Spurious correlation #5,884 · 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 Alphabet's stock price (GOOGL). The chart goes from 2005 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 Alphabet's stock price (GOOGL). The chart goes from 2005 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As American cheese consumption melted, so did the hearts of investors, causing a ripple effect in the stock market. As more people savored the idea of a cheesy investment, the demand for Alphabet's stock increased, leading to a gouda rise in their stock price. It seems like the secret to their success was simply to brie-lieve in the power of dairy deliciousness!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a cyberpunk kitchen setting, with neon lights illuminating the futuristic space. In the center of the scene, there is a sleek wooden cutting board where American cheese slices are melting, emitting a captivating glow. On one side, a group of jubilant investors are depicted, their expressions radiating joy as they gather around a computer screen displaying Alphabet's stock price rising steadily. The fusion of the melting cheese and the ascending stock chart symbolizes a harmonious blend of culinary satisfaction and financial success. 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 Alphabet's stock price (GOOGL). 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: Steampunk Cubism Cyberpunk Magic realism Cute 2D animation
ChatGPT: Cyberpunk

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 Alphabet's stock price (GOOGL), in the following situation: As American cheese consumption melted, so did the hearts of investors, causing a ripple effect in the stock market. As more people savored the idea of a cheesy investment, the demand for Alphabet's stock increased, leading to a gouda rise in their stock price. It seems like the secret to their success was simply to brie-lieve in the power of dairy deliciousness!. Keep the description simple.
ChatGPT: In a vibrant kitchen scene, a platter of American cheese slices is melting gently on a wooden cutting board, filling the room with a savory aroma. Meanwhile, a group of joyous investors are gathered around a computer screen, their hearts warmed by the sight of Alphabet's stock price (GOOGL) steadily rising on the monitor. The cheese and the stock chart create a harmonious blend of deliciousness and financial success, symbolizing the fusion of American cheese consumption and Alphabet's stock price.

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: [ Cyberpunk ] and depicting this scene: In a vibrant kitchen scene, a platter of American cheese slices is melting gently on a wooden cutting board, filling the room with a savory aroma. Meanwhile, a group of joyous investors are gathered around a computer screen, their hearts warmed by the sight of Alphabet's stock price (GOOGL) steadily rising on the monitor. The cheese and the stock chart create a harmonious blend of deliciousness and financial success, symbolizing the fusion of American cheese consumption and Alphabet's stock price. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a cyberpunk kitchen setting, with neon lights illuminating the futuristic space. In the center of the scene, there is a sleek wooden cutting board where American cheese slices are melting, emitting a captivating glow. On one side, a group of jubilant investors are depicted, their expressions radiating joy as they gather around a computer screen displaying Alphabet's stock price rising steadily. The fusion of the melting cheese and the ascending stock chart symbolizes a harmonious blend of culinary satisfaction and financial success.

*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 Alphabet's stock price (GOOGL) to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

The Journal of Dairy Economics and Financial Markets

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between American cheese consumption and Alphabet's stock price (GOOGL). Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

[[ABSTRACT]]
This paper delves into the cheesy world of American cheese and its unexpected influence on the stock price of Alphabet Inc. (GOOGL). Utilizing data from the USDA and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to unravel the tantalizing correlation between American cheese consumption and Alphabet's stock price. With a striking correlation coefficient of 0.9544431 and a p-value of less than 0.01 for the years 2005 to 2021, our findings suggest that there may be more to this gooey tale than meets the eye. Join us on this cheesy adventure as we explore the dairy-driven dynamics of the stock market and shed light on the unexpected power of American cheese.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The entanglement of American cheese and Alphabet Inc.'s stock price may elicit a chuckle or two from the uninitiated, but as the saying goes, "There's no use crying over spilled milk." This peculiar interconnection, though seemingly cheesy, has stirred up quite a gouda deal of interest among the financial and gastronomic communities alike.
Our investigation into this fromage fiasco was prompted by a curious blend of scientific intrigue and a penchant for puns. As we embarked on this research endeavor, we found ourselves knee-deep in both cheese curds and stock market data, an unlikely pairing that would make even the most seasoned economist do a double take. But as they say, "The proof of the pudding is in the eating," or in this case, the correlation coefficient and p-value.
With the data from the U.S. Department of Agriculture and LSEG Analytics (Refinitiv) in hand, we embarked on a statistical odyssey to disentangle the cheesy truth behind this unlikely relationship. Our statistical analyses, which encompassed the years 2005 to 2021, revealed a rather feta-stic correlation coefficient of 0.9544431 and a p-value of less than 0.01. These findings not only amused us but also led us to ponder the possibility that perhaps there's more to the stock market than bulls and bears – there might just be a place for dairy products, too.
In the following sections, we will embark on a whimsical journey through the dynamics of American cheese consumption and its unforeseen impact on Alphabet Inc.'s stock price. We invite you to join us as we expose the wheyward relationship between these seemingly unrelated variables and delve into the unexpected influence of everyone's favorite meltable commodity on the whims of Wall Street. So, grab a slice of your favorite cheddar and let's dive right into this cheesy adventure!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The surprising connection between American cheese consumption and Alphabet Inc.'s stock price has piqued the interest of researchers and enthusiasts alike. Smith, Doe, and Jones (2018) delved into the unsuspecting world of dairy and finance, shining a light on the unexpected correlation between these seemingly unrelated domains. Their findings uncovered a cheddar-tastic statistical relationship that left readers both bewildered and amused.

Expanding upon this dairy-driven phenomenon, "The Big Cheese: A Comprehensive Guide to Fromage Forecasts" by Sharp (2017) provides an in-depth exploration of the uncharted territory of cheese forecasting and its effect on financial markets. This tome of cheesy wisdom offers a gouda-deal of insight into the whimsical world of dairy's influence on stock prices, making it a must-read for aficionados of both cheese and finance.

On a more theoretical note, "The Swiss Connection: A Tale of Cheese and Stocks" by Brie (2015) delves into the history of cheese-themed financial indicators and their impact on investment strategies. The author's fondue-dness for wordplay and cheese-related puns adds a lighthearted touch to an otherwise weighty subject, making this book both informative and surprisingly humorous.

As we tread further into this peculiar intersection of fromage and financials, it becomes clear that fiction also holds a slice of the cheesy pie. "The Gouda Conundrum" by Feta (2010) is a whimsical novel that weaves a tale of intrigue and suspense centered around the clandestine world of cheese arbitrage and its influence on stock market fluctuations. While purely fictitious, the book's witty narrative and cheesy plot twists offer a delightful escape into the whimsical world of cheese-infused financial shenanigans.

Turning to the realm of animation and entertainment, it's worth noting the childhood classic "Wall Street Mice: The Big Cheese Heist." This beloved cartoon series follows the adventures of a group of savvy mice as they navigate the ins and outs of the stock market, all while making clever use of cheese-related puns and witty quips. While purely fictional and targeted at a younger audience, the show's subtle nods to the influence of dairy on financial markets offer a charming reflection of the real-world intrigue that has captured the attention of researchers and cheese enthusiasts alike.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between American cheese consumption and Alphabet's stock price (GOOGL). Make lots of 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 2005 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]]
The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

[[ABSTRACT]]
This paper delves into the cheesy world of American cheese and its unexpected influence on the stock price of Alphabet Inc. (GOOGL). Utilizing data from the USDA and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to unravel the tantalizing correlation between American cheese consumption and Alphabet's stock price. With a striking correlation coefficient of 0.9544431 and a p-value of less than 0.01 for the years 2005 to 2021, our findings suggest that there may be more to this gooey tale than meets the eye. Join us on this cheesy adventure as we explore the dairy-driven dynamics of the stock market and shed light on the unexpected power of American cheese.

[[INTRODUCTION]]
INTRODUCTION
The entanglement of American cheese and Alphabet Inc.'s stock price may elicit a chuckle or two from the uninitiated, but as the saying goes, "There's no use crying over spilled milk." This peculiar interconnection, though seemingly cheesy, has stirred up quite a gouda deal of interest among the financial and gastronomic communities alike.
Our investigation into this fromage fiasco was prompted by a curious blend of scientific intrigue and a penchant for puns. As we embarked on this research endeavor, we found ourselves knee-deep in both cheese curds and stock market data, an unlikely pairing that would make even the most seasoned economist do a double take. But as they say, "The proof of the pudding is in the eating," or in this case, the correlation coefficient and p-value.
With the data from the U.S. Department of Agriculture and LSEG Analytics (Refinitiv) in hand, we embarked on a statistical odyssey to disentangle the cheesy truth behind this unlikely relationship. Our statistical analyses, which encompassed the years 2005 to 2021, revealed a rather feta-stic correlation coefficient of 0.9544431 and a p-value of less than 0.01. These findings not only amused us but also led us to ponder the possibility that perhaps there's more to the stock market than bulls and bears – there might just be a place for dairy products, too.
In the following sections, we will embark on a whimsical journey through the dynamics of American cheese consumption and its unforeseen impact on Alphabet Inc.'s stock price. We invite you to join us as we expose the wheyward relationship between these seemingly unrelated variables and delve into the unexpected influence of everyone's favorite meltable commodity on the whims of Wall Street. So, grab a slice of your favorite cheddar and let's dive right into this cheesy adventure!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

METHODOLOGY

The quest to demystify the enigmatic relationship between American cheese consumption and Alphabet Inc.'s stock price necessitated a methodological approach as robust and flavorful as a well-aged gouda. Our research team assembled a comprehensive dataset from the U.S. Department of Agriculture for American cheese consumption and LSEG Analytics (Refinitiv) for Alphabet Inc.'s stock price, spanning the years 2005 to 2021.

To commence our analysis, we engaged in the time-honored tradition of data cleansing and wrangling, akin to the meticulous process of cheese-making – ridding the dataset of extraneous "whey" and curating it into a congealed mass of statistical goodness. Standardized statistical techniques, including time series analysis and cross-correlation functions, were employed to discern patterns and elucidate any clandestine coupling between the two variables.

In a display of statistical acrobatics, we harnessed the majestic power of Pearson's correlation coefficient to quantify the strength and direction of the relationship between American cheese consumption and Alphabet Inc.'s stock price. This statistical maneuver, akin to a delicate cheese tasting, allowed us to savor the nuances of the association and comprehend its depth beyond mere surface-level cheesiness.

Furthermore, we subjected our data to the rigors of regression analysis, unveiling the predictive prowess of American cheese consumption on the undulating undulations of Alphabet Inc.'s stock price. This analytical endeavor involved fitting a range of regression models, akin to experimenting with different cheese pairings to identify the most complementary and predictive combination.

Not content with simple linear relationships, our methodology availed itself of sophisticated time series modeling techniques, including autoregressive integrated moving average (ARIMA) models, to capture the temporal dependencies inherent in both American cheese consumption and Alphabet Inc.'s stock price. Such modeling, akin to allowing a cheese to mature gradually, enabled us to unravel the complex interplay between the two variables across time.

Lastly, our research availed itself of Granger causality testing to ascertain the potential directionality and temporal precedence between American cheese consumption and Alphabet Inc.'s stock price. This rigorous test, reminiscent of teasing out cause-and-effect amid a sea of cheese varietals, sought to uncover the flow of influence between these seemingly disparate domains.

Through this unorthodox yet rigorous methodological concoction, our intention was to not only uncover the empirical truth behind this unlikely relationship but also to infuse some levity and cheese-inspired whimsy into the staid world of statistical research. With bated breath and a sprinkling of intellectual curiosity, we proceeded to unravel the confounding tangle of American cheese and stock market dynamics.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between American cheese consumption and Alphabet's stock price (GOOGL). Make lots of 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]]
The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

[[ABSTRACT]]
This paper delves into the cheesy world of American cheese and its unexpected influence on the stock price of Alphabet Inc. (GOOGL). Utilizing data from the USDA and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to unravel the tantalizing correlation between American cheese consumption and Alphabet's stock price. With a striking correlation coefficient of 0.9544431 and a p-value of less than 0.01 for the years 2005 to 2021, our findings suggest that there may be more to this gooey tale than meets the eye. Join us on this cheesy adventure as we explore the dairy-driven dynamics of the stock market and shed light on the unexpected power of American cheese.

[[LITERATURE REVIEW]]
The surprising connection between American cheese consumption and Alphabet Inc.'s stock price has piqued the interest of researchers and enthusiasts alike. Smith, Doe, and Jones (2018) delved into the unsuspecting world of dairy and finance, shining a light on the unexpected correlation between these seemingly unrelated domains. Their findings uncovered a cheddar-tastic statistical relationship that left readers both bewildered and amused.
Expanding upon this dairy-driven phenomenon, "The Big Cheese: A Comprehensive Guide to Fromage Forecasts" by Sharp (2017) provides an in-depth exploration of the uncharted territory of cheese forecasting and its effect on financial markets. This tome of cheesy wisdom offers a gouda-deal of insight into the whimsical world of dairy's influence on stock prices, making it a must-read for aficionados of both cheese and finance.
On a more theoretical note, "The Swiss Connection: A Tale of Cheese and Stocks" by Brie (2015) delves into the history of cheese-themed financial indicators and their impact on investment strategies. The author's fondue-dness for wordplay and cheese-related puns adds a lighthearted touch to an otherwise weighty subject, making this book both informative and surprisingly humorous.
As we tread further into this peculiar intersection of fromage and financials, it becomes clear that fiction also holds a slice of the cheesy pie. "The Gouda Conundrum" by Feta (2010) is a whimsical novel that weaves a tale of intrigue and suspense centered around the clandestine world of cheese arbitrage and its influence on stock market fluctuations. While purely fictitious, the book's witty narrative and cheesy plot twists offer a delightful escape into the whimsical world of cheese-infused financial shenanigans.
Turning to the realm of animation and entertainment, it's worth noting the childhood classic "Wall Street Mice: The Big Cheese Heist." This beloved cartoon series follows the adventures of a group of savvy mice as they navigate the ins and outs of the stock market, all while making clever use of cheese-related puns and witty quips. While purely fictional and targeted at a younger audience, the show's subtle nods to the influence of dairy on financial markets offer a charming reflection of the real-world intrigue that has captured the attention of researchers and cheese enthusiasts alike.

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between American cheese consumption and Alphabet Inc.'s stock price (GOOGL) for the years 2005 to 2021. The correlation coefficient of 0.9544431 indicates a tantalizing association between these two seemingly unrelated variables. It appears that the whims of Wall Street may not be entirely independent of the cheesy cravings of the American populace.
Additionally, the r-squared value of 0.9109616 suggests that approximately 91.1% of the variability in Alphabet's stock price can be explained by changes in American cheese consumption. One might say that the stock market is as predictable as the meltability of a slice of American cheese on a hot summer day – remarkably so, yet with a hint of unpredictability.
The p-value of less than 0.01 further reinforces the robustness of this finding, indicating that the observed correlation is highly unlikely to have occurred by chance alone. It seems that the relationship between American cheese consumption and Alphabet Inc.'s stock price is not just a cheesy coincidence but a statistically significant phenomenon.
To visually encapsulate the striking correlation, we present the scatterplot in Figure 1, showcasing the tight clustering of data points around the upward trend line. Like a grilled cheese sandwich sizzling on a skillet, the positive correlation between American cheese consumption and Alphabet's stock price is undeniable.
These findings not only elucidate the unanticipated influence of American cheese on the stock market but also serve as a testament to the unpredictability and complexity of market dynamics. After all, who would have thought that the humble American cheese could hold such sway over the ebb and flow of stock prices? It seems that in the world of finance, as in the realm of dairy products, there's always room for the unexpected and the delightfully cheesy.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our investigation into the correlation between American cheese consumption and Alphabet Inc.'s stock price (GOOGL) present a cheddar-rific addition to the existing literature on the intersection of dairy and finance. Building upon the insightful works of Smith, Doe, and Jones (2018), Sharp's "The Big Cheese: A Comprehensive Guide to Fromage Forecasts" (2017), and Brie's "The Swiss Connection: A Tale of Cheese and Stocks" (2015), our results provide empirical support for the notion that the cheesy inclinations of the masses may indeed exert an unexpected pull on the gyrations of the stock market.

The robust correlation coefficient of 0.9544431 reinforces the notion that the ebb and flow of Alphabet's stock price may not be entirely independent of the fluctuations in American cheese consumption. Our findings align with the whimsically speculative insights of "The Gouda Conundrum" (Feta, 2010), which, while a work of fiction, artfully weaves a narrative that sublimely captures the enchanting notion of cheese-infused financial intrigue.

Moreover, the r-squared value of 0.9109616 suggests that the changes in American cheese consumption account for approximately 91.1% of the variability in Alphabet's stock price. This level of predictability rivals the meltability of a slice of American cheese on a summer day – mostly reliable, with a hint of delightful capriciousness. Furthermore, the p-value of less than 0.01 provides unequivocal support for the statistical significance of the observed correlation, quelling any doubts about the legitimacy of this curious dairy-driven phenomenon.

These results illustrate the enchanting complexity of market dynamics, inviting us to reconsider the conventional wisdom in financial analysis. Our findings suggest that the stock market may not be as impervious to the cheesy cravings of the populace as previously assumed. In the grand tale of finance, it appears that American cheese has secured its role as an unlikely protagonist in the whimsical world of market forces.

This unexpected revelation prompts a reexamination of the idiosyncratic influences that underpin the dynamics of financial markets. Perhaps, as "Wall Street Mice: The Big Cheese Heist" allegorically hints, the subtle interplay between cheese-related puns and witty quips in popular culture serves as a whimsical reflection of the captivating real-world intrigue that has ensnared both researchers and cheese enthusiasts alike.

In summary, our study's findings not only affirm the enchanting correlation between American cheese consumption and Alphabet Inc.'s stock price but also underscore the enduring allure of the inexplicable and delightfully cheesy in the world of finance.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between American cheese consumption and Alphabet's stock price (GOOGL). Make lots of 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]]
The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

[[ABSTRACT]]
This paper delves into the cheesy world of American cheese and its unexpected influence on the stock price of Alphabet Inc. (GOOGL). Utilizing data from the USDA and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to unravel the tantalizing correlation between American cheese consumption and Alphabet's stock price. With a striking correlation coefficient of 0.9544431 and a p-value of less than 0.01 for the years 2005 to 2021, our findings suggest that there may be more to this gooey tale than meets the eye. Join us on this cheesy adventure as we explore the dairy-driven dynamics of the stock market and shed light on the unexpected power of American cheese.

[[INTRDUCTION]]
INTRODUCTION
The entanglement of American cheese and Alphabet Inc.'s stock price may elicit a chuckle or two from the uninitiated, but as the saying goes, "There's no use crying over spilled milk." This peculiar interconnection, though seemingly cheesy, has stirred up quite a gouda deal of interest among the financial and gastronomic communities alike.
Our investigation into this fromage fiasco was prompted by a curious blend of scientific intrigue and a penchant for puns. As we embarked on this research endeavor, we found ourselves knee-deep in both cheese curds and stock market data, an unlikely pairing that would make even the most seasoned economist do a double take. But as they say, "The proof of the pudding is in the eating," or in this case, the correlation coefficient and p-value.
With the data from the U.S. Department of Agriculture and LSEG Analytics (Refinitiv) in hand, we embarked on a statistical odyssey to disentangle the cheesy truth behind this unlikely relationship. Our statistical analyses, which encompassed the years 2005 to 2021, revealed a rather feta-stic correlation coefficient of 0.9544431 and a p-value of less than 0.01. These findings not only amused us but also led us to ponder the possibility that perhaps there's more to the stock market than bulls and bears – there might just be a place for dairy products, too.
In the following sections, we will embark on a whimsical journey through the dynamics of American cheese consumption and its unforeseen impact on Alphabet Inc.'s stock price. We invite you to join us as we expose the wheyward relationship between these seemingly unrelated variables and delve into the unexpected influence of everyone's favorite meltable commodity on the whims of Wall Street. So, grab a slice of your favorite cheddar and let's dive right into this cheesy adventure!

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between American cheese consumption and Alphabet Inc.'s stock price (GOOGL) for the years 2005 to 2021. The correlation coefficient of 0.9544431 indicates a tantalizing association between these two seemingly unrelated variables. It appears that the whims of Wall Street may not be entirely independent of the cheesy cravings of the American populace.
Additionally, the r-squared value of 0.9109616 suggests that approximately 91.1% of the variability in Alphabet's stock price can be explained by changes in American cheese consumption. One might say that the stock market is as predictable as the meltability of a slice of American cheese on a hot summer day – remarkably so, yet with a hint of unpredictability.
The p-value of less than 0.01 further reinforces the robustness of this finding, indicating that the observed correlation is highly unlikely to have occurred by chance alone. It seems that the relationship between American cheese consumption and Alphabet Inc.'s stock price is not just a cheesy coincidence but a statistically significant phenomenon.
To visually encapsulate the striking correlation, we present the scatterplot in Figure 1, showcasing the tight clustering of data points around the upward trend line. Like a grilled cheese sandwich sizzling on a skillet, the positive correlation between American cheese consumption and Alphabet's stock price is undeniable.
These findings not only elucidate the unanticipated influence of American cheese on the stock market but also serve as a testament to the unpredictability and complexity of market dynamics. After all, who would have thought that the humble American cheese could hold such sway over the ebb and flow of stock prices? It seems that in the world of finance, as in the realm of dairy products, there's always room for the unexpected and the delightfully cheesy.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the connection between American cheese consumption and Alphabet Inc.'s stock price uncovers a cheddar of unexpected findings. The robust correlation coefficient and p-value suggest that the stock market may be no Gouda at standing alone but instead gets some potential flavor from the whims of dairy enthusiasts. The r-squared value illuminates that a whopping 91.1% of the stock price variability can be explained by changes in American cheese consumption, indicating that the market might not be as crackers as we once thought.

Much like a slice of Swiss, these results have myriad holes that leave room for future inquiries. While the correlation is mighty, causation has yet to be fondue. The mechanism behind this connection remains as enigmatic as the elusive perfect cheese pull. Is this relationship just a slice of cheesy luck, or is there a deeper, more profound bond between the dairy aisle and the stock market?

Nevertheless, one thing is for certain: no more research is needed in this area. It is gratefully apparent that the world of finance has been forever changed by the influence of American cheese, and it's a tale worth telling for generations to come. So, let's raise a toast – or should we say, a grilled cheese sandwich – to the unforeseen power of dairy products in the tumultuous sea of stock market fluctuations. Time to say "cheese" and call it a day!

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 The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Dairy Economics and Financial Markets

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 The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Dairy Economics and Financial Analysis

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price with an abstract of This paper delves into the cheesy world of American cheese and its unexpected influence on the stock price of Alphabet Inc. (GOOGL). Utilizing data from the USDA and LSEG Analytics (Refinitiv), we applied rigorous statistical analysis to unravel the tantalizing correlation between American cheese consumption and Alphabet's stock price. With a striking correlation coefficient of 0.9544431 and a p-value of less than 0.01 for the years 2005 to 2021, our findings suggest that there may be more to this gooey tale than meets the eye. Join us on this cheesy adventure as we explore the dairy-driven dynamics of the stock market and shed light on the unexpected power of American cheese.

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American cheese consumption, stock price correlation, Alphabet Inc., GOOGL, USDA data, LSEG Analytics, Refinitiv, statistical analysis, correlation coefficient, p-value, dairy market dynamics

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

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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

Alphabet's stock price (GOOGL)
Detailed data title: Opening price of Alphabet (GOOGL) 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 Alphabet's stock price (GOOGL)

Correlation r = 0.9544431 (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 = 0.9109616 (Coefficient of determination)
This means 91.1% of the change in the one variable (i.e., Alphabet's stock price (GOOGL)) is predictable based on the change in the other (i.e., American cheese consumption) over the 17 years from 2005 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.8E-9.^Show 0.0000000027943156667902900000
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 0.95 in 2.8E-7% of random cases. Said differently, if you correlated 357,869,375 random variables^Note You don't actually need 357 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 16 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 16 because we have two variables measured over a period of 17 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.

[ 0.88, 0.98 ] 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.

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
American cheese consumption (Pounds per person)	12.649	13.0656	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
Alphabet's stock price (GOOGL) (Stock price)	4.94	10.57	11.65	17.34	7.72	15.69	14.93	16.34	18	27.91	26.63	38.11	40.03	52.65	51.36	67.42	88

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.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

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([12.649,13.0656,12.7929,13.1306,13.3553,13.3048,13.0392,13.2569,13.3573,13.6656,14.0444,14.3621,15.0901,15.4027,15.5389,15.5,16.1,])
array_2 = np.array([4.94,10.57,11.65,17.34,7.72,15.69,14.93,16.34,18,27.91,26.63,38.11,40.03,52.65,51.36,67.42,88,])
array_1_name = "American cheese consumption"
array_2_name = "Alphabet's stock price (GOOGL)"

# 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 Alphabet's stock price (GOOGL)
AI-generated correlation image
The spurious research paper: The Big Cheese Squeeze: How American Cheese Consumption Swings Alphabet's Stock Price

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Correlation ID: 5884 · Black Variable ID: 553 · Red Variable ID: 1542


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