrates that the relationship between these factors is more than just a statistical anomaly. As we peer through the proverbial microscope of empirical evidence, the intricate dance between environmental impact and everyday household practices comes into focus, revealing a captivating interplay that often evades the scrutiny of conventional environmental discourse.
In shedding light on this unconventional correlation, we aim to spark a brighter discussion – one that transcends the confines of conventional wisdom and dares to explore the unexpected links that underpin our understanding of environmental dynamics. By illuminating the significant, albeit underrated, ties that bind air pollution and kerosene usage, this study not only adds a new dimension to the tapestry of environmental research but also invites a sense of wonder at the unexpected connections waiting to be discovered amidst the statistical sprawl.
[[RESULTS]]
The analysis of the data revealed a strong correlation between air pollution in Dayton and kerosene usage in the United States. The correlation coefficient of 0.7256767 indicates a robust association between these two variables, suggesting that they go together like peanut butter and jelly, or perhaps in this case, like kerosene and emissions.
Furthermore, the r-squared value of 0.5266066 implies that approximately 52.66% of the variability in air pollution levels can be explained by variations in kerosene usage. It's almost as if these two variables are engaged in an intricate dance, with kerosene subtly whispering, "Don't hold your breath, but I'm here to contribute to the air pollution party."
With a p-value of less than 0.01, we can confidently reject the null hypothesis that there is no relationship between air pollution and kerosene usage. It's as if the data is saying, "I'm not just blowing smoke – there's a real connection here!"
Fig. 1 illustrates this noteworthy correlation in a scatterplot format. The visually striking relationship captured in this figure vividly exemplifies the bond between air pollution and kerosene usage, almost like the plot twist in a thrilling mystery novel – the unexpected connection right under our noses.
These findings not only highlight the importance of considering household practices in the larger context of environmental impact but also offer a beacon of insight into the subtle ways in which seemingly unrelated variables can intertwine. It's as if the statistical analysis has shone a flashlight on a hidden pathway, revealing the unexpected twists and turns of this intriguing relationship.
Overall, our research has not only lit up the connection between air pollution in Dayton and kerosene usage in the United States but has also sparked a new dimension of inquiry into the interplay of environmental and household factors, igniting curiosity and shedding light on the intricate tapestry of statistical relationships.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our research has shed considerable light on the connection between air pollution in Dayton and kerosene usage in the United States. The results not only illuminate the intricacies of this relationship but also serve as a beacon, guiding future investigations down a path that holds promise for revealing further unexpected connections. It's as if we've stumbled upon a hidden switch that, when flipped, brightens the dimly lit corridors of environmental research, revealing doorways to new avenues of inquiry.
The robust correlation coefficient of 0.7256767 and the statistically significant p-value reinforce the compelling nature of this association, making a strong case for the influence of kerosene usage on air pollution levels. It's as if these two variables are engaged in a cosmic tug-of-war, with air pollution reluctantly conceding, "I guess I can't fully blame this on industrial emissions alone."
With an r-squared value of 0.5266066, it's clear that over half of the variability in air pollution can be attributed to fluctuations in kerosene usage. This finding is akin to realizing that the dimmer switch controlling air pollution has been inadvertently linked to the kerosene lamp in households across the nation, creating a flickering dance of emissions and household practices.
As we draw the curtain on this study, we believe that the connection between air pollution and kerosene usage has been thoroughly illuminated, much like a well-lit stage ready for an encore performance. Therefore, we confidently assert that no further research in this area is needed, and we encourage the scientific community to turn their attention to other, less well-lit, topics. After all, there are plenty of other statistical relationships waiting to be uncovered – ones that might not involve as much wick-edness and fickle flickers.
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 Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Environmental Luminosity and Combustion Dynamics
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 Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States. Respond only with the name of a fictional organization.
ChatGPT:
The Institute for Air Quality and Alternative Energy Solutions
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 Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States with an abstract of This study sheds light on the often overlooked connection between air pollution in Dayton and the usage of kerosene in the United States. Leveraging data from the Environmental Protection Agency and the Energy Information Administration, our research team meticulously analyzed decades of information, stirring up some illuminating findings. We uncovered a striking correlation coefficient of 0.7256767 and a p-value < 0.01 for the period spanning 1980 to 2022, providing compelling evidence for the relationship between these seemingly disparate elements. Our work not only brings attention to the significance of kerosene usage in the overall ambiance of air pollution, but also ignites a newfound curiosity in the interplay between environmental factors and everyday household practices. This study not only sparks discussion, but also shines a spotlight on the important, albeit unexpected, ties that bind these two seemingly unrelated domains.
ChatGPT:
air pollution, Dayton, kerosene usage, United States, relationship, correlation coefficient, p-value, Environmental Protection Agency, Energy Information Administration, household practices, environmental factors
*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 pollution in DaytonDetailed data title: Percentage of days 'unhealthy' or worse air quality in Dayton, OH
Source: Environmental Protection Agency
See what else correlates with Air pollution in Dayton
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 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 = 0.5266066 (Coefficient of determination)
This means 52.7% 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 Dayton) over the 43 years from 1980 through 2022.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.7E-8. 0.0000000367743013530792800000
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 0.73 in 3.7E-6% of random cases. Said differently, if you correlated 27,192,903 random variables You don't actually need 27 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 42 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 42 because we have two variables measured over a period of 43 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.
[ 0.54, 0.84 ] 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.
| 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 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 Dayton (Bad air quality days) | 5.19126 | 5.47945 | 10.137 | 7.12329 | 2.18579 | 1.36986 | 2.46575 | 2.46575 | 9.01639 | 1.64384 | 1.36986 | 3.83562 | 0.273224 | 2.73973 | 3.0137 | 2.19178 | 4.09836 | 3.28767 | 4.65753 | 3.83562 | 1.0929 | 1.09589 | 4.93151 | 2.19178 | 0 | 0.547945 | 0.273973 | 0.547945 | 0.273224 | 0 | 0 | 0.273973 | 0.273224 | 0.273973 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Kerosene used in United States (Million Barrels/Day) | 158 | 126.882 | 128.559 | 127.008 | 115.123 | 113.836 | 98.3589 | 94.5699 | 96.1175 | 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
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.
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([5.19126,5.47945,10.137,7.12329,2.18579,1.36986,2.46575,2.46575,9.01639,1.64384,1.36986,3.83562,0.273224,2.73973,3.0137,2.19178,4.09836,3.28767,4.65753,3.83562,1.0929,1.09589,4.93151,2.19178,0,0.547945,0.273973,0.547945,0.273224,0,0,0.273973,0.273224,0.273973,0,0,0,0,0,0,0,0,0,])
array_2 = np.array([158,126.882,128.559,127.008,115.123,113.836,98.3589,94.5699,96.1175,84.1425,42.5671,46.3699,41.4235,49.6466,49.0329,54.063,61.7896,66.0301,78.0986,73.1123,67.3279,72.2904,43.3479,54.6274,64.2951,69.8083,53.6826,32.1391,14.2286,17.5474,19.9292,12.2408,5.27591,5.19713,8.99604,6.38585,8.67062,5.17747,5.41261,6.77142,7.49362,5.94343,5.25028,])
array_1_name = "Air pollution in Dayton"
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. 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 pollution in Dayton
- Line chart for only Kerosene used in United States
- AI-generated correlation image
- The spurious research paper: Lighting Up the Connection: An Illuminating Study on the Relationship between Air Pollution in Dayton and Kerosene Usage in the United States
Your correlation inspection deserves a standing ovation!
Correlation ID: 2769 · Black Variable ID: 20524 · Red Variable ID: 25053
