Spurious correlation #3,181 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of locker room attendants in Michigan and the second variable is Kerosene used in Taiwan. The chart goes from 2003 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 The number of locker room attendants in Michigan and the second variable is Kerosene used in Taiwan. The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The fewer locker room attendants in Michigan meant less idle locker room chatter. This led to a decrease in hot air production, resulting in a reduced global demand for kerosene in a bizarrely interconnected way.

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image of a compelling Discovery magazine cover featuring the scene of a crowded Michigan locker room with fewer attendants, resulting in a line of people outside the locked door in frantic search of their misplaced keys. Meanwhile, capture the night sky in Taiwan dotted with the glow of kerosene lamps, signaling for help and causing an increase in kerosene usage. The composition should portray the contrast between the chaotic locker room and the tranquil yet urgent use of kerosene lamps, weaving together two seemingly unrelated situations into an intriguing visual narrative. 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: The number of locker room attendants in Michigan and Kerosene used in Taiwan. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: a Polaroid photo Cyberpunk A Discovery magazine cover image inspired by bauhaus Psychedelic
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of locker room attendants in Michigan and Kerosene used in Taiwan, in the following situation: Because as the number of locker room attendants in Michigan decreases, more people forget their keys and end up stranded. To signal for help, they start burning kerosene, increasing its usage in Taiwan.. Keep the description simple.
ChatGPT: In a Michigan locker room, there are fewer attendants present, leading to a line of people outside the locked door, frantically searching for their misplaced keys. Meanwhile, in Taiwan, the night sky is dotted with the glow of kerosene lamps, as more and more people use them to signal for help, causing the usage of kerosene to increase.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: In a Michigan locker room, there are fewer attendants present, leading to a line of people outside the locked door, frantically searching for their misplaced keys. Meanwhile, in Taiwan, the night sky is dotted with the glow of kerosene lamps, as more and more people use them to signal for help, causing the usage of kerosene to increase. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a compelling Discovery magazine cover featuring the scene of a crowded Michigan locker room with fewer attendants, resulting in a line of people outside the locked door in frantic search of their misplaced keys. Meanwhile, capture the night sky in Taiwan dotted with the glow of kerosene lamps, signaling for help and causing an increase in kerosene usage. The composition should portray the contrast between the chaotic locker room and the tranquil yet urgent use of kerosene lamps, weaving together two seemingly unrelated situations into an intriguing visual narrative.

*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 The number of locker room attendants in Michigan caused Kerosene used in Taiwan to decrease.

AI academic paper

(Because p < 0.01)

Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

The Journal of Zany Kinetic Kinesiology

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 The number of locker room attendants in Michigan and Kerosene used in Taiwan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

[[ABSTRACT]]
This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The perplexing correlations that emerge from seemingly unrelated variables have fascinated researchers for centuries. Just as the apple famously fell on Newton's head, sparking a revelation about gravity, so too do we find ourselves grappling with an unexpected association - the link between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. While this peculiar pairing may initially appear whimsical or even whimsy, the statistical analyses presented herein indicate a more robust relationship than one might expect. It is this head-scratching conundrum that motivates our inquiry into the uncharted territory of locker room labor and luminous liquid consumption.
To some, the notion of examining the staffing levels of locker rooms in Michigan alongside the consumption patterns of kerosene in Taiwan may seem far-fetched, if not downright absurd. Indeed, it is not every day that one encounters a correlation that seems as unlikely as finding a wet umbrella in a desert. However, as the old saying goes, "Truth is stranger than fiction," and in the realm of empirical data analysis, we must follow where the data leads, no matter how improbable the connections may seem.
This study sets out to unravel the enigmatic entanglement between these two disparate variables. Through the utilization of data from the Bureau of Labor Statistics and the Energy Information Administration, we endeavor to shed light on this quizzical quandary. The resulting statistical analyses reveal a remarkable correlation coefficient of 0.8435067 and a p-value < 0.01 over the period from 2003 to 2021. These findings, though unexpected, point to a surprisingly robust relationship that warrants further scrutiny, akin to finding a hidden door in a seemingly ordinary room.
Moreover, the unusual nature of this association beckons us to explore the underlying mechanisms at play. Just as a detective pursues leads in a baffling mystery, we are driven to unravel the intricate web of global economic influences that may be at the core of this perplexing puzzle. While it may seem as peculiar as a penguin in a desert, the potential insights gleaned from this unconventional inquiry could prove to be as illuminating as a lighthouse on a foggy night.
In the following sections, we will delve into the statistical methods employed, the results obtained, and the implications of this peculiar connection. By engaging in this whimsical pursuit, we hope to reveal hitherto overlooked dynamics and offer a glimpse into the uncanny nexus between seemingly unconnected phenomena. Just as a riddle invites scrutiny and curiosity, so too does this research endeavor into the mystifying marriage of locker room labor and luminous liquid consumption.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The linkage between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan is an unusual topic that has not received significant attention in academic literature. Remarkably, our search unearthed a surprising dearth of rigorous investigations into this perplexing association. However, a handful of studies have touched upon tangentially related areas, providing some potential insights into the underlying mechanisms at play.

In "The Economics of Locker Room Staffing" by Smith et al., the authors find a comprehensive analysis of labor allocation in various service industries, albeit without specific reference to Michigan or the implications for overseas kerosene usage. Similarly, Doe's "Energy Consumption Trends in East Asia" offers illuminating data on regional fuel consumption patterns, yet the linkage to locker room employment in the United States remains conspicuously absent from its analysis. Jones et al. examine the economic impact of unusual labor trends in "Unconventional Labor Dynamics," but regrettably, little attention is paid to the unlikely connection we explore in this study.

Turning to more general resources, "The Locker Room Chronicles" by Johnson provides an interesting exploration of the cultural significance of locker rooms, although it does not venture into the realm of economic analysis. In a similar vein, "Kerosene Dreams" by Martinez paints a vivid picture of life in Taiwan but remains silent on the peculiar influence of locker room staffing. Meanwhile, the movies "Michigan Mysteries: The Enigma of the Empty Lockers" and "Taiwan Tales: The Luminous Liquid Saga" offer tantalizing but anecdotal accounts that fall short of providing empirical evidence.

While the existing literature leaves much to be desired in terms of directly addressing the curious connection we seek to untangle, this dearth of prior research only serves to heighten the novelty and intrigue of our current investigation. In the absence of comprehensive scholarly treatment, our study aspires to fill this conspicuous gap and shed light on a baffling nexus that has largely escaped scholarly scrutiny.

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 The number of locker room attendants in Michigan and Kerosene used in Taiwan. 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 Bureau of Larbor Statistics and Energy Information Administration . You used data from 2003 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]]
Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

[[ABSTRACT]]
This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

[[INTRODUCTION]]
The perplexing correlations that emerge from seemingly unrelated variables have fascinated researchers for centuries. Just as the apple famously fell on Newton's head, sparking a revelation about gravity, so too do we find ourselves grappling with an unexpected association - the link between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. While this peculiar pairing may initially appear whimsical or even whimsy, the statistical analyses presented herein indicate a more robust relationship than one might expect. It is this head-scratching conundrum that motivates our inquiry into the uncharted territory of locker room labor and luminous liquid consumption.
To some, the notion of examining the staffing levels of locker rooms in Michigan alongside the consumption patterns of kerosene in Taiwan may seem far-fetched, if not downright absurd. Indeed, it is not every day that one encounters a correlation that seems as unlikely as finding a wet umbrella in a desert. However, as the old saying goes, "Truth is stranger than fiction," and in the realm of empirical data analysis, we must follow where the data leads, no matter how improbable the connections may seem.
This study sets out to unravel the enigmatic entanglement between these two disparate variables. Through the utilization of data from the Bureau of Labor Statistics and the Energy Information Administration, we endeavor to shed light on this quizzical quandary. The resulting statistical analyses reveal a remarkable correlation coefficient of 0.8435067 and a p-value < 0.01 over the period from 2003 to 2021. These findings, though unexpected, point to a surprisingly robust relationship that warrants further scrutiny, akin to finding a hidden door in a seemingly ordinary room.
Moreover, the unusual nature of this association beckons us to explore the underlying mechanisms at play. Just as a detective pursues leads in a baffling mystery, we are driven to unravel the intricate web of global economic influences that may be at the core of this perplexing puzzle. While it may seem as peculiar as a penguin in a desert, the potential insights gleaned from this unconventional inquiry could prove to be as illuminating as a lighthouse on a foggy night.
In the following sections, we will delve into the statistical methods employed, the results obtained, and the implications of this peculiar connection. By engaging in this whimsical pursuit, we hope to reveal hitherto overlooked dynamics and offer a glimpse into the uncanny nexus between seemingly unconnected phenomena. Just as a riddle invites scrutiny and curiosity, so too does this research endeavor into the mystifying marriage of locker room labor and luminous liquid consumption.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved a cascade of convoluted yet curiously effective research techniques to disentangle the relationship between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. The first step in this whimsical waltz was the aggregation of data from the Bureau of Labor Statistics and the Energy Information Administration. Through the expedient, albeit unorthodox, method of internet scouring, data spanning from 2003 to 2021 were acquired, as we cast our net on the digital sea like intrepid cyber fishermen.

The initial phase of the analysis entailed an intricate dance of statistical computations, reminiscent of assembling a jigsaw puzzle in a mirrored funhouse. The correlation between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan was determined using Pearson's correlation coefficient, yielding a surprising value of 0.8435067. One might say the connection between these variables is as strong as the aroma of freshly brewed coffee in the morning air or as feeble as a leaky umbrella in a rainstorm. Furthermore, the statistical significance of this association was assessed with a p-value < 0.01, indicating with a measure of certainty often eluding researchers in less eccentric endeavors, that the relationship is not a mere mirage in the desert of statistical noise.

To delve deeper into the implications of this unorthodox association, various time series analyses were conducted, akin to peering through a kaleidoscope to discern patterns within the swirling symphony of data. Through this kaleidoscopic lens, the temporal dynamics of locker room labor and luminous liquid consumption were examined, revealing an enigmatic narrative akin to decoding the movements of myriad fireflies in a moonlit meadow.

To augment our investigation, a series of control variables were incorporated into the analysis, akin to taming a fractious menagerie of statistical wildcards. These control variables included economic indicators such as gross domestic product, employment rates, and industrial production indices. By harnessing the erratic energies of these additional variables, we sought to isolate the unique influence of locker room labor on kerosene consumption, much like separating the gusts of wind that shape the patterns in a sand dune from the tumult of a desert storm.

Finally, the robustness of the results was scrutinized through a battery of robustness checks, reminiscent of stress-testing a flimsy umbrella in a tempest to ensure it could withstand the gales of skepticism. Sensitivity analyses, alternative model specifications, and outlier diagnostics were conducted to assess the stability of the findings, akin to scrutinizing the integrity of a house of cards in a light breeze.

Through this amalgamation of unorthodox yet rigorous methodologies, the tangled tale of locker room labor and luminous liquid consumption was unraveled, offering an unexpected glimpse into the intricate web of global economic influences.

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 The number of locker room attendants in Michigan and Kerosene used in Taiwan. 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 Bureau of Larbor Statistics and Energy Information Administration .

For the time period 2003 to 2021, you found a correlation 0.8435067, r-squared of 0.7115036, 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]]
Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

[[ABSTRACT]]
This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The perplexing correlations that emerge from seemingly unrelated variables have fascinated researchers for centuries. Just as the apple famously fell on Newton's head, sparking a revelation about gravity, so too do we find ourselves grappling with an unexpected association - the link between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. While this peculiar pairing may initially appear whimsical or even whimsy, the statistical analyses presented herein indicate a more robust relationship than one might expect. It is this head-scratching conundrum that motivates our inquiry into the uncharted territory of locker room labor and luminous liquid consumption.
To some, the notion of examining the staffing levels of locker rooms in Michigan alongside the consumption patterns of kerosene in Taiwan may seem far-fetched, if not downright absurd. Indeed, it is not every day that one encounters a correlation that seems as unlikely as finding a wet umbrella in a desert. However, as the old saying goes, "Truth is stranger than fiction," and in the realm of empirical data analysis, we must follow where the data leads, no matter how improbable the connections may seem.
This study sets out to unravel the enigmatic entanglement between these two disparate variables. Through the utilization of data from the Bureau of Labor Statistics and the Energy Information Administration, we endeavor to shed light on this quizzical quandary. The resulting statistical analyses reveal a remarkable correlation coefficient of 0.8435067 and a p-value < 0.01 over the period from 2003 to 2021. These findings, though unexpected, point to a surprisingly robust relationship that warrants further scrutiny, akin to finding a hidden door in a seemingly ordinary room.
Moreover, the unusual nature of this association beckons us to explore the underlying mechanisms at play. Just as a detective pursues leads in a baffling mystery, we are driven to unravel the intricate web of global economic influences that may be at the core of this perplexing puzzle. While it may seem as peculiar as a penguin in a desert, the potential insights gleaned from this unconventional inquiry could prove to be as illuminating as a lighthouse on a foggy night.
In the following sections, we will delve into the statistical methods employed, the results obtained, and the implications of this peculiar connection. By engaging in this whimsical pursuit, we hope to reveal hitherto overlooked dynamics and offer a glimpse into the uncanny nexus between seemingly unconnected phenomena. Just as a riddle invites scrutiny and curiosity, so too does this research endeavor into the mystifying marriage of locker room labor and luminous liquid consumption.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The data analysis revealed a substantial and statistically significant correlation between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. The correlation coefficient of 0.8435067 indicated a strong positive relationship, suggesting that as the number of locker room attendants in Michigan increased, so did the consumption of kerosene in Taiwan. This unexpected coupling may seem as unlikely as stumbling upon a treasure map in a library, yet the robustness of the correlation could not be ignored.

The r-squared value of 0.7115036 further emphasized the strength of this relationship, indicating that a considerable portion of the variation in kerosene consumption in Taiwan could be explained by the variation in the number of locker room attendants in Michigan. It is as if these two variables were engaged in an intricate dance, their movements harmonizing in ways that defy conventional expectations.

The p-value of less than 0.01 provided strong evidence against the null hypothesis of no correlation, confirming that the observed relationship was not a matter of sheer coincidence. This finding stood as steadfast as a lighthouse on a stormy night, guiding our attention to the compelling nature of this unanticipated association.

Given the bewildering nature of this connection, it is imperative to note that correlation does not imply causation. While the data revealed a striking linkage between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan, it remains an open question as to what underlying factors may be driving this curious correlation. The interplay between these variables could be as complex and multifaceted as solving a riddle within a puzzle, urging further investigation into the mechanisms at play.

Figure 1 provides visual evidence of the pronounced correlation between the number of locker room attendants in Michigan and kerosene consumption in Taiwan. The scatterplot illustrates the tightly clustered data points, painting a vivid picture of the compelling relationship uncovered in this study. The figure stands as a testament to the unexpected connections that can emerge from rigorous data analysis, akin to stumbling upon a breathtaking view in an otherwise mundane landscape.

In conclusion, the results of this investigation unearthed a substantial correlation between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan. The findings challenge conventional notions of causality in economic dynamics and beckon further exploration into the intricate web of global influences. The enigmatic bond between seemingly unrelated variables invites ongoing scrutiny, offering the potential for insights as unique as a rare gem in an unexplored cavern.

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 The number of locker room attendants in Michigan and Kerosene used in Taiwan. 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]]
Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

[[ABSTRACT]]
This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

[[LITERATURE REVIEW]]
The linkage between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan is an unusual topic that has not received significant attention in academic literature. Remarkably, our search unearthed a surprising dearth of rigorous investigations into this perplexing association. However, a handful of studies have touched upon tangentially related areas, providing some potential insights into the underlying mechanisms at play.
In "The Economics of Locker Room Staffing" by Smith et al., the authors find a comprehensive analysis of labor allocation in various service industries, albeit without specific reference to Michigan or the implications for overseas kerosene usage. Similarly, Doe's "Energy Consumption Trends in East Asia" offers illuminating data on regional fuel consumption patterns, yet the linkage to locker room employment in the United States remains conspicuously absent from its analysis. Jones et al. examine the economic impact of unusual labor trends in "Unconventional Labor Dynamics," but regrettably, little attention is paid to the unlikely connection we explore in this study.
Turning to more general resources, "The Locker Room Chronicles" by Johnson provides an interesting exploration of the cultural significance of locker rooms, although it does not venture into the realm of economic analysis. In a similar vein, "Kerosene Dreams" by Martinez paints a vivid picture of life in Taiwan but remains silent on the peculiar influence of locker room staffing. Meanwhile, the movies "Michigan Mysteries: The Enigma of the Empty Lockers" and "Taiwan Tales: The Luminous Liquid Saga" offer tantalizing but anecdotal accounts that fall short of providing empirical evidence.
While the existing literature leaves much to be desired in terms of directly addressing the curious connection we seek to untangle, this dearth of prior research only serves to heighten the novelty and intrigue of our current investigation. In the absence of comprehensive scholarly treatment, our study aspires to fill this conspicuous gap and shed light on a baffling nexus that has largely escaped scholarly scrutiny.

[[RESULTS]]
The data analysis revealed a substantial and statistically significant correlation between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. The correlation coefficient of 0.8435067 indicated a strong positive relationship, suggesting that as the number of locker room attendants in Michigan increased, so did the consumption of kerosene in Taiwan. This unexpected coupling may seem as unlikely as stumbling upon a treasure map in a library, yet the robustness of the correlation could not be ignored.
The r-squared value of 0.7115036 further emphasized the strength of this relationship, indicating that a considerable portion of the variation in kerosene consumption in Taiwan could be explained by the variation in the number of locker room attendants in Michigan. It is as if these two variables were engaged in an intricate dance, their movements harmonizing in ways that defy conventional expectations.
The p-value of less than 0.01 provided strong evidence against the null hypothesis of no correlation, confirming that the observed relationship was not a matter of sheer coincidence. This finding stood as steadfast as a lighthouse on a stormy night, guiding our attention to the compelling nature of this unanticipated association.
Given the bewildering nature of this connection, it is imperative to note that correlation does not imply causation. While the data revealed a striking linkage between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan, it remains an open question as to what underlying factors may be driving this curious correlation. The interplay between these variables could be as complex and multifaceted as solving a riddle within a puzzle, urging further investigation into the mechanisms at play.
Figure 1 provides visual evidence of the pronounced correlation between the number of locker room attendants in Michigan and kerosene consumption in Taiwan. The scatterplot illustrates the tightly clustered data points, painting a vivid picture of the compelling relationship uncovered in this study. The figure stands as a testament to the unexpected connections that can emerge from rigorous data analysis, akin to stumbling upon a breathtaking view in an otherwise mundane landscape.
In conclusion, the results of this investigation unearthed a substantial correlation between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan. The findings challenge conventional notions of causality in economic dynamics and beckon further exploration into the intricate web of global influences. The enigmatic bond between seemingly unrelated variables invites ongoing scrutiny, offering the potential for insights as unique as a rare gem in an unexplored cavern.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of the current study have brought to light an intriguing and ostensibly inexplicable relationship between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. While the connection may appear as incongruous as finding a snorkel in a snowstorm, the robust correlation uncovered in our analysis aligns with the unexpected, albeit scant, prior research on seemingly disparate economic and cultural phenomena.

Our findings, while initially puzzling, resonate with the work of Smith et al., whose exploration of labor allocation in service industries offers a broader context for understanding the allocation of resources within specific sectors of the economy. In a similar vein, Doe's examination of energy consumption trends in East Asia provides a backdrop against which to situate the notable influence of external factors on fuel usage, potentially touching upon the hitherto unexplored impact of transcontinental locker room dynamics.

The correlation coefficient of 0.8435067 observed in our study appears as robust as an oak tree in a storm, offering empirical support for the hitherto overlooked possibility of intricate connections between workforce dynamics in Michigan and kerosene demand in Taiwan. This aligns with the scholarly silence we encountered in the literature, amplifying the novelty and intrigue surrounding the curious association we interrogate in this study.

Indeed, the r-squared value of 0.7115036 serves as a further testament to the resilience of the relationship, providing a glimpse into the unanticipated strength of the connection between these unlikely bedfellows. This echoes the silence of existing literature while simultaneously underlining the surprising coherence of the data and the potential for unexpected patterns to emerge from rigorous analysis.

The p-value of less than 0.01 reinforces the significance of our findings, functioning as a confirmation akin to discovering that an unlikely pairing of wine and cheese is, in fact, a delectable combination. This statistical test, while traditionally used in hypothesis testing, serves as a robust reaffirmation of the validity of the observed connection, inviting further scrutiny of the complex and multifaceted interactions that may underlie the seemingly dichotomous variables.

In summary, the results of our investigation not only lend credence to the existence of an unexpected correlation between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan but also underscore the need for continued exploration of the underpinnings of this enigmatic relationship. As elusive as a mirage in the desert, the interplay between these variables cautions against hasty causal inferences, urging researchers to embark on the exciting pursuit of unraveling the intricate tapestry of global economic influences.

In essence, our study has unveiled a curious nexus that defies conventional economic reasoning, emphasizing the capacity for unexpected relationships to emerge from comprehensive data analysis. The peculiar bond between the number of locker room attendants in Michigan and kerosene consumption in Taiwan invites ongoing scrutiny, inspiring the pursuit of insights as unexpected and delightful as uncovering a hidden treasure in a forgotten attic.

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 The number of locker room attendants in Michigan and Kerosene used in Taiwan. 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]]
Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

[[ABSTRACT]]
This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

[[INTRDUCTION]]
The perplexing correlations that emerge from seemingly unrelated variables have fascinated researchers for centuries. Just as the apple famously fell on Newton's head, sparking a revelation about gravity, so too do we find ourselves grappling with an unexpected association - the link between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. While this peculiar pairing may initially appear whimsical or even whimsy, the statistical analyses presented herein indicate a more robust relationship than one might expect. It is this head-scratching conundrum that motivates our inquiry into the uncharted territory of locker room labor and luminous liquid consumption.
To some, the notion of examining the staffing levels of locker rooms in Michigan alongside the consumption patterns of kerosene in Taiwan may seem far-fetched, if not downright absurd. Indeed, it is not every day that one encounters a correlation that seems as unlikely as finding a wet umbrella in a desert. However, as the old saying goes, "Truth is stranger than fiction," and in the realm of empirical data analysis, we must follow where the data leads, no matter how improbable the connections may seem.
This study sets out to unravel the enigmatic entanglement between these two disparate variables. Through the utilization of data from the Bureau of Labor Statistics and the Energy Information Administration, we endeavor to shed light on this quizzical quandary. The resulting statistical analyses reveal a remarkable correlation coefficient of 0.8435067 and a p-value < 0.01 over the period from 2003 to 2021. These findings, though unexpected, point to a surprisingly robust relationship that warrants further scrutiny, akin to finding a hidden door in a seemingly ordinary room.
Moreover, the unusual nature of this association beckons us to explore the underlying mechanisms at play. Just as a detective pursues leads in a baffling mystery, we are driven to unravel the intricate web of global economic influences that may be at the core of this perplexing puzzle. While it may seem as peculiar as a penguin in a desert, the potential insights gleaned from this unconventional inquiry could prove to be as illuminating as a lighthouse on a foggy night.
In the following sections, we will delve into the statistical methods employed, the results obtained, and the implications of this peculiar connection. By engaging in this whimsical pursuit, we hope to reveal hitherto overlooked dynamics and offer a glimpse into the uncanny nexus between seemingly unconnected phenomena. Just as a riddle invites scrutiny and curiosity, so too does this research endeavor into the mystifying marriage of locker room labor and luminous liquid consumption.

[[RESULTS]]
The data analysis revealed a substantial and statistically significant correlation between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. The correlation coefficient of 0.8435067 indicated a strong positive relationship, suggesting that as the number of locker room attendants in Michigan increased, so did the consumption of kerosene in Taiwan. This unexpected coupling may seem as unlikely as stumbling upon a treasure map in a library, yet the robustness of the correlation could not be ignored.
The r-squared value of 0.7115036 further emphasized the strength of this relationship, indicating that a considerable portion of the variation in kerosene consumption in Taiwan could be explained by the variation in the number of locker room attendants in Michigan. It is as if these two variables were engaged in an intricate dance, their movements harmonizing in ways that defy conventional expectations.
The p-value of less than 0.01 provided strong evidence against the null hypothesis of no correlation, confirming that the observed relationship was not a matter of sheer coincidence. This finding stood as steadfast as a lighthouse on a stormy night, guiding our attention to the compelling nature of this unanticipated association.
Given the bewildering nature of this connection, it is imperative to note that correlation does not imply causation. While the data revealed a striking linkage between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan, it remains an open question as to what underlying factors may be driving this curious correlation. The interplay between these variables could be as complex and multifaceted as solving a riddle within a puzzle, urging further investigation into the mechanisms at play.
Figure 1 provides visual evidence of the pronounced correlation between the number of locker room attendants in Michigan and kerosene consumption in Taiwan. The scatterplot illustrates the tightly clustered data points, painting a vivid picture of the compelling relationship uncovered in this study. The figure stands as a testament to the unexpected connections that can emerge from rigorous data analysis, akin to stumbling upon a breathtaking view in an otherwise mundane landscape.
In conclusion, the results of this investigation unearthed a substantial correlation between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan. The findings challenge conventional notions of causality in economic dynamics and beckon further exploration into the intricate web of global influences. The enigmatic bond between seemingly unrelated variables invites ongoing scrutiny, offering the potential for insights as unique as a rare gem in an unexplored cavern.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In summary, the findings of this study uncover a surprisingly robust relationship between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan, akin to discovering a hidden treasure in an unexpected location. The substantial correlation coefficient and p-value < 0.01 highlight the compelling nature of this enigmatic association, urging us to delve further into this whimsical realm of inquiry. While the correlation between these variables may seem as unlikely as finding a penguin in a desert, the statistical evidence presented here demands serious attention, much like stumbling upon an unexpected plot twist in a classic mystery novel.

The visual depiction of the correlation in Figure 1 serves as a visual testament to the unexpected connections that rigorous data analysis can unveil, not unlike stumbling into a magnificent view in an otherwise mundane landscape. The unexpected dance between the staffing of locker rooms in Michigan and the consumption of kerosene in Taiwan defies conventional economic wisdom, resembling a surprising revelation in a complex puzzle.

Nevertheless, it is crucial to emphasize that correlation does not imply causation, and further exploration into the underlying mechanisms at play is warranted, just like unraveling the mysteries of a cryptic crossword puzzle. This peculiar nexus between seemingly unrelated variables invites continued investigation, offering the potential for insights as unique as a rare gem in an unexplored cavern.

In closing, it is evident that this eccentric examination into the connection between locker room labor and luminous liquid consumption has unearthed a remarkable relationship, challenging traditional notions of economic causality in a manner as unexpected as finding a wet umbrella in a desert. However, given the substantial findings of this study, it is our scholarly duty to assert that future research in this whimsical realm may yield diminishing returns. As such, it is our firm recommendation that no more research is needed in this area.

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 Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Zany Kinetic Kinesiology

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 Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination. Respond only with the name of a fictional organization.

ChatGPT:

The Organization for Anomalous Athletic Analysis

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 Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination with an abstract of This study examines the peculiar connection between the number of locker room attendants in Michigan and the consumption of kerosene in Taiwan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team sought to unravel this bizarre enigma. We found a remarkably high correlation coefficient of 0.8435067 and a p-value < 0.01 for the period spanning from 2003 to 2021. The results suggest a surprisingly robust relationship between these disparate variables, warranting further investigation into the underlying mechanisms at play. Despite the seemingly incongruous nature of this association, it appears that the enigmatic interplay between locker room labor and luminous liquid consumption may hold unexpected insights into the intricate web of global economic influences. Further research in this whimsical realm could shed light on hitherto overlooked dynamics and perhaps illuminate the curious nexus between seemingly unrelated phenomena.

ChatGPT:

locker room attendants, Michigan, consumption of kerosene, Taiwan, Bureau of Labor Statistics, Energy Information Administration, correlation coefficient, p-value, global economic influences, labor statistics, luminous liquid consumption

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

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Data details

The number of locker room attendants in Michigan
Detailed data title: BLS estimate of locker room, coatroom, and dressing room attendants in Michigan
Source: Bureau of Larbor Statistics
See what else correlates with The number of locker room attendants in Michigan

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

Correlation r = 0.8435067 (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 = 0.7115036 (Coefficient of determination)
This means 71.2% of the change in the one variable (i.e., Kerosene used in Taiwan) is predictable based on the change in the other (i.e., The number of locker room attendants in Michigan) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.71E-6.^Show 0.0000057091733699273050000000
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 0.84 in 0.000571% of random cases. Said differently, if you correlated 175,157 random variables^Note You don't actually need 175 thousand 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 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.63, 0.94 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of locker room attendants in Michigan (Laborers)	1120	1070	930	690	790	690	690	420	410	440	500	530	600	570	580	410	350	150	830
Kerosene used in Taiwan (Million Barrels/Day)	0.433273	0.392205	0.331541	0.278039	0.254036	0.186944	0.17772	0.132541	0.112174	0.0692683	0.0715258	0.0857241	0.092995	0.114393	0.0710726	0.081313	0.0434521	0.0869041	0.092427

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([1120,1070,930,690,790,690,690,420,410,440,500,530,600,570,580,410,350,150,830,])
array_2 = np.array([0.433273,0.392205,0.331541,0.278039,0.254036,0.186944,0.17772,0.132541,0.112174,0.0692683,0.0715258,0.0857241,0.092995,0.114393,0.0710726,0.081313,0.0434521,0.0869041,0.092427,])
array_1_name = "The number of locker room attendants in Michigan"
array_2_name = "Kerosene used in Taiwan"

# 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 The number of locker room attendants in Michigan
Line chart for only Kerosene used in Taiwan
AI-generated correlation image
The spurious research paper: Linking Locker Room Labor to Luminous Liquid: An Eccentric Examination

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Correlation ID: 3181 · Black Variable ID: 9737 · Red Variable ID: 24970


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