Spurious correlation #4,056 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Chicago and the second variable is Kerosene used in Peru. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Chicago and the second variable is Kerosene used in Peru. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The cleaner air in Chicago led to a decrease in seagull flatulence, which in turn reduced the kerosene production in Peru, since seagull flatulence is a crucial catalyst in the kerosene refinement process.

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an image that embodies the contrast between a bustling Chicago neighborhood and a serene Peruvian village as they transition to eco-friendly practices. In the Chicago scene, showcase the dispersing smog and the vibrant eco-friendly pigeons taking flight amidst the clearing air. In the Peruvian village, depict the locals gathered around a newly installed solar panel, showcasing their transition from kerosene lamps to the warm glow of sustainable energy. Emphasize the harmony between urban and rural environments embracing eco-friendly solutions. Use glitch art to create a surreal, visually striking representation of these contrasting scenes. 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 Chicago 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: Glitch art 3D Pixar Style Celtic art a funny image macro Oragami
ChatGPT: Glitch art

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 Chicago and Kerosene used in Peru, in the following situation: Because when air pollution in Chicago decreases, it leads to a rise in the population of eco-friendly pigeons. These pigeons have been migrating to Peru and inspiring the locals to switch from kerosene to cleaner energy sources, resulting in a decrease in kerosene usage.. Keep the description simple.
ChatGPT: In a bustling Chicago neighborhood, the smog begins to clear as fewer cars emit harmful fumes into the air. As the air quality improves, a flock of eco-friendly pigeons takes flight, their vibrant feathers shimmering in the sunlight. Meanwhile, in a quiet village in Peru, a group of locals gathers around a newly installed solar panel, intrigued by the clean and sustainable energy source. They eagerly switch from their kerosene lamps to the eco-friendly alternative, illuminating their village with a soft, warm glow.

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: [ Glitch art ] and depicting this scene: In a bustling Chicago neighborhood, the smog begins to clear as fewer cars emit harmful fumes into the air. As the air quality improves, a flock of eco-friendly pigeons takes flight, their vibrant feathers shimmering in the sunlight. Meanwhile, in a quiet village in Peru, a group of locals gathers around a newly installed solar panel, intrigued by the clean and sustainable energy source. They eagerly switch from their kerosene lamps to the eco-friendly alternative, illuminating their village with a soft, warm glow. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image that embodies the contrast between a bustling Chicago neighborhood and a serene Peruvian village as they transition to eco-friendly practices. In the Chicago scene, showcase the dispersing smog and the vibrant eco-friendly pigeons taking flight amidst the clearing air. In the Peruvian village, depict the locals gathered around a newly installed solar panel, showcasing their transition from kerosene lamps to the warm glow of sustainable energy. Emphasize the harmony between urban and rural environments embracing eco-friendly solutions. Use glitch art to create a surreal, visually striking representation of these contrasting scenes.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Air pollution in Chicago caused Kerosene used in Peru to decrease.

AI academic paper

(Because p < 0.01)

A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

The Journal of Ecological Connections

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Chicago and Kerosene used in Peru. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

[[ABSTRACT]]
The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.

Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

[[Insert dad joke related to kerosene]]
Did you hear about the fire at the kerosene factory? It was intense!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Air pollution is a serious concern with profound implications for public health and the environment. The Windy City of Chicago has long been grappling with air quality challenges, while in Peru, kerosene is a widely used source of energy. These two seemingly unrelated phenomena have been the subject of extensive research and policy interventions, but the connection between them has remained elusive, much like finding a needle in a statistical haystack.
As researchers, we are always on the lookout for unexpected correlations and unusual relationships in data. It's like being detectives of the numerical world, uncovering hidden connections and unraveling mysteries that baffle the conventional mind.
Our study sets out to shed light on the surprising bond between air pollution in Chicago and the utilization of kerosene in Peru. It's a bit like solving a scientific riddle with statistical clues, piecing together a puzzle where the pieces initially seem about as related as a fish and a bicycle.
We approached this investigation with a blend of scientific rigor and a hint of curiosity, akin to a scientific Sherlock Holmes, hunting for clues in the data and following the scent of statistical anomalies.
The correlation coefficient of 0.7467010 that we unearthed indicates a strong relationship between air pollution levels in Chicago and the quantity of kerosene used in Peru. It's as if these variables were engaged in a statistical tango, influencing each other across borders and oceans.
This unexpected link challenges traditional notions of cause and effect, much like realizing that the fruit doesn't fall far from the statistical tree. It's a reminder that in the complex web of global relationships, factors that may appear unrelated on the surface can, in fact, exert mutual influence, much like distant relatives showing unexpectedly similar traits at a family reunion.
We delved deep into the data, meticulously analyzing the information from the Environmental Protection Agency and the Energy Information Administration, navigating the statistical terrain with the precision of statistical cartographers charting unexplored territory.
Our findings not only suggest a connection between air pollution in Chicago and kerosene usage in Peru but also underscore the far-reaching impact of human activities on a global scale. It's a bit like discovering a scientific domino effect, where an action in one part of the world triggers a chain reaction with profound consequences elsewhere.
This discovery challenges traditional boundaries of environmental impact and calls for a reevaluation of strategies and policies aimed at mitigating air pollution and promoting sustainable energy use. It's like finding out the punchline to a long-running statistical joke – unexpected and thought-provoking.
As we present the results of our investigation, we invite fellow researchers and policymakers to contemplate the broader implications of these findings and join us in unraveling the intricate tapestry of environmental interconnectedness. After all, in the world of research, as in life, the most surprising connections are often the most revealing.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The current study seeks to provide a comprehensive analysis of the relationship between air pollution in Chicago and kerosene usage in Peru. Previous research by Smith (2010) and Doe (2015) has established the detrimental effects of air pollution on public health and the environment in urban areas. Furthermore, Jones (2018) has examined the widespread use of kerosene as a fuel source in developing regions, highlighting its implications for household energy consumption.

In "The Great Smog of India" by Siddharth Singh, the author delves into the complex dynamics of air pollution in India, shedding light on the multifaceted challenges posed by vehicular emissions, industrial activities, and agricultural practices. Similarly, "Energy Poverty: Global Challenges and Local Solutions" by Antoine Halff and Benjamin K. Sovacool addresses the use of kerosene as a primary source of lighting and cooking fuel in low-income households, emphasizing the associated economic and environmental repercussions.

On a more fictional note, "The Air We Breathe" by Andrea Barrett weaves a narrative set against the backdrop of industrialization and air quality concerns in a 19th-century American town. Likewise, "Kerosene" by Chris Wooding offers a fictitious exploration of alternative energy sources in a dystopian landscape, presenting a speculative take on fuel dependency and environmental sustainability.

In addition to scholarly works, the literature review incorporates unconventional sources of information, including the backs of shampoo bottles, which surprisingly provide insight into chemical compositions and environmental impacts, albeit in a rather unconventional manner. While the validity and rigor of such sources may be questionable in traditional research circles, their inclusion underscores the diverse and occasionally unexpected avenues through which knowledge can be acquired.

These diverse sources collectively serve to elucidate the broader context within which the current investigation is situated, offering a rich tapestry of perspectives that inform the understanding of the intricate interplay between air pollution in Chicago and kerosene usage in Peru.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Chicago and Kerosene used in Peru. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 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]]
A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

[[ABSTRACT]]
The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.

Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

[[Insert dad joke related to kerosene]]
Did you hear about the fire at the kerosene factory? It was intense!

[[INTRODUCTION]]
Air pollution is a serious concern with profound implications for public health and the environment. The Windy City of Chicago has long been grappling with air quality challenges, while in Peru, kerosene is a widely used source of energy. These two seemingly unrelated phenomena have been the subject of extensive research and policy interventions, but the connection between them has remained elusive, much like finding a needle in a statistical haystack.
As researchers, we are always on the lookout for unexpected correlations and unusual relationships in data. It's like being detectives of the numerical world, uncovering hidden connections and unraveling mysteries that baffle the conventional mind.
Our study sets out to shed light on the surprising bond between air pollution in Chicago and the utilization of kerosene in Peru. It's a bit like solving a scientific riddle with statistical clues, piecing together a puzzle where the pieces initially seem about as related as a fish and a bicycle.
We approached this investigation with a blend of scientific rigor and a hint of curiosity, akin to a scientific Sherlock Holmes, hunting for clues in the data and following the scent of statistical anomalies.
The correlation coefficient of 0.7467010 that we unearthed indicates a strong relationship between air pollution levels in Chicago and the quantity of kerosene used in Peru. It's as if these variables were engaged in a statistical tango, influencing each other across borders and oceans.
This unexpected link challenges traditional notions of cause and effect, much like realizing that the fruit doesn't fall far from the statistical tree. It's a reminder that in the complex web of global relationships, factors that may appear unrelated on the surface can, in fact, exert mutual influence, much like distant relatives showing unexpectedly similar traits at a family reunion.
We delved deep into the data, meticulously analyzing the information from the Environmental Protection Agency and the Energy Information Administration, navigating the statistical terrain with the precision of statistical cartographers charting unexplored territory.
Our findings not only suggest a connection between air pollution in Chicago and kerosene usage in Peru but also underscore the far-reaching impact of human activities on a global scale. It's a bit like discovering a scientific domino effect, where an action in one part of the world triggers a chain reaction with profound consequences elsewhere.
This discovery challenges traditional boundaries of environmental impact and calls for a reevaluation of strategies and policies aimed at mitigating air pollution and promoting sustainable energy use. It's like finding out the punchline to a long-running statistical joke – unexpected and thought-provoking.
As we present the results of our investigation, we invite fellow researchers and policymakers to contemplate the broader implications of these findings and join us in unraveling the intricate tapestry of environmental interconnectedness. After all, in the world of research, as in life, the most surprising connections are often the most revealing.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved the meticulous collection, aggregation, and analysis of data from the Environmental Protection Agency and the Energy Information Administration. The data spanned from 1980 to 2021, encompassing a comprehensive timeframe to capture variations in air pollution levels in Chicago and kerosene usage in Peru.

To establish the relationship between air pollution in Chicago and kerosene usage in Peru, we employed a hybrid approach blending econometric analysis and environmental impact assessment. This approach allowed for a nuanced exploration of the interplay between these seemingly disparate variables, much like a scientific waltz where the steps of statistics and environmental data gracefully intertwine.

First, to quantify air pollution levels in Chicago, we utilized data on key pollutants, including particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO), obtained from the Environmental Protection Agency. These data were then subjected to rigorous statistical analyses, akin to examining microscopic particles under the lens of statistical scrutiny.

For the assessment of kerosene usage in Peru, we delved into energy consumption patterns and fuel utilization trends, drawing on comprehensive data from the Energy Information Administration. The conversion of these energy consumption patterns into meaningful metrics involved an intricate dance of data manipulation and transformation, not unlike choreographing the steps of a statistical ballet.

Having quantified the variables of interest, we then applied econometric techniques, including time series analysis and panel data models, to discern patterns and relationships. This analytical process resembled unraveling a complex labyrinth of statistical dynamics, discerning hidden pathways of influence and interaction between air pollution in Chicago and kerosene usage in Peru.

The statistical software used for the analysis was as reliable as a well-behaved laboratory pet, ensuring the accuracy and validity of our findings. Robustness checks and sensitivity analyses were conducted to evaluate the stability and consistency of the results, akin to stress-testing the statistical hypothesis under various scenarios.

Furthermore, to account for potential confounding factors and spurious correlations, we integrated control variables such as population density, economic indicators, and meteorological data into our analytical framework. This approach guarded against the statistical equivalent of mistaking correlation for causation – a pitfall as common as confusing the correlation between kerosene usage and air pollution with the causation of an unexpected statistical fire.

Finally, the findings were subjected to peer review and validation by esteemed researchers in the fields of environmental science and econometrics, ensuring the rigorous scrutiny and robustness of the results. This validation process was akin to conducting a scientific symphony, harmonizing the diverse perspectives of experts to affirm the reliability and significance of our research findings.

In summary, the methodology employed in this study blended meticulous data collection, sophisticated econometric analysis, and stringent validation processes. This approach facilitated the unveiling of the intriguing and unexpected connection between air pollution in Chicago and kerosene usage in Peru, underscoring the significance of global environmental interconnectedness.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the 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 Chicago and Kerosene used in Peru. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 2021, you found a correlation 0.7467010, r-squared of 0.5575623, 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]]
A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

[[ABSTRACT]]
The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.

Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

[[Insert dad joke related to kerosene]]
Did you hear about the fire at the kerosene factory? It was intense!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Air pollution is a serious concern with profound implications for public health and the environment. The Windy City of Chicago has long been grappling with air quality challenges, while in Peru, kerosene is a widely used source of energy. These two seemingly unrelated phenomena have been the subject of extensive research and policy interventions, but the connection between them has remained elusive, much like finding a needle in a statistical haystack.
As researchers, we are always on the lookout for unexpected correlations and unusual relationships in data. It's like being detectives of the numerical world, uncovering hidden connections and unraveling mysteries that baffle the conventional mind.
Our study sets out to shed light on the surprising bond between air pollution in Chicago and the utilization of kerosene in Peru. It's a bit like solving a scientific riddle with statistical clues, piecing together a puzzle where the pieces initially seem about as related as a fish and a bicycle.
We approached this investigation with a blend of scientific rigor and a hint of curiosity, akin to a scientific Sherlock Holmes, hunting for clues in the data and following the scent of statistical anomalies.
The correlation coefficient of 0.7467010 that we unearthed indicates a strong relationship between air pollution levels in Chicago and the quantity of kerosene used in Peru. It's as if these variables were engaged in a statistical tango, influencing each other across borders and oceans.
This unexpected link challenges traditional notions of cause and effect, much like realizing that the fruit doesn't fall far from the statistical tree. It's a reminder that in the complex web of global relationships, factors that may appear unrelated on the surface can, in fact, exert mutual influence, much like distant relatives showing unexpectedly similar traits at a family reunion.
We delved deep into the data, meticulously analyzing the information from the Environmental Protection Agency and the Energy Information Administration, navigating the statistical terrain with the precision of statistical cartographers charting unexplored territory.
Our findings not only suggest a connection between air pollution in Chicago and kerosene usage in Peru but also underscore the far-reaching impact of human activities on a global scale. It's a bit like discovering a scientific domino effect, where an action in one part of the world triggers a chain reaction with profound consequences elsewhere.
This discovery challenges traditional boundaries of environmental impact and calls for a reevaluation of strategies and policies aimed at mitigating air pollution and promoting sustainable energy use. It's like finding out the punchline to a long-running statistical joke – unexpected and thought-provoking.
As we present the results of our investigation, we invite fellow researchers and policymakers to contemplate the broader implications of these findings and join us in unraveling the intricate tapestry of environmental interconnectedness. After all, in the world of research, as in life, the most surprising connections are often the most revealing.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our study reveal a significant correlation between air pollution in Chicago and the usage of kerosene in Peru from 1980 to 2021. The correlation coefficient of 0.7467010 suggests a strong relationship between these two disparate variables. It's as if these variables were long-lost statistical twins, finally reunited in a data-driven family reunion.

The r-squared value of 0.5575623 indicates that 55.76% of the variation in air pollution in Chicago can be explained by the amount of kerosene used in Peru. It's like unraveling a statistical mystery and finding that more than half of the puzzle pieces fit together perfectly, revealing a clearer picture of the unexpected connection between these seemingly unrelated factors.

The p-value of less than 0.01 indicates that the correlation is statistically significant. This suggests that the likelihood of observing such a strong relationship between air pollution in Chicago and kerosene usage in Peru by mere chance is less than 1 in 100. It's like finding a statistical needle in a haystack, except in this case, the needle is magnetically attracted to the kerosene-soaked statistical haystack.

Furthermore, our findings are visually represented in Figure 1, a scatterplot demonstrating the robust correlation between air pollution in Chicago and kerosene usage in Peru. The data points form a clear pattern, resembling a constellation of statistical stars aligning to reveal this surprising relationship. It's as if the data itself is saying, "Look! There's a connection here, and it's not just a statistical fluke."

[[Insert dad joke related to air pollution]]
I asked the air pollution for its opinion, but it was a little too air-rogant for my liking.

In conclusion, our research uncovers an unexpected and statistically significant bond between air pollution in Chicago and kerosene usage in Peru. This discovery challenges traditional notions of environmental interconnectedness and emphasizes the far-reaching impact of human activities on a global scale. It's a reminder that in the intricate dance of data analysis, the most remarkable connections can emerge from the most unexpected pairings, much like finding a diamond in the statistical rough.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Chicago and Kerosene used in Peru. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

[[ABSTRACT]]
The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.

Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

[[Insert dad joke related to kerosene]]
Did you hear about the fire at the kerosene factory? It was intense!

[[LITERATURE REVIEW]]
The current study seeks to provide a comprehensive analysis of the relationship between air pollution in Chicago and kerosene usage in Peru. Previous research by Smith (2010) and Doe (2015) has established the detrimental effects of air pollution on public health and the environment in urban areas. Furthermore, Jones (2018) has examined the widespread use of kerosene as a fuel source in developing regions, highlighting its implications for household energy consumption.
In "The Great Smog of India" by Siddharth Singh, the author delves into the complex dynamics of air pollution in India, shedding light on the multifaceted challenges posed by vehicular emissions, industrial activities, and agricultural practices. Similarly, "Energy Poverty: Global Challenges and Local Solutions" by Antoine Halff and Benjamin K. Sovacool addresses the use of kerosene as a primary source of lighting and cooking fuel in low-income households, emphasizing the associated economic and environmental repercussions.
On a more fictional note, "The Air We Breathe" by Andrea Barrett weaves a narrative set against the backdrop of industrialization and air quality concerns in a 19th-century American town. Likewise, "Kerosene" by Chris Wooding offers a fictitious exploration of alternative energy sources in a dystopian landscape, presenting a speculative take on fuel dependency and environmental sustainability.
In addition to scholarly works, the literature review incorporates unconventional sources of information, including the backs of shampoo bottles, which surprisingly provide insight into chemical compositions and environmental impacts, albeit in a rather unconventional manner. While the validity and rigor of such sources may be questionable in traditional research circles, their inclusion underscores the diverse and occasionally unexpected avenues through which knowledge can be acquired.
These diverse sources collectively serve to elucidate the broader context within which the current investigation is situated, offering a rich tapestry of perspectives that inform the understanding of the intricate interplay between air pollution in Chicago and kerosene usage in Peru.

[[RESULTS]]
The results of our study reveal a significant correlation between air pollution in Chicago and the usage of kerosene in Peru from 1980 to 2021. The correlation coefficient of 0.7467010 suggests a strong relationship between these two disparate variables. It's as if these variables were long-lost statistical twins, finally reunited in a data-driven family reunion.
The r-squared value of 0.5575623 indicates that 55.76% of the variation in air pollution in Chicago can be explained by the amount of kerosene used in Peru. It's like unraveling a statistical mystery and finding that more than half of the puzzle pieces fit together perfectly, revealing a clearer picture of the unexpected connection between these seemingly unrelated factors.
The p-value of less than 0.01 indicates that the correlation is statistically significant. This suggests that the likelihood of observing such a strong relationship between air pollution in Chicago and kerosene usage in Peru by mere chance is less than 1 in 100. It's like finding a statistical needle in a haystack, except in this case, the needle is magnetically attracted to the kerosene-soaked statistical haystack.
Furthermore, our findings are visually represented in Figure 1, a scatterplot demonstrating the robust correlation between air pollution in Chicago and kerosene usage in Peru. The data points form a clear pattern, resembling a constellation of statistical stars aligning to reveal this surprising relationship. It's as if the data itself is saying, "Look! There's a connection here, and it's not just a statistical fluke."

I asked the air pollution for its opinion, but it was a little too air-rogant for my liking.
In conclusion, our research uncovers an unexpected and statistically significant bond between air pollution in Chicago and kerosene usage in Peru. This discovery challenges traditional notions of environmental interconnectedness and emphasizes the far-reaching impact of human activities on a global scale. It's a reminder that in the intricate dance of data analysis, the most remarkable connections can emerge from the most unexpected pairings, much like finding a diamond in the statistical rough.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study supported and extended the prior research on the interplay between air pollution and energy usage. Our results align with the work of Smith (2010), Doe (2015), and Jones (2018), who highlighted the adverse effects of air pollution and the widespread use of kerosene as a fuel source. However, our study brings a unique twist by revealing a surprising connection between air pollution in Chicago and kerosene usage in Peru. It’s like discovering a hidden subplot in a well-known story, adding a new layer of complexity to the narrative of environmental impact.

This unexpected relationship challenges conventional wisdom and underscores the intricate web of global environmental dynamics. Much like a scientific plot twist, our findings defy expectations and prompt a reevaluation of traditional paradigms, emphasizing the need for an integrated approach to understanding the impact of human activities on a worldwide scale.

The positive correlation coefficient and statistically significant p-value unearthed in our analysis shed light on the strong association between air pollution in Chicago and kerosene usage in Peru. The statistical evidence reinforces the notion that these seemingly unrelated factors are, in fact, interconnected, much like uncovering a hidden link between two seemingly distinct characters in a storyline. The robustness of the relationship speaks to the influential role of kerosene combustion in shaping air quality, transcending geographical boundaries to influence urban environments across continents.

It's not every day you find such a link. It's like stumbling upon a scientific Easter egg, enriching our understanding of the intricate network of environmental interactions. Our results suggest that the impact of kerosene usage in Peru extends far beyond the borders of the South American nation, reaching across oceans to leave its imprint on the air quality of the bustling city of Chicago. It's as if the environmental repercussions of kerosene combustion have acquired a frequent flyer status, traversing international skies to make their presence felt in unexpected places.

The r-squared value further underscores the meaningful connection between air pollution in Chicago and kerosene usage in Peru. It's akin to unraveling a complex mathematical mystery, where more than half of the variance in air pollution can be attributed to the amount of kerosene used in Peru. This serves as a testament to the substantial impact of kerosene combustion on air quality, akin to a compelling subplot that significantly enriches the overarching narrative of environmental influence.

Our study adds a new layer of complexity to the ongoing discourse on the global implications of energy consumption and environmental quality. It's like introducing a compelling subplot in a larger narrative, expanding the scope of the environmental discourse to encompass unexpected connections and far-reaching influences. Our findings emphasize the need for a holistic approach to addressing environmental challenges, recognizing the interconnected nature of human activities and their environmental consequences.

The unexpected nexus between air pollution in Chicago and kerosene usage in Peru underscores the intricate interplay of environmental factors on a global scale. It's like discovering a hidden door in a labyrinth, revealing a path that leads to unexpected revelations about the complexity of our environmental landscape. Our study serves as a reminder that in the expansive tapestry of environmental dynamics, new connections and unexpected relationships continue to emerge, enriching our understanding of the world around us.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Chicago and Kerosene used in Peru. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

[[ABSTRACT]]
The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.

Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

[[Insert dad joke related to kerosene]]
Did you hear about the fire at the kerosene factory? It was intense!

[[INTRDUCTION]]
Air pollution is a serious concern with profound implications for public health and the environment. The Windy City of Chicago has long been grappling with air quality challenges, while in Peru, kerosene is a widely used source of energy. These two seemingly unrelated phenomena have been the subject of extensive research and policy interventions, but the connection between them has remained elusive, much like finding a needle in a statistical haystack.
As researchers, we are always on the lookout for unexpected correlations and unusual relationships in data. It's like being detectives of the numerical world, uncovering hidden connections and unraveling mysteries that baffle the conventional mind.
Our study sets out to shed light on the surprising bond between air pollution in Chicago and the utilization of kerosene in Peru. It's a bit like solving a scientific riddle with statistical clues, piecing together a puzzle where the pieces initially seem about as related as a fish and a bicycle.
We approached this investigation with a blend of scientific rigor and a hint of curiosity, akin to a scientific Sherlock Holmes, hunting for clues in the data and following the scent of statistical anomalies.
The correlation coefficient of 0.7467010 that we unearthed indicates a strong relationship between air pollution levels in Chicago and the quantity of kerosene used in Peru. It's as if these variables were engaged in a statistical tango, influencing each other across borders and oceans.
This unexpected link challenges traditional notions of cause and effect, much like realizing that the fruit doesn't fall far from the statistical tree. It's a reminder that in the complex web of global relationships, factors that may appear unrelated on the surface can, in fact, exert mutual influence, much like distant relatives showing unexpectedly similar traits at a family reunion.
We delved deep into the data, meticulously analyzing the information from the Environmental Protection Agency and the Energy Information Administration, navigating the statistical terrain with the precision of statistical cartographers charting unexplored territory.
Our findings not only suggest a connection between air pollution in Chicago and kerosene usage in Peru but also underscore the far-reaching impact of human activities on a global scale. It's a bit like discovering a scientific domino effect, where an action in one part of the world triggers a chain reaction with profound consequences elsewhere.
This discovery challenges traditional boundaries of environmental impact and calls for a reevaluation of strategies and policies aimed at mitigating air pollution and promoting sustainable energy use. It's like finding out the punchline to a long-running statistical joke – unexpected and thought-provoking.
As we present the results of our investigation, we invite fellow researchers and policymakers to contemplate the broader implications of these findings and join us in unraveling the intricate tapestry of environmental interconnectedness. After all, in the world of research, as in life, the most surprising connections are often the most revealing.

[[RESULTS]]
The results of our study reveal a significant correlation between air pollution in Chicago and the usage of kerosene in Peru from 1980 to 2021. The correlation coefficient of 0.7467010 suggests a strong relationship between these two disparate variables. It's as if these variables were long-lost statistical twins, finally reunited in a data-driven family reunion.
The r-squared value of 0.5575623 indicates that 55.76% of the variation in air pollution in Chicago can be explained by the amount of kerosene used in Peru. It's like unraveling a statistical mystery and finding that more than half of the puzzle pieces fit together perfectly, revealing a clearer picture of the unexpected connection between these seemingly unrelated factors.
The p-value of less than 0.01 indicates that the correlation is statistically significant. This suggests that the likelihood of observing such a strong relationship between air pollution in Chicago and kerosene usage in Peru by mere chance is less than 1 in 100. It's like finding a statistical needle in a haystack, except in this case, the needle is magnetically attracted to the kerosene-soaked statistical haystack.
Furthermore, our findings are visually represented in Figure 1, a scatterplot demonstrating the robust correlation between air pollution in Chicago and kerosene usage in Peru. The data points form a clear pattern, resembling a constellation of statistical stars aligning to reveal this surprising relationship. It's as if the data itself is saying, "Look! There's a connection here, and it's not just a statistical fluke."

I asked the air pollution for its opinion, but it was a little too air-rogant for my liking.
In conclusion, our research uncovers an unexpected and statistically significant bond between air pollution in Chicago and kerosene usage in Peru. This discovery challenges traditional notions of environmental interconnectedness and emphasizes the far-reaching impact of human activities on a global scale. It's a reminder that in the intricate dance of data analysis, the most remarkable connections can emerge from the most unexpected pairings, much like finding a diamond in the statistical rough.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has unearthed a compelling relationship between air pollution in Chicago and kerosene usage in Peru, shedding light on the surprising interconnectedness of these seemingly disparate factors. It's as if kerosene in Peru and air pollution in Chicago are engaging in a statistical tango, with each step influencing the other in a dance of data. Our study serves as a testament to the unexpected and thought-provoking connections that can be uncovered through meticulous analysis of empirical evidence. It's like finding a statistical Easter egg hidden in the vast garden of data.

Our findings challenge conventional wisdom and highlight the need for a reevaluation of environmental policies and strategies with a truly global perspective. It's a bit like realizing that the earth isn't flat but round in the statistical sense – a paradigm-shifting revelation. By recognizing the far-reaching impact of human activities on a global scale, we can strive for more holistic and effective approaches to environmental protection and sustainable energy use.

As for the dad joke you've all been waiting for – what did the kerosene say to the air pollution? "You really need to change your filter!"

In light of the robust correlation and statistically significant findings presented in this study, it is our firm belief that no further research is needed in this area. After all, in the world of scientific inquiry, there comes a point when one must say, "Enough is enough!"

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 A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Ecological Connections

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 A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Global Environmental Collaboration (IGEC)

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 A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru with an abstract of The study investigates the long-debated connection between air pollution in the wind city of Chicago and the seemingly unrelated kerosene usage in the South American country of Peru. We delved into this peculiar relationship, using data from the Environmental Protection Agency and the Energy Information Administration. Our research uncovered a striking correlation between these two apparently disparate factors, with a correlation coefficient of 0.7467010 and p < 0.01 from 1980 to 2021.
Our findings indicate that the levels of air pollution in Chicago are significantly linked to the amount of kerosene used in Peru, presenting an unexpected nexus between these geographically distant regions. It appears that the environmental impact of kerosene combustion in Peru reverberates across continents, influencing air quality in the urban metropolis of Chicago. This discovery challenges conventional wisdom and calls for a reevaluation of global environmental interconnectedness.

Did you hear about the fire at the kerosene factory? It was intense!

ChatGPT:

air pollution, Chicago, kerosene usage, Peru, environmental impact, correlation, combustion, global environmental interconnectedness

*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 Chicago
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Chicago-Naperville-Elgin, IL-IN-WI
Source: Environmental Protection Agency
See what else correlates with Air pollution in Chicago

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 r = 0.7467010 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.5575623 (Coefficient of determination)
This means 55.8% 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 Chicago) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.4E-8.^Show 0.0000000136214953468178800000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.75 in 1.4E-6% of random cases. Said differently, if you correlated 73,413,379 random variables^Note You don't actually need 73 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 41 degrees of freedom, ^Note 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.

[ 0.57, 0.86 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	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 Chicago (Bad air quality days)	10.929	5.20548	6.0274	13.1507	8.46994	3.83562	2.46575	6.84932	13.9344	8.21918	7.67123	10.9589	2.73224	1.36986	3.56164	7.39726	3.00546	3.83562	4.65753	5.47945	2.73224	7.94521	7.67123	3.28767	1.63934	6.84932	1.09589	2.73973	0	0.547945	0.547945	2.46575	3.00546	0.273973	0.273973	0.821918	1.36612	0.547945	1.64384	0.273973	1.36612	0.547945
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^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([10.929,5.20548,6.0274,13.1507,8.46994,3.83562,2.46575,6.84932,13.9344,8.21918,7.67123,10.9589,2.73224,1.36986,3.56164,7.39726,3.00546,3.83562,4.65753,5.47945,2.73224,7.94521,7.67123,3.28767,1.63934,6.84932,1.09589,2.73973,0,0.547945,0.547945,2.46575,3.00546,0.273973,0.273973,0.821918,1.36612,0.547945,1.64384,0.273973,1.36612,0.547945,])
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 Chicago"
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. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Chicago
Line chart for only Kerosene used in Peru
AI-generated correlation image
The spurious research paper: A Burning Connection: Unearthing the Surprising Relationship Between Air Pollution in Chicago and Kerosene Usage in Peru

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Correlation ID: 4056 · Black Variable ID: 20454 · Red Variable ID: 24811


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