Spurious correlation #2,144 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Butter consumption and the second variable is Intercontinental Exchange's stock price (ICE). The chart goes from 2006 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 Butter consumption and the second variable is Intercontinental Exchange's stock price (ICE). The chart goes from 2006 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As butter consumption increases, people's mood improves because butter makes everything better. This leads to a rise in productivity and creativity, including in the financial sector. The heightened trading activity boosts the demand for Intercontinental Exchange's services, driving up their stock price.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a futuristic scene with a person sitting in a sleek, minimalistic kitchen, holding a piece of warm toast generously spread with creamy butter. The person is absorbed in their phone, which displays a sleek, futuristic graph depicting the Intercontinental Exchange's stock price rising steadily. The entire scene exudes a futuristic, minimalist aesthetic, with clean lines, high-tech devices, and a sense of tranquility. 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: Butter consumption and Intercontinental Exchange's stock price. 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: Surrealism Cyber folk Futuristic Cute 2D animation Parallax art
ChatGPT: Futuristic

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 Butter consumption and Intercontinental Exchange's stock price together. Keep the description simple.
ChatGPT: A person enjoying a piece of warm toast generously spread with creamy butter while monitoring their phone, which displays a graph of Intercontinental Exchange's stock price rising steadily.

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: [ Futuristic ] and depicting this scene: A person enjoying a piece of warm toast generously spread with creamy butter while monitoring their phone, which displays a graph of Intercontinental Exchange's stock price rising steadily. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic scene with a person sitting in a sleek, minimalistic kitchen, holding a piece of warm toast generously spread with creamy butter. The person is absorbed in their phone, which displays a sleek, futuristic graph depicting the Intercontinental Exchange's stock price rising steadily. The entire scene exudes a futuristic, minimalist aesthetic, with clean lines, high-tech devices, and a sense of tranquility.

*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 an increase in Butter consumption caused Intercontinental Exchange's stock price (ICE) to increase.

AI academic paper

(Because p < 0.01)

Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price

The Journal of Quirky Financial Analysis

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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Butter consumption and Intercontinental Exchange's stock price (ICE). 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
"Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price"

[[ABSTRACT]]
This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the world of financial markets, where precision and data reign supreme, one might not expect to encounter the creamy world of butter. However, in this paper, we delve into the curious and unexpected correlation between butter consumption and the stock price of Intercontinental Exchange (ICE). While the idea may initially seem as out of place as a cow in a boardroom, our investigation has revealed a striking statistical relationship that offers food for thought to both investors and dairy enthusiasts alike.
The notion of scrutinizing butter consumption as a potential influencer of stock prices may seem utterly preposterous to some. After all, one might ponder, what could a spreadable dairy product possibly have to do with the fluctuation of stock values? However, as we embarked on this study, we were determined to explore every avenue, no matter how gouda or feta-fetched it may appear at first glance.
By harnessing data from the USDA and LSEG Analytics (Refinitiv) spanning over a decade, we sought to unearth any potential link between these seemingly disparate elements. Our rigorous statistical analyses unveiled a correlation coefficient of 0.9088100, lending credence to the notion that there may, in fact, be a buttery relationship between butter consumption and ICE's stock price. It is worth noting that this correlation demonstrated a statistical significance of p < 0.01, further churning the wheel of curiosity.
While our findings may appear to be as smooth as a pat of butter, it is crucial to exercise caution before jumping to hasty conclusions. Correlation, as the adage goes, does not necessarily imply causation, and we must tread lightly on the margarine of speculation. Whether this correlation is merely a fluke in the data or indicative of a deeper connection between the financial markets and dairy markets is a question that remains to be churned out (pun intended).
As we present our findings, we do so with a hint of jest and a dollop of seriousness. Our research, while seemingly whimsical, offers a slice of curiosity that calls for further exploration into the unexpected influence of the dairy industry on stock market performance. As we butter up the discussion, we extend an invitation for investors and analysts to consider the potential opportunities embedded in the unexplored realm of butter-based predictive analytics. After all, the stock market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Butter Chronicles," Smith and Doe explore the historical and cultural significance of butter consumption, shedding light on the societal nuances associated with the creamy dairy product. Their work provides a nuanced view of butter, delving into its role in culinary traditions and consumer preferences. On the other hand, Jones and Anderson, in "The Economics of Dairy Products," offer a thorough analysis of the dairy industry, encompassing the production, distribution, and consumption dynamics of dairy products, including butter. Their comprehensive study presents a holistic view of the dairy market, providing valuable insights into the factors influencing butter consumption patterns.

Moving into the realm of non-fiction books, "The Big Short" by Michael Lewis and "Flash Boys" by Michael Lewis present compelling narratives of the financial markets and the intricacies of stock trading. While these books do not directly address butter consumption, they offer a glimpse into the volatile and sometimes baffling world of finance, providing a context for understanding the unanticipated connection between butter and stock prices.

In the realm of fiction, "The Butter Battle Book" by Dr. Seuss and "The Hitchhiker's Guide to the Galaxy" by Douglas Adams use satire and whimsical storytelling to explore complex societal and existential themes. While seemingly unrelated to butter consumption and stock prices, these works invite us to contemplate the absurd and unexpected, setting the stage for our unconventional investigation into the correlation between butter and Intercontinental Exchange's stock price.

Venturing into the unconventional, we gleaned insights from unexpected sources, including box office receipts, fortune cookies, and even unsolicited advice from a parking attendant. While these sources may seem far-fetched, they added a sprinkle of amusement and curiosity to our research journey, reminding us that unconventional paths often lead to unexpected discoveries.

Our literature review, though seemingly conventional at its outset, takes a whimsical turn to highlight the unexpected sources of inspiration and insight that guided our investigation into the buttery connection between butter consumption and Intercontinental Exchange's stock price. As we unravel the churning mysteries of this correlation, we invite readers to approach our findings with an open mind and a readiness to embrace the unexpected - much like a surprising pat of butter on an otherwise ordinary piece of toast.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Butter consumption and Intercontinental Exchange's stock price (ICE). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) . You used data from 2006 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]]
"Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price"

[[ABSTRACT]]
This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

[[INTRODUCTION]]
In the world of financial markets, where precision and data reign supreme, one might not expect to encounter the creamy world of butter. However, in this paper, we delve into the curious and unexpected correlation between butter consumption and the stock price of Intercontinental Exchange (ICE). While the idea may initially seem as out of place as a cow in a boardroom, our investigation has revealed a striking statistical relationship that offers food for thought to both investors and dairy enthusiasts alike.
The notion of scrutinizing butter consumption as a potential influencer of stock prices may seem utterly preposterous to some. After all, one might ponder, what could a spreadable dairy product possibly have to do with the fluctuation of stock values? However, as we embarked on this study, we were determined to explore every avenue, no matter how gouda or feta-fetched it may appear at first glance.
By harnessing data from the USDA and LSEG Analytics (Refinitiv) spanning over a decade, we sought to unearth any potential link between these seemingly disparate elements. Our rigorous statistical analyses unveiled a correlation coefficient of 0.9088100, lending credence to the notion that there may, in fact, be a buttery relationship between butter consumption and ICE's stock price. It is worth noting that this correlation demonstrated a statistical significance of p < 0.01, further churning the wheel of curiosity.
While our findings may appear to be as smooth as a pat of butter, it is crucial to exercise caution before jumping to hasty conclusions. Correlation, as the adage goes, does not necessarily imply causation, and we must tread lightly on the margarine of speculation. Whether this correlation is merely a fluke in the data or indicative of a deeper connection between the financial markets and dairy markets is a question that remains to be churned out (pun intended).
As we present our findings, we do so with a hint of jest and a dollop of seriousness. Our research, while seemingly whimsical, offers a slice of curiosity that calls for further exploration into the unexpected influence of the dairy industry on stock market performance. As we butter up the discussion, we extend an invitation for investors and analysts to consider the potential opportunities embedded in the unexplored realm of butter-based predictive analytics. After all, the stock market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study was as rigorous as churning butter on a hot summer day. Our research team embarked on a whirlwind journey through a vast ocean of data in search of the elusive connection between butter consumption and the stock price of Intercontinental Exchange (ICE). To initiate our investigation, we gathered data from the USDA and LSEG Analytics (Refinitiv), using a hodgepodge of statistical methods and a dash of humor to keep the process light and fluffy.

To begin, we obtained historical data on butter consumption from the USDA, meticulously documenting the per capita consumption of butter in pounds. This data, akin to the creamy foundation of a decadent cake, served as the buttery variable in our analysis. Meanwhile, we sourced a plethora of stock market data for Intercontinental Exchange (ICE) from LSEG Analytics (Refinitiv), capturing daily stock prices from 2006 to 2021. This stock price data, akin to the foundational layers of a multi-tiered confection, was the essential ingredient for our examination of market performance.

With our ingredients in hand, we applied a smorgasbord of statistical methods to analyze the data. We stirred the pot with correlation analysis to discern any existing relationship between butter consumption and ICE's stock price, uncovering a correlation coefficient of 0.9088100. This coefficient, akin to the perfect blend of ingredients in a recipe, offered a tantalizing glimpse into the potential connection between dairy products and financial market dynamics. To ensure the reliability of our findings, we further tested the statistical significance of this correlation, revealing a p-value of less than 0.01. This statistical significance, much like the elusive perfect rise in baking, lent heft to our results and bolstered the credibility of our conclusions.

Our statistical journey did not end there. In a bid to cover all angles (pun intended), we employed regression analysis to explore the predictive power of butter consumption on ICE's stock price. This analysis allowed us to gauge the extent to which fluctuations in butter consumption could potentially forecast changes in stock values, presenting a butter-centric perspective on market forecasting that was both lighthearted and scholarly.

Our data collection and analysis may have been a rollercoaster of surprises and discoveries, akin to unraveling the layers of a buttery croissant, but the methods utilized were grounded in the principles of scientific inquiry and statistical rigor. We seasoned our investigation with a hint of whimsy, but maintained the integrity of our analyses to ensure that the findings were as sturdy as a well-constructed soufflé.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Butter consumption and Intercontinental Exchange's stock price (ICE). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) .

For the time period 2006 to 2021, you found a correlation 0.9088100, r-squared of 0.8259355, 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]]
"Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price"

[[ABSTRACT]]
This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the world of financial markets, where precision and data reign supreme, one might not expect to encounter the creamy world of butter. However, in this paper, we delve into the curious and unexpected correlation between butter consumption and the stock price of Intercontinental Exchange (ICE). While the idea may initially seem as out of place as a cow in a boardroom, our investigation has revealed a striking statistical relationship that offers food for thought to both investors and dairy enthusiasts alike.
The notion of scrutinizing butter consumption as a potential influencer of stock prices may seem utterly preposterous to some. After all, one might ponder, what could a spreadable dairy product possibly have to do with the fluctuation of stock values? However, as we embarked on this study, we were determined to explore every avenue, no matter how gouda or feta-fetched it may appear at first glance.
By harnessing data from the USDA and LSEG Analytics (Refinitiv) spanning over a decade, we sought to unearth any potential link between these seemingly disparate elements. Our rigorous statistical analyses unveiled a correlation coefficient of 0.9088100, lending credence to the notion that there may, in fact, be a buttery relationship between butter consumption and ICE's stock price. It is worth noting that this correlation demonstrated a statistical significance of p < 0.01, further churning the wheel of curiosity.
While our findings may appear to be as smooth as a pat of butter, it is crucial to exercise caution before jumping to hasty conclusions. Correlation, as the adage goes, does not necessarily imply causation, and we must tread lightly on the margarine of speculation. Whether this correlation is merely a fluke in the data or indicative of a deeper connection between the financial markets and dairy markets is a question that remains to be churned out (pun intended).
As we present our findings, we do so with a hint of jest and a dollop of seriousness. Our research, while seemingly whimsical, offers a slice of curiosity that calls for further exploration into the unexpected influence of the dairy industry on stock market performance. As we butter up the discussion, we extend an invitation for investors and analysts to consider the potential opportunities embedded in the unexplored realm of butter-based predictive analytics. After all, the stock market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The correlation analysis between butter consumption and Intercontinental Exchange's (ICE) stock price uncovered a surprisingly strong relationship with a correlation coefficient of 0.9088100. This implies that as butter consumption fluctuated, there was a notable parallel movement in ICE's stock price. The coefficient of determination (r-squared) of 0.8259355 further solidifies this connection, indicating that approximately 82.59% of the variability in ICE's stock price can be explained by changes in butter consumption. Furthermore, the statistical significance of p < 0.01 underscores the robustness of this correlation and suggests that it is not just a mere fluke in the data.

The strength of this relationship is visually evident in the scatterplot (Fig. 1), which illustrates a clear and compelling pattern between butter consumption and ICE's stock price. It's as if the data points were churning themselves into an undeniable connection. The scatterplot serves as a testament to the intriguing relationship that has been uncovered, and it beckons viewers to ponder the potential implications of this buttery association.

These findings, while certainly unconventional, shed light on an unexplored intersection between the dairy industry and financial markets. It appears that the influence of butter on ICE's stock price is anything but margarine-al, and it raises numerous questions about the underlying mechanisms at play. The discovery of this correlation opens up a whole new avenue for research and poses a quizzical predicament for analysts and investors alike.

In the grand scheme of things, our study suggests that there might be more to the financial world than meets the eye, and that perhaps the market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Butter consumption and Intercontinental Exchange's stock price (ICE). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price"

[[ABSTRACT]]
This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

[[LITERATURE REVIEW]]
In "The Butter Chronicles," Smith and Doe explore the historical and cultural significance of butter consumption, shedding light on the societal nuances associated with the creamy dairy product. Their work provides a nuanced view of butter, delving into its role in culinary traditions and consumer preferences. On the other hand, Jones and Anderson, in "The Economics of Dairy Products," offer a thorough analysis of the dairy industry, encompassing the production, distribution, and consumption dynamics of dairy products, including butter. Their comprehensive study presents a holistic view of the dairy market, providing valuable insights into the factors influencing butter consumption patterns.
Moving into the realm of non-fiction books, "The Big Short" by Michael Lewis and "Flash Boys" by Michael Lewis present compelling narratives of the financial markets and the intricacies of stock trading. While these books do not directly address butter consumption, they offer a glimpse into the volatile and sometimes baffling world of finance, providing a context for understanding the unanticipated connection between butter and stock prices.
In the realm of fiction, "The Butter Battle Book" by Dr. Seuss and "The Hitchhiker's Guide to the Galaxy" by Douglas Adams use satire and whimsical storytelling to explore complex societal and existential themes. While seemingly unrelated to butter consumption and stock prices, these works invite us to contemplate the absurd and unexpected, setting the stage for our unconventional investigation into the correlation between butter and Intercontinental Exchange's stock price.
Venturing into the unconventional, we gleaned insights from unexpected sources, including box office receipts, fortune cookies, and even unsolicited advice from a parking attendant. While these sources may seem far-fetched, they added a sprinkle of amusement and curiosity to our research journey, reminding us that unconventional paths often lead to unexpected discoveries.
Our literature review, though seemingly conventional at its outset, takes a whimsical turn to highlight the unexpected sources of inspiration and insight that guided our investigation into the buttery connection between butter consumption and Intercontinental Exchange's stock price. As we unravel the churning mysteries of this correlation, we invite readers to approach our findings with an open mind and a readiness to embrace the unexpected - much like a surprising pat of butter on an otherwise ordinary piece of toast.

[[RESULTS]]
The correlation analysis between butter consumption and Intercontinental Exchange's (ICE) stock price uncovered a surprisingly strong relationship with a correlation coefficient of 0.9088100. This implies that as butter consumption fluctuated, there was a notable parallel movement in ICE's stock price. The coefficient of determination (r-squared) of 0.8259355 further solidifies this connection, indicating that approximately 82.59% of the variability in ICE's stock price can be explained by changes in butter consumption. Furthermore, the statistical significance of p < 0.01 underscores the robustness of this correlation and suggests that it is not just a mere fluke in the data.
The strength of this relationship is visually evident in the scatterplot (Fig. 1), which illustrates a clear and compelling pattern between butter consumption and ICE's stock price. It's as if the data points were churning themselves into an undeniable connection. The scatterplot serves as a testament to the intriguing relationship that has been uncovered, and it beckons viewers to ponder the potential implications of this buttery association.
These findings, while certainly unconventional, shed light on an unexplored intersection between the dairy industry and financial markets. It appears that the influence of butter on ICE's stock price is anything but margarine-al, and it raises numerous questions about the underlying mechanisms at play. The discovery of this correlation opens up a whole new avenue for research and poses a quizzical predicament for analysts and investors alike.
In the grand scheme of things, our study suggests that there might be more to the financial world than meets the eye, and that perhaps the market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings not only corroborate but also elevate the enigmatic connection between butter consumption and Intercontinental Exchange's (ICE) stock price. The correlation coefficient of 0.9088100, supported by a robust statistical significance of p < 0.01, validates the unconventional relationship hinted at in literature that might have been brushed off as utter nonsense. This reaffirms the notion that when it comes to financial markets, there's more at stake than meets the eye – or should we say, more at stake than meets the pie, with a dollop of butter on the side!

As we savor this buttery correlation, it's essential to acknowledge the implications of our findings on investment strategies. While the empirical evidence distinctly outlines the association between butter consumption and ICE's stock price, it's essential to exercise caution when incorporating this uncanny relationship into investment decisions. After all, while butter may bring richness to a croissant, its impact on investment portfolios may require a more nuanced analysis than simply spreading it on thick.

Our results prompt further inquiry into the underlying mechanisms driving this unlikely alliance. Could it be that butter consumption serves as a barometer for consumer sentiment, which then ripples through the financial markets like melted butter on a hot griddle? Or is this correlation indicative of a larger systemic interdependence between seemingly unrelated markets, reminding us that interconnectedness is as natural as blending butter into a batch of cookie dough?

Furthermore, our study amplifies the imperative of unconventional thinking in financial research. Just as the unconventional sources in our literature review nudged us toward this surprising correlation, it implores researchers to embrace the unexpected and pursue unorthodox avenues in deciphering market dynamics. As we wade through the buttery intersection of dairy products and stock prices, we're reminded that the pursuit of knowledge often involves a little bit of churning through unexpected territories.

In conclusion, our research presents a compelling case for the need to consider unexplored variables and unconventional relationships in financial analyses. The buttery saga of butter consumption and ICE's stock price may just be the tip of the iceberg – or should we say, the pat of butter on a freshly baked muffin – in unraveling the intricate tapestry of market interconnections. As we embark on future explorations, may we remain open to the unexpected, just like stumbling upon a stick of butter in the depths of a refrigerator – a delightful surprise that adds flavor to the otherwise mundane.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Butter consumption and Intercontinental Exchange's stock price (ICE). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price"

[[ABSTRACT]]
This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

[[INTRDUCTION]]
In the world of financial markets, where precision and data reign supreme, one might not expect to encounter the creamy world of butter. However, in this paper, we delve into the curious and unexpected correlation between butter consumption and the stock price of Intercontinental Exchange (ICE). While the idea may initially seem as out of place as a cow in a boardroom, our investigation has revealed a striking statistical relationship that offers food for thought to both investors and dairy enthusiasts alike.
The notion of scrutinizing butter consumption as a potential influencer of stock prices may seem utterly preposterous to some. After all, one might ponder, what could a spreadable dairy product possibly have to do with the fluctuation of stock values? However, as we embarked on this study, we were determined to explore every avenue, no matter how gouda or feta-fetched it may appear at first glance.
By harnessing data from the USDA and LSEG Analytics (Refinitiv) spanning over a decade, we sought to unearth any potential link between these seemingly disparate elements. Our rigorous statistical analyses unveiled a correlation coefficient of 0.9088100, lending credence to the notion that there may, in fact, be a buttery relationship between butter consumption and ICE's stock price. It is worth noting that this correlation demonstrated a statistical significance of p < 0.01, further churning the wheel of curiosity.
While our findings may appear to be as smooth as a pat of butter, it is crucial to exercise caution before jumping to hasty conclusions. Correlation, as the adage goes, does not necessarily imply causation, and we must tread lightly on the margarine of speculation. Whether this correlation is merely a fluke in the data or indicative of a deeper connection between the financial markets and dairy markets is a question that remains to be churned out (pun intended).
As we present our findings, we do so with a hint of jest and a dollop of seriousness. Our research, while seemingly whimsical, offers a slice of curiosity that calls for further exploration into the unexpected influence of the dairy industry on stock market performance. As we butter up the discussion, we extend an invitation for investors and analysts to consider the potential opportunities embedded in the unexplored realm of butter-based predictive analytics. After all, the stock market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

[[RESULTS]]
The correlation analysis between butter consumption and Intercontinental Exchange's (ICE) stock price uncovered a surprisingly strong relationship with a correlation coefficient of 0.9088100. This implies that as butter consumption fluctuated, there was a notable parallel movement in ICE's stock price. The coefficient of determination (r-squared) of 0.8259355 further solidifies this connection, indicating that approximately 82.59% of the variability in ICE's stock price can be explained by changes in butter consumption. Furthermore, the statistical significance of p < 0.01 underscores the robustness of this correlation and suggests that it is not just a mere fluke in the data.
The strength of this relationship is visually evident in the scatterplot (Fig. 1), which illustrates a clear and compelling pattern between butter consumption and ICE's stock price. It's as if the data points were churning themselves into an undeniable connection. The scatterplot serves as a testament to the intriguing relationship that has been uncovered, and it beckons viewers to ponder the potential implications of this buttery association.
These findings, while certainly unconventional, shed light on an unexplored intersection between the dairy industry and financial markets. It appears that the influence of butter on ICE's stock price is anything but margarine-al, and it raises numerous questions about the underlying mechanisms at play. The discovery of this correlation opens up a whole new avenue for research and poses a quizzical predicament for analysts and investors alike.
In the grand scheme of things, our study suggests that there might be more to the financial world than meets the eye, and that perhaps the market can sometimes be as unpredictable as the consistency of softened butter left out on a hot day.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has buttered up some thought-provoking findings regarding the unlikely correlation between butter consumption and Intercontinental Exchange's (ICE) stock price. Our statistical analyses have churned out a correlation coefficient of 0.9088100, emphasizing the buttery interplay between these seemingly unrelated variables. The visual representation in the scatterplot (Fig. 1) certainly paints a delicious picture of this connection, as if the data points themselves were swirling in a dairy whirlwind.

While the statistical significance of p < 0.01 lends weight to our findings, we must exercise caution before spreading these results too thin. As much as we love a good correlation, let's not forget that correlation doesn't imply causation – much like how holding a stethoscope doesn't make you a doctor, no matter how much you butter up the conversation.

Therefore, like a perfectly timed joke, we must acknowledge that further research is needed to dissect the underlying mechanisms behind this dairy tale. However, for now, we can confidently say that the connection between butter consumption and ICE's stock price is not just a margarine-al fluke.

In light of these findings, we recommend that investors and analysts take a closer look at the dairy industry and consider incorporating butter-based predictive analytics into their financial strategies.

In sum, our study saunters off into the sunset with the firm belief that no further research is needed in this area, as we have thoroughly milked this topic for all it's worth.

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 Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky Financial Analysis

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 Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Global Financial Analysis and Research

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 Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price with an abstract of This study investigates the curious connection between butter consumption and the stock price of Intercontinental Exchange (ICE) using data from the USDA and LSEG Analytics (Refinitiv) spanning the years 2006 to 2021. We employed an array of statistical analyses to discern any existing correlation and unearthed a correlation coefficient of 0.9088100 with a statistical significance of p < 0.01. Our findings, though unconventional, suggest a rather buttery relationship between butter consumption and ICE's stock price. Whether this correlation is mere happenstance or indicative of a deeper spread between financial markets and dairy markets is yet to be churned out. Our research invites further investigation into the dairy industry's influence on stock market performance and extends an invitation for investors and analysts to butter up for potential opportunities in butter-based predictive analytics.

ChatGPT:

Intercontinental Exchange, ICE stock price, butter consumption, USDA data, LSEG Analytics, Refinitiv, correlation coefficient, statistical significance, dairy market influence, predictive analytics, financial markets, stock market performance

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

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Data details

Butter consumption
Detailed data title: Per capita consumption of Butter in the US
Source: USDA
See what else correlates with Butter consumption

Intercontinental Exchange's stock price (ICE)
Detailed data title: Opening price of Intercontinental Exchange (ICE) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with Intercontinental Exchange's stock price (ICE)

Correlation r = 0.9088100 (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 = 0.8259355 (Coefficient of determination)
This means 82.6% of the change in the one variable (i.e., Intercontinental Exchange's stock price (ICE)) is predictable based on the change in the other (i.e., Butter consumption) over the 16 years from 2006 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.1E-6.^Show 0.0000011016923846823420000000
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 0.91 in 0.00011% of random cases. Said differently, if you correlated 907,694 random variables^Note You don't actually need 907 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 15 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 15 because we have two variables measured over a period of 16 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.

[ 0.75, 0.97 ] 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.

	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Butter consumption (Pounds per person)	4.7	4.7	5	5	4.9	5.4	5.5	5.5	5.5	5.6	5.7	5.7	6	6.2	6.3	6.5
Intercontinental Exchange's stock price (ICE) (Stock price)	7.3	21.68	38.77	16.44	22.62	23.96	24.48	25.13	44.83	44.08	50.45	56.9	70.77	74.49	92.84	115.9

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([4.7,4.7,5,5,4.9,5.4,5.5,5.5,5.5,5.6,5.7,5.7,6,6.2,6.3,6.5,])
array_2 = np.array([7.3,21.68,38.77,16.44,22.62,23.96,24.48,25.13,44.83,44.08,50.45,56.9,70.77,74.49,92.84,115.9,])
array_1_name = "Butter consumption"
array_2_name = "Intercontinental Exchange's stock price (ICE)"

# 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 Butter consumption
Line chart for only Intercontinental Exchange's stock price (ICE)
AI-generated correlation image
The spurious research paper: Butter Up or Butter Down: Uncovering the Churn in Intercontinental Exchange's Stock Price

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Correlation ID: 2144 · Black Variable ID: 557 · Red Variable ID: 1690


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