Spurious correlation #3,371 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of fast food cooks in West Virginia and the second variable is Tesla's stock price (TSLA). The chart goes from 2011 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of fast food cooks in West Virginia and the second variable is Tesla's stock price (TSLA). The chart goes from 2011 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As more fast food cooks were employed in West Virginia, the state's average happiness levels skyrocketed. This unexpected surge in happiness created a ripple effect, causing a spike in demand for Tesla cars as people in the state suddenly felt more optimistic and willing to splurge on luxury electric vehicles. This uptick in demand was enough to catch the attention of investors, leading to a boost in Tesla's stock price. After all, it turns out that the secret sauce for Tesla's success may just be a sprinkle of joy from fast food cooks in West Virginia!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Create a futuristic scene set in a bustling West Virginia town. In the foreground, fast-food cooks work rapidly in a high-tech kitchen, surrounded by sizzling grills and automated food preparation systems. A long line of hungry customers eagerly waits, and advanced ordering kiosks or service robots process their requests. In the background, depict the busy stock exchange floor, with digital tickers displaying the soaring price of Tesla's stock (TSLA) amidst a flurry of high-tech trading activities. The scene should exude a futuristic vibe with sleek, advanced technology integrated seamlessly with everyday life. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of fast food cooks in West Virginia and Tesla's stock price (TSLA). 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 Futuristic A Discovery magazine cover image Realistic Woodblock painting
ChatGPT: Futuristic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of fast food cooks in West Virginia and Tesla's stock price (TSLA), in the following situation: Because more fast food cooks in West Virginia means more people eating unhealthy food, leading to an increase in heart-related health issues. This, in turn, creates more demand for electric cars like Tesla's, causing their stock price to surge. It's a classic case of fast food leading to fast electric car sales!. Keep the description simple.
ChatGPT: In a busy West Virginia town, a line of fast-food cooks work quickly in the kitchen as a long line of customers eagerly await their greasy meals. Meanwhile, on the bustling stock exchange floor, the price of Tesla's stock (TSLA) soars upward on the digital ticker, signaling a surge in demand.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Futuristic ] and depicting this scene: In a busy West Virginia town, a line of fast-food cooks work quickly in the kitchen as a long line of customers eagerly await their greasy meals. Meanwhile, on the bustling stock exchange floor, the price of Tesla's stock (TSLA) soars upward on the digital ticker, signaling a surge in demand. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Create a futuristic scene set in a bustling West Virginia town. In the foreground, fast-food cooks work rapidly in a high-tech kitchen, surrounded by sizzling grills and automated food preparation systems. A long line of hungry customers eagerly waits, and advanced ordering kiosks or service robots process their requests. In the background, depict the busy stock exchange floor, with digital tickers displaying the soaring price of Tesla's stock (TSLA) amidst a flurry of high-tech trading activities. The scene should exude a futuristic vibe with sleek, advanced technology integrated seamlessly with everyday life.

*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 The number of fast food cooks in West Virginia caused Tesla's stock price (TSLA) to increase.

AI academic paper

(Because p < 0.01)

West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

The Journal of Synchronistic Studies

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of fast food cooks in West Virginia and Tesla's stock price (TSLA). 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 the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

[[ABSTRACT]]
This study examines the curious correlation between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a thorough analysis spanning from 2011 to 2022 was conducted. Surprisingly, a correlation coefficient of 0.9883959 was discovered, with a p-value less than 0.01, suggesting a strong statistical association between these seemingly unrelated variables. The implications of this unforeseen connection are explored, casting light on the unexplored interplay between culinary activities and futuristic endeavors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The world of statistical analysis often uncovers unexpected relationships and correlations that defy conventional wisdom. In this study, we endeavor to explore the peculiar synchronization between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Despite seemingly belonging to disparate realms, culinary activities and futuristic enterprises may have a closer bond than initially assumed.
The incisive and often unexpected impact of seemingly unrelated variables on each other is a fascinating phenomenon to explore. While individuals might not view fast food cooks and electric vehicle manufacturing as directly interconnected, the world of statistical analysis brings to light surprising associations that merit attention and exploration.
With an appreciable touch of irony and dry humor, our journey into this unexplored territory aims to elucidate the intricate intersection between accustomed culinary routines and the high-stakes world of technological innovation and investment. The statistically robust correlation discovered prompts further investigation into this unlikely pairing, presenting an opportunity to delve into the uncharted terrain of seemingly disparate spheres of human activity.
Acknowledging the potential quirkiness of this association, this study aims to approach the subject matter with the academic rigor and seriousness it deserves, all the while embracing the light-heartedness of unexpected statistical revelations. As we embark on this scholarly yet whimsical exploration, we invite readers to indulge in the delightful unpredictability that statistical inquiry often unveils.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The exploration of surprising correlations and unexpected links between purportedly unrelated variables has been a recurring theme in statistical analysis and academic inquiry. The unexpected relationship between the number of fast food cooks in West Virginia and the stock price of Tesla (TSLA) falls within this realm of peculiar associations. The evolution of this inquiry necessitates a review of established research and literature, presented as follows.

Smith et al. (2015) conducted a comprehensive examination of employment trends in the fast-food industry, focusing on staffing patterns and their implications for regional economic indicators. Their findings highlighted the nuanced dynamics of fast-food labor markets and its ripple effects on broader economic activities. Similarly, Doe and colleagues (2018) delved into the intricacies of stock price movements, analyzing the impact of diverse factors on the valuation of companies, albeit in entirely different sectors. Jones' seminal work (2012) on unexpected correlations in statistics provided a theoretical framework for understanding the unpredictable connections that can emerge from empirical analysis.

Expanding beyond direct scholarly research, relevant non-fiction works such as "Fast Food Nation" by Eric Schlosser and "The Intelligent Investor" by Benjamin Graham shed light on the multifaceted dimensions of both fast food industry labor dynamics and stock market phenomena. These insightful publications enrich the contextual understanding of the variables under investigation, providing valuable perspectives that inform the present inquiry.

Transitioning to fictional literature, the convergence of culinary pursuits and technological advancements is subtly featured in works such as "The Age of Innocence" by Edith Wharton and "Neuromancer" by William Gibson. While not directly addressing fast food cooks or Tesla stock prices, these literary creations offer symbolic and allegorical interpretations of the interplay between tradition and innovation, mirroring the unexpected association at hand.

As the present investigation inclines towards the whimsical, it is imperative to acknowledge the unconventional sources consulted in this literature review. While traditional academic discourse and scholarly articles remain foundational, the authors discovered compelling insights from rather unconventional material. Perusing the backs of various shampoo bottles, with their quirky slogans and promises of transformation, offered a surprising parallel to the playful unpredictability inherent in statistical revelations.

In light of the multifaceted sources reviewed, the stage is set for an in-depth exploration of the unexpected synchronicity between fast food cooks in West Virginia and Tesla's stock price, informed by a tapestry of scholarly, non-fiction, fiction, and even unorthodox literary sources.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of fast food cooks in West Virginia and Tesla's stock price (TSLA). 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 Bureau of Larbor Statistics and LSEG Analytics (Refinitiv) . You used data from 2011 to 2022

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]]
West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

[[ABSTRACT]]
This study examines the curious correlation between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a thorough analysis spanning from 2011 to 2022 was conducted. Surprisingly, a correlation coefficient of 0.9883959 was discovered, with a p-value less than 0.01, suggesting a strong statistical association between these seemingly unrelated variables. The implications of this unforeseen connection are explored, casting light on the unexplored interplay between culinary activities and futuristic endeavors.

[[INTRODUCTION]]
INTRODUCTION
The world of statistical analysis often uncovers unexpected relationships and correlations that defy conventional wisdom. In this study, we endeavor to explore the peculiar synchronization between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Despite seemingly belonging to disparate realms, culinary activities and futuristic enterprises may have a closer bond than initially assumed.
The incisive and often unexpected impact of seemingly unrelated variables on each other is a fascinating phenomenon to explore. While individuals might not view fast food cooks and electric vehicle manufacturing as directly interconnected, the world of statistical analysis brings to light surprising associations that merit attention and exploration.
With an appreciable touch of irony and dry humor, our journey into this unexplored territory aims to elucidate the intricate intersection between accustomed culinary routines and the high-stakes world of technological innovation and investment. The statistically robust correlation discovered prompts further investigation into this unlikely pairing, presenting an opportunity to delve into the uncharted terrain of seemingly disparate spheres of human activity.
Acknowledging the potential quirkiness of this association, this study aims to approach the subject matter with the academic rigor and seriousness it deserves, all the while embracing the light-heartedness of unexpected statistical revelations. As we embark on this scholarly yet whimsical exploration, we invite readers to indulge in the delightful unpredictability that statistical inquiry often unveils.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:

The data utilized in this study was primarily sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), covering the period from 2011 to 2022. Due to the potentially whimsical nature of the research subject, a thorough process was undertaken to ensure the reliability and comprehensiveness of the data. However, the trawling of the internet also played a role in the data collection process, with an emphasis on gleaning information from reputable sources rather than falling prey to less savory statistical fare.

Variable Definition and Measurement:

The primary variables under examination in this study were the number of fast food cooks employed in West Virginia and the stock price of Tesla (TSLA). The classification of "fast food cooks" encompassed both the purveyors of delectable delights and the maestros of culinary craftsmanship, but notably excluded those of the slow-cooking persuasion.

Analytical Approach:

Statistical analysis of the collected data employed the rigorous scrutiny of exploratory data analysis, regression analysis, and correlation coefficients. To ensure the integrity of the findings, robust statistical models were utilized, acknowledging that the potential for spurious randomness in data cannot be dismissed, especially when venturing into the uncharted realms of culinary and technological harmonization.

Given the gravity of the research subject, the inferential statistics were applied with appropriate caution, recognizing the need to unearth genuine correlations amidst the statistical din that often permeates unexpected associations.

Ethical Considerations:

In adherence to academic integrity, the researchers exercised relentless vigilance in handling the data, recognizing the need to tread delicately when unearthing unanticipated connections. The protection of research subjects was paramount, particularly in the case of unsuspecting fast food cooks who inadvertently found themselves entangled in the whimsical world of statistical inquiry.

Limitations:

It is essential to acknowledge the limitations of this study, recognizing that the research findings, however robust, may still succumb to the capricious nature of statistical fortune. Additionally, the generalizability of the findings beyond the context of West Virginia and Tesla's stock price is subject to inherent limitations, as the profound interplay between culinary activities and technological achievements might manifest differently in alternate geographic and industry settings.

In conclusion, the methodology adopted in this study sought to strike a balance between scholarly rigor and the inherent lightheartedness of unexpected statistical revelations. The journey through the labyrinthine pathways of data analysis and interpretation endeavored to maintain a whimsical spirit, while steadfastly upholding the canons of robust statistical inquiry.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of fast food cooks in West Virginia and Tesla's stock price (TSLA). 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]]
West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

[[ABSTRACT]]
This study examines the curious correlation between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a thorough analysis spanning from 2011 to 2022 was conducted. Surprisingly, a correlation coefficient of 0.9883959 was discovered, with a p-value less than 0.01, suggesting a strong statistical association between these seemingly unrelated variables. The implications of this unforeseen connection are explored, casting light on the unexplored interplay between culinary activities and futuristic endeavors.

[[LITERATURE REVIEW]]
The exploration of surprising correlations and unexpected links between purportedly unrelated variables has been a recurring theme in statistical analysis and academic inquiry. The unexpected relationship between the number of fast food cooks in West Virginia and the stock price of Tesla (TSLA) falls within this realm of peculiar associations. The evolution of this inquiry necessitates a review of established research and literature, presented as follows.
Smith et al. (2015) conducted a comprehensive examination of employment trends in the fast-food industry, focusing on staffing patterns and their implications for regional economic indicators. Their findings highlighted the nuanced dynamics of fast-food labor markets and its ripple effects on broader economic activities. Similarly, Doe and colleagues (2018) delved into the intricacies of stock price movements, analyzing the impact of diverse factors on the valuation of companies, albeit in entirely different sectors. Jones' seminal work (2012) on unexpected correlations in statistics provided a theoretical framework for understanding the unpredictable connections that can emerge from empirical analysis.
Expanding beyond direct scholarly research, relevant non-fiction works such as "Fast Food Nation" by Eric Schlosser and "The Intelligent Investor" by Benjamin Graham shed light on the multifaceted dimensions of both fast food industry labor dynamics and stock market phenomena. These insightful publications enrich the contextual understanding of the variables under investigation, providing valuable perspectives that inform the present inquiry.
Transitioning to fictional literature, the convergence of culinary pursuits and technological advancements is subtly featured in works such as "The Age of Innocence" by Edith Wharton and "Neuromancer" by William Gibson. While not directly addressing fast food cooks or Tesla stock prices, these literary creations offer symbolic and allegorical interpretations of the interplay between tradition and innovation, mirroring the unexpected association at hand.
As the present investigation inclines towards the whimsical, it is imperative to acknowledge the unconventional sources consulted in this literature review. While traditional academic discourse and scholarly articles remain foundational, the authors discovered compelling insights from rather unconventional material. Perusing the backs of various shampoo bottles, with their quirky slogans and promises of transformation, offered a surprising parallel to the playful unpredictability inherent in statistical revelations.
In light of the multifaceted sources reviewed, the stage is set for an in-depth exploration of the unexpected synchronicity between fast food cooks in West Virginia and Tesla's stock price, informed by a tapestry of scholarly, non-fiction, fiction, and even unorthodox literary sources.

[[RESULTS]]
The analysis of the data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period of 2011 to 2022 revealed a remarkable correlation between the number of fast food cooks in West Virginia and Tesla's stock price (TSLA). A correlation coefficient of 0.9883959 was observed, suggesting a remarkably strong positive relationship between these two seemingly unrelated variables. The coefficient of determination (r-squared) was calculated to be 0.9769265, indicating that approximately 97.7% of the variability in Tesla's stock price can be explained by the number of fast food cooks in West Virginia. Additionally, the p-value, being less than 0.01, further supports the significance of this correlation.
Upon further exploration, the scatterplot (Fig. 1) visually illustrates the striking association between the number of fast food cooks in West Virginia and Tesla's stock price. The scatterplot showcases a near-linear relationship, offering a visual representation of the surprising synchronicity between these disparate factors.
The robustness of this statistical correlation prompts a reconsideration of the conventional boundaries of influence between culinary employment in West Virginia and the performance of a leading electric vehicle manufacturer. This unanticipated connection invites speculation and perhaps a certain whimsical pondering on the interplay of seemingly unrelated factors in the intricate web of economic and market dynamics.
The strength of this correlation defies mere coincidence and calls for further investigation into the underlying mechanisms that may link the number of fast food cooks in West Virginia to the stock price of Tesla. As we unravel this peculiar connection, we encourage a lighthearted appreciation for the unexpected discoveries that statistical analysis can unveil, often in the most unlikely of places.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study remarkably align with the curious connections that have been observed in the annals of academic research. The unexpected correlation between the number of fast food cooks in West Virginia and the stock price of Tesla (TSLA) not only defies traditional economic logic but also lends credence to the adage that "fries and highs" might indeed go hand in hand.

The literature review provided a comprehensive backdrop for the present investigation, highlighting previous studies that unraveled the intricate dynamics of both fast-food labor markets and stock price movements. Ironically, while some might scoff at the idea of lumping culinary activities and futuristic endeavors together, the results of this study overwhelmingly substantiate the prior research, etching a new chapter in the culinary-technological crossover saga.

Smith et al.'s (2015) in-depth analysis of staffing patterns in the fast-food industry suddenly assumes an air of significance when viewed through the lens of our findings. The staffing patterns, it seems, hold the key, not just to sizzling patties, but to the sizzling success of Tesla's stock. Doe and colleagues' (2018) examination of diverse factors impacting company valuations acquires a new flavor of relevance, as their insights now resonate in the correlation between fast food cooks and TSLA stock price. Even Jones' (2012) theoretical framework for unexpected correlations appears to have taken on a practical, profound flavor in the context of our study.

Furthermore, the literature review's unconventional sources, including the whimsy of fictional literary creations and, shall we say, "bottled wisdom", mirrored the unpredictability inherent in statistical revelations. Little did we anticipate that these tangential sources would provide a subtle undercurrent of support for the unanticipated synchronicity uncovered in our study. Notably, the multidimensional and multifaceted nature of the sources consulted indeed mirrored the complexity of the entwined variables in our investigation.

As we reflect on the unexpected alignment between fast food cooks in West Virginia and Tesla's stock price, it is evident that statistical analysis has once again pulled off a delightful surprise. This study transcends the mundane and ventures into the realm of whimsical pondering, reminding us that the world of statistical inquiry can yield colorful, unexpected confluences.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of fast food cooks in West Virginia and Tesla's stock price (TSLA). 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]]
West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

[[ABSTRACT]]
This study examines the curious correlation between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a thorough analysis spanning from 2011 to 2022 was conducted. Surprisingly, a correlation coefficient of 0.9883959 was discovered, with a p-value less than 0.01, suggesting a strong statistical association between these seemingly unrelated variables. The implications of this unforeseen connection are explored, casting light on the unexplored interplay between culinary activities and futuristic endeavors.

[[INTRDUCTION]]
INTRODUCTION
The world of statistical analysis often uncovers unexpected relationships and correlations that defy conventional wisdom. In this study, we endeavor to explore the peculiar synchronization between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Despite seemingly belonging to disparate realms, culinary activities and futuristic enterprises may have a closer bond than initially assumed.
The incisive and often unexpected impact of seemingly unrelated variables on each other is a fascinating phenomenon to explore. While individuals might not view fast food cooks and electric vehicle manufacturing as directly interconnected, the world of statistical analysis brings to light surprising associations that merit attention and exploration.
With an appreciable touch of irony and dry humor, our journey into this unexplored territory aims to elucidate the intricate intersection between accustomed culinary routines and the high-stakes world of technological innovation and investment. The statistically robust correlation discovered prompts further investigation into this unlikely pairing, presenting an opportunity to delve into the uncharted terrain of seemingly disparate spheres of human activity.
Acknowledging the potential quirkiness of this association, this study aims to approach the subject matter with the academic rigor and seriousness it deserves, all the while embracing the light-heartedness of unexpected statistical revelations. As we embark on this scholarly yet whimsical exploration, we invite readers to indulge in the delightful unpredictability that statistical inquiry often unveils.

[[RESULTS]]
The analysis of the data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for the period of 2011 to 2022 revealed a remarkable correlation between the number of fast food cooks in West Virginia and Tesla's stock price (TSLA). A correlation coefficient of 0.9883959 was observed, suggesting a remarkably strong positive relationship between these two seemingly unrelated variables. The coefficient of determination (r-squared) was calculated to be 0.9769265, indicating that approximately 97.7% of the variability in Tesla's stock price can be explained by the number of fast food cooks in West Virginia. Additionally, the p-value, being less than 0.01, further supports the significance of this correlation.
Upon further exploration, the scatterplot (Fig. 1) visually illustrates the striking association between the number of fast food cooks in West Virginia and Tesla's stock price. The scatterplot showcases a near-linear relationship, offering a visual representation of the surprising synchronicity between these disparate factors.
The robustness of this statistical correlation prompts a reconsideration of the conventional boundaries of influence between culinary employment in West Virginia and the performance of a leading electric vehicle manufacturer. This unanticipated connection invites speculation and perhaps a certain whimsical pondering on the interplay of seemingly unrelated factors in the intricate web of economic and market dynamics.
The strength of this correlation defies mere coincidence and calls for further investigation into the underlying mechanisms that may link the number of fast food cooks in West Virginia to the stock price of Tesla. As we unravel this peculiar connection, we encourage a lighthearted appreciation for the unexpected discoveries that statistical analysis can unveil, often in the most unlikely of places.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study have brought to light a remarkably robust correlation between the number of fast food cooks in West Virginia and Tesla's stock price (TSLA), presenting an unusual and intriguing area for further inquiry. The statistical association, with its correlation coefficient of 0.9883959 and a p-value less than 0.01, defies conventional expectations and invites both serious consideration and a touch of whimsy.

This unexpected synchronicity between culinary activities and the performance of a leading electric vehicle manufacturer begs the question: could there be a "fast food effect" on stock prices, or perhaps, a "drive-thru dividend" influencing market behavior? The implications of this unforeseen connection may extend beyond the realms of statistical correlation, offering a hint of the culinary arts in the traditionally austere world of stock market analysis.

While the connection remains enigmatic, it is clear that no more research is needed in this area. The findings stand as a testament to the delightful unpredictability of statistical inquiry and the unforeseen interplay between seemingly unrelated spheres of human activity.

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 West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Synchronistic Studies

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 West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Eccentric Energy Research and Innovation (SEERI)

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 West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity with an abstract of This study examines the curious correlation between the number of fast food cooks in the state of West Virginia and the stock price of Tesla (TSLA). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), a thorough analysis spanning from 2011 to 2022 was conducted. Surprisingly, a correlation coefficient of 0.9883959 was discovered, with a p-value less than 0.01, suggesting a strong statistical association between these seemingly unrelated variables. The implications of this unforeseen connection are explored, casting light on the unexplored interplay between culinary activities and futuristic endeavors.

ChatGPT:

West Virginia fast food industry, fast food cooks, West Virginia culinary workforce, Tesla stock price, Tesla (TSLA) trading, correlation analysis, data analysis, Bureau of Labor Statistics, LSEG Analytics (Refinitiv), statistical association, unexpected correlation, West Virginia employment trends, Tesla stock movement, culinary activities and stock prices

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

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Data details

The number of fast food cooks in West Virginia
Detailed data title: BLS estimate of cooks, fast food in West Virginia
Source: Bureau of Larbor Statistics
See what else correlates with The number of fast food cooks in West Virginia

Tesla's stock price (TSLA)
Detailed data title: Opening price of Tesla (TSLA) 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 Tesla's stock price (TSLA)

Correlation r = 0.9883959 (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 = 0.9769265 (Coefficient of determination)
This means 97.7% of the change in the one variable (i.e., Tesla's stock price (TSLA)) is predictable based on the change in the other (i.e., The number of fast food cooks in West Virginia) over the 12 years from 2011 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.6E-9.^Show 0.0000000016251214482939831000
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 0.99 in 1.6E-7% of random cases. Said differently, if you correlated 615,338,627 random variables^Note You don't actually need 615 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 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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.

[ 0.96, 1 ] 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.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
The number of fast food cooks in West Virginia (Cooks, Fast Food)	1740	1350	1480	1470	1770	1720	1590	1420	1190	1150	9290	11710
Tesla's stock price (TSLA) (Stock price)	1.79	1.93	2.33	9.99	14.86	15.38	14.32	20.8	20.41	28.3	239.82	382.58

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([1740,1350,1480,1470,1770,1720,1590,1420,1190,1150,9290,11710,])
array_2 = np.array([1.79,1.93,2.33,9.99,14.86,15.38,14.32,20.8,20.41,28.3,239.82,382.58,])
array_1_name = "The number of fast food cooks in West Virginia"
array_2_name = "Tesla's stock price (TSLA)"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of fast food cooks in West Virginia
Line chart for only Tesla's stock price (TSLA)
AI-generated correlation image
The spurious research paper: West Virginia's Fry Guys and Tesla's Highs: An Unlikely Synchronicity

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Correlation ID: 3371 · Black Variable ID: 16011 · Red Variable ID: 1545


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