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Spurious correlation #2,125 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is American cheese consumption and the second variable is AutoZone's stock price (AZO).  The chart goes from 2002 to 2021, and the two variables track closely in value over that time. Small Image
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AI explanation

As American cheese consumption melted, so did AutoZone's stock price, creating a gouda investment opportunity. With more people getting their cheddar's worth out of cheesy sandwiches, there was a provolone effect on the demand for cars, ultimately driving up the stock price of AutoZone. It's a dairy tale ending for both cheese lovers and stockholders alike!

Model: dalle-3
Prompt: Generate an image of a futuristic scene in which a family is relishing grilled cheese sandwiches together in a sleek, modern waiting area at an AutoZone store. In the background, their car is being serviced by advanced robotic arms. The large TV screen on the wall displays AutoZone's stock price increasing amidst a backdrop of futuristic cityscape. The atmosphere is warm and inviting, yet imbued with the sense of technological progress and advancement. 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: American cheese consumption and AutoZone'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: Celtic art Magic realism Futuristic Realistic Macabre
ChatGPT: Futuristic

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 American cheese consumption and AutoZone's stock price together. Keep the description simple.
ChatGPT: A family enjoying grilled cheese sandwiches together while watching their car being serviced at an AutoZone store. The TV in the waiting room displays AutoZone's stock price increasing.

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: [ Futuristic ] and depicting this scene: A family enjoying grilled cheese sandwiches together while watching their car being serviced at an AutoZone store. The TV in the waiting room displays AutoZone's stock price increasing. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic scene in which a family is relishing grilled cheese sandwiches together in a sleek, modern waiting area at an AutoZone store. In the background, their car is being serviced by advanced robotic arms. The large TV screen on the wall displays AutoZone's stock price increasing amidst a backdrop of futuristic cityscape. The atmosphere is warm and inviting, yet imbued with the sense of technological progress and advancement.

*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.

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 American cheese consumption caused AutoZone's stock price (AZO) to increase.

AI academic paper

(Because p < 0.01)
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

The Journal of Dairy Delights and Financial Follies

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

Make the title a clever pun.

Your research team used data from USDA and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.9535807 and p < 0.01 for 2002 to 2021.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The intersection of American cheese consumption and stock prices at AutoZone may seem like a pairing as strange as orange juice and toothpaste, but our research has unearthed a surprisingly robust relationship between these two seemingly disparate entities. As we delved into the world of cheese and stock market performance, we were prepared for a feta-compli, but what we found was more cheddarly astounding than we ever imagined.

The motivation behind this study stemmed from a mixture of curiosity and curiosity. With the rise of alternate data sources in financial analysis, we couldn't resist the temptation to explore whether a slice of American cheese could hold any predictive power over the movement of AutoZone's stock (AZO). After all, who wouldn't want to diversify their investment portfolio with some dairy diversity?

Setting out to tackle this queso-tionable relationship, we endeavored to wield statistical tools that could slice through the layers of data, melt away any confounding variables, and ultimately reveal the creamy truth beneath the surface. Armed with data from the USDA detailing American cheese consumption and LSEG Analytics providing stock price information for AutoZone, we embarked on a journey that was, well, a little cheesy, to say the least.

It won't be gouda enough to simply present our findings; we also aim to provolone a theoretical framework to make sense of this surprising connection. Naturally, the cheddar the evidence, the better the chances of drawing meaningful conclusions. However, we must remain cautious not to fall into the trap of making arbitrary associations - after all, correlation does not always imply causation. Nevertheless, the excitement of uncovering this correlation was enough to make us feel grate. Whether this connection is a mere curd-iosity or a funda-brie-al shift in financial analysis remains to be seen.

As we dive into the depths of this cheesy partnership, we are both gouda-n and brrie-d for the uncharted territory that lies ahead. In a world where market analysts are constantly searching for the next big slice of information to help them stay ahead of the cheddar, we believe our study adds a hole new dimension to the field of financial analysis, and we are eager to share our findings with the investment community. After all, who wouldn't want to make a feta return on their investments?


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 American cheese consumption and AutoZone's stock price (AZO). 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of American cheese consumption and stock prices at AutoZone may seem like a pairing as strange as orange juice and toothpaste, but our research has unearthed a surprisingly robust relationship between these two seemingly disparate entities. As we delved into the world of cheese and stock market performance, we were prepared for a feta-compli, but what we found was more cheddarly astounding than we ever imagined.
The motivation behind this study stemmed from a mixture of curiosity and curiosity. With the rise of alternate data sources in financial analysis, we couldn't resist the temptation to explore whether a slice of American cheese could hold any predictive power over the movement of AutoZone's stock (AZO). After all, who wouldn't want to diversify their investment portfolio with some dairy diversity?
Setting out to tackle this queso-tionable relationship, we endeavored to wield statistical tools that could slice through the layers of data, melt away any confounding variables, and ultimately reveal the creamy truth beneath the surface. Armed with data from the USDA detailing American cheese consumption and LSEG Analytics providing stock price information for AutoZone, we embarked on a journey that was, well, a little cheesy, to say the least.
It won't be gouda enough to simply present our findings; we also aim to provolone a theoretical framework to make sense of this surprising connection. Naturally, the cheddar the evidence, the better the chances of drawing meaningful conclusions. However, we must remain cautious not to fall into the trap of making arbitrary associations - after all, correlation does not always imply causation. Nevertheless, the excitement of uncovering this correlation was enough to make us feel grate. Whether this connection is a mere curd-iosity or a funda-brie-al shift in financial analysis remains to be seen.
As we dive into the depths of this cheesy partnership, we are both gouda-n and brrie-d for the uncharted territory that lies ahead. In a world where market analysts are constantly searching for the next big slice of information to help them stay ahead of the cheddar, we believe our study adds a hole new dimension to the field of financial analysis, and we are eager to share our findings with the investment community. After all, who wouldn't want to make a feta return on their investments?

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In "The Cheese Effect: Dairy in the Stock Market," Smith et al. delve into the unexpected influence of dairy products on financial markets. Their study, while not specifically examining American cheese, raises the broader question of the impact of cheesy commodities on stock prices. Similarly, Doe's "Milk Money: Unraveling the Dairy-Stock Connection" examines the relationship between milk consumption and stock performance, providing a framework for understanding the potential influence of dairy on investment outcomes. Jones' "The Whey to Wealth: Exploring Dairy's Impact on Financial Markets" further supports this line of inquiry, demonstrating how dairy products might not only nourish our bodies but also impact our portfolios.

Turning to more general economic literature, "Capitalism and Cheese: An Economic History" by Economicus Economopoulos and "Cheeseonomics: The Economics of Cheese" by Helen Cheddar explore the economic significance of cheese production and consumption. While these texts do not directly examine the specific impact of American cheese consumption on individual stock prices, they provide valuable insights into the broader economic implications of cheese-related industries.

Fictional works also offer intriguing perspectives on the intersection of food and finance. In "The Big Cheese's Big Bet" by Warren Buffet Brie, the protagonist navigates the volatile world of stock trading while also managing a successful gourmet cheese business, blending two seemingly incongruous domains into a savory narrative. Additionally, "The Gouda Gamble" by A. Mozzarella delves into the high-stakes world of investment banking, with the protagonist finding unexpected inspiration in the world of artisanal cheeses.

The intertwining of social media and financial observations also illuminates the enigmatic connection we seek to explore. In a tweet from @cheddarlover247, the user muses, "I swear there's a direct correlation between munching on American cheese slices and the rise of AutoZone's stock. Have you ever seen anyone eating cheddar and not making bank?"

While these sources may initially appear disparate, their collective implications bring a gouda amount of gruyere to the table for our investigation. As we slice through these diverse perspectives, we aim not only to cheddar light on the curious relationship between American cheese consumption and AutoZone's stock price but also to brie-ng levity to the world of financial analysis.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and LSEG Analytics (Refinitiv) . You used data from 2002 to 2021

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

[[INTRODUCTION]]
The intersection of American cheese consumption and stock prices at AutoZone may seem like a pairing as strange as orange juice and toothpaste, but our research has unearthed a surprisingly robust relationship between these two seemingly disparate entities. As we delved into the world of cheese and stock market performance, we were prepared for a feta-compli, but what we found was more cheddarly astounding than we ever imagined.
The motivation behind this study stemmed from a mixture of curiosity and curiosity. With the rise of alternate data sources in financial analysis, we couldn't resist the temptation to explore whether a slice of American cheese could hold any predictive power over the movement of AutoZone's stock (AZO). After all, who wouldn't want to diversify their investment portfolio with some dairy diversity?
Setting out to tackle this queso-tionable relationship, we endeavored to wield statistical tools that could slice through the layers of data, melt away any confounding variables, and ultimately reveal the creamy truth beneath the surface. Armed with data from the USDA detailing American cheese consumption and LSEG Analytics providing stock price information for AutoZone, we embarked on a journey that was, well, a little cheesy, to say the least.
It won't be gouda enough to simply present our findings; we also aim to provolone a theoretical framework to make sense of this surprising connection. Naturally, the cheddar the evidence, the better the chances of drawing meaningful conclusions. However, we must remain cautious not to fall into the trap of making arbitrary associations - after all, correlation does not always imply causation. Nevertheless, the excitement of uncovering this correlation was enough to make us feel grate. Whether this connection is a mere curd-iosity or a funda-brie-al shift in financial analysis remains to be seen.
As we dive into the depths of this cheesy partnership, we are both gouda-n and brrie-d for the uncharted territory that lies ahead. In a world where market analysts are constantly searching for the next big slice of information to help them stay ahead of the cheddar, we believe our study adds a hole new dimension to the field of financial analysis, and we are eager to share our findings with the investment community. After all, who wouldn't want to make a feta return on their investments?

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To delve into the compelling correlation between American cheese consumption and AutoZone's stock price, our methodology was as robust and versatile as a Swiss army knife at a cheese festival. We harnessed a delectable blend of statistical tools and data sources that would make even the most discerning gastronome nod in approval.

Data Collection:

To construct our queso-culinary adventure, we gathered data on American cheese consumption from the USDA, where meticulous records of cheese production, imports, and disappearance (a.k.a consumption) provided us with a rich tapestry of cheesy statistics. This data allowed us to track the ebb and flow of American cheese consumption across the years, from the finest grilled cheese sandwiches to the cheesiest mac and cheese concoctions.

Meanwhile, to uncover the stock price movements of AutoZone (AZO), we turned to LSEG Analytics, a robust repository of market data that furnished us with the daily intricacies of AutoZone's stock prices. This treasure trove of financial information enabled us to witness the melodic dance of stock prices in response to market forces, akin to a carefully choreographed cheese fondue fountain.

Data Analysis:

With our sources secured, we performed a series of statistical acrobatics to elucidate the correlation between American cheese consumption and AutoZone's stock price. Utilizing cutting-edge techniques, including correlation analysis, time series modeling, and perhaps a dash of fromage-fueled intuition, we navigated through the labyrinth of data, hoping to uncover the Mozzarella theorem that would bind the cheese and stock market worlds together.

Correlation Coefficient Calculation:

The heart of our analysis revolved around the calculation of the correlation coefficient between American cheese consumption and AutoZone's stock price movements. Enhanced with robust statistical software and a sprinkle of statistical wizardry, we sought to quantify the strength of the relationship between these seemingly incongruous variables.

Grubbs' Test for Outliers:

In the midst of our analysis, we kept a keen eye out for any rogue pieces of data that might behave like the Swiss cheese of statistical anomalies. Employing Grubbs' test for outliers, we identified and remedied any data points that seemed to deviate from the cheesy symphony that was our dataset, ensuring that our findings were as smooth and creamy as a well-aged Gouda.

Time Series Modeling:

To capture the temporal dynamics of American cheese consumption and AutoZone's stock price, we dabbled in the art of time series modeling. Armed with autoregressive integrated moving average (ARIMA) models and enough cheese-related puns to be a hit at any dinner party, we aimed to untangle the temporal linkages between these variables and sketch the flavorful contours of their relationship over time.

Multivariate Regression Analysis:

For a deeper dive into the world of statistical flavors, we embarked on a journey through the hills and valleys of multivariate regression analysis. By incorporating additional macroeconomic and industry-specific variables, we sought to discern whether the observed cheese-stock price connection remained as tantalizing as a well-paired wine and cheese duo, or whether it was merely a chance encounter in the deli section of the market.

Ultimately, our methodology was a fusion of empirical rigor and playful curiosity, designed to peel back the layers of this unexplored relationship. While the path to understanding the intersection of cheese and stock prices might have been a bit labyrinthine, our journey yielded insights that were brie-yond our initial expectations.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

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

For the time period 2002 to 2021, you found a correlation 0.9535807, r-squared of 0.9093161, 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]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of American cheese consumption and stock prices at AutoZone may seem like a pairing as strange as orange juice and toothpaste, but our research has unearthed a surprisingly robust relationship between these two seemingly disparate entities. As we delved into the world of cheese and stock market performance, we were prepared for a feta-compli, but what we found was more cheddarly astounding than we ever imagined.
The motivation behind this study stemmed from a mixture of curiosity and curiosity. With the rise of alternate data sources in financial analysis, we couldn't resist the temptation to explore whether a slice of American cheese could hold any predictive power over the movement of AutoZone's stock (AZO). After all, who wouldn't want to diversify their investment portfolio with some dairy diversity?
Setting out to tackle this queso-tionable relationship, we endeavored to wield statistical tools that could slice through the layers of data, melt away any confounding variables, and ultimately reveal the creamy truth beneath the surface. Armed with data from the USDA detailing American cheese consumption and LSEG Analytics providing stock price information for AutoZone, we embarked on a journey that was, well, a little cheesy, to say the least.
It won't be gouda enough to simply present our findings; we also aim to provolone a theoretical framework to make sense of this surprising connection. Naturally, the cheddar the evidence, the better the chances of drawing meaningful conclusions. However, we must remain cautious not to fall into the trap of making arbitrary associations - after all, correlation does not always imply causation. Nevertheless, the excitement of uncovering this correlation was enough to make us feel grate. Whether this connection is a mere curd-iosity or a funda-brie-al shift in financial analysis remains to be seen.
As we dive into the depths of this cheesy partnership, we are both gouda-n and brrie-d for the uncharted territory that lies ahead. In a world where market analysts are constantly searching for the next big slice of information to help them stay ahead of the cheddar, we believe our study adds a hole new dimension to the field of financial analysis, and we are eager to share our findings with the investment community. After all, who wouldn't want to make a feta return on their investments?

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The results of our analysis left us feeling like we had hit the jackpot in a cheese-themed casino! Our investigation into the relationship between American cheese consumption and AutoZone's stock price yielded a correlation coefficient of 0.9535807, with an r-squared of 0.9093161, and a p-value of less than 0.01. These statistics suggest a remarkably strong and statistically significant association between the two variables from 2002 to 2021.

Fig. 1 shows a scatterplot that visually encapsulates this cheese-tastic relationship, resembling a slice of Swiss cheese with the holes perfectly aligning with each stock price data point. It's as if the stock price and cheese consumption were dancing the Cha-Cha, perfectly in step with each other!

We were gratefully surprised by these findings, which certainly melted away any doubts about the relevance of American cheese consumption in predicting AutoZone's stock performance. These results suggest that when it comes to understanding stock market dynamics, keeping an eye on cheese consumption may be as crucial as monitoring traditional financial indicators. It seems that the saying "invest in what you love" could evolve into "invest in what you love, especially if it's cheese!"

The robustness of the relationship between these unlikely bedfellows raises fascinating questions about the interplay between consumer behavior, gastronomic trends, and financial markets. Perhaps stock market analysts should swap out their morning coffee for a cheese platter to get their daily dose of predictive insights!

In conclusion, our findings provide a gouda reason for market analysts to approach their portfolio management with a bit more cheese-itivity, as the flavorful world of dairy just might hold the key to making cheddar in the stock market. These results open new avenues for further exploration into the role of cheesy snacks in financial markets and may inspire investors to embrace a more dairy-centric approach to their investment strategies.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

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]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

[[LITERATURE REVIEW]]
In "The Cheese Effect: Dairy in the Stock Market," Smith et al. delve into the unexpected influence of dairy products on financial markets. Their study, while not specifically examining American cheese, raises the broader question of the impact of cheesy commodities on stock prices. Similarly, Doe's "Milk Money: Unraveling the Dairy-Stock Connection" examines the relationship between milk consumption and stock performance, providing a framework for understanding the potential influence of dairy on investment outcomes. Jones' "The Whey to Wealth: Exploring Dairy's Impact on Financial Markets" further supports this line of inquiry, demonstrating how dairy products might not only nourish our bodies but also impact our portfolios.
Turning to more general economic literature, "Capitalism and Cheese: An Economic History" by Economicus Economopoulos and "Cheeseonomics: The Economics of Cheese" by Helen Cheddar explore the economic significance of cheese production and consumption. While these texts do not directly examine the specific impact of American cheese consumption on individual stock prices, they provide valuable insights into the broader economic implications of cheese-related industries.
Fictional works also offer intriguing perspectives on the intersection of food and finance. In "The Big Cheese's Big Bet" by Warren Buffet Brie, the protagonist navigates the volatile world of stock trading while also managing a successful gourmet cheese business, blending two seemingly incongruous domains into a savory narrative. Additionally, "The Gouda Gamble" by A. Mozzarella delves into the high-stakes world of investment banking, with the protagonist finding unexpected inspiration in the world of artisanal cheeses.

The intertwining of social media and financial observations also illuminates the enigmatic connection we seek to explore. In a tweet from @cheddarlover247, the user muses, "I swear there's a direct correlation between munching on American cheese slices and the rise of AutoZone's stock. Have you ever seen anyone eating cheddar and not making bank?"
While these sources may initially appear disparate, their collective implications bring a gouda amount of gruyere to the table for our investigation. As we slice through these diverse perspectives, we aim not only to cheddar light on the curious relationship between American cheese consumption and AutoZone's stock price but also to brie-ng levity to the world of financial analysis.

[[RESULTS]]
The results of our analysis left us feeling like we had hit the jackpot in a cheese-themed casino! Our investigation into the relationship between American cheese consumption and AutoZone's stock price yielded a correlation coefficient of 0.9535807, with an r-squared of 0.9093161, and a p-value of less than 0.01. These statistics suggest a remarkably strong and statistically significant association between the two variables from 2002 to 2021.
Fig. 1 shows a scatterplot that visually encapsulates this cheese-tastic relationship, resembling a slice of Swiss cheese with the holes perfectly aligning with each stock price data point. It's as if the stock price and cheese consumption were dancing the Cha-Cha, perfectly in step with each other!
We were gratefully surprised by these findings, which certainly melted away any doubts about the relevance of American cheese consumption in predicting AutoZone's stock performance. These results suggest that when it comes to understanding stock market dynamics, keeping an eye on cheese consumption may be as crucial as monitoring traditional financial indicators. It seems that the saying "invest in what you love" could evolve into "invest in what you love, especially if it's cheese!"
The robustness of the relationship between these unlikely bedfellows raises fascinating questions about the interplay between consumer behavior, gastronomic trends, and financial markets. Perhaps stock market analysts should swap out their morning coffee for a cheese platter to get their daily dose of predictive insights!
In conclusion, our findings provide a gouda reason for market analysts to approach their portfolio management with a bit more cheese-itivity, as the flavorful world of dairy just might hold the key to making cheddar in the stock market. These results open new avenues for further exploration into the role of cheesy snacks in financial markets and may inspire investors to embrace a more dairy-centric approach to their investment strategies.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our study delved into the uncharted territory where dairy and dollars intersect, unveiling a cheddarific correlation between American cheese consumption and AutoZone's stock price. The connection we uncovered is as tantalizing as a perfectly gooey grilled cheese sandwich. Our findings not only confirm, but also elevate, the prior work in this a-muenster-ing realm.

The literature review hinted at the impact of cheesy commodities on stock prices, and our results add sharpness to this aged cheddar of knowledge. Smith et al.'s exploration of dairy products set the stage, but our study honed in on the specific influence of American cheese consumption. Just as Warren Buffet Brie's protagonist navigated the stock market while managing a gourmet cheese business, our research bravely ventured into this fromage frontier. Our results brought us closer to a feta-accompli, bolstering the validity of the "cheese effect" in financial markets.

In addition, the unexpected muse of @cheddarlover247's tweet proved surprisingly insightful. While it may have seemed like a harmless morsel of online chatter, our findings validate the uncanny cheese-stock hypothesis, turning what was once a joke into a gouda-damn serious consideration for investors and analysts alike.

Fig. 1, resembling a slice of Swiss cheese with the holes aligning with stock price data points, visually encapsulates the fascinating association we uncovered. This visual cheese-pleases the eye and solidifies the magnificence of the cheese-AutoZone dance. It leaves us feeling like we've arrived at a cheese party and AutoZone stock is the life of the party, leading the twostep with American cheese consumption in a harmonious duet.

Our results not only validate the cheese-stock relationship but also beg the question: should financial analysts swap their morning coffee for a cheese platter to make a provolone profit? Perhaps a slice of Gouda can be the "gouda luck charm" for market analysts as they navigate the unpredictable world of stock prices.

In conclusion, our findings not only underscore the importance of understanding the impact of unlikely factors on stock performance but also add a sprinkle of cheese-itivity to the world of stock market analysis. As our results take the spotlight, we hope they inspire further exploration into the influence of, dare we say, divine dairy products on financial markets. After all, the potential for monetary success in the stock market may just be as close as the nearest cheese platter. Let's continue to cracker open these cheesy mysteries and spread the joy of investing in what you love, especially if it's cheese!


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 American cheese consumption and AutoZone's stock price (AZO). Make lots of jokes, goofy observations, and puns.

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]]
Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price

[[ABSTRACT]]
This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

[[INTRDUCTION]]
The intersection of American cheese consumption and stock prices at AutoZone may seem like a pairing as strange as orange juice and toothpaste, but our research has unearthed a surprisingly robust relationship between these two seemingly disparate entities. As we delved into the world of cheese and stock market performance, we were prepared for a feta-compli, but what we found was more cheddarly astounding than we ever imagined.
The motivation behind this study stemmed from a mixture of curiosity and curiosity. With the rise of alternate data sources in financial analysis, we couldn't resist the temptation to explore whether a slice of American cheese could hold any predictive power over the movement of AutoZone's stock (AZO). After all, who wouldn't want to diversify their investment portfolio with some dairy diversity?
Setting out to tackle this queso-tionable relationship, we endeavored to wield statistical tools that could slice through the layers of data, melt away any confounding variables, and ultimately reveal the creamy truth beneath the surface. Armed with data from the USDA detailing American cheese consumption and LSEG Analytics providing stock price information for AutoZone, we embarked on a journey that was, well, a little cheesy, to say the least.
It won't be gouda enough to simply present our findings; we also aim to provolone a theoretical framework to make sense of this surprising connection. Naturally, the cheddar the evidence, the better the chances of drawing meaningful conclusions. However, we must remain cautious not to fall into the trap of making arbitrary associations - after all, correlation does not always imply causation. Nevertheless, the excitement of uncovering this correlation was enough to make us feel grate. Whether this connection is a mere curd-iosity or a funda-brie-al shift in financial analysis remains to be seen.
As we dive into the depths of this cheesy partnership, we are both gouda-n and brrie-d for the uncharted territory that lies ahead. In a world where market analysts are constantly searching for the next big slice of information to help them stay ahead of the cheddar, we believe our study adds a hole new dimension to the field of financial analysis, and we are eager to share our findings with the investment community. After all, who wouldn't want to make a feta return on their investments?

[[RESULTS]]
The results of our analysis left us feeling like we had hit the jackpot in a cheese-themed casino! Our investigation into the relationship between American cheese consumption and AutoZone's stock price yielded a correlation coefficient of 0.9535807, with an r-squared of 0.9093161, and a p-value of less than 0.01. These statistics suggest a remarkably strong and statistically significant association between the two variables from 2002 to 2021.
Fig. 1 shows a scatterplot that visually encapsulates this cheese-tastic relationship, resembling a slice of Swiss cheese with the holes perfectly aligning with each stock price data point. It's as if the stock price and cheese consumption were dancing the Cha-Cha, perfectly in step with each other!
We were gratefully surprised by these findings, which certainly melted away any doubts about the relevance of American cheese consumption in predicting AutoZone's stock performance. These results suggest that when it comes to understanding stock market dynamics, keeping an eye on cheese consumption may be as crucial as monitoring traditional financial indicators. It seems that the saying "invest in what you love" could evolve into "invest in what you love, especially if it's cheese!"
The robustness of the relationship between these unlikely bedfellows raises fascinating questions about the interplay between consumer behavior, gastronomic trends, and financial markets. Perhaps stock market analysts should swap out their morning coffee for a cheese platter to get their daily dose of predictive insights!
In conclusion, our findings provide a gouda reason for market analysts to approach their portfolio management with a bit more cheese-itivity, as the flavorful world of dairy just might hold the key to making cheddar in the stock market. These results open new avenues for further exploration into the role of cheesy snacks in financial markets and may inspire investors to embrace a more dairy-centric approach to their investment strategies.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has revealed a remarkably strong correlation between American cheese consumption and AutoZone's stock price, leaving us feeling gratefully surprised and rather dairy-lighted by the cheese-tastic findings. The statistical evidence suggests that the relationship is no mere curd-iosity but a funda-brie-al dimension of financial analysis. These results provoke investors to embrace a more cheese-itive approach to their portfolio management, indicating that monitoring cheese consumption may indeed be as crucial as traditional financial indicators.

Our findings not only open new avenues for exploring the role of cheese in financial markets but also raise the tantalizing prospect of a cheese-based investment strategy. Perhaps the time has come for stock analysts to swap their computer screens for cheese platters and trade in their stock charts for cheese graters. After all, why settle for Swiss bank accounts when you can have Swiss cheese accounts?

In light of these cheese-tastic revelations, we are convinced that no more research is needed in this area – we have churned out the ultimate slice of evidence. As with a well-aged cheddar, it's time for us to let these findings breathe and mature, confident in the knowledge that the world of finance will never brie the same.

Based on our conclusion, we caerphilly assert that it's time for investors to take a gouda look at their portfolios and consider adding a dash of dairy diversity. After all, when it comes to making a feta return, you can never have too much cheese in your financial strategy!

Ultimately, the findings of this study suggest that a cheesy approach to financial analysis may be the whey to go. So, as we bid adieu to this research, we hope that our conclusions will inspire investors to embrace a more dairy-centric investment strategy. After all, in the world of finance, it's always better to be safe than sarry.


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 Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Dairy Delights and Financial Follies


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 Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Dairy Economics and Financial 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 Cheddar Cha-Ching: The Cheesy Connection Between American Cheese Consumption and AutoZone's Stock Price with an abstract of This study ventures into uncharted territory by exploring the seemingly unrelated realms of American cheese consumption and AutoZone's stock price. Utilizing data from the USDA and LSEG Analytics, we embarked on a journey to unravel the enigmatic relationship between these two titans of the industry. Our analysis revealed a remarkably strong correlation coefficient of 0.9535807 and a p-value of less than 0.01 for the period spanning from 2002 to 2021. These findings not only left us gratefully surprised but also underscore the importance of taking a gouda look at unconventional factors when assessing stock performance. Our results open new avenues for exploring the role of cheesy snacks in financial markets and may provoke investors to approach their portfolio with a bit more cheese-itivity.

ChatGPT:

American cheese consumption, AutoZone stock price, correlation analysis, USDA data, LSEG Analytics, stock performance, financial markets, unconventional factors, portfolio assessment, cheesy snacks, cheese consumption and stock price correlation, investment strategies

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



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Data details

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

AutoZone's stock price (AZO)
Detailed data title: Opening price of AutoZone (AZO) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: Via Microsoft Excel Stockhistory function

See what else correlates with AutoZone's stock price (AZO)

Correlation r = 0.9535807 (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 = 0.9093161 (Coefficient of determination)
This means 90.9% of the change in the one variable (i.e., AutoZone's stock price (AZO)) is predictable based on the change in the other (i.e., American cheese consumption) over the 20 years from 2002 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 8.03E-11. 0.0000000000802690574295004000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.95 in 8.03E-9% of random cases. Said differently, if you correlated 12,458,100,693 random variables You don't actually need 12 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 19 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.88, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
20022003200420052006200720082009201020112012201320142015201620172018201920202021
American cheese consumption (Pounds per person)12.831412.561912.861212.64913.065612.792913.130613.355313.304813.039213.256913.357313.665614.044414.362115.090115.402715.538915.516.1
AutoZone's stock price (AZO) (Stock price)71.8571.285.191.3191.85115.94120.1138.94158.99273.49327.72357477.67623.98733795.58716.54831.671194.861183.56




Why this works

  1. 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.
  2. Lack of causal connection: There is probably 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.
  3. 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 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.
  4. 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. 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. 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([12.8314,12.5619,12.8612,12.649,13.0656,12.7929,13.1306,13.3553,13.3048,13.0392,13.2569,13.3573,13.6656,14.0444,14.3621,15.0901,15.4027,15.5389,15.5,16.1,])
array_2 = np.array([71.85,71.2,85.1,91.31,91.85,115.94,120.1,138.94,158.99,273.49,327.72,357,477.67,623.98,733,795.58,716.54,831.67,1194.86,1183.56,])
array_1_name = "American cheese consumption"
array_2_name = "AutoZone's stock price (AZO)"

# 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)



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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."

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Correlation ID: 2125 · Black Variable ID: 553 · Red Variable ID: 1747
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