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Spurious correlation #3,197 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on meats, poultry, fish, and eggs and the second variable is Tractor Supply Company's stock price (TSCO).  The chart goes from 2002 to 2022, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As household spending on meat, poultry, fish, and eggs rose, more people rekindled their love for grilling. This led to a surge in backyard barbecues, causing a sudden spike in demand for all things related to outdoor cooking, including Tractor Supply Company's products. It's like the stock price was on a grill and just couldn't resist the sizzle of profits!

Model: dalle-3
Prompt: [Prompt]: Create a hyper-realistic image of a group of friends and family gathered in the backyard of a charming suburban home, surrounded by lush greenery and a white picket fence. In the center, a sizzling grill emits mouth-watering aromas as various meats, poultry, fish, and eggs cook to perfection. The table, adorned with an assortment of premium ingredients, showcases the increased household spending on these items. In the backdrop, a sturdy garden shed from Tractor Supply Company proudly stands, stocked with outdoor living supplies such as high-quality patio furniture and grilling accessories. The stock price of TSCO rises like a plume of smoke from the grill, reflecting the surge in demand for such products. The scene is bathed in warm, golden sunlight, enhancing the vibrant colors of the gathering and evoking a sense of joy and togetherness. Keep the visual depiction simple and focus on capturing the lively atmosphere and the richness of the surroundings. 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: Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). 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: Steampunk a meme Voxel art Hyper-realistic Art nouveau
ChatGPT: Hyper-realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO), in the following situation: Because as household spending on meats, poultry, fish, and eggs increased, more people started grilling and hosting backyard barbecues. This led to a surge in demand for outdoor living supplies, including items sold by Tractor Supply Company. As a result, TSCO stock price rose like a perfectly cooked steak on a sizzling grill!. Keep the description simple.
ChatGPT: In the backyard of a charming suburban home, a group of friends and family gather around a sizzling grill, enjoying the aroma of barbecue. The table is laden with a variety of meats, poultry, fish, and eggs, showcasing the increased household spending on these items. In the background, a sturdy garden shed from Tractor Supply Company stands proudly, stocked with outdoor living supplies like patio furniture and grilling accessories. The stock price of TSCO rises like a plume of smoke from the grill, reflecting the surge in demand for such 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: [ Hyper-realistic ] and depicting this scene: In the backyard of a charming suburban home, a group of friends and family gather around a sizzling grill, enjoying the aroma of barbecue. The table is laden with a variety of meats, poultry, fish, and eggs, showcasing the increased household spending on these items. In the background, a sturdy garden shed from Tractor Supply Company stands proudly, stocked with outdoor living supplies like patio furniture and grilling accessories. The stock price of TSCO rises like a plume of smoke from the grill, reflecting the surge in demand for such products. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [Prompt]: Create a hyper-realistic image of a group of friends and family gathered in the backyard of a charming suburban home, surrounded by lush greenery and a white picket fence. In the center, a sizzling grill emits mouth-watering aromas as various meats, poultry, fish, and eggs cook to perfection. The table, adorned with an assortment of premium ingredients, showcases the increased household spending on these items. In the backdrop, a sturdy garden shed from Tractor Supply Company proudly stands, stocked with outdoor living supplies such as high-quality patio furniture and grilling accessories. The stock price of TSCO rises like a plume of smoke from the grill, reflecting the surge in demand for such products. The scene is bathed in warm, golden sunlight, enhancing the vibrant colors of the gathering and evoking a sense of joy and togetherness. Keep the visual depiction simple and focus on capturing the lively atmosphere and the richness of the surroundings.

*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 Annual US household spending on meats, poultry, fish, and eggs caused Tractor Supply Company's stock price (TSCO) to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price

Journal of Agricultural Economics and Financial Markets

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 researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title seem clever.

Your research team used data from Bureau of Labor Statistics and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.9292542 and p < 0.01 for 2002 to 2022.

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]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
Introduction

Amidst the hustle and bustle of financial market analyses and consumer behavior studies, a peculiar relationship has quietly simmered beneath the surface – the tantalizing connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). While some might argue that attempting to correlate these seemingly distinct entities is as fruitful as squeezing water from a stone, our inquisitive minds were captivated by the prospect of uncovering unexpected patterns in the world of finance and consumer preferences.

As seasoned observers of market trends and aficionados of puns, we embarked on this quantitatively rigorous pursuit, armed with a data-driven magnifying glass to scrutinize the interplay between carnivorous inclinations and the financial performance of a purveyor of all things "tractorific." Our endeavor sought to unearth more than mere numerical associations; we aimed to provide a meaty insight into a domain where the lines between consumer habits and stock market behavior have been as murky as a rare steak.

In the following sections, we present our detailed exploration, showcasing a substantial correlation coefficient that would make any statistician's heart skip a beat, along with a p-value so low that it would blend in seamlessly with the market's propensity to "p"ounce on significant findings. Join us as we dissect the fabric of this unexpected entanglement, shining a light on the improbable symmetry between backyard barbeques and the rise and fall of TSCO stock.

As we embark on this unconventional odyssey through data, we invite you to don your financial spectacles and saddle up for a journey filled with surprises, chuckles, and the prospect of unearthing investment opportunities that are as rare as a cow in a poultry farm.

Let us commence with our scholarly escapade into the realm of "meating" investments.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
Amidst the hustle and bustle of financial market analyses and consumer behavior studies, a peculiar relationship has quietly simmered beneath the surface – the tantalizing connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). While some might argue that attempting to correlate these seemingly distinct entities is as fruitful as squeezing water from a stone, our inquisitive minds were captivated by the prospect of uncovering unexpected patterns in the world of finance and consumer preferences.
As seasoned observers of market trends and aficionados of puns, we embarked on this quantitatively rigorous pursuit, armed with a data-driven magnifying glass to scrutinize the interplay between carnivorous inclinations and the financial performance of a purveyor of all things "tractorific." Our endeavor sought to unearth more than mere numerical associations; we aimed to provide a meaty insight into a domain where the lines between consumer habits and stock market behavior have been as murky as a rare steak.
In the following sections, we present our detailed exploration, showcasing a substantial correlation coefficient that would make any statistician's heart skip a beat, along with a p-value so low that it would blend in seamlessly with the market's propensity to "p"ounce on significant findings. Join us as we dissect the fabric of this unexpected entanglement, shining a light on the improbable symmetry between backyard barbeques and the rise and fall of TSCO stock.
As we embark on this unconventional odyssey through data, we invite you to don your financial spectacles and saddle up for a journey filled with surprises, chuckles, and the prospect of unearthing investment opportunities that are as rare as a cow in a poultry farm.
Let us commence with our scholarly escapade into the realm of "meating" investments.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The literature surrounding the intriguing intersection of consumer behavior and stock market dynamics has been an area of substantial academic interest. Smith et al. (2015) conducted a comprehensive analysis of household spending patterns and their impact on market trends, illuminating the complexities of consumer preferences and financial outcomes. In their study, they deftly navigated through the labyrinth of economic data, uncovering correlations that extended beyond the conventional wisdom of market analysis.

Doe and Jones (2018) further delved into the intricacies of consumer purchasing power and its ripple effects on the stock performance of retail companies. Their meticulous examination articulated the subtle nuances of consumer sentiment and its influence on investment decisions, painting a vivid tapestry of the intricate dance between the marketplace and the dining table.

Building upon this foundation, Lorem and Ipsum (2020) delved into the specific realm of meat and poultry consumption, unearthing deeper connections between carnivorous inclinations and financial outcomes. Their work shed light on the profound impact of meat-related expenditures on the stock market, compelling readers to contemplate the unforeseen implications of dietary choices on investment portfolios.

In the realm of non-fiction literature, works such as "The Omnivore's Dilemma" by Michael Pollan and "Eating Animals" by Jonathan Safran Foer have offered thought-provoking insights into the cultural, ethical, and environmental dimensions of meat consumption. These seminal works have shaped societal discourses on dietary habits and ecological considerations, stirring a pot of contemplation around the interconnectedness of food choices and broader societal trends.

Turning to the realm of fiction, literary gems like "The Jungle" by Upton Sinclair and "Animal Farm" by George Orwell have masterfully woven narratives around agricultural themes, lending a lens to the multifaceted dynamics of food production and societal structures. While these works may not provide direct empirical evidence of the relationship between meat consumption and stock prices, their thematic resonance with agricultural ecosystems offers a whimsical backdrop for contextualizing our research endeavor.

Noteworthy social media posts have also contributed to our understanding of the underlying currents in consumer behavior. A Twitter user humorously quipped, "Investing in steak dinners seems to have a rare medium well done effect on my portfolio. #MeatYourInvestments," encapsulating a blend of culinary wit and financial acumen in a succinct 240-character package. Similarly, a Reddit thread titled "Eggcellent Investments: Cracking the Code on Poultry Spending and Stock Gains" provided a platform for lighthearted banter coupled with astute observations on the interplay between household expenditures and market performances.

As we navigate through this eclectic tapestry of literature and popular discourse, we are reminded of the rich tapestry of human experiences that underpin the enigmatic relationship between consumer choices and financial markets. The convergence of empirical studies, literary narratives, and online musings beckons us to embrace a multidimensional perspective as we unravel the intriguing fabric of "meating" investments.



Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and LSEG Analytics (Refinitiv) . You used data from 2002 to 2022

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

[[INTRODUCTION]]
Introduction
Amidst the hustle and bustle of financial market analyses and consumer behavior studies, a peculiar relationship has quietly simmered beneath the surface – the tantalizing connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). While some might argue that attempting to correlate these seemingly distinct entities is as fruitful as squeezing water from a stone, our inquisitive minds were captivated by the prospect of uncovering unexpected patterns in the world of finance and consumer preferences.
As seasoned observers of market trends and aficionados of puns, we embarked on this quantitatively rigorous pursuit, armed with a data-driven magnifying glass to scrutinize the interplay between carnivorous inclinations and the financial performance of a purveyor of all things "tractorific." Our endeavor sought to unearth more than mere numerical associations; we aimed to provide a meaty insight into a domain where the lines between consumer habits and stock market behavior have been as murky as a rare steak.
In the following sections, we present our detailed exploration, showcasing a substantial correlation coefficient that would make any statistician's heart skip a beat, along with a p-value so low that it would blend in seamlessly with the market's propensity to "p"ounce on significant findings. Join us as we dissect the fabric of this unexpected entanglement, shining a light on the improbable symmetry between backyard barbeques and the rise and fall of TSCO stock.
As we embark on this unconventional odyssey through data, we invite you to don your financial spectacles and saddle up for a journey filled with surprises, chuckles, and the prospect of unearthing investment opportunities that are as rare as a cow in a poultry farm.
Let us commence with our scholarly escapade into the realm of "meating" investments.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

Sample Selection:
To begin our carnivorous crusade, we harnessed the data prowess of the Bureau of Labor Statistics and LSEG Analytics (Refinitiv) to obtain comprehensive and delectable details on annual US household spending on meats, poultry, fish, and eggs. We then scoured the historical records of Tractor Supply Company's (TSCO) stock prices, extracting every nugget of information from the intricate labyrinth of financial databases spanning the years 2002 to 2022. Our sampling approach was as meticulous as handpicking the juiciest cut at a butcher's shop, ensuring that our dataset was as robust and succulent as it could be.

Data Analysis:
Armed with spreadsheets laden with numbers that would make even the most die-hard mathematician halt for a moment of awe, we set forth on a statistical odyssey through the realms of correlation analysis. Employing sophisticated statistical software, we embarked on a veritable journey of number crunching, watching with bated breath as the numbers danced before our very eyes, revealing the intricate web of relationships between consumer spending on animal products and the ebb and flow of TSCO stock prices.

Correlation Coefficient Calculation:
In our pursuit of numerical understandings, we calculated the Pearson correlation coefficient, aiming to quantify the strength and direction of the relationship between household spending on meats, poultry, fish, and eggs, and the performance of TSCO stock. The resulting coefficient, akin to the perfect sizzle of a well-seasoned steak on a summer grill, revealed a strikingly high degree of correlation, prompting gasps of astonishment from our team and perhaps a gentle nod of approval from the financial gods.

Regression Analysis:
Intrigued by the depth of this seemingly meaty association, we dived even further into the statistical cauldron, subjecting our data to the rigors of regression analysis. With each iteration of the regression model, we prodded and probed the data, inching closer to unraveling the enigmatic link between carnivorous spending patterns and the stock price gyrations of TSCO. The regression analysis fortified our comprehension of this cryptic relationship, laying bare the predictive potential lurking within the labyrinthine paths of meat-filled shopping carts and stock market valuations.

Statistical Significance Testing:
On the precipice of statistical revelation, we engaged in a series of significance tests, teasing out every nuanced signal from the midst of statistical noise. The calculated p-value, as eminent as a beef Wellington at a gala dinner, affirmed the significance of the relationship, beckoning investors and researchers alike to contemplate the unforeseen implications of carnivorous consumer habits on financial market dynamics.

Robustness Checks:
With an eye for meticulousness rivaled only by the scrutinizing gaze of a discerning butcher, we subjected our findings to a battery of robustness checks. Sensitivity analyses, alternate specifications, and diagnostic tests were conducted with unwavering focus, ensuring that our findings remained as savory and robust as a perfectly smoked brisket at a backyard barbecue.

Ethical Considerations:
In our relentless pursuit of illuminating the symbiotic dance between meat-centric consumption patterns and stock market dynamics, we maintained an unwavering commitment to ethical data utilization. Our analysis adhered to the highest standards of data privacy and integrity, ensuring that the information harvested for our study was treated with the utmost respect and sensitivity.

In conclusion, our methodology was an intricate pas de deux between data acquisition, statistical wizardry, and unyielding curiosity, leading us on a journey through the captivating nexus of carnivorous consumer habits and the enigmatic undulations of Tractor Supply Company's stock price. With our robust approach and unbridled enthusiasm, we set the stage for an in-depth exploration of this unexpected and tantalizing entanglement.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

For the time period 2002 to 2022, you found a correlation 0.9292542, r-squared of 0.8635134, 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]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Introduction
Amidst the hustle and bustle of financial market analyses and consumer behavior studies, a peculiar relationship has quietly simmered beneath the surface – the tantalizing connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). While some might argue that attempting to correlate these seemingly distinct entities is as fruitful as squeezing water from a stone, our inquisitive minds were captivated by the prospect of uncovering unexpected patterns in the world of finance and consumer preferences.
As seasoned observers of market trends and aficionados of puns, we embarked on this quantitatively rigorous pursuit, armed with a data-driven magnifying glass to scrutinize the interplay between carnivorous inclinations and the financial performance of a purveyor of all things "tractorific." Our endeavor sought to unearth more than mere numerical associations; we aimed to provide a meaty insight into a domain where the lines between consumer habits and stock market behavior have been as murky as a rare steak.
In the following sections, we present our detailed exploration, showcasing a substantial correlation coefficient that would make any statistician's heart skip a beat, along with a p-value so low that it would blend in seamlessly with the market's propensity to "p"ounce on significant findings. Join us as we dissect the fabric of this unexpected entanglement, shining a light on the improbable symmetry between backyard barbeques and the rise and fall of TSCO stock.
As we embark on this unconventional odyssey through data, we invite you to don your financial spectacles and saddle up for a journey filled with surprises, chuckles, and the prospect of unearthing investment opportunities that are as rare as a cow in a poultry farm.
Let us commence with our scholarly escapade into the realm of "meating" investments.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

A rigorous analysis of the data collected between 2002 and 2022 has uncovered a striking correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). The correlation coefficient of 0.9292542 indicates a notably strong positive relationship between these variables. Additionally, the r-squared value of 0.8635134 suggests that a substantial proportion of the variability in TSCO stock price can be explained by changes in household spending on animal products.

The statistical significance of this relationship is further accentuated by the p-value of less than 0.01. In other words, the likelihood of observing such a strong association between carnivorous culinary preferences and TSCO stock performance by mere chance is as rare as finding a vegetarian at a sausage festival.

Furthermore, our findings are encapsulated in Fig. 1, a scatterplot that vividly portrays the robust correlation between annual US household spending on animal products and the stock price of TSCO. This graphical representation serves as a visual testament to the meaty nature of this unexpected financial association, compelling even the most skeptical observers to ponder the extent of its implications.

In summary, our investigation has unearthed a compelling correlation between the consumption patterns of carnivorous delights and the performance of Tractor Supply Company in the stock market. These findings not only enrich our understanding of the intricate interplay between consumer behavior and financial markets but also offer the investment community a prime opportunity to savor the rewards of "meating" their investment aspirations.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

[[LITERATURE REVIEW]]
The literature surrounding the intriguing intersection of consumer behavior and stock market dynamics has been an area of substantial academic interest. Smith et al. (2015) conducted a comprehensive analysis of household spending patterns and their impact on market trends, illuminating the complexities of consumer preferences and financial outcomes. In their study, they deftly navigated through the labyrinth of economic data, uncovering correlations that extended beyond the conventional wisdom of market analysis.
Doe and Jones (2018) further delved into the intricacies of consumer purchasing power and its ripple effects on the stock performance of retail companies. Their meticulous examination articulated the subtle nuances of consumer sentiment and its influence on investment decisions, painting a vivid tapestry of the intricate dance between the marketplace and the dining table.
Building upon this foundation, Lorem and Ipsum (2020) delved into the specific realm of meat and poultry consumption, unearthing deeper connections between carnivorous inclinations and financial outcomes. Their work shed light on the profound impact of meat-related expenditures on the stock market, compelling readers to contemplate the unforeseen implications of dietary choices on investment portfolios.
In the realm of non-fiction literature, works such as "The Omnivore's Dilemma" by Michael Pollan and "Eating Animals" by Jonathan Safran Foer have offered thought-provoking insights into the cultural, ethical, and environmental dimensions of meat consumption. These seminal works have shaped societal discourses on dietary habits and ecological considerations, stirring a pot of contemplation around the interconnectedness of food choices and broader societal trends.
Turning to the realm of fiction, literary gems like "The Jungle" by Upton Sinclair and "Animal Farm" by George Orwell have masterfully woven narratives around agricultural themes, lending a lens to the multifaceted dynamics of food production and societal structures. While these works may not provide direct empirical evidence of the relationship between meat consumption and stock prices, their thematic resonance with agricultural ecosystems offers a whimsical backdrop for contextualizing our research endeavor.
Noteworthy social media posts have also contributed to our understanding of the underlying currents in consumer behavior. A Twitter user humorously quipped, "Investing in steak dinners seems to have a rare medium well done effect on my portfolio. #MeatYourInvestments," encapsulating a blend of culinary wit and financial acumen in a succinct 240-character package. Similarly, a Reddit thread titled "Eggcellent Investments: Cracking the Code on Poultry Spending and Stock Gains" provided a platform for lighthearted banter coupled with astute observations on the interplay between household expenditures and market performances.
As we navigate through this eclectic tapestry of literature and popular discourse, we are reminded of the rich tapestry of human experiences that underpin the enigmatic relationship between consumer choices and financial markets. The convergence of empirical studies, literary narratives, and online musings beckons us to embrace a multidimensional perspective as we unravel the intriguing fabric of "meating" investments.


[[RESULTS]]
A rigorous analysis of the data collected between 2002 and 2022 has uncovered a striking correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). The correlation coefficient of 0.9292542 indicates a notably strong positive relationship between these variables. Additionally, the r-squared value of 0.8635134 suggests that a substantial proportion of the variability in TSCO stock price can be explained by changes in household spending on animal products.
The statistical significance of this relationship is further accentuated by the p-value of less than 0.01. In other words, the likelihood of observing such a strong association between carnivorous culinary preferences and TSCO stock performance by mere chance is as rare as finding a vegetarian at a sausage festival.
Furthermore, our findings are encapsulated in Fig. 1, a scatterplot that vividly portrays the robust correlation between annual US household spending on animal products and the stock price of TSCO. This graphical representation serves as a visual testament to the meaty nature of this unexpected financial association, compelling even the most skeptical observers to ponder the extent of its implications.
In summary, our investigation has unearthed a compelling correlation between the consumption patterns of carnivorous delights and the performance of Tractor Supply Company in the stock market. These findings not only enrich our understanding of the intricate interplay between consumer behavior and financial markets but also offer the investment community a prime opportunity to savor the rewards of "meating" their investment aspirations.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The results of our investigation have brought to light a connection between the demand for meat, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO) that is as clear as a sunny-side-up egg. Our findings align closely with previous research, notably the work of Lorem and Ipsum (2020), who adeptly sliced through the complexities of animal product consumption and its impact on financial outcomes. It appears that the carnivorous cravings of consumers exert a significant influence on the performance of agricultural retail giants, much like the gravitational pull of a meaty feast draws in hungry diners.

The strong positive correlation we discovered underscores the profound influence of household spending on TSCO stock prices and reinforces the work of Smith et al. (2015), who skillfully navigated the labyrinth of consumer preferences and market trends, emerging with an understanding that goes beyond the conventional wisdom of market analysis. Our results, ripe with statistical significance, echo the sentiments of a Twitter user who humorously mused about the rare medium well-done effect of steak dinners on their portfolio, capturing the essence of how culinary choices can leave their sizzling mark on financial ventures.

It is apparent that the narrative woven by previous research and literary works has interwoven with our findings, creating a whimsical backdrop that accentuates the multifaceted nature of this "meaty" financial relationship. The thematic resonance with agricultural ecosystems, as showcased in literary gems such as "The Jungle" by Upton Sinclair, adds a layer of depth to our understanding of how consumer choices intertwine with the dynamics of financial markets, akin to seasoning enhancing the flavor of a well-crafted dish.

Furthermore, our results have significant implications for investors seeking to tenderize their portfolios for success. The visually compelling scatterplot encapsulates the meaty nature of this financial association, akin to a succulent dish that convinces even the most skeptical observers to savor its robust flavors. As we reflect on our findings, it becomes evident that a deeper understanding of consumer dietary patterns can indeed provide a recipe for financial success, allowing investors to sink their teeth into the enticing opportunities presented by this unexpected correlation.

In unraveling the intricate fabric of consumer choices and financial markets, our study not only enriches the academic discourse but also offers a tantalizing opportunity for investors to relish the rewards of "meating" their investment aspirations. As we savor the implications of this "meaty" financial relationship, it becomes clear that the dinner table and the stock market may share a closer bond than previously thought, serving a reminder that in the world of investments, one must be prepared to tackle both the bull and the bear – and perhaps the steer as well.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Annual US household spending on meats, poultry, fish, and eggs and Tractor Supply Company's stock price (TSCO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price"

[[ABSTRACT]]
In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

[[INTRDUCTION]]
Introduction
Amidst the hustle and bustle of financial market analyses and consumer behavior studies, a peculiar relationship has quietly simmered beneath the surface – the tantalizing connection between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). While some might argue that attempting to correlate these seemingly distinct entities is as fruitful as squeezing water from a stone, our inquisitive minds were captivated by the prospect of uncovering unexpected patterns in the world of finance and consumer preferences.
As seasoned observers of market trends and aficionados of puns, we embarked on this quantitatively rigorous pursuit, armed with a data-driven magnifying glass to scrutinize the interplay between carnivorous inclinations and the financial performance of a purveyor of all things "tractorific." Our endeavor sought to unearth more than mere numerical associations; we aimed to provide a meaty insight into a domain where the lines between consumer habits and stock market behavior have been as murky as a rare steak.
In the following sections, we present our detailed exploration, showcasing a substantial correlation coefficient that would make any statistician's heart skip a beat, along with a p-value so low that it would blend in seamlessly with the market's propensity to "p"ounce on significant findings. Join us as we dissect the fabric of this unexpected entanglement, shining a light on the improbable symmetry between backyard barbeques and the rise and fall of TSCO stock.
As we embark on this unconventional odyssey through data, we invite you to don your financial spectacles and saddle up for a journey filled with surprises, chuckles, and the prospect of unearthing investment opportunities that are as rare as a cow in a poultry farm.
Let us commence with our scholarly escapade into the realm of "meating" investments.

[[RESULTS]]
A rigorous analysis of the data collected between 2002 and 2022 has uncovered a striking correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). The correlation coefficient of 0.9292542 indicates a notably strong positive relationship between these variables. Additionally, the r-squared value of 0.8635134 suggests that a substantial proportion of the variability in TSCO stock price can be explained by changes in household spending on animal products.
The statistical significance of this relationship is further accentuated by the p-value of less than 0.01. In other words, the likelihood of observing such a strong association between carnivorous culinary preferences and TSCO stock performance by mere chance is as rare as finding a vegetarian at a sausage festival.
Furthermore, our findings are encapsulated in Fig. 1, a scatterplot that vividly portrays the robust correlation between annual US household spending on animal products and the stock price of TSCO. This graphical representation serves as a visual testament to the meaty nature of this unexpected financial association, compelling even the most skeptical observers to ponder the extent of its implications.
In summary, our investigation has unearthed a compelling correlation between the consumption patterns of carnivorous delights and the performance of Tractor Supply Company in the stock market. These findings not only enrich our understanding of the intricate interplay between consumer behavior and financial markets but also offer the investment community a prime opportunity to savor the rewards of "meating" their investment aspirations.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our research has boldly ventured into the realm where consumer spending on meats, poultry, fish, and eggs intersects with the stock price of Tractor Supply Company (TSCO). The substantial correlation coefficient and the statistically significant p-value unearthed in our analysis have undoubtedly provided a meaty insight into this uncharted territory of financial analysis and culinary commerce. Our findings not only reinforce the enthralling nature of this unexpected relationship but also tantalize investors with the potential to capitalize on meat-centric market insights.

As we wrap up our exploration, it's clear that the "beefy" correlation between carnivorous consumption patterns and TSCO's stock performance is no mere "chicken feed." Indeed, the implications of our findings are as substantial as a Sunday roast, offering investors an opportunity to sink their teeth into a potentially lucrative avenue for investment. Our investigation has skilfully carved a niche in the landscape of financial analysis, blending the flavors of consumer behavior and stock market performance with a dash of unconventional pairing.

In the grand buffet of research on financial markets, our study has certainly served up a juicy finding that is as savory as a well-marinated steak. With such robust results in hand, it's safe to say that further research in this area would be akin to beating a dead horse – unnecessary and unpalatable. It seems that, for now, our findings offer a full plate of insights, leaving little room for further probing into this particular intersection of carnivorous inclinations and stock market trajectories. It's time to savor the meat of our labor and allow our findings to marinate in the annals of financial discovery.

In the words of the esteemed philosopher Socrates, "The unexamined life is not worth living," but in the case of our research, we can confidently assert that the thoroughly examined meat is well worth investing in.


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 Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agricultural Economics and Financial Markets


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 Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Sustainable Consumption Research


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 Meat Your Investments: A Quantitative Study of the Relationship Between Household Spending on Animal Products and Tractor Supply Company's Stock Price with an abstract of In this paper, we delve into the intriguing world of consumer spending habits and financial markets by investigating the correlation between annual US household spending on meats, poultry, fish, and eggs and the stock price of Tractor Supply Company (TSCO). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), we conducted a meticulous analysis spanning the years 2002 to 2022. Our findings reveal a remarkably high correlation coefficient of 0.9292542 and a statistically significant level of p < 0.01, shedding light on the unexpected, yet compelling relationship between carnivorous consumption patterns and the performance of a renowned agricultural supply retailer in the stock market. We explore the implications of these findings and discuss the potential for investors to "meat" their financial goals through a deeper understanding of this intriguing association.

ChatGPT:

household spending, animal products, meat consumption, poultry consumption, fish consumption, egg consumption, Tractor Supply Company, TSCO stock price, consumer spending habits, financial markets, correlation study, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, stock market performance, carnivorous consumption patterns, investment implications, quantitative study

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



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Data details

Annual US household spending on meats, poultry, fish, and eggs
Detailed data title: Average annual household spend on meats, poultry, fish, and eggs
Source: Bureau of Labor Statistics
See what else correlates with Annual US household spending on meats, poultry, fish, and eggs

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

See what else correlates with Tractor Supply Company's stock price (TSCO)

Correlation r = 0.9292542 (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 = 0.8635134 (Coefficient of determination)
This means 86.4% of the change in the one variable (i.e., Tractor Supply Company's stock price (TSCO)) is predictable based on the change in the other (i.e., Annual US household spending on meats, poultry, fish, and eggs) over the 21 years from 2002 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.2E-9. 0.0000000011718127363041675000
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 0.93 in 1.2E-7% of random cases. Said differently, if you correlated 853,378,675 random variables You don't actually need 853 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 20 degrees of freedom, 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.

[ 0.83, 0.97 ] 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.
200220032004200520062007200820092010201120122013201420152016201720182019202020212022
Annual US household spending on meats, poultry, fish, and eggs (Household spend)798825880764797777846841784832852856892896890944961980107511151216
Tractor Supply Company's stock price (TSCO) (Stock price)2.144.769.749.3813.3411.248.929.2213.324.5435.4245.4177.678.883.576.975.3581.8193.69140.9238.35




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([798,825,880,764,797,777,846,841,784,832,852,856,892,896,890,944,961,980,1075,1115,1216,])
array_2 = np.array([2.14,4.76,9.74,9.38,13.34,11.24,8.92,9.22,13.3,24.54,35.42,45.41,77.6,78.8,83.5,76.9,75.35,81.81,93.69,140.9,238.35,])
array_1_name = "Annual US household spending on meats, poultry, fish, and eggs"
array_2_name = "Tractor Supply Company's stock price (TSCO)"

# 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."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

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Correlation ID: 3197 · Black Variable ID: 19888 · Red Variable ID: 1886
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