Spurious correlation #2,151 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is US household spending on fresh milk and cream and the second variable is Vodafone Group's stock price (VOD). 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 US household spending on fresh milk and cream and the second variable is Vodafone Group's stock price (VOD). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As US household spending on fresh milk and cream decreased, it led to a surplus of dairy products. This surplus led to an increase in the popularity of dairy-based challenges on social media, such as the gallon milk challenge. As more and more people participated in these challenges, there was a surge in data usage to upload and view videos, causing a strain on telecom networks. This increased demand for data put pressure on companies like Vodafone, leading to a decrease in their stock price as they scrambled to upgrade their infrastructure to support the udderly ridiculous trend.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a black-and-white newspaper illustration-style image depicting a family standing in a grocery store, carefully selecting and placing fresh milk and cream in their shopping cart. In the background, a digital screen is displaying the Vodafone Group's stock price graph fluctuating throughout the day. Ensure the emphasis is on the family and the products they are choosing, with the stock price graph serving as a secondary visual element. 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: US household spending on fresh milk and cream and Vodafone Group'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: A black-and-white newspaper illustration Realistic Vaporwave An image that looks like it is a scene from a Pixar movie Cyberpunk
ChatGPT: A black-and-white newspaper illustration

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 US household spending on fresh milk and cream and Vodafone Group's stock price together. Keep the description simple.
ChatGPT: In the scene, a family standing in a grocery store is seen carefully selecting and placing fresh milk and cream in their shopping cart. Meanwhile, in the background, a digital screen displays the Vodafone Group's stock price graph fluctuating throughout the day.

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: [ A black-and-white newspaper illustration ] and depicting this scene: In the scene, a family standing in a grocery store is seen carefully selecting and placing fresh milk and cream in their shopping cart. Meanwhile, in the background, a digital screen displays the Vodafone Group's stock price graph fluctuating throughout the day. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a black-and-white newspaper illustration-style image depicting a family standing in a grocery store, carefully selecting and placing fresh milk and cream in their shopping cart. In the background, a digital screen is displaying the Vodafone Group's stock price graph fluctuating throughout the day. Ensure the emphasis is on the family and the products they are choosing, with the stock price graph serving as a secondary visual element.

*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 a decrease in US household spending on fresh milk and cream caused Vodafone Group's stock price (VOD) to decrease.

AI academic paper

(Because p < 0.01)

Moo-ving Markets: The Milky Way to Stock Market Analysis

The Journal of Bovine Economics and Financial Analysis

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 US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). 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 the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
"Moo-ving Markets: The Milky Way to Stock Market Analysis"

[[ABSTRACT]]
In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the old adage goes, "Don't cry over spilled milk," but perhaps one should shed a tear for missed stock market opportunities linked to cow's milk. The intersection of milk consumption and stock prices may seem utterly unrelated, much like an unexpected dairy-free trend in an ice cream parlor. However, our research aims to demonstrate that beneath the creamy surface lies a surprising association worth exploring.
The link between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) may initially appear as unlikely as finding a lactose-intolerant cow. Nevertheless, our study seeks to unravel the whey in which these seemingly disparate elements intersect. By diving into the lactose-laden depths of household expenditure data and stock market fluctuations, we aim to shed light on an unusual yet compelling relationship.
While economists and analysts often focus on traditional market indicators, our research boldly ventures into a new pasture, where the bovine meets the bulls and bears. We aim to steer the conversation away from the standard market fundamentals and instead milk the data for intriguing insights (and perhaps the occasional cow pun).
As we embark on this conceivably unconventional journey, it is vital to acknowledge the inherent skepticism that may greet such an unorthodox study. After all, associating dairy products with stock market performance may seem as uncanny as a lactose-intolerant superhero. However, as the saying goes, "the proof is in the pudding," and we intend to churn out the evidence to support our findings.
So, grab your favorite dairy beverage, be it a tall glass of milk or a frothy latte, and join us as we navigate the Milky Way of stock market analysis. This study promises to be a milkshake of statistical analysis, where those who are lactose intolerant may feel a bit cheesed off. Nevertheless, we assure you that our findings will be udderly captivating and may just butter up your understanding of the curious connections between dairy consumption and stock market trends.
In the following sections, we will engage in a rigorous analysis of the data, exploring the correlations, implications, and potential market insights derived from our dairy-driven investigation. So, hold onto your hats, or perhaps your cow-printed aprons, as we journey through the lactose-laden landscape of financial analysis.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
**[[LITERATURE REVIEW]]**

In "Dairy Economics Quarterly," Smith et al. (2015) explore the impact of US household spending on dairy products, including fresh milk and cream, on various sectors of the economy. Their findings reveal a positive association between dairy consumption and economic indicators, but their analysis falls short of udderly connecting the creamy comestibles to stock market performance.

Similarly, Doe and Jones (2018) delve into the consumer behavior regarding dairy products in "Milk Money Matters." Their study uncovers intriguing patterns in household spending but stops short of churning out any concrete evidence linking milk and cream consumption to stock market movements.

Turning now to the broader market context, "Stocks and Stones" by Wall Street Elite (2017) presents an in-depth analysis of stock price determinants, with nary a mention of dairy products or their potential impact on market dynamics. Likewise, "Moo-ving Markets: A Holistic Approach to Stock Analysis" by Investment Gurus United (2020) fails to include the delightful dairy dimension in their market assessments.

Venturing beyond the hallowed halls of academic tomes, let us consider non-fiction literature that may hold relevance to our peculiar pursuits. "Got Milked: The Economics of Dairy" by E. Cream and C. A. Youthere (2019) provides an intriguing exploration of dairy economics, but regrettably lacks information on stock market correlations. In a similar vein, "Cream of the Crop: The Business of Fresh Milk" by B. Uttermilk (2021) offers enriching insights into the dairy industry but omits any connection to stock market trends.

Transitioning to a dairy-flavored detour, we contemplate the fictional landscape for potential insights. Could the magical realism of Haruki Murakami's "Kafka on the Shore" inadvertently sprinkle stock market wisdom amidst its fantastical felines and fishy tales? Unfortunately, the connection remains as elusive as a lactose-free milkshake in a dairy bar.

Delving ever deeper into literary realms, we ponder the dystopian masterpiece "Brave New World" by Aldous Huxley, where societal order and consumerism collide. Alas, while the novel offers profound societal commentary, its stock market musings seem as absent as a lactose-intolerant bovine at a dairy farm.

With our pursuit of relevant literature taking on a whimsical hue, we must confess to exploring unorthodox avenues in our quest for knowledge. From perusing the backs of dairy product containers to scrutinizing the nutritional information on cereal boxes, our efforts know no bounds. And as we navigated the labyrinthine passages of grocery store aisles, we found ourselves hoodwinked by the captivating tales of breakfast cereal characters, but alas, no stock market revelations emerged from the marshmallow-studded chaos.

In our quest for enlightenment, we even dared to consider the potential wisdom tucked within the seemingly mundane prose of shampoo bottle labels, but alas, our follicle-centric foray yielded little more than conditioning tips and hair care jargon.

As we conclude our whimsical journey through the annals of literature, both factual and fanciful, we eagerly anticipate the dairy-rich findings that await us in the empirical analysis of our study. So, brace yourselves for the creamy correlations and stock market sagas that lie ahead!

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 US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Make lots of 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 Bureau of Labor Statistics 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]]
"Moo-ving Markets: The Milky Way to Stock Market Analysis"

[[ABSTRACT]]
In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

[[INTRODUCTION]]
As the old adage goes, "Don't cry over spilled milk," but perhaps one should shed a tear for missed stock market opportunities linked to cow's milk. The intersection of milk consumption and stock prices may seem utterly unrelated, much like an unexpected dairy-free trend in an ice cream parlor. However, our research aims to demonstrate that beneath the creamy surface lies a surprising association worth exploring.
The link between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) may initially appear as unlikely as finding a lactose-intolerant cow. Nevertheless, our study seeks to unravel the whey in which these seemingly disparate elements intersect. By diving into the lactose-laden depths of household expenditure data and stock market fluctuations, we aim to shed light on an unusual yet compelling relationship.
While economists and analysts often focus on traditional market indicators, our research boldly ventures into a new pasture, where the bovine meets the bulls and bears. We aim to steer the conversation away from the standard market fundamentals and instead milk the data for intriguing insights (and perhaps the occasional cow pun).
As we embark on this conceivably unconventional journey, it is vital to acknowledge the inherent skepticism that may greet such an unorthodox study. After all, associating dairy products with stock market performance may seem as uncanny as a lactose-intolerant superhero. However, as the saying goes, "the proof is in the pudding," and we intend to churn out the evidence to support our findings.
So, grab your favorite dairy beverage, be it a tall glass of milk or a frothy latte, and join us as we navigate the Milky Way of stock market analysis. This study promises to be a milkshake of statistical analysis, where those who are lactose intolerant may feel a bit cheesed off. Nevertheless, we assure you that our findings will be udderly captivating and may just butter up your understanding of the curious connections between dairy consumption and stock market trends.
In the following sections, we will engage in a rigorous analysis of the data, exploring the correlations, implications, and potential market insights derived from our dairy-driven investigation. So, hold onto your hats, or perhaps your cow-printed aprons, as we journey through the lactose-laden landscape of financial analysis.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the creamy correlations between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD), our research team embarked on a dairy-driven odyssey through the data pastures. We milked the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) for all the relevant information, separating the wheat from the chaff and ensuring our datasets were as pure as a glass of full-fat milk.

First, we herded the data from 2002 to 2022 like diligent cowhands, making sure no outliers or rogue data points escaped our scrutiny. We then applied rigorous statistical techniques, more meticulous than a bovine pedicure, to clean, homogenize, and pasteurize the data for analysis. We stirred in a dash of complex econometric models, treating them like the subtle flavors of a well-aged cheese – adding depth and richness to our investigation.

Our approach involved fitting a series of sophisticated time-series models, as complex as untangling a herd of cows from a barbed wire fence, to capture the nuanced relationship between household dairy expenditures and stock prices. We employed ARIMA models to sift through the fluctuations, ensuring that no potential trends got lost in the bovine chaos, and utilized robust regression analyses to churn out statistically significant results.

Once we had corralled the data and tamed its wild tendencies, we calculated a variety of statistical indicators, measuring the associations with a precision that would make even the most fastidious dairy farmer proud. This process involved more number crunching than a herd of cows chewing cud, but the results were well worth the effort.

To test the significance of our findings, we employed a battery of hypothesis tests, examining the relationships with a scrutiny as sharp as a branding iron. We made sure our inferences were as sturdy as the gates of a cattle ranch, leaving no room for doubt or misinterpretation in our conclusions.

Finally, we conducted sensitivity analyses, robustness checks, and scenario simulations, ensuring that our results could withstand the financial equivalent of the stampeding hooves of a herd of dairy cows – because in the volatile world of stock markets, nothing is certain, and our findings had to be as resilient as a steel milk can.

In summary, our methodology was more thorough than checking for hidden chocolate chips in a bowl of cookie dough ice cream, and our dedication to precision was as unwavering as a cow's commitment to grazing in a sunlit meadow. With this approach, we navigated the treacherous terrain of data analysis, ensuring that our findings would hold up under the scrutiny of both financial analysts and discerning dairy enthusiasts alike.

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 results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Make lots of 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 Bureau of Labor Statistics and LSEG Analytics (Refinitiv) .

For the time period 2002 to 2022, you found a correlation 0.8187223, r-squared of 0.6703062, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
"Moo-ving Markets: The Milky Way to Stock Market Analysis"

[[ABSTRACT]]
In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
As the old adage goes, "Don't cry over spilled milk," but perhaps one should shed a tear for missed stock market opportunities linked to cow's milk. The intersection of milk consumption and stock prices may seem utterly unrelated, much like an unexpected dairy-free trend in an ice cream parlor. However, our research aims to demonstrate that beneath the creamy surface lies a surprising association worth exploring.
The link between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) may initially appear as unlikely as finding a lactose-intolerant cow. Nevertheless, our study seeks to unravel the whey in which these seemingly disparate elements intersect. By diving into the lactose-laden depths of household expenditure data and stock market fluctuations, we aim to shed light on an unusual yet compelling relationship.
While economists and analysts often focus on traditional market indicators, our research boldly ventures into a new pasture, where the bovine meets the bulls and bears. We aim to steer the conversation away from the standard market fundamentals and instead milk the data for intriguing insights (and perhaps the occasional cow pun).
As we embark on this conceivably unconventional journey, it is vital to acknowledge the inherent skepticism that may greet such an unorthodox study. After all, associating dairy products with stock market performance may seem as uncanny as a lactose-intolerant superhero. However, as the saying goes, "the proof is in the pudding," and we intend to churn out the evidence to support our findings.
So, grab your favorite dairy beverage, be it a tall glass of milk or a frothy latte, and join us as we navigate the Milky Way of stock market analysis. This study promises to be a milkshake of statistical analysis, where those who are lactose intolerant may feel a bit cheesed off. Nevertheless, we assure you that our findings will be udderly captivating and may just butter up your understanding of the curious connections between dairy consumption and stock market trends.
In the following sections, we will engage in a rigorous analysis of the data, exploring the correlations, implications, and potential market insights derived from our dairy-driven investigation. So, hold onto your hats, or perhaps your cow-printed aprons, as we journey through the lactose-laden landscape of financial analysis.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our study unveiled an intriguing correlation between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) from 2002 to 2022. Our analysis revealed a striking correlation coefficient of 0.8187223, elucidating a robust relationship between these seemingly unrelated variables. This correlation suggests that as US household spending on fresh milk and cream fluctuated, so did the stock price of Vodafone Group, with an r-squared value of 0.6703062, indicating that approximately 67% of the variability in Vodafone Group's stock price can be explained by changes in US household spending on fresh milk and cream.

Fig. 1 provides a visual representation of this dairy-driven correlation, showcasing the strong relationship between these variables. The scatterplot leaves little room for doubt that there is indeed something white and fluffy linking the lactose-laden world of dairy and the volatile realm of stock prices.

It is worth noting that the p-value of less than 0.01 highlights the statistical significance of this correlation. In other words, the likelihood of observing such a strong relationship between US household spending on fresh milk and cream and Vodafone Group's stock price by random chance alone is less than 1%, indicating that these findings are not merely a result of chance or coincidence. This statistically significant relationship reinforces the notion that the dairy market may have a greater influence on the stock market than previously accounted for, much like a cow's unexpected ability to jump over the moon.

In summary, our results suggest that there is indeed a meaningful association between US household spending on fresh milk and cream and Vodafone Group's stock price, challenging conventional wisdom and raising the question: could the dairy aisle be providing more than just sustenance for our bodies, but also food for thought for our investment portfolios? These findings open a new pasture for future research in the intersection of dairy consumption and financial markets, as we continue to milk every last drop of insight from this unusual yet captivating correlation.

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 US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Make lots of 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]]
"Moo-ving Markets: The Milky Way to Stock Market Analysis"

[[ABSTRACT]]
In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

[[LITERATURE REVIEW]]
In "Dairy Economics Quarterly," Smith et al. (2015) explore the impact of US household spending on dairy products, including fresh milk and cream, on various sectors of the economy. Their findings reveal a positive association between dairy consumption and economic indicators, but their analysis falls short of udderly connecting the creamy comestibles to stock market performance.
Similarly, Doe and Jones (2018) delve into the consumer behavior regarding dairy products in "Milk Money Matters." Their study uncovers intriguing patterns in household spending but stops short of churning out any concrete evidence linking milk and cream consumption to stock market movements.
Turning now to the broader market context, "Stocks and Stones" by Wall Street Elite (2017) presents an in-depth analysis of stock price determinants, with nary a mention of dairy products or their potential impact on market dynamics. Likewise, "Moo-ving Markets: A Holistic Approach to Stock Analysis" by Investment Gurus United (2020) fails to include the delightful dairy dimension in their market assessments.
Venturing beyond the hallowed halls of academic tomes, let us consider non-fiction literature that may hold relevance to our peculiar pursuits. "Got Milked: The Economics of Dairy" by E. Cream and C. A. Youthere (2019) provides an intriguing exploration of dairy economics, but regrettably lacks information on stock market correlations. In a similar vein, "Cream of the Crop: The Business of Fresh Milk" by B. Uttermilk (2021) offers enriching insights into the dairy industry but omits any connection to stock market trends.
Transitioning to a dairy-flavored detour, we contemplate the fictional landscape for potential insights. Could the magical realism of Haruki Murakami's "Kafka on the Shore" inadvertently sprinkle stock market wisdom amidst its fantastical felines and fishy tales? Unfortunately, the connection remains as elusive as a lactose-free milkshake in a dairy bar.
Delving ever deeper into literary realms, we ponder the dystopian masterpiece "Brave New World" by Aldous Huxley, where societal order and consumerism collide. Alas, while the novel offers profound societal commentary, its stock market musings seem as absent as a lactose-intolerant bovine at a dairy farm.
With our pursuit of relevant literature taking on a whimsical hue, we must confess to exploring unorthodox avenues in our quest for knowledge. From perusing the backs of dairy product containers to scrutinizing the nutritional information on cereal boxes, our efforts know no bounds. And as we navigated the labyrinthine passages of grocery store aisles, we found ourselves hoodwinked by the captivating tales of breakfast cereal characters, but alas, no stock market revelations emerged from the marshmallow-studded chaos.
In our quest for enlightenment, we even dared to consider the potential wisdom tucked within the seemingly mundane prose of shampoo bottle labels, but alas, our follicle-centric foray yielded little more than conditioning tips and hair care jargon.
As we conclude our whimsical journey through the annals of literature, both factual and fanciful, we eagerly anticipate the dairy-rich findings that await us in the empirical analysis of our study. So, brace yourselves for the creamy correlations and stock market sagas that lie ahead!

[[RESULTS]]
The results of our study unveiled an intriguing correlation between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) from 2002 to 2022. Our analysis revealed a striking correlation coefficient of 0.8187223, elucidating a robust relationship between these seemingly unrelated variables. This correlation suggests that as US household spending on fresh milk and cream fluctuated, so did the stock price of Vodafone Group, with an r-squared value of 0.6703062, indicating that approximately 67% of the variability in Vodafone Group's stock price can be explained by changes in US household spending on fresh milk and cream.
Fig. 1 provides a visual representation of this dairy-driven correlation, showcasing the strong relationship between these variables. The scatterplot leaves little room for doubt that there is indeed something white and fluffy linking the lactose-laden world of dairy and the volatile realm of stock prices.
It is worth noting that the p-value of less than 0.01 highlights the statistical significance of this correlation. In other words, the likelihood of observing such a strong relationship between US household spending on fresh milk and cream and Vodafone Group's stock price by random chance alone is less than 1%, indicating that these findings are not merely a result of chance or coincidence. This statistically significant relationship reinforces the notion that the dairy market may have a greater influence on the stock market than previously accounted for, much like a cow's unexpected ability to jump over the moon.
In summary, our results suggest that there is indeed a meaningful association between US household spending on fresh milk and cream and Vodafone Group's stock price, challenging conventional wisdom and raising the question: could the dairy aisle be providing more than just sustenance for our bodies, but also food for thought for our investment portfolios? These findings open a new pasture for future research in the intersection of dairy consumption and financial markets, as we continue to milk every last drop of insight from this unusual yet captivating correlation.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our study has shed light on the bovine bounty of connections between U.S. household spending on fresh milk and cream and the stock price of Vodafone Group (VOD). Our findings corroborate the earlier research by Smith et al. (2015) and Doe and Jones (2018), who, albeit unintentionally, paved the whey for our dairy-driven exploration. While their studies may not have directly prodded the udderly intriguing relationship we uncovered, they laid the foundation for our cheese-tastic revelations.

The statistical correlation coefficient of 0.8187223, with a p-value of less than 0.01, supports the notion that the dairy market may hold more sway over stock market fluctuations than previously thought. This robust correlation speaks volumes about the potential impact of dairy consumption habits on the highly volatile world of stock prices, prompting us to ponder whether the dairy aisle is churning out more than just milk and cream—perhaps it's also churning out stock market insights. It seems the dairy section is not just a place for lighthearted mooo-ves, but rather a surprisingly creamy predictor of financial trends.

Our results have truly turned the investment landscape into a pasture of possibilities, offering a new lens through which to view the stock market. The strength of the correlation and its statistical significance cannot be brushed aside as mere cow-incidence, but rather as a dairy-fueled force to behold. As we contemplate the implications of these findings, we find ourselves musing on the potential applicability of dairy-based investment strategies—a prospect that might just milk the most out of the market's potential.

In conclusion, our study has revealed a creamy correlation between dairy consumption and stock prices, encouraging further exploration of the moo-nificent mysteries underlying market dynamics. Who knew that the humble consumption of fresh milk and cream could have such stock-tacular implications? We eagerly anticipate the potential ripple effects in the field of financial analysis, as we milk every last drop of insight from this unlikely yet utterly captivating correlation.

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 US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Make lots of 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]]
"Moo-ving Markets: The Milky Way to Stock Market Analysis"

[[ABSTRACT]]
In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

[[INTRDUCTION]]
As the old adage goes, "Don't cry over spilled milk," but perhaps one should shed a tear for missed stock market opportunities linked to cow's milk. The intersection of milk consumption and stock prices may seem utterly unrelated, much like an unexpected dairy-free trend in an ice cream parlor. However, our research aims to demonstrate that beneath the creamy surface lies a surprising association worth exploring.
The link between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) may initially appear as unlikely as finding a lactose-intolerant cow. Nevertheless, our study seeks to unravel the whey in which these seemingly disparate elements intersect. By diving into the lactose-laden depths of household expenditure data and stock market fluctuations, we aim to shed light on an unusual yet compelling relationship.
While economists and analysts often focus on traditional market indicators, our research boldly ventures into a new pasture, where the bovine meets the bulls and bears. We aim to steer the conversation away from the standard market fundamentals and instead milk the data for intriguing insights (and perhaps the occasional cow pun).
As we embark on this conceivably unconventional journey, it is vital to acknowledge the inherent skepticism that may greet such an unorthodox study. After all, associating dairy products with stock market performance may seem as uncanny as a lactose-intolerant superhero. However, as the saying goes, "the proof is in the pudding," and we intend to churn out the evidence to support our findings.
So, grab your favorite dairy beverage, be it a tall glass of milk or a frothy latte, and join us as we navigate the Milky Way of stock market analysis. This study promises to be a milkshake of statistical analysis, where those who are lactose intolerant may feel a bit cheesed off. Nevertheless, we assure you that our findings will be udderly captivating and may just butter up your understanding of the curious connections between dairy consumption and stock market trends.
In the following sections, we will engage in a rigorous analysis of the data, exploring the correlations, implications, and potential market insights derived from our dairy-driven investigation. So, hold onto your hats, or perhaps your cow-printed aprons, as we journey through the lactose-laden landscape of financial analysis.

[[RESULTS]]
The results of our study unveiled an intriguing correlation between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD) from 2002 to 2022. Our analysis revealed a striking correlation coefficient of 0.8187223, elucidating a robust relationship between these seemingly unrelated variables. This correlation suggests that as US household spending on fresh milk and cream fluctuated, so did the stock price of Vodafone Group, with an r-squared value of 0.6703062, indicating that approximately 67% of the variability in Vodafone Group's stock price can be explained by changes in US household spending on fresh milk and cream.
Fig. 1 provides a visual representation of this dairy-driven correlation, showcasing the strong relationship between these variables. The scatterplot leaves little room for doubt that there is indeed something white and fluffy linking the lactose-laden world of dairy and the volatile realm of stock prices.
It is worth noting that the p-value of less than 0.01 highlights the statistical significance of this correlation. In other words, the likelihood of observing such a strong relationship between US household spending on fresh milk and cream and Vodafone Group's stock price by random chance alone is less than 1%, indicating that these findings are not merely a result of chance or coincidence. This statistically significant relationship reinforces the notion that the dairy market may have a greater influence on the stock market than previously accounted for, much like a cow's unexpected ability to jump over the moon.
In summary, our results suggest that there is indeed a meaningful association between US household spending on fresh milk and cream and Vodafone Group's stock price, challenging conventional wisdom and raising the question: could the dairy aisle be providing more than just sustenance for our bodies, but also food for thought for our investment portfolios? These findings open a new pasture for future research in the intersection of dairy consumption and financial markets, as we continue to milk every last drop of insight from this unusual yet captivating correlation.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has produced robust evidence of a surprisingly strong correlation between US household spending on fresh milk and cream and the stock price of Vodafone Group (VOD). It seems that when it comes to financial analysis, there's a whole udder world of potential factors at play.

Our findings not only highlight the statistical significance of this correlation but also underscore the potential impact of dairy consumption on stock market dynamics. It turns out that when markets are in a slump, a little milk might do the stock prices good – a real moo-d booster, if you will.

While our results may initially seem as unexpected as a lactose-intolerant cow, they open the door to a pasture of further research in this unconventional realm. Who would have thought that dairy products could hold such sway over stock prices? It's like finding out that the secret to market success has been sitting in the dairy aisle all along.

This study might just prompt a new stock market adage: "Don't have a cow, have a correlation with fresh milk and cream expenditure." It's a phrase that could truly milk the potential of unconventional market indicators.

In light of these findings, it's clear that future research in this area may lead to a churn in traditional market analysis – perhaps our next study will uncover the cheese consumption-stock price connection. But for now, it seems that there's no need for further research in this area. We've milked this correlation for all it's worth!

So, as we bid adieu to this bovine-driven journey through financial and dairy markets, we hope our findings have left you feeling both enlightened and, dare we say, a little bit lactose-fascinated!

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 Moo-ving Markets: The Milky Way to Stock Market Analysis. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Bovine Economics and Financial Analysis

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 Moo-ving Markets: The Milky Way to Stock Market Analysis. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Bovine Economics and Financial Patterning

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 Moo-ving Markets: The Milky Way to Stock Market Analysis with an abstract of In this research paper, we delve into the fascinating world of dairy and stock market analysis to investigate the connection between US household spending on fresh milk and cream and Vodafone Group's stock price (VOD). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team undertook a statistical journey fit for the lactose tolerant. Our findings reveal a correlation coefficient of 0.8187223, with p < 0.01, covering the period from 2002 to 2022. Grab your dairy cow and buckle up, as we unravel the creamy connections between cow's milk and stock prices in this udderly intriguing study!

ChatGPT:

dairy consumption, stock market analysis, US household spending, milk and cream expenditure, Vodafone Group stock price, correlation coefficient, statistical analysis, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, dairy industry, stock price correlation, dairy product sales

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

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Data details

US household spending on fresh milk and cream
Detailed data title: Annual US household spend on fresh milk and cream, as a percentage of total household spend
Source: Bureau of Labor Statistics
See what else correlates with US household spending on fresh milk and cream

Vodafone Group's stock price (VOD)
Detailed data title: Opening price of Vodafone Group (VOD) 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 Vodafone Group's stock price (VOD)

Correlation r = 0.8187223 (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 = 0.6703062 (Coefficient of determination)
This means 67% of the change in the one variable (i.e., Vodafone Group's stock price (VOD)) is predictable based on the change in the other (i.e., US household spending on fresh milk and cream) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.7E-6.^Show 0.0000057027470003525340000000
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 0.82 in 0.00057% of random cases. Said differently, if you correlated 175,354 random variables^Note You don't actually need 175 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 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.

[ 0.6, 0.92 ] 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
US household spending on fresh milk and cream (Household spend)	0.312216	0.311145	0.331835	0.314594	0.289256	0.310246	0.332766	0.293476	0.293084	0.30178	0.295478	0.297456	0.274792	0.250098	0.242536	0.244755	0.240102	0.222095	0.239671	0.23458	0.230241
Vodafone Group's stock price (VOD) (Stock price)	54.37	38.41	52.88	57.51	46.18	51.74	68.37	36.47	42.53	48.46	51.61	47.23	71.5	34.08	32.12	24.84	32.16	19.3	19.38	17.04	15.2

Why this works

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

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

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.312216,0.311145,0.331835,0.314594,0.289256,0.310246,0.332766,0.293476,0.293084,0.30178,0.295478,0.297456,0.274792,0.250098,0.242536,0.244755,0.240102,0.222095,0.239671,0.23458,0.230241,])
array_2 = np.array([54.37,38.41,52.88,57.51,46.18,51.74,68.37,36.47,42.53,48.46,51.61,47.23,71.5,34.08,32.12,24.84,32.16,19.3,19.38,17.04,15.2,])
array_1_name = "US household spending on fresh milk and cream"
array_2_name = "Vodafone Group's stock price (VOD)"

# 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 US household spending on fresh milk and cream
Line chart for only Vodafone Group's stock price (VOD)
AI-generated correlation image
The spurious research paper: Moo-ving Markets: The Milky Way to Stock Market Analysis

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Correlation ID: 2151 · Black Variable ID: 19954 · Red Variable ID: 1857


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