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AI explanation
As the air pollution in Washington, D.C. decreased, it created a ripple effect on global air currents. These redirected air currents just happened to carry away kerosene molecules from Peru, leading to a decrease in kerosene used in the country. It's like a real-life game of environmental chess, with D.C. making the first move and Peru getting kerosene'd in the end!
Model: dalle-3
Prompt: Image Generation Prompt: Create an image of Washington, D.C., with clear skies and a departing jet stream, contrasting with the smog particles struggling to catch the ride. Show a hint of frustration and motion in the particles. Additionally, depict a cozy Peruvian village where families are gleefully switching off kerosene-fueled night lights. The grumbling smog particles, left behind, should be subtly contrasted with the joyous atmosphere of the village. Capture the contrast in the scenes in a clear and visually impactful way. 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 Washington, D.C. and Kerosene used in Peru. 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: Cutsy graphics A TIME magazine cover image Psychedelic Psychedelic A TIME magazine cover image
ChatGPT: A TIME 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 Washington, D.C. and Kerosene used in Peru, in the following situation: Because as air pollution decreased in Washington, D.C., the smog particles couldn't hitch a ride on the jet stream to Peru, forcing them to carpool instead. But since the smog particles were terrible backseat drivers, the other particles voted to leave them on the side of the road, reducing the need for kerosene to fuel their night light.. Keep the description simple.
ChatGPT: In Washington, D.C., the skies clear as smog particles struggle to catch a ride on the departing jet stream. Meanwhile, in Peru, families happily switch off their kerosene-fueled night lights, as the smog particles, now left behind, grumble by the roadside.
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 TIME magazine cover image ] and depicting this scene: In Washington, D.C., the skies clear as smog particles struggle to catch a ride on the departing jet stream. Meanwhile, in Peru, families happily switch off their kerosene-fueled night lights, as the smog particles, now left behind, grumble by the roadside. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Image Generation Prompt: Create an image of Washington, D.C., with clear skies and a departing jet stream, contrasting with the smog particles struggling to catch the ride. Show a hint of frustration and motion in the particles. Additionally, depict a cozy Peruvian village where families are gleefully switching off kerosene-fueled night lights. The grumbling smog particles, left behind, should be subtly contrasted with the joyous atmosphere of the village. Capture the contrast in the scenes in a clear and visually impactful way.
*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 Washington, D.C. caused Kerosene used in Peru to decrease.
AI academic paper
 |
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C. The International Journal of Ecological Economics and Political Ecology Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Make the title an alliteration.
Your research team used data from Environmental Protection Agency and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.8487147 and p < 0.01 for 1980 to 2021.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the introduction section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the introduction section of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Here is the title and abstract of the paper:
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
Dimly lit households in the picturesque Peruvian countryside may seem worlds away from the bustling political precincts of Washington, D.C., but our research aims to illuminate an unexpected connection between these two disparate settings. While one may think that kerosene lamps and political maneuverings have nothing in common, our findings suggest otherwise.
As we delve into this unlikely pairing, it's worth noting that the correlation between kerosene consumption in Peru and air pollution in the esteemed city of Washington, D.C. was not initially on our radar. However, serendipity often plays a role in scientific inquiry, leading us down unforeseen paths that ultimately reveal thought-provoking connections.
The juxtaposition of rural kerosene use and urban air pollution may seem comically incongruous at first glance, eliciting a wry chuckle from even the most stoic researcher. Yet, as we peered beneath the surface, our chuckles turned into gasps of surprise as the data unveiled a compelling relationship that transcended geographical and cultural divides.
Moving beyond the superficial disparities, our endeavor sheds light on the intricate interplay between seemingly isolated phenomena—a scholarly pursuit that is as intellectually stimulating as it is unexpected. As we embark on this intellectual adventure, one cannot help but marvel at the delightful complexity of our natural and societal systems and their sometimes whimsical interconnections.
We invite readers to join us on this enthralling journey, where the seemingly mundane act of kerosene consumption intertwines with the sophisticated dance of political decision-making, defying conventional scientific silos and inviting a fresh perspective on the synergies inherent in our global ecosystem. Let us venture forth to unravel this curious tapestry that showcases both the intricacies and quirks of our world.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a couple popular internet memes that are related to one of the topics.
Here is the title and abstract of the paper:
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Dimly lit households in the picturesque Peruvian countryside may seem worlds away from the bustling political precincts of Washington, D.C., but our research aims to illuminate an unexpected connection between these two disparate settings. While one may think that kerosene lamps and political maneuverings have nothing in common, our findings suggest otherwise.
As we delve into this unlikely pairing, it's worth noting that the correlation between kerosene consumption in Peru and air pollution in the esteemed city of Washington, D.C. was not initially on our radar. However, serendipity often plays a role in scientific inquiry, leading us down unforeseen paths that ultimately reveal thought-provoking connections.
The juxtaposition of rural kerosene use and urban air pollution may seem comically incongruous at first glance, eliciting a wry chuckle from even the most stoic researcher. Yet, as we peered beneath the surface, our chuckles turned into gasps of surprise as the data unveiled a compelling relationship that transcended geographical and cultural divides.
Moving beyond the superficial disparities, our endeavor sheds light on the intricate interplay between seemingly isolated phenomena—a scholarly pursuit that is as intellectually stimulating as it is unexpected. As we embark on this intellectual adventure, one cannot help but marvel at the delightful complexity of our natural and societal systems and their sometimes whimsical interconnections.
We invite readers to join us on this enthralling journey, where the seemingly mundane act of kerosene consumption intertwines with the sophisticated dance of political decision-making, defying conventional scientific silos and inviting a fresh perspective on the synergies inherent in our global ecosystem. Let us venture forth to unravel this curious tapestry that showcases both the intricacies and quirks of our world.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In their seminal work, Smith and Doe (2005) meticulously dissected the environmental consequences of kerosene consumption in rural areas, providing a comprehensive framework for understanding the impacts on local air quality. Similarly, Jones et al. (2010) conducted an exhaustive analysis of air pollution patterns in urban centers, unraveling the intricate web of atmospheric dynamics. These studies, along with the broader literature on energy consumption and air quality, form the foundation upon which our investigation rests.
Building upon this serious scholarly groundwork, "The Kerosene Chronicles" by Environmentalist Expert (2018) offers a compelling exposé of the challenges and opportunities in transitioning away from kerosene use in developing regions. This enlightening resource delves into the cultural, economic, and environmental dimensions of kerosene usage, painting a rich tapestry of human-environment interactions that transcend geographic boundaries.
Turning to the realm of fiction, "A Tale of Two Cities" by Charles Dickens (1859) provides a vivid portrayal of urban life and societal disparities, reminding us that the city of Washington, D.C. is no stranger to historical tomes and literary intrigue. In a similar vein, the dystopian classic "Brave New World" by Aldous Huxley (1932) prompts contemplation of futuristic societal structures, perhaps offering a whimsical parallel to the unexpected link between kerosene in Peru and air pollution in the political precincts of Washington, D.C.
On a more lighthearted note, the internet meme "Distracted Boyfriend" perfectly encapsulates the surprising nature of our research findings. Just as the meme's protagonist diverts his attention from the expected path to gaze at an unexpected object, so too does our research redirect scholarly focus to the unconventional connection between kerosene consumption and urban air pollution. The juxtaposition of seemingly unrelated elements elicits a chuckle, and yet, beneath the humor lies a thought-provoking insight into the interconnectedness of our world.
As we traverse the landscape of literature, both scholarly and imaginative, one cannot help but marvel at the delightful twists and turns that bring us to the precipice of discovery. Just as unexpected encounters can lead to the most riveting conversations, so too does this research journey venture into unexpected realms, inviting scholars and enthusiasts alike to ponder the peculiar harmonies of our global ecosystem.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration . You used data from 1980 to 2021
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
[[INTRODUCTION]]
Dimly lit households in the picturesque Peruvian countryside may seem worlds away from the bustling political precincts of Washington, D.C., but our research aims to illuminate an unexpected connection between these two disparate settings. While one may think that kerosene lamps and political maneuverings have nothing in common, our findings suggest otherwise.
As we delve into this unlikely pairing, it's worth noting that the correlation between kerosene consumption in Peru and air pollution in the esteemed city of Washington, D.C. was not initially on our radar. However, serendipity often plays a role in scientific inquiry, leading us down unforeseen paths that ultimately reveal thought-provoking connections.
The juxtaposition of rural kerosene use and urban air pollution may seem comically incongruous at first glance, eliciting a wry chuckle from even the most stoic researcher. Yet, as we peered beneath the surface, our chuckles turned into gasps of surprise as the data unveiled a compelling relationship that transcended geographical and cultural divides.
Moving beyond the superficial disparities, our endeavor sheds light on the intricate interplay between seemingly isolated phenomena—a scholarly pursuit that is as intellectually stimulating as it is unexpected. As we embark on this intellectual adventure, one cannot help but marvel at the delightful complexity of our natural and societal systems and their sometimes whimsical interconnections.
We invite readers to join us on this enthralling journey, where the seemingly mundane act of kerosene consumption intertwines with the sophisticated dance of political decision-making, defying conventional scientific silos and inviting a fresh perspective on the synergies inherent in our global ecosystem. Let us venture forth to unravel this curious tapestry that showcases both the intricacies and quirks of our world.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
Sample Selection:
To embark on this whimsical voyage of connection, our research team carefully selected the data sources for our study. With a strategic eye akin to a watchful hawk, we scoured the vast expanse of the internet, foraging for datasets that would serve as the bedrock of our investigation. The Environmental Protection Agency and the Energy Information Administration emerged as the sturdy tendrils from which we drew our data, casting a scholarly net that spanned from 1980 to 2021.
Data Compilation:
With the tenacity of an archeologist unearthing ancient relics, our team compiled data on kerosene consumption in the tranquil landscapes of Peru and air pollution levels in the bustling thoroughfares of Washington, D.C. We meticulously extracted, collated, and curated the data, akin to tending to a vibrant garden of statistical blooms, nurturing the numbers with gentle care and attention.
Statistical Analysis:
Armed with our treasure trove of data, we unleashed the prowess of statistical analysis, employing techniques that would make even the most stoic of mathematicians crack a wry smile. Through the arcane arts of correlation analysis, we probed the data for whispers of connection, unraveling the threads of relationship between kerosene consumption and air pollution with the finesse of a maestro conducting a symphony. The correlation coefficient of 0.8487147 stood resolute, casting a bright spotlight on the surprising interplay between these seemingly disparate entities, leaving our research team both astounded and amused by the striking strength of the correlation.
Subgroup Analysis:
Like intrepid explorers venturing into uncharted territories, we further dissected the data to unravel the nuances of this captivating link. Our journey took us into the labyrinthine corridors of subgroup analysis, where we navigated through the murky waters of demographic variables and temporal fluctuations with the agility of acrobats, teasing out subtleties that added layers of intrigue to our findings. The robustness of the correlation persisted across subgroups, cementing the unconventional bond between kerosene use in Peru and air pollution in Washington, D.C. with an unwavering tenacity that mirrored the unyielding spirit of a mountain goat traversing steep cliffs.
Sensitivity Analysis:
Akin to craftsmen fine-tuning a delicate instrument, we subjected our findings to the rigors of sensitivity analysis, probing for potential lurking confounders and alternate explanations with a skeptical eye. Each twist and turn in our analysis, akin to a sleight of hand, sought to unravel any hidden veils that might obscure the purity of our discoveries.
Ethical Considerations:
Ensuring the ethical integrity of our study was a paramount concern, akin to a vigilant guardian watching over a precious artifact. We upheld the principles of academic conduct with unwavering dedication, fostering an environment of transparency and rigor in our research endeavors. All measures were taken to ensure that our findings would stand as sturdy pillars in the scholarly edifice, tempered by the fires of academic scrutiny.
Conclusion:
In the end, our methodological odyssey brought to light the fascinating nexus between kerosene consumption in Peru and air pollution in Washington, D.C., weaving a tale that, while unexpected, exudes the sparkle of revelation and the allure of intellectual adventure. The unexpected linkage between these phenomena, elusive and captivating as a specter in the night, invites further exploration and contemplation, transcending the boundaries of conventional wisdom and delighting in the dance of scholarly inquiry.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration .
For the time period 1980 to 2021, you found a correlation 0.8487147, r-squared of 0.7203166, 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]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
Dimly lit households in the picturesque Peruvian countryside may seem worlds away from the bustling political precincts of Washington, D.C., but our research aims to illuminate an unexpected connection between these two disparate settings. While one may think that kerosene lamps and political maneuverings have nothing in common, our findings suggest otherwise.
As we delve into this unlikely pairing, it's worth noting that the correlation between kerosene consumption in Peru and air pollution in the esteemed city of Washington, D.C. was not initially on our radar. However, serendipity often plays a role in scientific inquiry, leading us down unforeseen paths that ultimately reveal thought-provoking connections.
The juxtaposition of rural kerosene use and urban air pollution may seem comically incongruous at first glance, eliciting a wry chuckle from even the most stoic researcher. Yet, as we peered beneath the surface, our chuckles turned into gasps of surprise as the data unveiled a compelling relationship that transcended geographical and cultural divides.
Moving beyond the superficial disparities, our endeavor sheds light on the intricate interplay between seemingly isolated phenomena—a scholarly pursuit that is as intellectually stimulating as it is unexpected. As we embark on this intellectual adventure, one cannot help but marvel at the delightful complexity of our natural and societal systems and their sometimes whimsical interconnections.
We invite readers to join us on this enthralling journey, where the seemingly mundane act of kerosene consumption intertwines with the sophisticated dance of political decision-making, defying conventional scientific silos and inviting a fresh perspective on the synergies inherent in our global ecosystem. Let us venture forth to unravel this curious tapestry that showcases both the intricacies and quirks of our world.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
Our analysis of the data revealed a remarkably strong correlation between kerosene consumption in Peru and air pollution levels in Washington, D.C. For the time period from 1980 to 2021, the correlation coefficient stood at a striking 0.8487147, with an r-squared value of 0.7203166. Moreover, the p-value was less than 0.01, indicating a statistically significant relationship.
Figure 1 displays a scatterplot illustrating the robust correlation between kerosene usage in Peru and air pollution in Washington, D.C. The plot aptly captures the surprising entwining of these seemingly unrelated variables, serving as a visual testament to the unexpected bond that our research has uncovered.
These findings challenge traditional assumptions about the disconnected nature of urban air quality and rural energy consumption. While it may seem as surprising as encountering a llama in the Senate chamber, our results exemplify the whimsical interconnectedness that can be unearthed through rigorous scientific inquiry.
As we continue to unpack the enigmatic nexus between these dissimilar domains, our research raises thought-provoking questions and opens new avenues for scholarly investigation. The intersection of kerosene usage in Peru and air pollution in Washington, D.C. may just be the tip of the iceberg in the curious world of interconnected phenomena, urging researchers to delve deeper into the multifaceted tapestry that forms our global ecosystem.
As we navigate this uncharted territory, the seemingly discordant pairing of kerosene and air pollution beckons us to embrace the delightful complexity of our natural and societal systems. Our study not only challenges scientific boundaries but also sparks a lively intellectual dalliance, inviting scholarly minds to ponder the unexpected interactions that animate our world.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 500 tokens.
Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.
Do not write a conclusion. I will add a conclusion after this.
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
[[LITERATURE REVIEW]]
In their seminal work, Smith and Doe (2005) meticulously dissected the environmental consequences of kerosene consumption in rural areas, providing a comprehensive framework for understanding the impacts on local air quality. Similarly, Jones et al. (2010) conducted an exhaustive analysis of air pollution patterns in urban centers, unraveling the intricate web of atmospheric dynamics. These studies, along with the broader literature on energy consumption and air quality, form the foundation upon which our investigation rests.
Building upon this serious scholarly groundwork, "The Kerosene Chronicles" by Environmentalist Expert (2018) offers a compelling exposé of the challenges and opportunities in transitioning away from kerosene use in developing regions. This enlightening resource delves into the cultural, economic, and environmental dimensions of kerosene usage, painting a rich tapestry of human-environment interactions that transcend geographic boundaries.
Turning to the realm of fiction, "A Tale of Two Cities" by Charles Dickens (1859) provides a vivid portrayal of urban life and societal disparities, reminding us that the city of Washington, D.C. is no stranger to historical tomes and literary intrigue. In a similar vein, the dystopian classic "Brave New World" by Aldous Huxley (1932) prompts contemplation of futuristic societal structures, perhaps offering a whimsical parallel to the unexpected link between kerosene in Peru and air pollution in the political precincts of Washington, D.C.
On a more lighthearted note, the internet meme "Distracted Boyfriend" perfectly encapsulates the surprising nature of our research findings. Just as the meme's protagonist diverts his attention from the expected path to gaze at an unexpected object, so too does our research redirect scholarly focus to the unconventional connection between kerosene consumption and urban air pollution. The juxtaposition of seemingly unrelated elements elicits a chuckle, and yet, beneath the humor lies a thought-provoking insight into the interconnectedness of our world.
As we traverse the landscape of literature, both scholarly and imaginative, one cannot help but marvel at the delightful twists and turns that bring us to the precipice of discovery. Just as unexpected encounters can lead to the most riveting conversations, so too does this research journey venture into unexpected realms, inviting scholars and enthusiasts alike to ponder the peculiar harmonies of our global ecosystem.
[[RESULTS]]
Our analysis of the data revealed a remarkably strong correlation between kerosene consumption in Peru and air pollution levels in Washington, D.C. For the time period from 1980 to 2021, the correlation coefficient stood at a striking 0.8487147, with an r-squared value of 0.7203166. Moreover, the p-value was less than 0.01, indicating a statistically significant relationship.
Figure 1 displays a scatterplot illustrating the robust correlation between kerosene usage in Peru and air pollution in Washington, D.C. The plot aptly captures the surprising entwining of these seemingly unrelated variables, serving as a visual testament to the unexpected bond that our research has uncovered.
These findings challenge traditional assumptions about the disconnected nature of urban air quality and rural energy consumption. While it may seem as surprising as encountering a llama in the Senate chamber, our results exemplify the whimsical interconnectedness that can be unearthed through rigorous scientific inquiry.
As we continue to unpack the enigmatic nexus between these dissimilar domains, our research raises thought-provoking questions and opens new avenues for scholarly investigation. The intersection of kerosene usage in Peru and air pollution in Washington, D.C. may just be the tip of the iceberg in the curious world of interconnected phenomena, urging researchers to delve deeper into the multifaceted tapestry that forms our global ecosystem.
As we navigate this uncharted territory, the seemingly discordant pairing of kerosene and air pollution beckons us to embrace the delightful complexity of our natural and societal systems. Our study not only challenges scientific boundaries but also sparks a lively intellectual dalliance, inviting scholarly minds to ponder the unexpected interactions that animate our world.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The striking correlation between kerosene consumption in the rural stretches of Peru and air pollution levels in the bustling confines of Washington, D.C. unveils a captivating symbiosis that defies conventional academic silos. As we dive deeper into the implications of our findings, it is evident that the whimsical interplay between seemingly disparate entities can yield substantial scholarly fruit, much like a pineapple lodged in an apple tree.
Our results resonate intriguingly with prior research. The comprehensive framework delineated by Smith and Doe (2005) regarding the environmental impacts of kerosene usage finds resonance in our study, painting a picture of intricate environmental entanglement that is as thought-provoking as finding a needle in a haystack. Similarly, the nuanced analysis of air pollution patterns in urban centers by Jones et al. (2010) is corroborated by the robust correlation revealed in our investigation, akin to finding a needle in an even larger haystack.
The insight provided by Environmentalist Expert (2018) in "The Kerosene Chronicles" echoes our findings, emphasizing the profound influence of energy consumption on air quality, much like a matchstick igniting a thought-provoking conversation. Our unexpected research pas de deux between kerosene in Peru and air pollution in Washington, D.C. seems to unfold as a climactic twist akin to the breathtaking reveal in a first-rate mystery novel.
Drawing from the lighthearted corners of literature, we are reminded of the whimsical nature of unexpected discoveries through Charles Dickens' "A Tale of Two Cities." Just as cities tell complex stories that intertwine across distances, our research illuminates the surprising connection between distant domains, akin to spotting a jaguar in the urban jungle. Furthermore, the dystopian classic "Brave New World" by Aldous Huxley finds an unexpected echo in our research, challenging us to ponder the futuristic implications of our findings, much like a robot vacuum cleaner treading unpredictably through the corridors of academia.
In conclusion, our study not only enriches the scholarly discourse but also cultivates a spark of curiosity and humor, inviting researchers to embrace the unexpected harmonies that permeate our global ecosystem. As we unravel the enigmatic nexus between kerosene consumption in Peru and air pollution in Washington, D.C., we find ourselves at the crossroads of serendipity and scholarly revelation, much like an unexpected encounter unveiling the hidden delights of our interconnected world.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Washington, D.C. and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.
Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
[[ABSTRACT]]
This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
[[INTRDUCTION]]
Dimly lit households in the picturesque Peruvian countryside may seem worlds away from the bustling political precincts of Washington, D.C., but our research aims to illuminate an unexpected connection between these two disparate settings. While one may think that kerosene lamps and political maneuverings have nothing in common, our findings suggest otherwise.
As we delve into this unlikely pairing, it's worth noting that the correlation between kerosene consumption in Peru and air pollution in the esteemed city of Washington, D.C. was not initially on our radar. However, serendipity often plays a role in scientific inquiry, leading us down unforeseen paths that ultimately reveal thought-provoking connections.
The juxtaposition of rural kerosene use and urban air pollution may seem comically incongruous at first glance, eliciting a wry chuckle from even the most stoic researcher. Yet, as we peered beneath the surface, our chuckles turned into gasps of surprise as the data unveiled a compelling relationship that transcended geographical and cultural divides.
Moving beyond the superficial disparities, our endeavor sheds light on the intricate interplay between seemingly isolated phenomena—a scholarly pursuit that is as intellectually stimulating as it is unexpected. As we embark on this intellectual adventure, one cannot help but marvel at the delightful complexity of our natural and societal systems and their sometimes whimsical interconnections.
We invite readers to join us on this enthralling journey, where the seemingly mundane act of kerosene consumption intertwines with the sophisticated dance of political decision-making, defying conventional scientific silos and inviting a fresh perspective on the synergies inherent in our global ecosystem. Let us venture forth to unravel this curious tapestry that showcases both the intricacies and quirks of our world.
[[RESULTS]]
Our analysis of the data revealed a remarkably strong correlation between kerosene consumption in Peru and air pollution levels in Washington, D.C. For the time period from 1980 to 2021, the correlation coefficient stood at a striking 0.8487147, with an r-squared value of 0.7203166. Moreover, the p-value was less than 0.01, indicating a statistically significant relationship.
Figure 1 displays a scatterplot illustrating the robust correlation between kerosene usage in Peru and air pollution in Washington, D.C. The plot aptly captures the surprising entwining of these seemingly unrelated variables, serving as a visual testament to the unexpected bond that our research has uncovered.
These findings challenge traditional assumptions about the disconnected nature of urban air quality and rural energy consumption. While it may seem as surprising as encountering a llama in the Senate chamber, our results exemplify the whimsical interconnectedness that can be unearthed through rigorous scientific inquiry.
As we continue to unpack the enigmatic nexus between these dissimilar domains, our research raises thought-provoking questions and opens new avenues for scholarly investigation. The intersection of kerosene usage in Peru and air pollution in Washington, D.C. may just be the tip of the iceberg in the curious world of interconnected phenomena, urging researchers to delve deeper into the multifaceted tapestry that forms our global ecosystem.
As we navigate this uncharted territory, the seemingly discordant pairing of kerosene and air pollution beckons us to embrace the delightful complexity of our natural and societal systems. Our study not only challenges scientific boundaries but also sparks a lively intellectual dalliance, inviting scholarly minds to ponder the unexpected interactions that animate our world.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our examination of the curious correlation between kerosene consumption in Peru and air pollution in Washington, D.C. has unveiled a compelling relationship that transcends geographical and cultural boundaries. The statistical analysis revealed a remarkably strong correlation coefficient of 0.8487147, with a p-value less than 0.01, illustrating a connection that is as surprising as seeing a llama in a political debate.
The juxtaposition of these seemingly incongruous elements may elicit a wry chuckle from even the most stoic of researchers, yet our findings challenge conventional wisdom and foster a sense of intellectual curiosity akin to stumbling upon a rare artifact in an unexpected location. The visual testament provided by Figure 1 serves as a whimsical reminder of the intricate interplay between rural energy consumption and urban air quality, inviting scholarly minds to embark on a captivating journey of exploration.
As we bid adieu to this delightful expedition into the world of unexpected scientific correlations, we are compelled to assert that further research in this domain may prove as futile as searching for a needle in a haystack. Our findings stand as a testament to the whimsical interconnectedness that underlies our global ecosystem, urging researchers to embrace the delightful complexity of our natural and societal systems without necessarily embarking on a quixotic quest for additional correlations.
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 Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.. Respond only with the name of a fictional academic journal.
ChatGPT:
The International Journal of Ecological Economics and Political Ecology
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 Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.. Respond only with the name of a fictional organization.
ChatGPT:
The Cosmic Sprocket Research Institute
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 Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C. with an abstract of This paper presents the surprising and unconventional link between air pollution in the city of Washington, D.C. and the use of kerosene in the cozier confines of Peru. Using data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into this seemingly unrelated duo with scholarly scrutiny. Through rigorous statistical analysis, we uncovered a correlation coefficient of 0.8487147 and p < 0.01 for the period spanning from 1980 to 2021, revealing a remarkably strong connection. The findings shed light on the perplexing interplay between urban air quality and rural energy consumption, challenging conventional wisdom and igniting a research frontier that is far from fossilized in its implications. Join us on this enlightening journey of exploration, where seemingly distinct entities come together in an unconventional research pas de deux.
ChatGPT:
Kerosene consumption, air pollution, Peru, Washington DC, correlation, urban air quality, rural energy consumption, environmental impact, pollution data, Energy Information Administration, Environmental Protection Agency, statistical analysis, research implications
*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 Washington, D.C.Detailed data title: Percentage of days 'unhealthy' or worse air quality in Washington-Arlington-Alexandria, DC-VA-MD-WV
Source: Environmental Protection Agency
See what else correlates with Air pollution in Washington, D.C.
Kerosene used in Peru
Detailed data title: Volume of kerosene used consumed in Peru in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in Peru
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 = 0.7203166 (Coefficient of determination)
This means 72% of the change in the one variable (i.e., Kerosene used in Peru) is predictable based on the change in the other (i.e., Air pollution in Washington, D.C.) over the 42 years from 1980 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.3E-12. 0.0000000000012555624818544333
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 0.85 in 1.3E-10% of random cases. Said differently, if you correlated 796,455,783,326 random variables You don't actually need 796 billion 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 41 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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.
[ 0.73, 0.92 ] 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 | |
| Air pollution in Washington, D.C. (Bad air quality days) | 14.2077 | 10.137 | 11.5068 | 16.9863 | 9.28962 | 11.2329 | 10.9589 | 13.4247 | 14.4809 | 5.20548 | 6.0274 | 13.6986 | 3.82514 | 12.6027 | 5.75342 | 8.76712 | 4.64481 | 8.21918 | 12.8767 | 10.9589 | 3.27869 | 6.0274 | 9.86301 | 2.19178 | 1.91257 | 4.93151 | 5.75342 | 4.65753 | 2.18579 | 0 | 3.56164 | 2.73973 | 2.45902 | 0 | 0.273973 | 0.273973 | 0 | 0 | 0.273973 | 0.273973 | 0.273224 | 0.273973 |
| Kerosene used in Peru (Million Barrels/Day) | 18.6082 | 19.1679 | 19.028 | 17.349 | 17.7688 | 17.489 | 19 | 22 | 22 | 18 | 16 | 15 | 6.9 | 15 | 13 | 14 | 14 | 14 | 14 | 14 | 14.0449 | 14.8882 | 14.9517 | 10.8008 | 6.84295 | 4.42622 | 1.77896 | 1.16479 | 0.97153 | 1.52482 | 1.41893 | 1.39775 | 1.03489 | 1.14362 | 1.24951 | 1 | 1 | 0.782137 | 0.412795 | 0.0651781 | 0.0434521 | 0.0434521 |
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([14.2077,10.137,11.5068,16.9863,9.28962,11.2329,10.9589,13.4247,14.4809,5.20548,6.0274,13.6986,3.82514,12.6027,5.75342,8.76712,4.64481,8.21918,12.8767,10.9589,3.27869,6.0274,9.86301,2.19178,1.91257,4.93151,5.75342,4.65753,2.18579,0,3.56164,2.73973,2.45902,0,0.273973,0.273973,0,0,0.273973,0.273973,0.273224,0.273973,])
array_2 = np.array([18.6082,19.1679,19.028,17.349,17.7688,17.489,19,22,22,18,16,15,6.9,15,13,14,14,14,14,14,14.0449,14.8882,14.9517,10.8008,6.84295,4.42622,1.77896,1.16479,0.97153,1.52482,1.41893,1.39775,1.03489,1.14362,1.24951,1,1,0.782137,0.412795,0.0651781,0.0434521,0.0434521,])
array_1_name = "Air pollution in Washington, D.C."
array_2_name = "Kerosene used in Peru"
# 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 Washington, D.C.
- Line chart for only Kerosene used in Peru
- AI-generated correlation image
- The spurious research paper: Correlating Kerosene Consumption in Peru with Pollution in the Political Precincts of Washington, D.C.
Your correlation inspection deserves a standing ovation!
Correlation ID: 3166 · Black Variable ID: 21419 · Red Variable ID: 24811
