Spurious correlation #2,540 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is US Bottled Water Consumption per Person and the second variable is Constellation Brands' stock price (STZ). 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 Bottled Water Consumption per Person and the second variable is Constellation Brands' stock price (STZ). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Because as bottled water consumption rose, so did the need for the perfect chaser. As people stayed hydrated, they were also creating a new market for water-enhanced wine: H2-Oh, the latest in boozy hydration! Who knew staying refreshed could be so grape? It's a win-win situation for Constellation Brands, as they ride the wave of this liquid luck!

^{Show image generation prompt} Model: dalle-3
Prompt: Generating Prompt: [ DALLE-3's attention to details and ability to interpret a diverse range of subjects ] Depict a scene where a person is holding a bottle of water and taking a refreshing sip. They are confidently checking the stock price of Constellation Brands on their phone, and as they continue to drink water, their expression becomes more pleased as they observe the stock price steadily rising. In the background, the constellation-shaped logo of Constellation Brands shines brightly, representing the growing confidence in the company's products. The image should capture the person's satisfaction, the refreshing moment of drinking water, and the subtle glow of the constellation-shaped logo in the background. The visual should emphasize the dynamics of the person's expression and the evolving stock price, imbuing the scene with a sense of optimism and success. 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 Bottled Water Consumption per Person and Constellation Brands' stock price (STZ). 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: Cyberpunk Cute cartoon Scalar art Cute cartoon Steampunk
ChatGPT: Scalar art

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 Bottled Water Consumption per Person and Constellation Brands' stock price (STZ), in the following situation: As people drank more water, they became better at making sober investment decisions, thus driving up the stock price of Constellation Brands. This led to a surge in confidence in the company's products, including their alcoholic beverages, ultimately boosting their stock price.. Keep the description simple.
ChatGPT: A person is seen holding a bottle of water, taking a refreshing sip, and then confidently checking the stock price of Constellation Brands on their phone. As they continue to drink water, their expression becomes more pleased as they observe the stock price steadily rising. In the background, the constellation-shaped logo of Constellation Brands shines brightly, representing the growing confidence in the company's products.

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: [ Scalar art ] and depicting this scene: A person is seen holding a bottle of water, taking a refreshing sip, and then confidently checking the stock price of Constellation Brands on their phone. As they continue to drink water, their expression becomes more pleased as they observe the stock price steadily rising. In the background, the constellation-shaped logo of Constellation Brands shines brightly, representing the growing confidence in the company's products. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generating Prompt: [ DALLE-3's attention to details and ability to interpret a diverse range of subjects ] Depict a scene where a person is holding a bottle of water and taking a refreshing sip. They are confidently checking the stock price of Constellation Brands on their phone, and as they continue to drink water, their expression becomes more pleased as they observe the stock price steadily rising. In the background, the constellation-shaped logo of Constellation Brands shines brightly, representing the growing confidence in the company's products. The image should capture the person's satisfaction, the refreshing moment of drinking water, and the subtle glow of the constellation-shaped logo in the background. The visual should emphasize the dynamics of the person's expression and the evolving stock price, imbuing the scene with a sense of optimism and success.

*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 US Bottled Water Consumption per Person caused Constellation Brands' stock price (STZ) to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

The Journal of Financial Hydrodynamics

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between US Bottled Water Consumption per Person and Constellation Brands' stock price (STZ). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

[[ABSTRACT]]
The ever-growing demand for bottled water has flooded the market, cascading into various sectors, including the stock market. This study dived into the complex relationship between US bottled water consumption per person and the stock price of Constellation Brands. With data from Statista and LSEG Analytics (Refinitiv), a remarkable correlation coefficient of 0.9399801 and p < 0.01 for the period from 2002 to 2022 was observed.

As the data flow of bottled water consumption trickled in, an unanticipated correlation with Constellation Brands' stock price emerged. It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands. One might say the stock rose to the occasion, mirroring the upward trend in bottled water consumption.

The findings of this study make a splash in the world of market analysis, shedding light on the hydrologic forces at play in the entwined fates of consumer beverage habits and stock market performance. With such a strong correlation, it's clear that the aquatic allure of bottled water has made waves in the realm of investment. Investors might need to stay afloat and ride the swell of this watery stock market correlation.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the ordinary consumer reaches for a refreshing bottle of water, little do they realize the potential impact their hydration habits may have on the stock market. This study delves into the surprising relationship between US bottled water consumption per person and the stock price of Constellation Brands (STZ), a connection that is sure to make a splash in the world of market analysis.
The pronounced increase in bottled water consumption over the past few decades has been quite the fountain of youth for the beverage industry, affecting various players in the market. It seems that as Americans continue to guzzle bottled water, Constellation Brands' stock price has also bubbled up in response. It's as if the market is crying out, "Water we doing without all this bottled water data?"
This study aims to quench the thirst of investors and analysts alike for a deeper understanding of the forces at play in the relationship between bottled water consumption and stock market performance. Understanding this buoyant relationship could provide valuable insights for market predictions and investment strategies – turning perhaps the tide for those seeking to stay afloat in the world of stock trading.
The correlation between US bottled water consumption and Constellation Brands' stock price is as clear as the water itself. It's as if the stock market is saying, "Let's dive in and chart the depths of this aquatic connection."

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between consumer behaviors and stock market performance has garnered substantial attention in the academic literature. Smith and Doe (2015) investigated the influence of consumer beverage preferences on stock prices, while Jones (2017) delved into the impact of changing consumption patterns on investment decisions. These studies shed light on the intricate connections between consumer behavior and stock market dynamics, providing a foundation for exploring the relationship between US bottled water consumption per person and Constellation Brands' stock price.

In "The Bottled Water Industry: Market Analysis and Forecast 2021-2026," lorem and ipsum trace the growth trajectory of the bottled water industry and its impact on consumer choices. Additionally, "Stocks and Water: An Exploration of Aquatic Assets" by lorem and ipsum provides a comprehensive overview of the economic implications of water-related industries on stock market performance.

Conversely, fictional works such as "The Water Wealth Theory" by John Aqua and "Bubbles and Stocks: A Tale of Liquid Assets" by Lily H2O, while not empirical research, offer imaginative narratives that portray the thematic resonance of water and finance. These literary representations may inspire creative thinking in relation to the aquatic analysis of stock market correlates.

In a less traditional approach to literature review, the researchers also considered the insights derived from unconventional sources. The study paid heed to the lexical prose inscribed on the back labels of various bottled water products, with a humorous anecdote from a popular brand suggesting, "Why did the water go to school? Because it wanted to be a little 'bright' thinker."

The diversification of literature sources allowed for a comprehensive understanding of the varied perspectives on the connection between US bottled water consumption per person and Constellation Brands' stock price, encompassing empirical studies, market analyses, fictional narratives, and even the whimsical musings found on everyday consumer products.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between US Bottled Water Consumption per Person and Constellation Brands' stock price (STZ). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Statista 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]]
The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

[[ABSTRACT]]
The ever-growing demand for bottled water has flooded the market, cascading into various sectors, including the stock market. This study dived into the complex relationship between US bottled water consumption per person and the stock price of Constellation Brands. With data from Statista and LSEG Analytics (Refinitiv), a remarkable correlation coefficient of 0.9399801 and p < 0.01 for the period from 2002 to 2022 was observed.

As the data flow of bottled water consumption trickled in, an unanticipated correlation with Constellation Brands' stock price emerged. It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands. One might say the stock rose to the occasion, mirroring the upward trend in bottled water consumption.

The findings of this study make a splash in the world of market analysis, shedding light on the hydrologic forces at play in the entwined fates of consumer beverage habits and stock market performance. With such a strong correlation, it's clear that the aquatic allure of bottled water has made waves in the realm of investment. Investors might need to stay afloat and ride the swell of this watery stock market correlation.

[[INTRODUCTION]]
As the ordinary consumer reaches for a refreshing bottle of water, little do they realize the potential impact their hydration habits may have on the stock market. This study delves into the surprising relationship between US bottled water consumption per person and the stock price of Constellation Brands (STZ), a connection that is sure to make a splash in the world of market analysis.
The pronounced increase in bottled water consumption over the past few decades has been quite the fountain of youth for the beverage industry, affecting various players in the market. It seems that as Americans continue to guzzle bottled water, Constellation Brands' stock price has also bubbled up in response. It's as if the market is crying out, "Water we doing without all this bottled water data?"
This study aims to quench the thirst of investors and analysts alike for a deeper understanding of the forces at play in the relationship between bottled water consumption and stock market performance. Understanding this buoyant relationship could provide valuable insights for market predictions and investment strategies – turning perhaps the tide for those seeking to stay afloat in the world of stock trading.
The correlation between US bottled water consumption and Constellation Brands' stock price is as clear as the water itself. It's as if the stock market is saying, "Let's dive in and chart the depths of this aquatic connection."

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The research team made a splash in the world of data collection by diving into various sources such as Statista and LSEG Analytics (Refinitiv). The team collected historical data on US bottled water consumption per person, as well as the stock price of Constellation Brands (STZ), spanning the years 2002 to 2022. The data was then carefully filtered and purified to ensure its accuracy and reliability, much like the process of filtering water to remove impurities. One might say the data collection process flowed smoothly, with no major waves of discrepancies encountered.

Statistical Analysis:
The collected data was subjected to a series of statistical analyses to uncover any underlying relationships between US bottled water consumption per person and Constellation Brands' stock price. Various statistical tests, including correlation analysis, were conducted to gauge the strength and significance of the association. It was a bit like testing the waters, but with numbers and equations rather than swimsuits and sunscreen.

Correlation Calculation:
The research team utilized correlative analysis to measure the degree of linear relationship between US bottled water consumption per person and Constellation Brands' stock price. The Pearson correlation coefficient was employed to assess the strength and direction of the association, akin to determining the flow and current of a river. The statistician even quipped, "It's like finding the perfect balance of water and wine – a correlation that's just right, not too strong and not too weak."

Testing for Significance:
In addition to computing the correlation coefficient, the research team tested the significance of the correlation using p-values. This step served to ascertain whether the observed association between bottled water consumption and stock price was statistically meaningful or simply the result of chance. The results indicated a p-value of less than 0.01, indicating a highly significant relationship that was not merely a drop in the ocean of randomness.

Control Variables:
To ensure the robustness of the findings, the study also considered potential confounding factors that could influence the observed correlation. Control variables such as macroeconomic indicators and industry-specific trends were incorporated into the analysis to minimize the risk of spurious relationships. Consideration of these additional factors helped to maintain the clarity and purity of the association between bottled water consumption and stock price, just like adding a filter to achieve crystal-clear tap water.

Sensitivity Analysis:
Lastly, the research team conducted sensitivity analyses to test the robustness of the results and the stability of the observed correlation over time. Various scenarios and adjustments were explored to evaluate the resilience of the relationship under different conditions. This step was akin to testing the waters under varying temperatures and pressures, ensuring that the association held strong regardless of external influences.

Ethical Considerations:
In compliance with research ethics and integrity, all data sources and methodologies were transparently documented, with proper attribution given to the original providers of the information. The research team also maintained a commitment to accuracy and objectivity, steering clear of any murky practices that could cloud the validity of the findings.

The research methodology adopted in this study aimed to unravel the enigmatic connection between US bottled water consumption per person and Constellation Brands' stock price, serving as a beacon of clarity in the often turbulent sea of market analysis. Through a rigorous and systematic approach, the study sought to ascertain the reliability and validity of the observed correlation, ensuring that the findings flowed smoothly and upheld the high standards of academic research.

NOTICE: This response has been drafted solely for the purpose of entertainment and does not reflect any actual research methodology.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between US Bottled Water Consumption per Person and Constellation Brands' stock price (STZ). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

[[ABSTRACT]]
The ever-growing demand for bottled water has flooded the market, cascading into various sectors, including the stock market. This study dived into the complex relationship between US bottled water consumption per person and the stock price of Constellation Brands. With data from Statista and LSEG Analytics (Refinitiv), a remarkable correlation coefficient of 0.9399801 and p < 0.01 for the period from 2002 to 2022 was observed.

As the data flow of bottled water consumption trickled in, an unanticipated correlation with Constellation Brands' stock price emerged. It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands. One might say the stock rose to the occasion, mirroring the upward trend in bottled water consumption.

The findings of this study make a splash in the world of market analysis, shedding light on the hydrologic forces at play in the entwined fates of consumer beverage habits and stock market performance. With such a strong correlation, it's clear that the aquatic allure of bottled water has made waves in the realm of investment. Investors might need to stay afloat and ride the swell of this watery stock market correlation.

[[LITERATURE REVIEW]]
The relationship between consumer behaviors and stock market performance has garnered substantial attention in the academic literature. Smith and Doe (2015) investigated the influence of consumer beverage preferences on stock prices, while Jones (2017) delved into the impact of changing consumption patterns on investment decisions. These studies shed light on the intricate connections between consumer behavior and stock market dynamics, providing a foundation for exploring the relationship between US bottled water consumption per person and Constellation Brands' stock price.
In "The Bottled Water Industry: Market Analysis and Forecast 2021-2026," lorem and ipsum trace the growth trajectory of the bottled water industry and its impact on consumer choices. Additionally, "Stocks and Water: An Exploration of Aquatic Assets" by lorem and ipsum provides a comprehensive overview of the economic implications of water-related industries on stock market performance.
Conversely, fictional works such as "The Water Wealth Theory" by John Aqua and "Bubbles and Stocks: A Tale of Liquid Assets" by Lily H2O, while not empirical research, offer imaginative narratives that portray the thematic resonance of water and finance. These literary representations may inspire creative thinking in relation to the aquatic analysis of stock market correlates.
In a less traditional approach to literature review, the researchers also considered the insights derived from unconventional sources. The study paid heed to the lexical prose inscribed on the back labels of various bottled water products, with a humorous anecdote from a popular brand suggesting, "Why did the water go to school? Because it wanted to be a little 'bright' thinker."
The diversification of literature sources allowed for a comprehensive understanding of the varied perspectives on the connection between US bottled water consumption per person and Constellation Brands' stock price, encompassing empirical studies, market analyses, fictional narratives, and even the whimsical musings found on everyday consumer products.

[[RESULTS]]
The analysis of the data collected from Statista and LSEG Analytics (Refinitiv) revealed a striking correlation between US bottled water consumption per person and Constellation Brands' stock price. The correlation coefficient of 0.9399801 indicates a strong positive linear relationship between these two variables. One might say that the market has been making a splash in response to the demand for bottled water.
The r-squared value of 0.8835625 suggests that approximately 88.36% of the variability in Constellation Brands' stock price can be explained by the changing levels of US bottled water consumption. It appears that the stock price was quite thirsty for this particular type of information, as it drank up the trends in bottled water consumption like a parched hiker at an oasis.
The significance level (p < 0.01) demonstrates that the observed relationship between bottled water consumption and stock price is not due to random chance. This finding indicates that the connection between these two variables is indeed a notable feature of the market landscape. One could say that the correlation between these two phenomena is as clear as... well, bottled water.
The scatterplot in Figure 1 visually illustrates the robust positive correlation between US bottled water consumption per person and Constellation Brands' stock price. The data points form a pattern that is about as clear as the transparency of the bottled water itself. It's indeed a refreshing sight in the often murky waters of stock market analysis.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of this study provide strong support for the prior research that has delved into the captivating connection between consumer beverage habits and stock market performance. The remarkable correlation coefficient of 0.9399801 aligns with the findings of Smith and Doe (2015), whose investigation also uncovered a significant influence of consumer beverage preferences on stock prices. It seems that the stock price, much like a leaky faucet, responded to the increasing national consumption of bottled water.

Moreover, the unanticipated correlation between US bottled water consumption per person and Constellation Brands' stock price mirrors the findings of Jones (2017), who explored the impact of changing consumption patterns on investment decisions. In this case, it appears that the stock price flowed in tandem with the surge in bottled water consumption, much like a well-crafted punchline in a comedy routine.

The unexpected relationship between these two seemingly unrelated phenomena also resonates with the imaginative narratives depicted in the fictional works of John Aqua and Lily H2O. Though not empirical research, these literary representations envisioned a world where water and finance intertwined in unexpected ways, much like the surprising connection uncovered in our empirical analysis. It seems that the stock market can indeed be influenced by the ebb and flow of consumer beverage preferences, much to the surprise of market analysts and researchers alike.

In conclusion, this study's findings contribute to our understanding of the intricate connections between consumer behavior and stock market dynamics. The robust positive correlation between US bottled water consumption per person and Constellation Brands' stock price reveals a previously uncharted aquatic terrain in the world of market analysis. As investors navigate these watery depths, they may find that staying afloat in the stock market requires keeping a keen eye on the tide of consumer beverage habits. After all, it seems that in the world of stock markets, when it rains, it pours – both in terms of volatility and bottled water consumption.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between US Bottled Water Consumption per Person and Constellation Brands' stock price (STZ). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

[[ABSTRACT]]
The ever-growing demand for bottled water has flooded the market, cascading into various sectors, including the stock market. This study dived into the complex relationship between US bottled water consumption per person and the stock price of Constellation Brands. With data from Statista and LSEG Analytics (Refinitiv), a remarkable correlation coefficient of 0.9399801 and p < 0.01 for the period from 2002 to 2022 was observed.

As the data flow of bottled water consumption trickled in, an unanticipated correlation with Constellation Brands' stock price emerged. It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands. One might say the stock rose to the occasion, mirroring the upward trend in bottled water consumption.

The findings of this study make a splash in the world of market analysis, shedding light on the hydrologic forces at play in the entwined fates of consumer beverage habits and stock market performance. With such a strong correlation, it's clear that the aquatic allure of bottled water has made waves in the realm of investment. Investors might need to stay afloat and ride the swell of this watery stock market correlation.

[[INTRDUCTION]]
As the ordinary consumer reaches for a refreshing bottle of water, little do they realize the potential impact their hydration habits may have on the stock market. This study delves into the surprising relationship between US bottled water consumption per person and the stock price of Constellation Brands (STZ), a connection that is sure to make a splash in the world of market analysis.
The pronounced increase in bottled water consumption over the past few decades has been quite the fountain of youth for the beverage industry, affecting various players in the market. It seems that as Americans continue to guzzle bottled water, Constellation Brands' stock price has also bubbled up in response. It's as if the market is crying out, "Water we doing without all this bottled water data?"
This study aims to quench the thirst of investors and analysts alike for a deeper understanding of the forces at play in the relationship between bottled water consumption and stock market performance. Understanding this buoyant relationship could provide valuable insights for market predictions and investment strategies – turning perhaps the tide for those seeking to stay afloat in the world of stock trading.
The correlation between US bottled water consumption and Constellation Brands' stock price is as clear as the water itself. It's as if the stock market is saying, "Let's dive in and chart the depths of this aquatic connection."

[[RESULTS]]
The analysis of the data collected from Statista and LSEG Analytics (Refinitiv) revealed a striking correlation between US bottled water consumption per person and Constellation Brands' stock price. The correlation coefficient of 0.9399801 indicates a strong positive linear relationship between these two variables. One might say that the market has been making a splash in response to the demand for bottled water.
The r-squared value of 0.8835625 suggests that approximately 88.36% of the variability in Constellation Brands' stock price can be explained by the changing levels of US bottled water consumption. It appears that the stock price was quite thirsty for this particular type of information, as it drank up the trends in bottled water consumption like a parched hiker at an oasis.
The significance level (p < 0.01) demonstrates that the observed relationship between bottled water consumption and stock price is not due to random chance. This finding indicates that the connection between these two variables is indeed a notable feature of the market landscape. One could say that the correlation between these two phenomena is as clear as... well, bottled water.
The scatterplot in Figure 1 visually illustrates the robust positive correlation between US bottled water consumption per person and Constellation Brands' stock price. The data points form a pattern that is about as clear as the transparency of the bottled water itself. It's indeed a refreshing sight in the often murky waters of stock market analysis.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study reveal a remarkably strong correlation between US bottled water consumption per person and the stock price of Constellation Brands (STZ). It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands, demonstrating a clear aquatically-inclined relationship between these two variables. This connection between consumer beverage habits and stock market performance is as refreshing as a cool drink of water on a hot summer day.

Investors might do well to consider the ripple effects of bottled water consumption on the stock market, as it seems to have quite the buoyant influence on Constellation Brands' stock price. Perhaps it's time for analysts to dive in and swim with the tide of this correlation, riding the wave of bottled water consumption data to inform investment decisions. With such a strong correlation, it seems the market is saying, "Water we doing without considering the impact of bottled water consumption?"

This study has provided valuable insights into the hydrologic forces at play in the intertwined fate of consumer beverage habits and stock market performance. With the evidence of this correlation as clear as the purest bottled water, it's evident that the aquatic allure of bottled water has made significant waves in the realm of investment.

Given the compelling nature of the correlation between US bottled water consumption and Constellation Brands' stock price, it appears that no further research is needed in this area. It seems we've already quenched our thirst for knowledge in this particular avenue of market analysis. Now, the question remains: who's ready to take the plunge and invest in this aquatic association?

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 The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Financial Hydrodynamics

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 The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Aquatic Economic Research and Analysis

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 The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price with an abstract of The ever-growing demand for bottled water has flooded the market, cascading into various sectors, including the stock market. This study dived into the complex relationship between US bottled water consumption per person and the stock price of Constellation Brands. With data from Statista and LSEG Analytics (Refinitiv), a remarkable correlation coefficient of 0.9399801 and p < 0.01 for the period from 2002 to 2022 was observed.
As the data flow of bottled water consumption trickled in, an unanticipated correlation with Constellation Brands' stock price emerged. It seems that as Americans' thirst for bottled water increased, so did the stock price of Constellation Brands. One might say the stock rose to the occasion, mirroring the upward trend in bottled water consumption.
The findings of this study make a splash in the world of market analysis, shedding light on the hydrologic forces at play in the entwined fates of consumer beverage habits and stock market performance. With such a strong correlation, it's clear that the aquatic allure of bottled water has made waves in the realm of investment. Investors might need to stay afloat and ride the swell of this watery stock market correlation.

ChatGPT:

bottled water consumption, US, stock price, Constellation Brands, market analysis, correlation coefficient, consumer beverage habits, investment, hydrologic forces, market performance, stock market correlation

*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 Bottled Water Consumption per Person
Source: Statista
See what else correlates with US Bottled Water Consumption per Person

Constellation Brands' stock price (STZ)
Detailed data title: Opening price of Constellation Brands (STZ) 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 Constellation Brands' stock price (STZ)

Correlation r = 0.9399801 (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 = 0.8835625 (Coefficient of determination)
This means 88.4% of the change in the one variable (i.e., Constellation Brands' stock price (STZ)) is predictable based on the change in the other (i.e., US Bottled Water Consumption per Person) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.6E-10.^Show 0.0000000002564047395599518400
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 0.94 in 2.6E-8% of random cases. Said differently, if you correlated 3,900,083,913 random variables^Note You don't actually need 3 billion 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.

[ 0.86, 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.

	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
US Bottled Water Consumption per Person (Gallons)	20.1	21.6	23.2	25.4	27.6	29	28.5	27.6	28.3	29.2	30.8	32	34.1	36.5	39	42	42.3	44	45.2	47	46.5
Constellation Brands' stock price (STZ) (Stock price)	10.71	11.93	16.45	23.26	26.35	28.8	23.59	15.82	16.02	22.18	20.96	35.95	70.02	98.71	141.03	155.01	229	160.06	189.97	220	251.25

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([20.1,21.6,23.2,25.4,27.6,29,28.5,27.6,28.3,29.2,30.8,32,34.1,36.5,39,42,42.3,44,45.2,47,46.5,])
array_2 = np.array([10.71,11.93,16.45,23.26,26.35,28.8,23.59,15.82,16.02,22.18,20.96,35.95,70.02,98.71,141.03,155.01,229,160.06,189.97,220,251.25,])
array_1_name = "US Bottled Water Consumption per Person"
array_2_name = "Constellation Brands' stock price (STZ)"

# 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 Bottled Water Consumption per Person
Line chart for only Constellation Brands' stock price (STZ)
AI-generated correlation image
The spurious research paper: The Thirsty Stock Market: An Aquatic Analysis of the Relationship Between US Bottled Water Consumption and Constellation Brands' Stock Price

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Correlation ID: 2540 · Black Variable ID: 32 · Red Variable ID: 1739


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