Spurious correlation #2,274 · 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 PACCAR's stock price (PCAR). 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 PACCAR's stock price (PCAR). 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, more people were drawn to the idea of crafting and fine-tuning their own products. This renewed interest in craftsmanship extended to other industries, including the manufacturing of heavy-duty trucks. PACCAR, being a leading truck manufacturer, benefited from this cultural shift as investors hop(d) on board, believing that the company would continue to brew up success in this new market environment. Who knew that the secret ingredient to PACCAR's stock price going up wasn't just high-quality trucks, but also a taste for artisanal pursuits!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a realistic scene featuring the United States map as the background, filled with colorful brewery icons scattered across the country. In the foreground, display a stock market ticker showing PACCAR's stock price rising steadily, with a green upward arrow indicating its positive trajectory. The breweries should be vibrant and distinct, while the stock market ticker should be clear and easily legible. Ensure that the overall visual depiction is simple yet enticing. 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 PACCAR's stock price. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Symbolism 3D Pixar Style Surrealism Steampunk Realistic
ChatGPT: Realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of Breweries in the United States and PACCAR's stock price together. Keep the description simple.
ChatGPT: In the scene, the United States map is filled with various colorful brewery icons scattered across the country, representing the growing number of breweries. In the foreground, a stock market ticker displays PACCAR's stock price rising steadily, with a green upward arrow indicating its positive trajectory.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Realistic ] and depicting this scene: In the scene, the United States map is filled with various colorful brewery icons scattered across the country, representing the growing number of breweries. In the foreground, a stock market ticker displays PACCAR's stock price rising steadily, with a green upward arrow indicating its positive trajectory. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a realistic scene featuring the United States map as the background, filled with colorful brewery icons scattered across the country. In the foreground, display a stock market ticker showing PACCAR's stock price rising steadily, with a green upward arrow indicating its positive trajectory. The breweries should be vibrant and distinct, while the stock market ticker should be clear and easily legible. Ensure that the overall visual depiction is simple yet enticing.

*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 PACCAR's stock price (PCAR) to increase.

AI academic paper

(Because p < 0.01)

Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR'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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and PACCAR's stock price (PCAR). Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price

[[ABSTRACT]]
In this research, we delve into the frothy world of economics by analyzing the curious correlation between the burgeoning craft beer scene in the United States and the stock price of PACCAR. With data sourced from the Brewers Association and LSEG Analytics (Refinitiv), our findings reveal a staggering correlation coefficient of 0.8801309 and p < 0.01 from 2002 to 2022. Our study sheds light on the intoxicating relationship between hops and PACCAR's stock, providing a refreshing perspective on market influences. Join us as we raise a glass to unconventional economic indicators and hoppy returns.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Economics and beer may seem like an odd couple, but in today's research, we aim to unravel the sudsy connection between the number of breweries in the United States and the stock price of PACCAR (PCAR). While some researchers may prefer to stick with traditional economic indicators, we believe it's time to tap into the unexplored world of craft beer to foamulate a fresh perspective on market influences.
As craft beer continues to ferment its way into the hearts (and livers) of Americans, the economic implications are nothing to alegnore. With the rise of microbreweries, brewpubs, and nanobreweries, we're witnessing a beerenaissance unlike anything seen since the Prohibition days. But what do these hoptastic trends have to do with PACCAR's stock price, you may wonder? Well, pour yourself a pint and join us as we dive into the frothy sea of data and statistics to extract the hoppy truth behind this correlation.
Our investigation into this hoppy hypothesis is not just random ale-mentation. It's grounded in solid data sourced from the Brewers Association and LSEG Analytics (Refinitiv). Armed with these statistical alements, we set out to brew a unique blend of economic analysis that transcends the ordinary and embraces the hoppportunity to uncover unexpected correlations.
In this paper, we'll explore the historical context of both the craft beer industry and PACCAR's stock performance, frothing at the mouth to reveal the surprising correlation coefficient and p-value that may have even the most seasoned economists rubbing their eyes in disbelief. So sit back, relax, and hop along with us as we explore the bubbly relationship between breweries and stock prices, reminding us that in the world of economics, just like in brewing, there's always something hoppening. Cheers to unconventional indicators and hoppy returns!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al." the authors find that the number of breweries in the United States has experienced a significant upward trend in recent years, mirroring the exponential growth of IPAs and barrel-aged stouts on tap menus across the nation. This proliferation of craft breweries has not only brought a deluge of artisanal ales and creative concoctions but has also stirred frothy debates about the economic impact of this sudsy revolution.

Doe's study "The Brew Economy" delves into the relationship between craft beer production and local economies, highlighting the foamidable influence of breweries on job creation, tourism, and community revitalization. As the craft beer wave washes ashore, it leaves behind a trail of hopheads and beer-curious travelers flocking to tasting rooms and brewpubs, signaling a lucrative lager-acy for some regions.

Jones' work "Aleconomics: Breweries and the Bottom Line" uncovers the frothy implications of craft beer on stock prices, revealing that microbrewery industry benchmarks and hops-related trends can have ripple effects on certain sectors of the stock market. The study proposes the notion that the beer market is not just an ale-ternative investment but may indeed serve as a meaningful indicator of broader economic shifts.

As we wade deeper into the foamy depths of literature, we encounter "Brewonomics: Fermenting Fortune" by Fermenta Hoptimista, an insightful exploration of the economic ramifications of beer culture. Hoptimista's meticulous analysis of hop price fluctuations and malt-to-market dynamics offers a refreshing perspective on the interconnectedness of brewing and the stock market, reminding us that in the world of finance, every bubble has its brew-nchmark.

Turning our attention to more unconventional sources, we can't neglect the timeless wisdom imparted in "Charlie and the Brewery Factory" by Roald Hops, which, although a work of fiction, offers lessons on the transformative power of imagination and innovation in the brewing industry. Likewise, "The Grapes of Lager" by John Steinbrew reveals the intertwined destinies of craft beer and the stock market, albeit in a parallel universe where lager flows freely and hops hold the power to sway financial fortunes.

In our relentless pursuit of understanding the brewtiful relationship between breweries and stock prices, we turned to unexpected sources such as the cryptic messages lurking in grocery store receipts (those CVS records are surprisingly hoppy!) and the whispers of ancient brewing recipes hiding within the pages of long-forgotten alchemical texts. After all, when it comes to uncovering hidden correlations, a pint of curiosity and a dash of whimsy might just be the secret ingredients we need.

With this eclectic blend of serious scholarship, literary musings, and perhaps a dash of pure hops-induced delirium, we set the stage for our own exploration of the enigmatic ties between breweries, beer, and stock market performance. So, as we embark on this journey into the ale-luring world of finance and foamy beverages, let's raise a toast to the unexpected, the unprecedented, and the irresistibly absurd. Cheers to unconventional indicators and hoppy returns!

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and PACCAR's stock price (PCAR). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from 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]]
Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price

[[ABSTRACT]]
In this research, we delve into the frothy world of economics by analyzing the curious correlation between the burgeoning craft beer scene in the United States and the stock price of PACCAR. With data sourced from the Brewers Association and LSEG Analytics (Refinitiv), our findings reveal a staggering correlation coefficient of 0.8801309 and p < 0.01 from 2002 to 2022. Our study sheds light on the intoxicating relationship between hops and PACCAR's stock, providing a refreshing perspective on market influences. Join us as we raise a glass to unconventional economic indicators and hoppy returns.

[[INTRODUCTION]]
Economics and beer may seem like an odd couple, but in today's research, we aim to unravel the sudsy connection between the number of breweries in the United States and the stock price of PACCAR (PCAR). While some researchers may prefer to stick with traditional economic indicators, we believe it's time to tap into the unexplored world of craft beer to foamulate a fresh perspective on market influences.
As craft beer continues to ferment its way into the hearts (and livers) of Americans, the economic implications are nothing to alegnore. With the rise of microbreweries, brewpubs, and nanobreweries, we're witnessing a beerenaissance unlike anything seen since the Prohibition days. But what do these hoptastic trends have to do with PACCAR's stock price, you may wonder? Well, pour yourself a pint and join us as we dive into the frothy sea of data and statistics to extract the hoppy truth behind this correlation.
Our investigation into this hoppy hypothesis is not just random ale-mentation. It's grounded in solid data sourced from the Brewers Association and LSEG Analytics (Refinitiv). Armed with these statistical alements, we set out to brew a unique blend of economic analysis that transcends the ordinary and embraces the hoppportunity to uncover unexpected correlations.
In this paper, we'll explore the historical context of both the craft beer industry and PACCAR's stock performance, frothing at the mouth to reveal the surprising correlation coefficient and p-value that may have even the most seasoned economists rubbing their eyes in disbelief. So sit back, relax, and hop along with us as we explore the bubbly relationship between breweries and stock prices, reminding us that in the world of economics, just like in brewing, there's always something hoppening. Cheers to unconventional indicators and hoppy returns!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the sudsy connection between the number of breweries in the United States and PACCAR's stock price, we embarked on a scientific adventure that would make even the most fervent beer connoisseur raise an eyebrow in curiosity. Our methodology comprised a blend of statistical analysis, data wrangling, and a touch of hops-infused intuition.

Data Collection:
Our research team scoured the depths of the internet, utilizing data from reputable sources such as the Brewers Association and LSEG Analytics (Refinitiv). We selected data spanning from 2002 to 2022, allowing us to capture the full-bodied essence of the craft beer revolution and its potential impact on the stock market. With the precision of a brewmaster crafting the perfect IPA, we meticulously collected information on the number of breweries in the United States and PACCAR's stock price, ensuring a robust dataset that would withstand the rigors of statistical scrutiny.

Statistical Analysis:
Armed with our treasure trove of data, we rolled up our sleeves and prepared for the fermentable feast of statistical analysis. Our first order of business was to calculate the correlation coefficient between the number of breweries and PACCAR's stock price. Employing time-series analysis and regression models, we sought to uncork the essence of their relationship, ensuring that our findings would not be watered down by statistical noise.

Data Cleaning and Transformation:
Like a skilled cellarman meticulously tending to aging barrels of beer, we devoted ample time to clean and transform the raw data into a format suitable for our analytical endeavors. Outliers were treated with the same level of skepticism as a questionable tasting batch of homebrew, and missing values were imputed with the caution of a brewmaster adjusting a recipe. Our goal was to present a dataset that exuded the aroma of reliability and statistical robustness.

Control Variables:
In the spirit of scientific inquiry, we considered the potential influence of extraneous factors on PACCAR's stock price. Variables such as overall market trends, economic indicators, and the price of barley were carefully considered in our analysis, ensuring that we did not fall victim to the intoxicating allure of spurious relationships.

Econometric Modeling:
Applying the art of econometric modeling, we sought to brew an empirical framework that encapsulated the dynamic interplay between brewery numbers and PACCAR's stock performance. Our models were concocted with a judicious blend of time-series analysis, panel data techniques, and a hint of statistical wizardry, aiming to distill the essence of this peculiar relationship into a coherent and digestible form.

Ethical Considerations:
In adherence to research ethics, our team ensured that no actual beer was consumed during the data analysis process. While the temptation to conduct a "taste test" alongside regression diagnostics was certainly present, we maintained the utmost professionalism and refrained from allowing our research to become a "pub crawl" in disguise.

In conclusion, our methodology combined the precision of quantitative analysis with the exploratory spirit of a brewery tour, providing a robust framework for unraveling the sudsy connection between breweries and stock prices. With our data in hand and a dash of statistical flair, we were ready to embark on a journey into the frothy realms of economic analysis, armed with the knowledge that in the world of research, just like in brewing, there's always something hoppening. Cheers to unconventional indicators and hoppy returns!

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and PACCAR's stock price (PCAR). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price

[[ABSTRACT]]
In this research, we delve into the frothy world of economics by analyzing the curious correlation between the burgeoning craft beer scene in the United States and the stock price of PACCAR. With data sourced from the Brewers Association and LSEG Analytics (Refinitiv), our findings reveal a staggering correlation coefficient of 0.8801309 and p < 0.01 from 2002 to 2022. Our study sheds light on the intoxicating relationship between hops and PACCAR's stock, providing a refreshing perspective on market influences. Join us as we raise a glass to unconventional economic indicators and hoppy returns.

[[LITERATURE REVIEW]]
In "Smith et al." the authors find that the number of breweries in the United States has experienced a significant upward trend in recent years, mirroring the exponential growth of IPAs and barrel-aged stouts on tap menus across the nation. This proliferation of craft breweries has not only brought a deluge of artisanal ales and creative concoctions but has also stirred frothy debates about the economic impact of this sudsy revolution.
Doe's study "The Brew Economy" delves into the relationship between craft beer production and local economies, highlighting the foamidable influence of breweries on job creation, tourism, and community revitalization. As the craft beer wave washes ashore, it leaves behind a trail of hopheads and beer-curious travelers flocking to tasting rooms and brewpubs, signaling a lucrative lager-acy for some regions.
Jones' work "Aleconomics: Breweries and the Bottom Line" uncovers the frothy implications of craft beer on stock prices, revealing that microbrewery industry benchmarks and hops-related trends can have ripple effects on certain sectors of the stock market. The study proposes the notion that the beer market is not just an ale-ternative investment but may indeed serve as a meaningful indicator of broader economic shifts.
As we wade deeper into the foamy depths of literature, we encounter "Brewonomics: Fermenting Fortune" by Fermenta Hoptimista, an insightful exploration of the economic ramifications of beer culture. Hoptimista's meticulous analysis of hop price fluctuations and malt-to-market dynamics offers a refreshing perspective on the interconnectedness of brewing and the stock market, reminding us that in the world of finance, every bubble has its brew-nchmark.
Turning our attention to more unconventional sources, we can't neglect the timeless wisdom imparted in "Charlie and the Brewery Factory" by Roald Hops, which, although a work of fiction, offers lessons on the transformative power of imagination and innovation in the brewing industry. Likewise, "The Grapes of Lager" by John Steinbrew reveals the intertwined destinies of craft beer and the stock market, albeit in a parallel universe where lager flows freely and hops hold the power to sway financial fortunes.
In our relentless pursuit of understanding the brewtiful relationship between breweries and stock prices, we turned to unexpected sources such as the cryptic messages lurking in grocery store receipts (those CVS records are surprisingly hoppy!) and the whispers of ancient brewing recipes hiding within the pages of long-forgotten alchemical texts. After all, when it comes to uncovering hidden correlations, a pint of curiosity and a dash of whimsy might just be the secret ingredients we need.
With this eclectic blend of serious scholarship, literary musings, and perhaps a dash of pure hops-induced delirium, we set the stage for our own exploration of the enigmatic ties between breweries, beer, and stock market performance. So, as we embark on this journey into the ale-luring world of finance and foamy beverages, let's raise a toast to the unexpected, the unprecedented, and the irresistibly absurd. Cheers to unconventional indicators and hoppy returns!

[[RESULTS]]
The results of our sudsy investigation into the connection between the number of breweries in the United States and PACCAR's stock price (PCAR) from 2002 to 2022 have left us all buzzing with excitement. We found a positively intoxicating correlation coefficient of 0.8801309, which had us positively bubbling over with academic glee. The corresponding r-squared value of 0.7746303 spoke to the robustness of the relationship, further fermenting our belief in the significance of our findings. Not to mention, the p-value less than 0.01 had us feeling statistically hoppy about the strength of our results.
Fig. 1 illustrates the frothy relationship between these variables, presenting a scatterplot that, if you squint, somewhat resembles a pint of beer. It's as if the data itself is toasting to the undeniable connection between craft beer and stock prices, bidding a hoppy "cheers" to our research team's efforts.
Our results speak volumes about the potential market impacts of the ever-growing craft beer scene. The brew-haha of microbreweries and brewpubs seems to have a noticeable influence on PACCAR's stock price, proving that sometimes, when it comes to economic indicators, it's better to think outside the beer box. Our findings lend support to the idea that, when it comes to predicting stock prices, there may be more than a grain of truth in the world of brewonomics.
In conclusion, our study uncovers a hoppy correlation between the frothy excitement of the craft beer industry and the stocks of PACCAR, shedding light on a refreshing perspective that certainly won't leave investors feeling lukewarm. Let's raise a glass to unconventional economic analysis and the surprising correlations that can sometimes emerge when statistics and sudsy beverages collide. Cheers to hoppy returns and the thrill of uncovering unexpected connections in the world of economics!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings present a compelling case for the frothy connection between the number of breweries in the United States and PACCAR's stock price. As the "Ales of Industry" model proposed by Jones (2015) suggests, the craft beer revolution has indeed sent ripples through the stock market, and our research provides empirical support for this ale-mighty influence.

The positively intoxicating correlation coefficient of 0.8801309 revealed in our study seems to suggest that when it comes to predicting PACCAR's stock price, the number of breweries may be a brew-tifully potent indicator. This robust relationship echoes the sentiments expressed in Steinbrew's "The Grapes of Lager," subtly hinting at the intertwined destinies of craft beer and the stock market – though, regrettably, without the abundant lager flows of a parallel universe.

Our results serve as a refreshing brew-minder of the broader economic implications of the craft beer industry, in line with the findings of Doe's "The Brew Economy." The significant upward trend in the number of breweries found in Smith et al.'s research aligns with the hoppy notion that the craft beer wave has indeed lager-ed its mark on local economies and, evidently, PACCAR's stock performance.

It is evident that our study, much like Fermenta Hoptimista's "Brewonomics: Fermenting Fortune," has contributed to the evolving understanding of the interconnectedness of brewing and the stock market. Through the lens of statistics and economic analysis, our findings leap off the page like a lively yeast culture, fermenting the idea that perhaps, there's more than just froth and bubble to the relationship between breweries and stock prices.

While the connection between breweries and stock prices may seem as whimsical as a cryptic message lurking in a grocery store receipt, our statistical analysis has added a dash of empirical weight to this surprising relationship. Indeed, this study highlights the brew-tiful complexity of economic indicators and the unexpectedly hoppy returns that may emerge when unconventional variables are brought to the table.

In the spirit of "Charlie and the Brewery Factory," our research serves as a testament to the transformative power of embracing unconventional indicators and the ale-ment of surprise in economic analysis. As we raise a metaphorical glass to our findings, let's revel in the hoppy returns of our research and the undeniable excitement of uncorking unexpected connections in the world of economics. Cheers to the playful pursuit of knowledge and the way it can make us all feel hoppy in more ways than one!

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and PACCAR's stock price (PCAR). Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price

[[ABSTRACT]]
In this research, we delve into the frothy world of economics by analyzing the curious correlation between the burgeoning craft beer scene in the United States and the stock price of PACCAR. With data sourced from the Brewers Association and LSEG Analytics (Refinitiv), our findings reveal a staggering correlation coefficient of 0.8801309 and p < 0.01 from 2002 to 2022. Our study sheds light on the intoxicating relationship between hops and PACCAR's stock, providing a refreshing perspective on market influences. Join us as we raise a glass to unconventional economic indicators and hoppy returns.

[[INTRDUCTION]]
Economics and beer may seem like an odd couple, but in today's research, we aim to unravel the sudsy connection between the number of breweries in the United States and the stock price of PACCAR (PCAR). While some researchers may prefer to stick with traditional economic indicators, we believe it's time to tap into the unexplored world of craft beer to foamulate a fresh perspective on market influences.
As craft beer continues to ferment its way into the hearts (and livers) of Americans, the economic implications are nothing to alegnore. With the rise of microbreweries, brewpubs, and nanobreweries, we're witnessing a beerenaissance unlike anything seen since the Prohibition days. But what do these hoptastic trends have to do with PACCAR's stock price, you may wonder? Well, pour yourself a pint and join us as we dive into the frothy sea of data and statistics to extract the hoppy truth behind this correlation.
Our investigation into this hoppy hypothesis is not just random ale-mentation. It's grounded in solid data sourced from the Brewers Association and LSEG Analytics (Refinitiv). Armed with these statistical alements, we set out to brew a unique blend of economic analysis that transcends the ordinary and embraces the hoppportunity to uncover unexpected correlations.
In this paper, we'll explore the historical context of both the craft beer industry and PACCAR's stock performance, frothing at the mouth to reveal the surprising correlation coefficient and p-value that may have even the most seasoned economists rubbing their eyes in disbelief. So sit back, relax, and hop along with us as we explore the bubbly relationship between breweries and stock prices, reminding us that in the world of economics, just like in brewing, there's always something hoppening. Cheers to unconventional indicators and hoppy returns!

[[RESULTS]]
The results of our sudsy investigation into the connection between the number of breweries in the United States and PACCAR's stock price (PCAR) from 2002 to 2022 have left us all buzzing with excitement. We found a positively intoxicating correlation coefficient of 0.8801309, which had us positively bubbling over with academic glee. The corresponding r-squared value of 0.7746303 spoke to the robustness of the relationship, further fermenting our belief in the significance of our findings. Not to mention, the p-value less than 0.01 had us feeling statistically hoppy about the strength of our results.
Fig. 1 illustrates the frothy relationship between these variables, presenting a scatterplot that, if you squint, somewhat resembles a pint of beer. It's as if the data itself is toasting to the undeniable connection between craft beer and stock prices, bidding a hoppy "cheers" to our research team's efforts.
Our results speak volumes about the potential market impacts of the ever-growing craft beer scene. The brew-haha of microbreweries and brewpubs seems to have a noticeable influence on PACCAR's stock price, proving that sometimes, when it comes to economic indicators, it's better to think outside the beer box. Our findings lend support to the idea that, when it comes to predicting stock prices, there may be more than a grain of truth in the world of brewonomics.
In conclusion, our study uncovers a hoppy correlation between the frothy excitement of the craft beer industry and the stocks of PACCAR, shedding light on a refreshing perspective that certainly won't leave investors feeling lukewarm. Let's raise a glass to unconventional economic analysis and the surprising correlations that can sometimes emerge when statistics and sudsy beverages collide. Cheers to hoppy returns and the thrill of uncovering unexpected connections in the world of economics!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In wrapping up our study, it's crystal lager-clear that the relationship between the number of breweries in the United States and PACCAR's stock price (PCAR) is not just frothy speculation. Our findings pour a cold one on the traditional indicators and instead hopscotch into the world of brewonomics. With a correlation coefficient that's as strong as a double IPA and a p-value that's statistically redder than a cherry ale, it's evident that this isn't just a mere ale-usion.

Our research highlights the stein-credible influence of craft beer on stock prices, demonstrating that when it comes to economic indicators, sometimes you need to think outside the beer box. After all, why settle for ho-hum statistics when you can raise a hoppy toast to the unexpected connections in the world of economics?

So, as we lift our pints to unconventional economic analysis and hoppy returns, it's time to put a cap on this particular study. With findings as solid as an ice-cold pilsner, it's safe to say that no more research is needed in this area. The brew-haha of the brew-economy has spoken, and it's time for us to start brewing up new research adventures. Cheers to pouring over unusual connections and to the unBEERlievable quirks of the economic world!

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 Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR'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 Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Fermentation Economics and Beverage Studies

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 Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price with an abstract of In this research, we delve into the frothy world of economics by analyzing the curious correlation between the burgeoning craft beer scene in the United States and the stock price of PACCAR. With data sourced from the Brewers Association and LSEG Analytics (Refinitiv), our findings reveal a staggering correlation coefficient of 0.8801309 and p < 0.01 from 2002 to 2022. Our study sheds light on the intoxicating relationship between hops and PACCAR's stock, providing a refreshing perspective on market influences. Join us as we raise a glass to unconventional economic indicators and hoppy returns.

ChatGPT:

craft beer industry, breweries in the United States, PACCAR stock price, correlation between craft beer and stock price, economic indicators, market influences, Brewers Association data, LSEG Analytics data, unconventional economic indicators, stock price correlation, craft beer market, PACCAR stock analysis,

*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

PACCAR's stock price (PCAR)
Detailed data title: Opening price of PACCAR (PCAR) 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 PACCAR's stock price (PCAR)

Correlation r = 0.8801309 (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 = 0.7746303 (Coefficient of determination)
This means 77.5% of the change in the one variable (i.e., PACCAR's stock price (PCAR)) 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 1.4E-7.^Show 0.0000001442018707210847500000
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 0.88 in 1.4E-5% of random cases. Said differently, if you correlated 6,934,723 random variables^Note You don't actually need 6 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 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.

[ 0.72, 0.95 ] 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
PACCAR's stock price (PCAR) (Stock price)	7.93	8.36	15.37	21.94	18.92	26.68	33.14	17.5	22.63	35.5	23.59	28.27	36.62	43.03	30.21	42.36	48.38	37.51	52.79	57.74	58.84

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([7.93,8.36,15.37,21.94,18.92,26.68,33.14,17.5,22.63,35.5,23.59,28.27,36.62,43.03,30.21,42.36,48.38,37.51,52.79,57.74,58.84,])
array_1_name = "The number of Breweries in the United States"
array_2_name = "PACCAR's stock price (PCAR)"

# 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 Breweries in the United States
Line chart for only PACCAR's stock price (PCAR)
AI-generated correlation image
The spurious research paper: Pour Decisions: Exploring the Sudsy Connection Between the Number of Breweries in the United States and PACCAR's Stock Price

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Correlation ID: 2274 · Black Variable ID: 34 · Red Variable ID: 1756


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