Spurious correlation #2,826 · 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 Breweries in the United States and the second variable is Humana's stock price (HUM). The chart goes from 2002 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 Breweries in the United States and the second variable is Humana's stock price (HUM). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of breweries in the United States increased, there was a corresponding rise in beer consumption. This led to a sudden surge in people attempting new and daring feats, igniting a nationwide interest in extreme sports and activities. Unexpectedly, this created a higher demand for insurance coverage, particularly from Humana, as more and more individuals sought protection against the potential mishaps of their newfound adventurous hobbies. As a result, Humana's stock price experienced an upswing, all thanks to the frothy cascade of events set in motion by the burgeoning brewery industry.

^{Show image generation prompt} Model: dalle-3
Prompt: An image that looks like it is a scene from a Disney movie, showing a bustling cityscape with colorful and whimsical breweries popping up on every corner. The buildings are adorned with vibrant signs, and outdoor seating is filled with animated characters enjoying craft beer. Meanwhile, a prominent digital screen displays the soaring stock price of Humana (HUM) intertwined with playful visuals representing the increase in healthcare coverage demand due to beer-related injuries. 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 Breweries in the United States and Humana's stock price (HUM). 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: Symbolism Magic realism A scene from Skyrim Voxel art An image that looks like it is a scene from A Disney Movie
ChatGPT: An image that looks like it is a scene from A Disney Movie

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 Breweries in the United States and Humana's stock price (HUM), in the following situation: Because as the number of breweries in the US increased, so did the demand for beer, fueling a surge in Humana's stock price. More brewskis meant more hospital visits, and Humana, being a health insurance company, saw this as a golden opportunity to capitalize on the inevitable increase in beer-related injuries and hangovers. As the saying goes, when the beer flows, the stocks grow!. Keep the description simple.
ChatGPT: In a bustling cityscape, the number of breweries in the United States is showcased by the sight of several new craft beer establishments opening up, with vibrant signage and busy outdoor seating. Meanwhile, the digital screen of a stock market ticker prominently displays Humana's stock price (HUM) soaring upwards in response to the increased demand for healthcare coverage stemming from the rise in beer-related injuries and visits to the hospital.

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: [ An image that looks like it is a scene from A Disney Movie ] and depicting this scene: In a bustling cityscape, the number of breweries in the United States is showcased by the sight of several new craft beer establishments opening up, with vibrant signage and busy outdoor seating. Meanwhile, the digital screen of a stock market ticker prominently displays Humana's stock price (HUM) soaring upwards in response to the increased demand for healthcare coverage stemming from the rise in beer-related injuries and visits to the hospital. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: An image that looks like it is a scene from a Disney movie, showing a bustling cityscape with colorful and whimsical breweries popping up on every corner. The buildings are adorned with vibrant signs, and outdoor seating is filled with animated characters enjoying craft beer. Meanwhile, a prominent digital screen displays the soaring stock price of Humana (HUM) intertwined with playful visuals representing the increase in healthcare coverage demand due to beer-related injuries.

*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 Breweries in the United States caused Humana's stock price (HUM) to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price

The Journal of Fermented Finance

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 Breweries in the United States and Humana's stock price (HUM). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price"

[[ABSTRACT]]
This study delves into the peculiar intersection of craft beer culture and financial markets, examining the curious correlation between the number of breweries in the United States and the stock price of healthcare giant Humana (HUM). Leveraging data from the Brewers Association and LSEG Analytics (Refinitiv), our analysis spans the period from 2002 to 2022. Our findings reveal a striking correlation coefficient of 0.9710823 and p < 0.01, suggesting a robust relationship between these seemingly disparate variables. Unraveling the frothy connection between beer and stock prices yields insights that bubble to the surface with potential implications for both investors and ale enthusiasts alike.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The mysterious and often surprisingly delightful world of economics and finance frequently presents opportunities for exploring unexpected and unconventional relationships. In this study, we embark on a sudsy journey that delves into the captivating entanglement of craft beer culture and the stock price of Humana, a prominent player in the healthcare sector. The seemingly incongruous connection between the number of breweries in the United States and the stock performance of Humana (HUM) forms the fermenting foundation of this investigation.
At first glance, the notion of linking the count of breweries to a healthcare company’s stock price might appear as elusive as a disappearing head on a poorly poured pint. However, as we venture into the frothy depths of this peculiar association, we uncover intriguing correlations and correlations that are as pronounced as the head on a freshly poured ale.
The present study endeavors to disentangle the convoluted web of economic and market forces at play, teasing out potential implications and shedding light on the unexpected interplay between seemingly disparate realms. In doing so, we aim to provide not only scholarly insights but also a dash of whimsy for those who enjoy a well-crafted pun or a clever statistical quip.
Our investigation draws upon data sourced from the Brewers Association, encapsulating the lively landscape of the U.S. brewery count, and leverages stock price data from LSEG Analytics (Refinitiv) to capture the undulating trajectory of Humana’s stock price from 2002 to 2022. Through rigorous statistical analysis and a touch of irreverent curiosity, we untangle the intertwined threads that bind brew-nomics and stock spirits, revealing a correlation that raises a glass to the often unpredictable, yet oddly harmonious, nature of financial markets.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The interplay between seemingly unrelated variables has long captivated the intellectual curiosity of scholars across disciplines. To contextualize the unexpected and somewhat whimsical relationship between the number of breweries in the United States and the stock price of Humana (HUM), we review a range of studies and writings that shed light on this peculiar phenomenon.

Smith et al. (2015) provide a comprehensive analysis of the macroeconomic factors influencing the beverage industry, with a focus on the interconnectivity between alcohol consumption patterns and consumer spending habits. Their work delves into the intricate web of market forces that govern the profitability of alcoholic beverage producers, offering valuable insights into the potential ripple effects on related sectors, albeit not specifically addressing the correlation with healthcare stock prices.

In "The Brewing Industry: A Guide to Historical Records" by Doe (2009), an extensive exploration of the historical evolution of breweries in the United States unfolds, illuminating the societal and economic dynamics that have shaped this industry. While Doe's work centers on historical records, it offers a foundational understanding of the ebb and flow of brewery establishments, setting the stage for examining their contemporary economic implications.

Jones (2018) presents a comprehensive analysis of stock price movements in the healthcare sector, focusing on the impact of regulatory changes and technological advancements. Though not directly addressing the influence of breweries, Jones's work underscores the multifaceted nature of factors that contribute to stock price fluctuations within the healthcare industry.

Expanding beyond traditional academic literature, "The Oxford Companion to Beer" offers a wealth of knowledge on the rich tapestry of beer culture, spanning from brewing traditions to the social significance of beer. While not a treatise on financial markets, this compilation provides a broader perspective on the cultural significance of breweries, teasing out their potential impacts on consumer behavior and societal trends.

In the realm of fiction, "Brew to Kill: A Coffeehouse Mystery" by Java (2017) and "The Lager Queen of Minnesota" by Barley (2019) offer entertaining narratives set against the backdrop of brewery culture. While purely fictitious, these works serve as playful reminders of the pervasive influence of breweries in popular imagination and storytelling.

Turning to the realm of animated entertainment, the animated series "The Simpsons," particularly the episodes featuring Homer Simpson's escapades in home brewing and beer appreciation, playfully epitomize the role of breweries in pop culture. Additionally, the educational children's show "Arthur" features episodes that subtly embed themes of responsible consumption and entrepreneurship within the context of a charming fictional town, subtly acknowledging the cultural presence of breweries.

In synthesizing the diverse literature and cultural references, our study seeks to bridge the gap between financial analysis and the frothy world of breweries, embarking on a lighthearted yet rigorous exploration of the curious correlations that intertwine these seemingly disparate realms.

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 Breweries in the United States and Humana's stock price (HUM). 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 Brewers Association and LSEG Analytics (Refinitiv) . You used data from 2002 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]]
"Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price"

[[ABSTRACT]]
This study delves into the peculiar intersection of craft beer culture and financial markets, examining the curious correlation between the number of breweries in the United States and the stock price of healthcare giant Humana (HUM). Leveraging data from the Brewers Association and LSEG Analytics (Refinitiv), our analysis spans the period from 2002 to 2022. Our findings reveal a striking correlation coefficient of 0.9710823 and p < 0.01, suggesting a robust relationship between these seemingly disparate variables. Unraveling the frothy connection between beer and stock prices yields insights that bubble to the surface with potential implications for both investors and ale enthusiasts alike.

[[INTRODUCTION]]
INTRODUCTION
The mysterious and often surprisingly delightful world of economics and finance frequently presents opportunities for exploring unexpected and unconventional relationships. In this study, we embark on a sudsy journey that delves into the captivating entanglement of craft beer culture and the stock price of Humana, a prominent player in the healthcare sector. The seemingly incongruous connection between the number of breweries in the United States and the stock performance of Humana (HUM) forms the fermenting foundation of this investigation.
At first glance, the notion of linking the count of breweries to a healthcare company’s stock price might appear as elusive as a disappearing head on a poorly poured pint. However, as we venture into the frothy depths of this peculiar association, we uncover intriguing correlations and correlations that are as pronounced as the head on a freshly poured ale.
The present study endeavors to disentangle the convoluted web of economic and market forces at play, teasing out potential implications and shedding light on the unexpected interplay between seemingly disparate realms. In doing so, we aim to provide not only scholarly insights but also a dash of whimsy for those who enjoy a well-crafted pun or a clever statistical quip.
Our investigation draws upon data sourced from the Brewers Association, encapsulating the lively landscape of the U.S. brewery count, and leverages stock price data from LSEG Analytics (Refinitiv) to capture the undulating trajectory of Humana’s stock price from 2002 to 2022. Through rigorous statistical analysis and a touch of irreverent curiosity, we untangle the intertwined threads that bind brew-nomics and stock spirits, revealing a correlation that raises a glass to the often unpredictable, yet oddly harmonious, nature of financial markets.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this research endeavor aimed to capture the effervescent essence of the relationship between the number of breweries in the United States and Humana's stock price (HUM). The data used for this analysis spanned the period from 2002 to 2022 and was meticulously gathered from reputable sources, primarily the Brewers Association and LSEG Analytics (Refinitiv).

The first step involved "hopping" onto the internet to peruse the digital brew of information available. The BrewSearch 9000 algorithm, a cutting-edge web-scraping tool designed by our very own tech-savvy intern, meticulously combed through online databases, brewery websites, and beer-related forums to compile a comprehensive dataset of U.S. brewery counts. No stone was left unturned, nor was any keg left untapped in our quest for brew-tyful data.

Meanwhile, in the realm of stock market sorcery, the LSEG Analytics (Refinitiv) database served as our bubbling cauldron of stock price information. We diligently gathered historical stock price data for Humana (HUM), dissecting the daily, weekly, and monthly fluctuations with the precision of a seasoned brewmaster perfecting the art of fermentation.

With an inventory of data at our disposal that was robust enough to make even the most ardent beer lover's heart leap for joy, we ventured into the world of statistical analysis. Utilizing an eclectic mix of regression techniques, time series models, and correlation coefficients, we probed the potential relationships brewing within our dataset.

Our statistical arsenal was not just a bland pot of oatmeal; it was brimming with flavor-enhancing spices of advanced econometric methods, simmering until the p-values popped like freshly uncorked bottles of champagne. The inclusion of covariates such as macroeconomic indicators and industry-specific variables added a depth of flavor to our analysis, ensuring that we didn't end up with a flat, one-note brew of results.

After marinating our dataset with careful consideration and not a small amount of statistical seasoning, we scrutinized the findings with the discerning eye of a cicerone evaluating a rare, barrel-aged stout. The captivating correlation coefficient of 0.9710823 and a twinkling p-value of less than 0.01 emerged from the frothy depths, signaling a robust and statistically significant relationship between the number of breweries in the United States and Humana's stock price.

Once our findings had been meticulously decanted, we proceeded to perform robustness checks and sensitivity analyses to ensure that our results were as sturdy as a well-constructed beer flight. Sensitivity analyses were conducted to test the impact of extreme outliers and to validate the resilience of our results to potential data quirks, thus fortifying the credibility of our findings.

In summary, the methodology adopted in this investigation was a carefully crafted blend of technological prowess, statistical acumen, and a hint of brewing metaphors. By navigating the labyrinthine depths of data collection, embracing the art of statistical sorcery, and exercising a discerning palate for robustness, we unfurled the frothy nexus between brew-nomics and stock spirits, all while savoring the occasional pun-filled chuckle along the way.

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 Breweries in the United States and Humana's stock price (HUM). 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]]
"Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price"

[[ABSTRACT]]
This study delves into the peculiar intersection of craft beer culture and financial markets, examining the curious correlation between the number of breweries in the United States and the stock price of healthcare giant Humana (HUM). Leveraging data from the Brewers Association and LSEG Analytics (Refinitiv), our analysis spans the period from 2002 to 2022. Our findings reveal a striking correlation coefficient of 0.9710823 and p < 0.01, suggesting a robust relationship between these seemingly disparate variables. Unraveling the frothy connection between beer and stock prices yields insights that bubble to the surface with potential implications for both investors and ale enthusiasts alike.

[[LITERATURE REVIEW]]
The interplay between seemingly unrelated variables has long captivated the intellectual curiosity of scholars across disciplines. To contextualize the unexpected and somewhat whimsical relationship between the number of breweries in the United States and the stock price of Humana (HUM), we review a range of studies and writings that shed light on this peculiar phenomenon.
Smith et al. (2015) provide a comprehensive analysis of the macroeconomic factors influencing the beverage industry, with a focus on the interconnectivity between alcohol consumption patterns and consumer spending habits. Their work delves into the intricate web of market forces that govern the profitability of alcoholic beverage producers, offering valuable insights into the potential ripple effects on related sectors, albeit not specifically addressing the correlation with healthcare stock prices.
In "The Brewing Industry: A Guide to Historical Records" by Doe (2009), an extensive exploration of the historical evolution of breweries in the United States unfolds, illuminating the societal and economic dynamics that have shaped this industry. While Doe's work centers on historical records, it offers a foundational understanding of the ebb and flow of brewery establishments, setting the stage for examining their contemporary economic implications.
Jones (2018) presents a comprehensive analysis of stock price movements in the healthcare sector, focusing on the impact of regulatory changes and technological advancements. Though not directly addressing the influence of breweries, Jones's work underscores the multifaceted nature of factors that contribute to stock price fluctuations within the healthcare industry.
Expanding beyond traditional academic literature, "The Oxford Companion to Beer" offers a wealth of knowledge on the rich tapestry of beer culture, spanning from brewing traditions to the social significance of beer. While not a treatise on financial markets, this compilation provides a broader perspective on the cultural significance of breweries, teasing out their potential impacts on consumer behavior and societal trends.
In the realm of fiction, "Brew to Kill: A Coffeehouse Mystery" by Java (2017) and "The Lager Queen of Minnesota" by Barley (2019) offer entertaining narratives set against the backdrop of brewery culture. While purely fictitious, these works serve as playful reminders of the pervasive influence of breweries in popular imagination and storytelling.
Turning to the realm of animated entertainment, the animated series "The Simpsons," particularly the episodes featuring Homer Simpson's escapades in home brewing and beer appreciation, playfully epitomize the role of breweries in pop culture. Additionally, the educational children's show "Arthur" features episodes that subtly embed themes of responsible consumption and entrepreneurship within the context of a charming fictional town, subtly acknowledging the cultural presence of breweries.
In synthesizing the diverse literature and cultural references, our study seeks to bridge the gap between financial analysis and the frothy world of breweries, embarking on a lighthearted yet rigorous exploration of the curious correlations that intertwine these seemingly disparate realms.

[[RESULTS]]
The investigation revealed a substantial correlation between the number of breweries in the United States and the stock price of Humana (HUM) from 2002 to 2022. The correlation coefficient of 0.9710823 suggests a remarkably strong relationship between these seemingly unrelated variables. This robust correlation brings to mind the satisfying sight of a well-poured beer with a thick, frothy head that refuses to dissipate – much like this intriguing statistical connection.
The high R-squared value of 0.9430008 further supports the notion that changes in the U.S. brewery count can explain a significant proportion of the variation in Humana's stock price. This finding is as clear as the effervescence in a freshly uncorked bottle of craft beer, demonstrating the notable influence of this unexpected variable on the stock price dynamics of a healthcare behemoth.
Moreover, the p-value of less than 0.01 underscores the statistical significance of this relationship, leaving little room for doubt regarding the impact of the U.S. brewery count on Humana's stock price. This result is as unequivocal as a straightforward ale recommendation at a brewery tour – it simply cannot be ignored.
The accompanying scatterplot (Fig. 1) visually encapsulates the striking correlation between the number of breweries and Humana's stock price. Just as a perfectly blended beer harmonizes distinct flavors, this scatterplot harmonizes two seemingly disparate variables into a compelling visual narrative that exemplifies the buoyant relationship uncovered by our analysis.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this investigation lend support to the notion that the number of breweries in the United States exerts a discernible influence on the stock price of Humana (HUM). While the relationship may seem as unexpected as finding a bottle of champagne in a beer cooler, our results confirm the robustness of this statistical association. The high correlation coefficient echoes the harmonious symphony of flavors in a well-crafted brew, underscoring the surprisingly close link between these disparate variables.

Our results resonate with the prior research by Smith et al. (2015), highlighting the broader economic implications of beverage industry dynamics. Just as a diverse array of hops and malts contributes to a rich and complex beer, the multifaceted market forces at play in the beverage industry appear to intertwine with healthcare stock prices in unforeseen ways. We also find resonance with Doe's (2009) exploration of historical brewery evolution, as our findings hint at the enduring impact of this industry on contemporary economic dynamics.

The statistical significance of the relationship, as indicated by the p-value, is akin to a resounding consensus among beer aficionados on the quality of a particularly exceptional brew; it cannot be dismissed lightly. Our analysis supports the view that the number of breweries in the United States is not just a casual variable in the stock price equation but rather a key ingredient, much like barley in a classic beer recipe, contributing significantly to the overall flavor profile of Humana's stock.

In light of these findings, this study offers a frothy perspective on the intricate dance between seemingly unrelated variables, calling for a deeper exploration of the economic and sociocultural interplay between breweries and stock market dynamics. Just as a well-crafted beer inspires conversations and connections, the intersection of breweries and stock prices may yield fruitful discussions in both financial and beer enthusiast circles, blending academia with an unexpected dash of whimsy.

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 Breweries in the United States and Humana's stock price (HUM). 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]]
"Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price"

[[ABSTRACT]]
This study delves into the peculiar intersection of craft beer culture and financial markets, examining the curious correlation between the number of breweries in the United States and the stock price of healthcare giant Humana (HUM). Leveraging data from the Brewers Association and LSEG Analytics (Refinitiv), our analysis spans the period from 2002 to 2022. Our findings reveal a striking correlation coefficient of 0.9710823 and p < 0.01, suggesting a robust relationship between these seemingly disparate variables. Unraveling the frothy connection between beer and stock prices yields insights that bubble to the surface with potential implications for both investors and ale enthusiasts alike.

[[INTRDUCTION]]
INTRODUCTION
The mysterious and often surprisingly delightful world of economics and finance frequently presents opportunities for exploring unexpected and unconventional relationships. In this study, we embark on a sudsy journey that delves into the captivating entanglement of craft beer culture and the stock price of Humana, a prominent player in the healthcare sector. The seemingly incongruous connection between the number of breweries in the United States and the stock performance of Humana (HUM) forms the fermenting foundation of this investigation.
At first glance, the notion of linking the count of breweries to a healthcare company’s stock price might appear as elusive as a disappearing head on a poorly poured pint. However, as we venture into the frothy depths of this peculiar association, we uncover intriguing correlations and correlations that are as pronounced as the head on a freshly poured ale.
The present study endeavors to disentangle the convoluted web of economic and market forces at play, teasing out potential implications and shedding light on the unexpected interplay between seemingly disparate realms. In doing so, we aim to provide not only scholarly insights but also a dash of whimsy for those who enjoy a well-crafted pun or a clever statistical quip.
Our investigation draws upon data sourced from the Brewers Association, encapsulating the lively landscape of the U.S. brewery count, and leverages stock price data from LSEG Analytics (Refinitiv) to capture the undulating trajectory of Humana’s stock price from 2002 to 2022. Through rigorous statistical analysis and a touch of irreverent curiosity, we untangle the intertwined threads that bind brew-nomics and stock spirits, revealing a correlation that raises a glass to the often unpredictable, yet oddly harmonious, nature of financial markets.

[[RESULTS]]
The investigation revealed a substantial correlation between the number of breweries in the United States and the stock price of Humana (HUM) from 2002 to 2022. The correlation coefficient of 0.9710823 suggests a remarkably strong relationship between these seemingly unrelated variables. This robust correlation brings to mind the satisfying sight of a well-poured beer with a thick, frothy head that refuses to dissipate – much like this intriguing statistical connection.
The high R-squared value of 0.9430008 further supports the notion that changes in the U.S. brewery count can explain a significant proportion of the variation in Humana's stock price. This finding is as clear as the effervescence in a freshly uncorked bottle of craft beer, demonstrating the notable influence of this unexpected variable on the stock price dynamics of a healthcare behemoth.
Moreover, the p-value of less than 0.01 underscores the statistical significance of this relationship, leaving little room for doubt regarding the impact of the U.S. brewery count on Humana's stock price. This result is as unequivocal as a straightforward ale recommendation at a brewery tour – it simply cannot be ignored.
The accompanying scatterplot (Fig. 1) visually encapsulates the striking correlation between the number of breweries and Humana's stock price. Just as a perfectly blended beer harmonizes distinct flavors, this scatterplot harmonizes two seemingly disparate variables into a compelling visual narrative that exemplifies the buoyant relationship uncovered by our analysis.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation has uncorked a surprisingly robust correlation between the number of breweries in the United States and the stock price of Humana. The effervescent correlation coefficient and the statistically significant p-value point to a connection as undeniable as the appeal of a cold beer on a hot day. This unexpected relationship serves as a reminder that in the vast and diverse landscape of economic forces, even the most unlikely pairings can yield meaningful insights.

The implications of this research reach beyond the realm of finance, offering a frothy perspective on the interconnectedness of seemingly unrelated phenomena. While this correlation may elicit a chuckle or two, it also underscores the intricate and often perplexing nature of market dynamics. After all, who would have thought that the craft beer scene could exert such influence over the stock price of a healthcare giant? It's a testament to the flavorful unpredictability of economic interactions.

As we raise a metaphorical glass to the intriguing findings of this study, we must acknowledge that further research in this area may yield diminishing returns. The evidence presented here is as compelling as a well-crafted brew, and additional inquiries may only result in diminishing froth. Thus, we posit that the relationship between the number of breweries in the United States and Humana's stock price has been thoroughly examined, leaving little yeast for doubt. It's time to savor these findings and resist the temptation to stir the pot – or perhaps, in this case, the fermenter – any further.

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 Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Fermented Finance

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 Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Hopademic Society

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 Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price with an abstract of This study delves into the peculiar intersection of craft beer culture and financial markets, examining the curious correlation between the number of breweries in the United States and the stock price of healthcare giant Humana (HUM). Leveraging data from the Brewers Association and LSEG Analytics (Refinitiv), our analysis spans the period from 2002 to 2022. Our findings reveal a striking correlation coefficient of 0.9710823 and p < 0.01, suggesting a robust relationship between these seemingly disparate variables. Unraveling the frothy connection between beer and stock prices yields insights that bubble to the surface with potential implications for both investors and ale enthusiasts alike.

ChatGPT:

brewery count, stock price, correlation, craft beer culture, financial markets, United States breweries, Humana stock price, data analysis, Brewers Association, LSEG Analytics, Refinitiv, correlation coefficient, healthcare stock, stock market relationship, ale enthusiasts, investment implications

*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 Breweries in the United States
Detailed data title: Number of Breweries in the United States
Source: Brewers Association
See what else correlates with The number of Breweries in the United States

Humana's stock price (HUM)
Detailed data title: Opening price of Humana (HUM) 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 Humana's stock price (HUM)

Correlation r = 0.9710823 (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 = 0.9430008 (Coefficient of determination)
This means 94.3% of the change in the one variable (i.e., Humana's stock price (HUM)) is predictable based on the change in the other (i.e., The number of Breweries in the United States) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.8E-13.^Show 0.0000000000002812596715683773
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 0.97 in 2.8E-11% of random cases. Said differently, if you correlated 3,555,433,292,031 random variables^Note You don't actually need 3 trillion 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 20 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 20 because we have two variables measured over a period of 21 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.

[ 0.93, 0.99 ] 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.

	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
The number of Breweries in the United States (Number of breweries)	1575	1629	1635	1612	1741	1805	1896	1933	2131	2525	2670	3162	4014	4847	5780	6767	7722	8557	9092	9384	9709
Humana's stock price (HUM) (Stock price)	11.8	10.1	23.05	29.86	54.91	55.46	76.51	37.38	44.43	55.06	89.21	69.74	102.76	144.95	177.67	202.87	249.36	283.31	367.16	417.82	461.38

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([1575,1629,1635,1612,1741,1805,1896,1933,2131,2525,2670,3162,4014,4847,5780,6767,7722,8557,9092,9384,9709,])
array_2 = np.array([11.8,10.1,23.05,29.86,54.91,55.46,76.51,37.38,44.43,55.06,89.21,69.74,102.76,144.95,177.67,202.87,249.36,283.31,367.16,417.82,461.38,])
array_1_name = "The number of Breweries in the United States"
array_2_name = "Humana's stock price (HUM)"

# 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
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of Breweries in the United States
Line chart for only Humana's stock price (HUM)
AI-generated correlation image
The spurious research paper: Brew-nomics and Stock Spirits: A Sudsy Look at the Relationship Between U.S. Brewery Count and Humana's Stock Price

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Correlation ID: 2826 · Black Variable ID: 34 · Red Variable ID: 1708


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