Spurious correlation #3,774 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Butter consumption and the second variable is Ulta Beauty's stock price (ULTA). The chart goes from 2008 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Butter consumption and the second variable is Ulta Beauty's stock price (ULTA). The chart goes from 2008 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The increase in butter consumption led to an increase in delicious, buttery pastries being made. This in turn caused more people to indulge in sweets, leading to an increase in acne. As a result, more people shopped at Ulta Beauty for skincare products, ultimately boosting Ulta Beauty's stock price. Because who knew that the path to stock market success was paved with buttery croissants and clear, glowing skin!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a hyper-realistic kitchen scene where people are joyfully spreading lavish amounts of butter on soft, freshly-baked bread. In the background, through the window, a line of individuals eagerly glance at their reflections in the storefront windows of an Ulta Beauty store. An electronic board prominently displays the steady upward climb of the ULTA stock price. 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: Butter consumption and Ulta Beauty's stock price (ULTA). 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: Oragami Concept art Hyper-realistic Art nouveau Minimalism
ChatGPT: Hyper-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 Butter consumption and Ulta Beauty's stock price (ULTA), in the following situation: The increase in butter consumption led to more people buttering themselves up before looking in the mirror, which caused a surge in visits to Ulta Beauty, boosting their stock price.. Keep the description simple.
ChatGPT: In a cozy kitchen, people are happily spreading generous amounts of butter on warm, freshly-baked bread. Meanwhile, a line of individuals can be seen outside an Ulta Beauty store, eagerly checking their reflections in the storefront windows, with the ULTA stock price displayed prominently on an electronic board, showing a steady upward climb.

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: [ Hyper-realistic ] and depicting this scene: In a cozy kitchen, people are happily spreading generous amounts of butter on warm, freshly-baked bread. Meanwhile, a line of individuals can be seen outside an Ulta Beauty store, eagerly checking their reflections in the storefront windows, with the ULTA stock price displayed prominently on an electronic board, showing a steady upward climb. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a hyper-realistic kitchen scene where people are joyfully spreading lavish amounts of butter on soft, freshly-baked bread. In the background, through the window, a line of individuals eagerly glance at their reflections in the storefront windows of an Ulta Beauty store. An electronic board prominently displays the steady upward climb of the ULTA stock price.

*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 Butter consumption caused Ulta Beauty's stock price (ULTA) to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

The Journal of Dairy Economics and Consumer Behavior

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 Butter consumption and Ulta Beauty's stock price (ULTA). 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 a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

[[ABSTRACT]]
This study delves into the unconventional and unexpectedly buttery world of finance by examining the relationship between butter consumption and Ulta Beauty's stock price (ULTA). Utilizing data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on a quest to unravel this seemingly unconnected duo. Our findings reveal a correlation coefficient of 0.9246834 for the period spanning from 2008 to 2021, with a significance level of p < 0.01. The paper goes on to butter up the reader with insightful observations that go against the grain of traditional financial analysis, highlighting the dairy-sweet nuances in the stock market. In essence, our research churns out evidence of a potential butter-lie relationship that may churn the tide of future investment strategies, shedding light on the cream of the crop in financial forecasting.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The interplay between seemingly unrelated factors and financial markets has long intrigued researchers and investors alike. In this study, we explore the curious connection between butter consumption and Ulta Beauty’s stock price (ULTA). While these two entities may appear to have less in common than a cow and a cosmetics store, our investigation aims to uncover any hidden financial buttery churnings that may underlie this apparent disconnection.
The enigmatic relationship between food and finance has been the subject of scholarly interest for decades. From the buttery smoothness of a well-performing stock to the potential for churning through market volatility, the parallels between the dairy aisle and the trading floor have not escaped the attention of keen observers. However, our study takes a unique and, dare I say, spreadable approach by delving into the specific impact of butter consumption on the stock price of a prominent beauty retailer.
As we embark on this scholarly dairy expedition, it is essential to acknowledge the wide-ranging implications of our findings. By shedding light on the previously unexplored link between butter yearn and Ulta’s earnings spurn, we aim to challenge conventional financial wisdom and perhaps even butter up the investment strategies of the future.
Our investigation draws upon a comprehensive dataset sourced from the United States Department of Agriculture (USDA) to capture the fluctuations in butter consumption over the past decade. Meanwhile, financial data from LSEG Analytics (Refinitiv) serves as the butter knife, cutting through the complexities of Ulta Beauty’s stock performance. Through rigorous analysis and statistical modeling, we aim to butter our readers up with empirical evidence of a potential correlation that has remained largely unexamined until now.
In the subsequent sections of this paper, we will spread our findings across a careful examination of the data, a detailed discussion of the statistical methods employed, and an assessment of the implications for financial theory and practice. As the results unfold, we encourage readers to approach them with an open mind and a generous dollop of humor, for what is academic research without the occasional sprinkle of levity?
In essence, this study promises to churn the tide of financial analysis, offering a tantalizing glimpse into the unexpected intersections of consumer behavior and stock market dynamics. Just as butter lends richness to the most flavorful dishes, our research aims to infuse the realm of finance with newfound insights and, of course, a dash of whimsy.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
LITERATURE REVIEW

The authors find that the interplay between butter consumption and Ulta Beauty's stock price (ULTA) is an area of research that has been surprisingly underexplored in the finance literature. It is a topic that, much like unsalted butter, has flown under the radar despite its potential to add flavor to the field of financial analysis. However, a few notable works have touched upon related themes, albeit not directly addressing the butter-beauty linkage in the manner that this paper endeavors to do.

Smith et al. (2015) examined the impact of dairy product prices on consumer spending patterns, revealing intriguing patterns in the purchase of beauty and personal care products during periods of fluctuating butter costs. Meanwhile, Doe's (2018) comprehensive review of food commodity prices and stock market performance hinted at the possibility of hidden correlations between culinary preferences and investment behavior, though the specific mention of butter's influence remained somewhat spread thin.

Jones' (2019) seminal work, "From Cows to Cosmetics: Unveiling Financial Secrets in the Farm-to-Table-to-Face Universe," offered a broad exploration of the connections between agricultural products, consumer goods, and financial markets. While not focused specifically on the butter-Ulta relationship, Jones' work did lay a creamy foundation for understanding the potential impact of seemingly unrelated consumer trends on stock performance in the beauty industry.

Moving beyond academic research, a number of non-fiction books have also touched upon topics that indirectly relate to our current investigation. "The Economics of Butter: A Spreadable History" by Economist X provides a comprehensive overview of the global butter market and its ripple effects on various industries, inviting readers to churn over the economic implications of dairy delicacies. Similarly, "Beauty and the Markets: Financial Fairy Tales" by Analyst Y weaves together anecdotes from the world of beauty and finance, offering tantalizing glimpses into the unexpected parallels that underpin these seemingly distinct domains.

In the realm of fiction, the novels "The Wealthy Butter Connoisseur" by Author Z and "The Stock Market Murders: A Financial Beauty Mystery" by Novelist W offer imaginative interpretations of the intertwining worlds of butter and beauty, hinting at the potential for hidden financial dramas to unfold beneath the seemingly mundane surfaces of everyday consumer choices.

In a more unconventional form of research, the authors opted to gain insights from popular culture, including TV shows that may hold relevance to our inquiry. "Baking Bonanza: The Great Butter Showdown" and "Ulta Ulta Everywhere" are two reality TV series that follow the trials and tribulations of butter enthusiasts and beauty aficionados, respectively. While not directly addressing our research question, these shows provided valuable context on the cultural significance and public perception of both butter and beauty products.

As the literature review demonstrates, the investigation into the connection between butter consumption and Ulta Beauty's stock price is an underexplored yet undeniably rich area of inquiry. The sources consulted offer valuable insights and intriguing parallels, setting the stage for our own rigorous examination of this buttery conundrum. With this foundation in mind, we can confidently proceed to churn the tides of financial analysis and shed light on the potential ties between butter yearn and Ulta's earnings spurn.

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 Butter consumption and Ulta Beauty's stock price (ULTA). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) . You used data from 2008 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

[[ABSTRACT]]
This study delves into the unconventional and unexpectedly buttery world of finance by examining the relationship between butter consumption and Ulta Beauty's stock price (ULTA). Utilizing data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on a quest to unravel this seemingly unconnected duo. Our findings reveal a correlation coefficient of 0.9246834 for the period spanning from 2008 to 2021, with a significance level of p < 0.01. The paper goes on to butter up the reader with insightful observations that go against the grain of traditional financial analysis, highlighting the dairy-sweet nuances in the stock market. In essence, our research churns out evidence of a potential butter-lie relationship that may churn the tide of future investment strategies, shedding light on the cream of the crop in financial forecasting.

[[INTRODUCTION]]
INTRODUCTION
The interplay between seemingly unrelated factors and financial markets has long intrigued researchers and investors alike. In this study, we explore the curious connection between butter consumption and Ulta Beauty’s stock price (ULTA). While these two entities may appear to have less in common than a cow and a cosmetics store, our investigation aims to uncover any hidden financial buttery churnings that may underlie this apparent disconnection.
The enigmatic relationship between food and finance has been the subject of scholarly interest for decades. From the buttery smoothness of a well-performing stock to the potential for churning through market volatility, the parallels between the dairy aisle and the trading floor have not escaped the attention of keen observers. However, our study takes a unique and, dare I say, spreadable approach by delving into the specific impact of butter consumption on the stock price of a prominent beauty retailer.
As we embark on this scholarly dairy expedition, it is essential to acknowledge the wide-ranging implications of our findings. By shedding light on the previously unexplored link between butter yearn and Ulta’s earnings spurn, we aim to challenge conventional financial wisdom and perhaps even butter up the investment strategies of the future.
Our investigation draws upon a comprehensive dataset sourced from the United States Department of Agriculture (USDA) to capture the fluctuations in butter consumption over the past decade. Meanwhile, financial data from LSEG Analytics (Refinitiv) serves as the butter knife, cutting through the complexities of Ulta Beauty’s stock performance. Through rigorous analysis and statistical modeling, we aim to butter our readers up with empirical evidence of a potential correlation that has remained largely unexamined until now.
In the subsequent sections of this paper, we will spread our findings across a careful examination of the data, a detailed discussion of the statistical methods employed, and an assessment of the implications for financial theory and practice. As the results unfold, we encourage readers to approach them with an open mind and a generous dollop of humor, for what is academic research without the occasional sprinkle of levity?
In essence, this study promises to churn the tide of financial analysis, offering a tantalizing glimpse into the unexpected intersections of consumer behavior and stock market dynamics. Just as butter lends richness to the most flavorful dishes, our research aims to infuse the realm of finance with newfound insights and, of course, a dash of whimsy.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

METHODOLOGY

In order to unravel the mystery behind the interwoven realms of butter consumption and Ulta Beauty's stock price, our research employed a multi-faceted approach that is as layered as a flaky croissant. The methodology encompassed data collection, statistical analysis, and modeling techniques designed to churn out robust and credible findings.

Data Collection:
Our study drew upon a rich tapestry of information sourced from the United States Department of Agriculture (USDA), capturing the butter consumption patterns across the United States from 2008 to 2021. The USDA data provided the raw ingredients for our analysis, allowing us to gauge the fluctuations and trends in butter demand over the specified period. At times, navigating through this dataset felt akin to unraveling the complexities of a multi-layered strudel – dense with information yet ultimately satisfying.

On the financial front, we harnessed the instrumental prowess of LSEG Analytics (Refinitiv) to procure Ulta Beauty's stock price data for the corresponding timeframe. This process involved sifting through market intricacies and volatility, akin to separating the cream from the churn in a reality show where the contestants are stock prices.

Statistical Analysis:
Having assembled this delectable assortment of data, we employed an array of statistical methods to churn out the robustness of our findings. Our analysis unleashed the power of correlation coefficients, demonstrating the strength and direction of the relationship between butter consumption and Ulta Beauty's stock price. We didn't spread ourselves thin at this stage and ensured proper statistical significance, unlike a toaster that always burns one side of the toast.

Modeling Techniques:
In addition to correlation analysis, we transcended the bread-and-butter statistical approaches by engaging in intricate modeling techniques. Utilizing time series analysis, we crafted a veritable soufflé of econometric models to capture the changing dynamics between butter consumption and stock prices. Our modeling process resembled the meticulous artistry of a pastry chef, delicately folding in variables to present a dish (or in this case, model) that was both visually appealing and substantively nourishing.

It is important to note that our research team approached this methodology with the gravity of a soufflé – being fully aware of the potential risks, they proceeded with a deft touch and ample precautions. The resulting rigor helped ensure that our findings were not just a flash in the pan, but rather, a well-baked and thoroughly tested set of conclusions.

In sum, our methodology was carefully crafted to ensure the integrity of our analysis and the robustness of our conclusions. The fusion of disparate data sources, statistical techniques, and modeling artistry allowed us to craft a comprehensive research design that is as rich and satisfying as a perfectly-spread layer of butter on warm toast.

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 Butter consumption and Ulta Beauty's stock price (ULTA). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

[[ABSTRACT]]
This study delves into the unconventional and unexpectedly buttery world of finance by examining the relationship between butter consumption and Ulta Beauty's stock price (ULTA). Utilizing data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on a quest to unravel this seemingly unconnected duo. Our findings reveal a correlation coefficient of 0.9246834 for the period spanning from 2008 to 2021, with a significance level of p < 0.01. The paper goes on to butter up the reader with insightful observations that go against the grain of traditional financial analysis, highlighting the dairy-sweet nuances in the stock market. In essence, our research churns out evidence of a potential butter-lie relationship that may churn the tide of future investment strategies, shedding light on the cream of the crop in financial forecasting.

[[LITERATURE REVIEW]]
LITERATURE REVIEW
The authors find that the interplay between butter consumption and Ulta Beauty's stock price (ULTA) is an area of research that has been surprisingly underexplored in the finance literature. It is a topic that, much like unsalted butter, has flown under the radar despite its potential to add flavor to the field of financial analysis. However, a few notable works have touched upon related themes, albeit not directly addressing the butter-beauty linkage in the manner that this paper endeavors to do.
Smith et al. (2015) examined the impact of dairy product prices on consumer spending patterns, revealing intriguing patterns in the purchase of beauty and personal care products during periods of fluctuating butter costs. Meanwhile, Doe's (2018) comprehensive review of food commodity prices and stock market performance hinted at the possibility of hidden correlations between culinary preferences and investment behavior, though the specific mention of butter's influence remained somewhat spread thin.
Jones' (2019) seminal work, "From Cows to Cosmetics: Unveiling Financial Secrets in the Farm-to-Table-to-Face Universe," offered a broad exploration of the connections between agricultural products, consumer goods, and financial markets. While not focused specifically on the butter-Ulta relationship, Jones' work did lay a creamy foundation for understanding the potential impact of seemingly unrelated consumer trends on stock performance in the beauty industry.
Moving beyond academic research, a number of non-fiction books have also touched upon topics that indirectly relate to our current investigation. "The Economics of Butter: A Spreadable History" by Economist X provides a comprehensive overview of the global butter market and its ripple effects on various industries, inviting readers to churn over the economic implications of dairy delicacies. Similarly, "Beauty and the Markets: Financial Fairy Tales" by Analyst Y weaves together anecdotes from the world of beauty and finance, offering tantalizing glimpses into the unexpected parallels that underpin these seemingly distinct domains.
In the realm of fiction, the novels "The Wealthy Butter Connoisseur" by Author Z and "The Stock Market Murders: A Financial Beauty Mystery" by Novelist W offer imaginative interpretations of the intertwining worlds of butter and beauty, hinting at the potential for hidden financial dramas to unfold beneath the seemingly mundane surfaces of everyday consumer choices.
In a more unconventional form of research, the authors opted to gain insights from popular culture, including TV shows that may hold relevance to our inquiry. "Baking Bonanza: The Great Butter Showdown" and "Ulta Ulta Everywhere" are two reality TV series that follow the trials and tribulations of butter enthusiasts and beauty aficionados, respectively. While not directly addressing our research question, these shows provided valuable context on the cultural significance and public perception of both butter and beauty products.
As the literature review demonstrates, the investigation into the connection between butter consumption and Ulta Beauty's stock price is an underexplored yet undeniably rich area of inquiry. The sources consulted offer valuable insights and intriguing parallels, setting the stage for our own rigorous examination of this buttery conundrum. With this foundation in mind, we can confidently proceed to churn the tides of financial analysis and shed light on the potential ties between butter yearn and Ulta's earnings spurn.

[[RESULTS]]
The statistical analysis of the relationship between butter consumption and Ulta Beauty's stock price (ULTA) revealed a remarkably strong correlation coefficient of 0.9246834. This rather "gouda" correlation, with an r-squared of 0.8550393, suggests that there is a high degree of association between the two variables. In other words, as butter consumption waxes or wanes, the stock price of Ulta Beauty mirrors it quite closely, which is undoubtedly an interesting and unconventional finding in the world of financial research.
Furthermore, with a p-value of less than 0.01, our results indicate that the observed correlation is statistically significant. This suggests that the likelihood of the correlation occurring by pure chance is as rare as finding a cow jumping over the moon.
The figure (Fig. 1) included herein visually depicts the strong positive correlation between butter consumption and Ulta Beauty's stock price, further emphasizing the buttery smooth nature of this relationship. It showcases the ups and downs of both variables, resembling the undulating hills and valleys of a freshly churned butter sculpture.
This study contributes to the growing body of interdisciplinary research that transcends the conventional boundaries of finance and food. Our findings not only churn the conventional wisdom of financial analysis but also spread a layer of intrigue over the unexpected connections between seemingly unrelated phenomena. Thus, our results not only serve as a pat of butter on the warm bread of scientific inquiry but also hold promise for influencing future investment strategies with a unique flavor.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study provide compelling evidence of a robust association between butter consumption and Ulta Beauty's stock price (ULTA), supporting previous research that hinted at the existence of hidden correlations in the financial and culinary realms. Contrary to the mild skepticism that may have initially accompanied the consideration of butter's impact on stock performance, our findings add an unexpected dollop of credibility to the convergence of dairy delights and financial valuations.

Drawing inspiration from Smith et al.'s (2015) exploration of dairy product prices and consumer spending patterns, as well as Doe's (2018) tantalizing speculations on the influence of food commodity prices on stock market performance, our research dives deeper into the buttery abyss and unearths a creamy consistency in the synchronicity of butter consumption and Ulta's stock price.

Moreover, Jones' (2019) seminal work provides a broad understanding of the interconnectedness between agricultural products, consumer goods, and financial markets, setting the stage for our own examination of this dairy-fueled financial ballet. While our study particularly focuses on the butter-Ulta relationship, the implications extend far beyond these seemingly distinct domains, suggesting an interplay that is as interconnected as a perfectly layered croissant.

In addition to the seemingly lighthearted musings in popular culture, our results solidify the relevance of such unconventional sources in shedding light on complex financial phenomena. Much like the unexpected twists in TV shows "Baking Bonanza: The Great Butter Showdown" and "Ulta Ulta Everywhere," our findings infuse a sense of whimsy into the staid world of finance, proving that sometimes, reality can be just as delightful and surprising as fiction.

Ultimately, our study not only buttresses the existing literature's implications but also churns out a fresh perspective on the potential ties between butter yearn and Ulta's earnings spurn. The "gouda" correlation coefficient and its statistical significance carve out a place for the butter-beauty relationship in the hallowed halls of financial inquiry and forecast, reaffirming the surreal yet tangible impact of butter consumption on stock market dynamics. By churning the tides of conventional financial wisdom, our research emphasizes that even the most unexpected connections can lend a tantalizing flavor to investment strategies, making for an udderly intriguing addition to the ever-expanding buffet of financial knowledge.

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 Butter consumption and Ulta Beauty's stock price (ULTA). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

[[ABSTRACT]]
This study delves into the unconventional and unexpectedly buttery world of finance by examining the relationship between butter consumption and Ulta Beauty's stock price (ULTA). Utilizing data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on a quest to unravel this seemingly unconnected duo. Our findings reveal a correlation coefficient of 0.9246834 for the period spanning from 2008 to 2021, with a significance level of p < 0.01. The paper goes on to butter up the reader with insightful observations that go against the grain of traditional financial analysis, highlighting the dairy-sweet nuances in the stock market. In essence, our research churns out evidence of a potential butter-lie relationship that may churn the tide of future investment strategies, shedding light on the cream of the crop in financial forecasting.

[[INTRDUCTION]]
INTRODUCTION
The interplay between seemingly unrelated factors and financial markets has long intrigued researchers and investors alike. In this study, we explore the curious connection between butter consumption and Ulta Beauty’s stock price (ULTA). While these two entities may appear to have less in common than a cow and a cosmetics store, our investigation aims to uncover any hidden financial buttery churnings that may underlie this apparent disconnection.
The enigmatic relationship between food and finance has been the subject of scholarly interest for decades. From the buttery smoothness of a well-performing stock to the potential for churning through market volatility, the parallels between the dairy aisle and the trading floor have not escaped the attention of keen observers. However, our study takes a unique and, dare I say, spreadable approach by delving into the specific impact of butter consumption on the stock price of a prominent beauty retailer.
As we embark on this scholarly dairy expedition, it is essential to acknowledge the wide-ranging implications of our findings. By shedding light on the previously unexplored link between butter yearn and Ulta’s earnings spurn, we aim to challenge conventional financial wisdom and perhaps even butter up the investment strategies of the future.
Our investigation draws upon a comprehensive dataset sourced from the United States Department of Agriculture (USDA) to capture the fluctuations in butter consumption over the past decade. Meanwhile, financial data from LSEG Analytics (Refinitiv) serves as the butter knife, cutting through the complexities of Ulta Beauty’s stock performance. Through rigorous analysis and statistical modeling, we aim to butter our readers up with empirical evidence of a potential correlation that has remained largely unexamined until now.
In the subsequent sections of this paper, we will spread our findings across a careful examination of the data, a detailed discussion of the statistical methods employed, and an assessment of the implications for financial theory and practice. As the results unfold, we encourage readers to approach them with an open mind and a generous dollop of humor, for what is academic research without the occasional sprinkle of levity?
In essence, this study promises to churn the tide of financial analysis, offering a tantalizing glimpse into the unexpected intersections of consumer behavior and stock market dynamics. Just as butter lends richness to the most flavorful dishes, our research aims to infuse the realm of finance with newfound insights and, of course, a dash of whimsy.

[[RESULTS]]
The statistical analysis of the relationship between butter consumption and Ulta Beauty's stock price (ULTA) revealed a remarkably strong correlation coefficient of 0.9246834. This rather "gouda" correlation, with an r-squared of 0.8550393, suggests that there is a high degree of association between the two variables. In other words, as butter consumption waxes or wanes, the stock price of Ulta Beauty mirrors it quite closely, which is undoubtedly an interesting and unconventional finding in the world of financial research.
Furthermore, with a p-value of less than 0.01, our results indicate that the observed correlation is statistically significant. This suggests that the likelihood of the correlation occurring by pure chance is as rare as finding a cow jumping over the moon.
The figure (Fig. 1) included herein visually depicts the strong positive correlation between butter consumption and Ulta Beauty's stock price, further emphasizing the buttery smooth nature of this relationship. It showcases the ups and downs of both variables, resembling the undulating hills and valleys of a freshly churned butter sculpture.
This study contributes to the growing body of interdisciplinary research that transcends the conventional boundaries of finance and food. Our findings not only churn the conventional wisdom of financial analysis but also spread a layer of intrigue over the unexpected connections between seemingly unrelated phenomena. Thus, our results not only serve as a pat of butter on the warm bread of scientific inquiry but also hold promise for influencing future investment strategies with a unique flavor.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the peculiar relationship between butter consumption and Ulta Beauty's stock price has churned out some undoubtedly intriguing findings. The robust correlation coefficient and exceptionally low p-value suggest that there may indeed be more than meets the eye in this seemingly unrelated pair. It appears that as the butter melts, so does the stock price of Ulta Beauty, painting a picture that is as rich and smooth as a perfectly churned batch of dairy goodness. The statistical significance of our findings serves as a strong endorsement of the tantalizing connection between these two disparate domains, highlighting the dairy-sweet nuances in the stock market that are often overshadowed by more conventional analyses.

As we skim the surface of these results, it is imperative to acknowledge the potential implications for financial forecasting and investment strategies. While our study may seem unconventional, dare I say a bit "cheesy," it nonetheless offers a fresh perspective on the intricate tapestry of market dynamics. After all, who would have thought that a pat of butter could hold the key to predicting stock prices in the beauty industry?

In the grand scheme of financial research, our study leaves a lingering aroma of curiosity, challenging traditional notions and inviting a deeper investigation into the unexpected intersections of consumer behavior and stock performance. However, it may be time to close the cookbook on this particular inquiry, as our findings have unveiled a correlation so strong, it's almost as undeniable as the allure of a freshly baked croissant. Therefore, it is with a touch of whimsy and a dollop of certainty that we assert: no more research is needed in this buttery realm of financial inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Dairy Economics and Consumer Behavior

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 Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Dairy Economics and Analysis (IDEA)

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 Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn with an abstract of This study delves into the unconventional and unexpectedly buttery world of finance by examining the relationship between butter consumption and Ulta Beauty's stock price (ULTA). Utilizing data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on a quest to unravel this seemingly unconnected duo. Our findings reveal a correlation coefficient of 0.9246834 for the period spanning from 2008 to 2021, with a significance level of p < 0.01. The paper goes on to butter up the reader with insightful observations that go against the grain of traditional financial analysis, highlighting the dairy-sweet nuances in the stock market. In essence, our research churns out evidence of a potential butter-lie relationship that may churn the tide of future investment strategies, shedding light on the cream of the crop in financial forecasting.

ChatGPT:

butter consumption, Ulta Beauty stock price, finance, correlation coefficient, traditional financial analysis, dairy products, stock market, investment strategies, financial forecasting, USDA data, LSEG Analytics, Refinitiv, butter consumption and stock prices

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

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Data details

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

Ulta Beauty's stock price (ULTA)
Detailed data title: Opening price of Ulta Beauty (ULTA) 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 Ulta Beauty's stock price (ULTA)

Correlation r = 0.9246834 (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 = 0.8550393 (Coefficient of determination)
This means 85.5% of the change in the one variable (i.e., Ulta Beauty's stock price (ULTA)) is predictable based on the change in the other (i.e., Butter consumption) over the 14 years from 2008 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.2E-6.^Show 0.0000022371269591039580000000
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 0.92 in 0.00022% of random cases. Said differently, if you correlated 447,002 random variables^Note You don't actually need 447 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 13 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 13 because we have two variables measured over a period of 14 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.

[ 0.77, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Butter consumption (Pounds per person)	5	5	4.9	5.4	5.5	5.5	5.5	5.6	5.7	5.7	6	6.2	6.3	6.5
Ulta Beauty's stock price (ULTA) (Stock price)	17	8.31	19.23	34.13	65.9	100.26	96.21	128.13	182.67	256.2	224.44	239.96	254.32	287.16

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([5,5,4.9,5.4,5.5,5.5,5.5,5.6,5.7,5.7,6,6.2,6.3,6.5,])
array_2 = np.array([17,8.31,19.23,34.13,65.9,100.26,96.21,128.13,182.67,256.2,224.44,239.96,254.32,287.16,])
array_1_name = "Butter consumption"
array_2_name = "Ulta Beauty's stock price (ULTA)"

# 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 Butter consumption
Line chart for only Ulta Beauty's stock price (ULTA)
AI-generated correlation image
The spurious research paper: Churn and Return: Relations Between Butter Yearn and Ulta's Earnings Spurn

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Correlation ID: 3774 · Black Variable ID: 557 · Red Variable ID: 1919


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