Spurious correlation #2,004 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is American cheese consumption and the second variable is Mastercard's stock price (MA). The chart goes from 2007 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 American cheese consumption and the second variable is Mastercard's stock price (MA). The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As American cheese consumption melts, so does the resistance to indulgent spending, causing a nacho-ral rise in demand for credit transactions. This has led to a gouda-wnward pressure on Mastercard's stock, as investors brie-lieve in the power of cheese to drive economic cheddar-tunities. It seems the saying is true: In cheese, and in finance, it's all about the Havarti!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a family sitting at a wooden dining table, smiling and chatting while enjoying classic cheeseburgers with American cheese slices. The table is set with ketchup, mustard, pickles, and fries. The father is holding a smartphone, checking the Mastercard stock price, while the mother helps the kids with their meals. The scene captures a warm, family gathering with a hint of modernity through the use of technology. The emphasis should be on the joyful family moment with subtle indications of the smartphone activity. 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: American cheese consumption and Mastercard'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: Data cyber art Concept art Realistic inspired by bauhaus Voxel art
ChatGPT: Realistic

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 American cheese consumption and Mastercard's stock price together. Keep the description simple.
ChatGPT: A family enjoying a classic cheeseburger meal with American cheese slices while checking their Mastercard stock price on their smartphone.

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: [ Realistic ] and depicting this scene: A family enjoying a classic cheeseburger meal with American cheese slices while checking their Mastercard stock price on their smartphone. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a family sitting at a wooden dining table, smiling and chatting while enjoying classic cheeseburgers with American cheese slices. The table is set with ketchup, mustard, pickles, and fries. The father is holding a smartphone, checking the Mastercard stock price, while the mother helps the kids with their meals. The scene captures a warm, family gathering with a hint of modernity through the use of technology. The emphasis should be on the joyful family moment with subtle indications of the smartphone activity.

*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 American cheese consumption caused Mastercard's stock price (MA) to increase.

AI academic paper

(Because p < 0.01)

Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)

The Journal of Dairy Economics and Financial Transactions

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 American cheese consumption and Mastercard's stock price (MA). 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]]
"Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)"

[[ABSTRACT]]
In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the esteemed realm of financial analysis, it is customary to scrutinize a myriad of variables and factors in an attempt to unearth the elusive patterns and relationships that guide the fluctuations of stock prices. From interest rates and economic indicators to company performance and market sentiment, the quest for identifying key drivers of stock prices is akin to a perilous expedition through a dense jungle of data. However, in this jungle of financial analysis, one may unexpectedly stumble upon the unlikely presence of a block of American cheese, nestled among the formidable statistics and trend lines.
The notion of a connection between American cheese consumption and the stock price of Mastercard (MA) may appear as a comical rendezvous of unrelated entities, akin to a serendipitous meeting between a mathematician and a stand-up comedian. However, as we embark on this curious endeavor, it becomes increasingly clear that in the labyrinthine world of market dynamics, even the most unassuming variables may hold clandestine sway over the ebb and flow of stock prices. Hence, we are propelled to investigate this peculiar duo with the same fervor and inquisitiveness usually reserved for more traditional factors.
Our exploration into this quizzical correlation was motivated by an innate curiosity and a penchant for embracing the unconventional in the realm of financial analysis. The initial murmurings of a potential relationship between American cheese consumption and Mastercard's stock price beckoned us to take a closer look, leading to a journey that would involve traversing through years of data and statistical analyses. As we navigated through the convoluted landscape of cheese consumption and market performance, we encountered surprising revelations and peculiar patterns that challenged the traditional boundaries of financial analysis.
It is essential to note that our investigation was not undertaken with a haphazard abandon, but rather with the fervent dedication and meticulous rigor befitting any scholarly pursuit. Leveraging data from the United States Department of Agriculture (USDA) and the esteemed LSEG Analytics (Refinitiv), we meticulously combed through the years from 2007 to 2021, subjecting the data to an array of statistical tests and methodologies. The unearthing of a correlation coefficient of 0.9056200 and a p-value of less than 0.01 dangled before us like a tantalizing enigma, demanding further scrutiny and contemplation.
As we delve deeper into the emmental connection between American cheese consumption and Mastercard's stock price, we invite our esteemed colleagues and scholars to join us on this unconventional odyssey. The ambitions of our investigation extend beyond the mere discovery of an unexpected correlation; rather, we seek to unveil the underlying mechanisms and potential implications that this unlikely relationship may harbor for the landscape of stock analysis. With a blend of scientific rigor and an openness to unanticipated connections, our foray into this uncharted territory promises not only to broaden the horizons of financial analysis but also to elicit a few wry smiles amidst the stern countenances of statistical analysis.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The scholarly exploration of the curious connection between American cheese consumption and the stock price of Mastercard (MA) has led researchers down a veritable rabbit hole of investigations and analyses. Numerous studies have shed light on the intricate relationship between consumer behavior and financial markets, with a particular emphasis on the unorthodox interplay between seemingly unrelated variables. Smith et al. (2015) delved into the psychological factors influencing consumer spending patterns and their impact on stock prices, while Doe and Jones (2017) sought to unravel the cryptic influence of cultural trends on market fluctuations. These seminal works provided a solid foundation for understanding the nuanced dynamics of consumer behavior and financial markets, setting the stage for our own unconventional exploration.

Turning to the realm of non-fiction, authors such as "Freakonomics" by Steven D. Levitt and Stephen J. Dubner and "Predictably Irrational" by Dan Ariely have traversed the boundaries of traditional economic analysis, uncovering unexpected connections and behavioral anomalies that lie beneath the surface of economic phenomena. While not directly addressing the curious link between American cheese consumption and stock prices, these works beckon us to adopt a broader perspective and embrace the unpredictable nature of economic relationships.

Venturing into the realm of fiction, novels like "The Cheese Monkeys" by Chip Kidd and "The Money Culture" by Michael Lewis offer a whimsical and satirical take on the world of consumerism, financial markets, and the intricacies of human behavior. Although purely works of imagination, these literary pieces encourage us to approach our investigation with a hint of levity and an appreciation for the unconventional.

In a departure from traditional literature sources, our inquiry took an unconventional turn as we delved into the depths of everyday life, scouring through grocery receipts, including those from the famed CVS, to glean insights into consumer purchasing habits and, by extension, their potential impact on stock prices. While this approach may raise an eyebrow among purveyors of scholarly conventions, it allowed us to tap into the pulse of consumer behavior through a somewhat unorthodox but undeniably fascinating window.

As we navigate this peculiar and often whimsical landscape of research, we invite our esteemed colleagues to embrace the unorthodox and quirky nature of our investigation. The findings to follow not only promise to expand the horizons of financial analysis but also elicit a few chuckles amidst the otherwise serious discourse of academic research.

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 American cheese consumption and Mastercard's stock price (MA). 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 2007 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]]
"Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)"

[[ABSTRACT]]
In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

[[INTRODUCTION]]
In the esteemed realm of financial analysis, it is customary to scrutinize a myriad of variables and factors in an attempt to unearth the elusive patterns and relationships that guide the fluctuations of stock prices. From interest rates and economic indicators to company performance and market sentiment, the quest for identifying key drivers of stock prices is akin to a perilous expedition through a dense jungle of data. However, in this jungle of financial analysis, one may unexpectedly stumble upon the unlikely presence of a block of American cheese, nestled among the formidable statistics and trend lines.
The notion of a connection between American cheese consumption and the stock price of Mastercard (MA) may appear as a comical rendezvous of unrelated entities, akin to a serendipitous meeting between a mathematician and a stand-up comedian. However, as we embark on this curious endeavor, it becomes increasingly clear that in the labyrinthine world of market dynamics, even the most unassuming variables may hold clandestine sway over the ebb and flow of stock prices. Hence, we are propelled to investigate this peculiar duo with the same fervor and inquisitiveness usually reserved for more traditional factors.
Our exploration into this quizzical correlation was motivated by an innate curiosity and a penchant for embracing the unconventional in the realm of financial analysis. The initial murmurings of a potential relationship between American cheese consumption and Mastercard's stock price beckoned us to take a closer look, leading to a journey that would involve traversing through years of data and statistical analyses. As we navigated through the convoluted landscape of cheese consumption and market performance, we encountered surprising revelations and peculiar patterns that challenged the traditional boundaries of financial analysis.
It is essential to note that our investigation was not undertaken with a haphazard abandon, but rather with the fervent dedication and meticulous rigor befitting any scholarly pursuit. Leveraging data from the United States Department of Agriculture (USDA) and the esteemed LSEG Analytics (Refinitiv), we meticulously combed through the years from 2007 to 2021, subjecting the data to an array of statistical tests and methodologies. The unearthing of a correlation coefficient of 0.9056200 and a p-value of less than 0.01 dangled before us like a tantalizing enigma, demanding further scrutiny and contemplation.
As we delve deeper into the emmental connection between American cheese consumption and Mastercard's stock price, we invite our esteemed colleagues and scholars to join us on this unconventional odyssey. The ambitions of our investigation extend beyond the mere discovery of an unexpected correlation; rather, we seek to unveil the underlying mechanisms and potential implications that this unlikely relationship may harbor for the landscape of stock analysis. With a blend of scientific rigor and an openness to unanticipated connections, our foray into this uncharted territory promises not only to broaden the horizons of financial analysis but also to elicit a few wry smiles amidst the stern countenances of statistical analysis.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To dissect the enigmatic correlation between American cheese consumption and Mastercard's stock price (MA), we employed a blend of traditional statistical methods and a whimsical twist of unconventional analyses. Our data delving extravaganza commenced with a comprehensive collection of information from the expanse of the internet, focusing our keen gazes on the treasure troves of the United States Department of Agriculture (USDA) and the esteemed LSEG Analytics (Refinitiv).

Our examination covered the chronicles from 2007 to 2021, allowing for an extensive exploration of the temporal relationship between these seemingly disparate variables. To calculate American cheese consumption, we artfully amalgamated data from the USDA, taking into account the consumption patterns that mirrored the ebb and flow of societal whims and culinary inclinations.

As for the stock price of Mastercard (MA), we diligently observed its movements through the lens of LSEG Analytics (Refinitiv), employing their exquisite data to capture the undulating manifestations of market dynamics. The utilization of this refined data offered us a grand tableau to unravel the intricate dance between stock price and dairy delicacies.

This monumental marriage of data from disparate sources laid the fertile groundwork for our statistical analysis. We began by conducting a Pearson correlation coefficient analysis, seeking to unveil the entwined fluctuations of American cheese consumption and Mastercard's stock price. Our insatiable hunger for statistical rigour led us to scrutinize the strength and direction of this relationship, paving the way for a revelatory correlation coefficient of 0.9056200 that sparkled with statistical significance.

Furthermore, we employed a simple linear regression analysis to unravel the nuanced interplay between American cheese consumption and stock price, uncovering hidden patterns and trends that danced with the finesse of a maestro orchestra conductor. The prodigious p-value of less than 0.01 tantalized us with the prospect of a true relationship, beckoning us to delve deeper into the whimsical world of finance and dairy comestibles.

It is worth noting that our rigorous methodologies were underpinned by a spirit of lighthearted curiosity, a passion for unearthing unconventional connections, and a willingness to embrace the unexpected in the alluring embrace of scientific inquiry. Our study emerged from the crucible of unconventional exploration, blending the solemnity of statistical analyses with the gaiety of unexpected correlations, as we unveiled the emmental connection between American cheese consumption and Mastercard's stock price.

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 American cheese consumption and Mastercard's stock price (MA). 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 2007 to 2021, you found a correlation 0.9056200, r-squared of 0.8201476, 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]]
"Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)"

[[ABSTRACT]]
In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the esteemed realm of financial analysis, it is customary to scrutinize a myriad of variables and factors in an attempt to unearth the elusive patterns and relationships that guide the fluctuations of stock prices. From interest rates and economic indicators to company performance and market sentiment, the quest for identifying key drivers of stock prices is akin to a perilous expedition through a dense jungle of data. However, in this jungle of financial analysis, one may unexpectedly stumble upon the unlikely presence of a block of American cheese, nestled among the formidable statistics and trend lines.
The notion of a connection between American cheese consumption and the stock price of Mastercard (MA) may appear as a comical rendezvous of unrelated entities, akin to a serendipitous meeting between a mathematician and a stand-up comedian. However, as we embark on this curious endeavor, it becomes increasingly clear that in the labyrinthine world of market dynamics, even the most unassuming variables may hold clandestine sway over the ebb and flow of stock prices. Hence, we are propelled to investigate this peculiar duo with the same fervor and inquisitiveness usually reserved for more traditional factors.
Our exploration into this quizzical correlation was motivated by an innate curiosity and a penchant for embracing the unconventional in the realm of financial analysis. The initial murmurings of a potential relationship between American cheese consumption and Mastercard's stock price beckoned us to take a closer look, leading to a journey that would involve traversing through years of data and statistical analyses. As we navigated through the convoluted landscape of cheese consumption and market performance, we encountered surprising revelations and peculiar patterns that challenged the traditional boundaries of financial analysis.
It is essential to note that our investigation was not undertaken with a haphazard abandon, but rather with the fervent dedication and meticulous rigor befitting any scholarly pursuit. Leveraging data from the United States Department of Agriculture (USDA) and the esteemed LSEG Analytics (Refinitiv), we meticulously combed through the years from 2007 to 2021, subjecting the data to an array of statistical tests and methodologies. The unearthing of a correlation coefficient of 0.9056200 and a p-value of less than 0.01 dangled before us like a tantalizing enigma, demanding further scrutiny and contemplation.
As we delve deeper into the emmental connection between American cheese consumption and Mastercard's stock price, we invite our esteemed colleagues and scholars to join us on this unconventional odyssey. The ambitions of our investigation extend beyond the mere discovery of an unexpected correlation; rather, we seek to unveil the underlying mechanisms and potential implications that this unlikely relationship may harbor for the landscape of stock analysis. With a blend of scientific rigor and an openness to unanticipated connections, our foray into this uncharted territory promises not only to broaden the horizons of financial analysis but also to elicit a few wry smiles amidst the stern countenances of statistical analysis.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

Our investigation into the intriguing link between American cheese consumption and Mastercard's stock price (MA) from 2007 to 2021 yielded some rather "grate" results. The correlation coefficient of 0.9056200, accompanied by an r-squared value of 0.8201476, revealed a remarkably strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further solidified the statistical significance of this association, leaving us to ponder the provolone possibilities that lay beyond this unexpected correlation.

To visually emphasize the robustness of this correlation, we present the scatterplot in Figure 1, which encapsulates the undeniable affinity between American cheese consumption and Mastercard's stock price. The scatterplot, akin to the Mona Lisa of statistical representation, astonishes with its portrayal of the convergence of dairy delight and financial fervor.

While some may be quick to dismiss the notion of cheese consumption influencing stock prices as a mere "fondue" of imagination, the data speaks volumes about the unanticipated sway of this unassuming dairy delight. We are reminded that amidst the cold, hard world of stock analysis, there exists a warm, melty center of unexpected relationships waiting to be uncovered.

Our findings beckon the curious minds of scholars and analysts to not only ponder the peculiar partnership of cheddar and stock prices but also to consider the broader implications for market dynamics. As the saying goes, "It's not easy being cheesy", but in the world of financial analysis, embracing the unexpected may just lead to a feta future of insight and discovery.

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 American cheese consumption and Mastercard's stock price (MA). 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]]
"Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)"

[[ABSTRACT]]
In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

[[LITERATURE REVIEW]]
The scholarly exploration of the curious connection between American cheese consumption and the stock price of Mastercard (MA) has led researchers down a veritable rabbit hole of investigations and analyses. Numerous studies have shed light on the intricate relationship between consumer behavior and financial markets, with a particular emphasis on the unorthodox interplay between seemingly unrelated variables. Smith et al. (2015) delved into the psychological factors influencing consumer spending patterns and their impact on stock prices, while Doe and Jones (2017) sought to unravel the cryptic influence of cultural trends on market fluctuations. These seminal works provided a solid foundation for understanding the nuanced dynamics of consumer behavior and financial markets, setting the stage for our own unconventional exploration.
Turning to the realm of non-fiction, authors such as "Freakonomics" by Steven D. Levitt and Stephen J. Dubner and "Predictably Irrational" by Dan Ariely have traversed the boundaries of traditional economic analysis, uncovering unexpected connections and behavioral anomalies that lie beneath the surface of economic phenomena. While not directly addressing the curious link between American cheese consumption and stock prices, these works beckon us to adopt a broader perspective and embrace the unpredictable nature of economic relationships.
Venturing into the realm of fiction, novels like "The Cheese Monkeys" by Chip Kidd and "The Money Culture" by Michael Lewis offer a whimsical and satirical take on the world of consumerism, financial markets, and the intricacies of human behavior. Although purely works of imagination, these literary pieces encourage us to approach our investigation with a hint of levity and an appreciation for the unconventional.
In a departure from traditional literature sources, our inquiry took an unconventional turn as we delved into the depths of everyday life, scouring through grocery receipts, including those from the famed CVS, to glean insights into consumer purchasing habits and, by extension, their potential impact on stock prices. While this approach may raise an eyebrow among purveyors of scholarly conventions, it allowed us to tap into the pulse of consumer behavior through a somewhat unorthodox but undeniably fascinating window.
As we navigate this peculiar and often whimsical landscape of research, we invite our esteemed colleagues to embrace the unorthodox and quirky nature of our investigation. The findings to follow not only promise to expand the horizons of financial analysis but also elicit a few chuckles amidst the otherwise serious discourse of academic research.

[[RESULTS]]
Our investigation into the intriguing link between American cheese consumption and Mastercard's stock price (MA) from 2007 to 2021 yielded some rather "grate" results. The correlation coefficient of 0.9056200, accompanied by an r-squared value of 0.8201476, revealed a remarkably strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further solidified the statistical significance of this association, leaving us to ponder the provolone possibilities that lay beyond this unexpected correlation.
To visually emphasize the robustness of this correlation, we present the scatterplot in Figure 1, which encapsulates the undeniable affinity between American cheese consumption and Mastercard's stock price. The scatterplot, akin to the Mona Lisa of statistical representation, astonishes with its portrayal of the convergence of dairy delight and financial fervor.
While some may be quick to dismiss the notion of cheese consumption influencing stock prices as a mere "fondue" of imagination, the data speaks volumes about the unanticipated sway of this unassuming dairy delight. We are reminded that amidst the cold, hard world of stock analysis, there exists a warm, melty center of unexpected relationships waiting to be uncovered.
Our findings beckon the curious minds of scholars and analysts to not only ponder the peculiar partnership of cheddar and stock prices but also to consider the broader implications for market dynamics. As the saying goes, "It's not easy being cheesy", but in the world of financial analysis, embracing the unexpected may just lead to a feta future of insight and discovery.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation into the correlation between American cheese consumption and Mastercard's stock price (MA) has unveiled a perceptive puzzle that defies the surface-level expectations of market analysis. Delving into the literature, it becomes clear that the interplay between cheese and stocks extends far beyond mere curdiosity - pun intended. While some may initially dismiss the idea of a dairy product influencing stock prices as a mere "whey"ward fantasy, existing research has paved the whey for our results to be taken with the utmost seriousness. Undoubtedly, this study contributes a cheddar wedge of insight to a field often characterized by cold, hard numbers.

A profound foundation for our investigation can be found in the works of Smith et al. (2015) and Doe and Jones (2017), who delved into the intricate web of consumer behavior and its impact on financial markets. Their scholarly endeavors, although not directly related to cheese, set the stage for our own unorthodox exploration. The unconventional nature of our inquiry is emphasized by drawing inspiration from not only traditional non-fiction works but also from the whimsical and satirical world of fiction. Indeed, it may seem rather cheesy to invoke Chip Kidd's "The Cheese Monkeys" in a scholarly context, but as demonstrated by our results, what may appear as crackpot when taken at first "grate" can melt into a solid foundation for unexpected insights.

Our findings robustly supported the prior research by uncovering a remarkably strong correlation between American cheese consumption and Mastercard's stock price, with a correlation coefficient of 0.9056200. This statistical relationship, much like a well-aged Gouda, has matured over time, rendering it a not-so-fondue of imagination but a tangible connection worthy of careful consideration. Furthermore, the scatterplot in Figure 1 serves as visual testimony to the undeniably strong relationship between these variables, reminiscent of a Monet painting that holds unorthodox beauty.

The powdery implications of our results extend beyond the seemingly whimsical realm of cheese consumption and stock prices, promising to provoke a "grater" understanding of market dynamics. Our study reminds us that behind the seemingly mundane lies a "wheely" compelling network of influences worthy of further exploration. As researchers, we are called to embrace the unconventional and perhaps even delight in the occasional whimsical quirk that the realm of science and statistics may offer. After all, in the world of research, a little sprinkle of levity can make the most serious endeavors a bit more "gouda".

Stay "curdy" for more insights from the bries of academia!

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 American cheese consumption and Mastercard's stock price (MA). 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]]
"Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)"

[[ABSTRACT]]
In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

[[INTRDUCTION]]
In the esteemed realm of financial analysis, it is customary to scrutinize a myriad of variables and factors in an attempt to unearth the elusive patterns and relationships that guide the fluctuations of stock prices. From interest rates and economic indicators to company performance and market sentiment, the quest for identifying key drivers of stock prices is akin to a perilous expedition through a dense jungle of data. However, in this jungle of financial analysis, one may unexpectedly stumble upon the unlikely presence of a block of American cheese, nestled among the formidable statistics and trend lines.
The notion of a connection between American cheese consumption and the stock price of Mastercard (MA) may appear as a comical rendezvous of unrelated entities, akin to a serendipitous meeting between a mathematician and a stand-up comedian. However, as we embark on this curious endeavor, it becomes increasingly clear that in the labyrinthine world of market dynamics, even the most unassuming variables may hold clandestine sway over the ebb and flow of stock prices. Hence, we are propelled to investigate this peculiar duo with the same fervor and inquisitiveness usually reserved for more traditional factors.
Our exploration into this quizzical correlation was motivated by an innate curiosity and a penchant for embracing the unconventional in the realm of financial analysis. The initial murmurings of a potential relationship between American cheese consumption and Mastercard's stock price beckoned us to take a closer look, leading to a journey that would involve traversing through years of data and statistical analyses. As we navigated through the convoluted landscape of cheese consumption and market performance, we encountered surprising revelations and peculiar patterns that challenged the traditional boundaries of financial analysis.
It is essential to note that our investigation was not undertaken with a haphazard abandon, but rather with the fervent dedication and meticulous rigor befitting any scholarly pursuit. Leveraging data from the United States Department of Agriculture (USDA) and the esteemed LSEG Analytics (Refinitiv), we meticulously combed through the years from 2007 to 2021, subjecting the data to an array of statistical tests and methodologies. The unearthing of a correlation coefficient of 0.9056200 and a p-value of less than 0.01 dangled before us like a tantalizing enigma, demanding further scrutiny and contemplation.
As we delve deeper into the emmental connection between American cheese consumption and Mastercard's stock price, we invite our esteemed colleagues and scholars to join us on this unconventional odyssey. The ambitions of our investigation extend beyond the mere discovery of an unexpected correlation; rather, we seek to unveil the underlying mechanisms and potential implications that this unlikely relationship may harbor for the landscape of stock analysis. With a blend of scientific rigor and an openness to unanticipated connections, our foray into this uncharted territory promises not only to broaden the horizons of financial analysis but also to elicit a few wry smiles amidst the stern countenances of statistical analysis.

[[RESULTS]]
Our investigation into the intriguing link between American cheese consumption and Mastercard's stock price (MA) from 2007 to 2021 yielded some rather "grate" results. The correlation coefficient of 0.9056200, accompanied by an r-squared value of 0.8201476, revealed a remarkably strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further solidified the statistical significance of this association, leaving us to ponder the provolone possibilities that lay beyond this unexpected correlation.
To visually emphasize the robustness of this correlation, we present the scatterplot in Figure 1, which encapsulates the undeniable affinity between American cheese consumption and Mastercard's stock price. The scatterplot, akin to the Mona Lisa of statistical representation, astonishes with its portrayal of the convergence of dairy delight and financial fervor.
While some may be quick to dismiss the notion of cheese consumption influencing stock prices as a mere "fondue" of imagination, the data speaks volumes about the unanticipated sway of this unassuming dairy delight. We are reminded that amidst the cold, hard world of stock analysis, there exists a warm, melty center of unexpected relationships waiting to be uncovered.
Our findings beckon the curious minds of scholars and analysts to not only ponder the peculiar partnership of cheddar and stock prices but also to consider the broader implications for market dynamics. As the saying goes, "It's not easy being cheesy", but in the world of financial analysis, embracing the unexpected may just lead to a feta future of insight and discovery.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our examination of the unlikely relationship between American cheese consumption and Mastercard's stock price has unveiled a strong and statistically significant correlation, transcending the traditional boundaries of financial analysis. The robust correlation coefficient, akin to a wheel of aged cheddar, speaks to the surprising depth of connection between these seemingly disparate variables. The findings, though peppered with a hint of fromage flair, underscore the need for a broader exploration of unorthodox influences in market dynamics.

The insatiable appetite of statistical inquiry has often led us to unexpected destinations, much like stumbling onto a slice of American cheese in the annals of stock analysis. However, as scholars, we must exercise caution in not overreaching in our interpretations and attributing causality to an association, lest we fall into the trap of Swiss cheese logic, where the holes in our reasoning become all too apparent.

While our findings beckon further exploration, it is prudent to recognize the limits of our current endeavor. As much as we relish the charm of unraveling the enigmatic dance between dairy delicacies and stock prices, it is time to declare that no more research is needed in this particular area. We have done our "whey" in illuminating this peculiar relationship, and it is now for others to build upon this gruyere of knowledge, steering clear of any "cheesy" missteps that may lead us astray in the labyrinth of financial analysis. Our focus now shifts to other unexplored territories, as the world of statistical curiosities continues to unfold before us like a grand buffet of inquiry.

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 Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA). Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Dairy Economics and Financial Transactions

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 Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA). Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Dairy Economics and Financial Ruminations

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 Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA) with an abstract of In this diligent investigation, we delved into the curious correlation between American cheese consumption and the stock price of one of the titans of the financial sector, Mastercard (MA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we rigorously analyzed the relationship between these unexpected bedfellows from 2007 to 2021. Strikingly, our findings revealed a robust correlation coefficient of 0.9056200 and a statistically significant p-value of less than 0.01. This surprising association, veiled in the seemingly bland world of cheese consumption and stock prices, opens the door to a gouda deal of further investigation and introspection. Our results beckon the careful consideration of newfound possibilities in understanding market dynamics, with the potential to provoke a (cheddar) cheesy grin from those immersed in the frontiers of stock analysis.

ChatGPT:

American cheese, consumption, stock price, Mastercard, MA, correlation, USDA, LSEG Analytics, Refinitiv, 2007-2021, correlation coefficient, p-value, market dynamics, stock analysis

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

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

Mastercard's stock price (MA)
Detailed data title: Opening price of Mastercard (MA) 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 Mastercard's stock price (MA)

Correlation r = 0.9056200 (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 = 0.8201476 (Coefficient of determination)
This means 82% of the change in the one variable (i.e., Mastercard's stock price (MA)) is predictable based on the change in the other (i.e., American cheese consumption) over the 15 years from 2007 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.4E-6.^Show 0.0000033921486895211280000000
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 0.00034% of random cases. Said differently, if you correlated 294,798 random variables^Note You don't actually need 294 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 14 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.73, 0.97 ] 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.

	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
American cheese consumption (Pounds per person)	12.7929	13.1306	13.3553	13.3048	13.0392	13.2569	13.3573	13.6656	14.0444	14.3621	15.0901	15.4027	15.5389	15.5	16.1
Mastercard's stock price (MA) (Stock price)	9.96	21.79	14.41	25.8	22.64	37.84	50.63	83.74	86.68	95.37	104.41	152.01	185.83	300.46	358

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([12.7929,13.1306,13.3553,13.3048,13.0392,13.2569,13.3573,13.6656,14.0444,14.3621,15.0901,15.4027,15.5389,15.5,16.1,])
array_2 = np.array([9.96,21.79,14.41,25.8,22.64,37.84,50.63,83.74,86.68,95.37,104.41,152.01,185.83,300.46,358,])
array_1_name = "American cheese consumption"
array_2_name = "Mastercard's stock price (MA)"

# 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 American cheese consumption
Line chart for only Mastercard's stock price (MA)
AI-generated correlation image
The spurious research paper: Cheddar, Swiping, and Stock Prices: The Emmental Connection Between American Cheese Consumption and Mastercard's Stock (MA)

View another random correlation

How fun was this correlation?

Your rating skills are legendary!

Correlation ID: 2004 · Black Variable ID: 553 · Red Variable ID: 1557


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: