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Spurious correlation #3,045 · 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 Microsoft's stock price (MSFT).  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 and poultry went up, more people were inspired to host backyard barbecues. This led to a surge in demand for grills, and coincidentally, Microsoft's Xbox division saw a boost in sales of their popular game 'Cooking Mama: Grill Off Challenge'. This unexpected windfall in the gaming sector impressed investors, causing an uptick in Microsoft's stock price.

Model: dalle-3
Prompt: Generate an image that replicates the scenic feel of a Pixar movie. The scene should depict a lively neighborhood with families engaging in various activities at a meat and poultry market. People are seen picking out fresh cuts and eggs, showcasing a sense of community and connectivity. Simultaneously, a digital billboard above the market displays the soaring stock price of Microsoft (MSFT), creating an air of excitement as investors anticipate the success of the new "CluckSoft" product. The scene should capture the warmth and energy of the bustling neighborhood, evoking the whimsical charm typical of a Pixar movie. 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 Microsoft's stock price (MSFT). 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: Surrealism Realistic Data cyber art Voxel art An image that looks like it is a scene from a Pixar movie
ChatGPT: An image that looks like it is a scene from a Pixar movie

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 Microsoft's stock price (MSFT), in the following situation: As households spent more on meat, Microsoft introduced "CluckSoft," a new line of poultry-based software. Investors flocked to the stock, expecting it to "egg-scel" in the market. This caused a rise in Microsoft's stock price.. Keep the description simple.
ChatGPT: In a bustling neighborhood, families are seen at a local meat and poultry market, picking out fresh cuts and eggs for their meals. At the same time, on a digital billboard above the market, the stock price of Microsoft (MSFT) is soaring upwards, as excited investors envision the new "CluckSoft" product taking off.

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: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: In a bustling neighborhood, families are seen at a local meat and poultry market, picking out fresh cuts and eggs for their meals. At the same time, on a digital billboard above the market, the stock price of Microsoft (MSFT) is soaring upwards, as excited investors envision the new "CluckSoft" product taking off. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that replicates the scenic feel of a Pixar movie. The scene should depict a lively neighborhood with families engaging in various activities at a meat and poultry market. People are seen picking out fresh cuts and eggs, showcasing a sense of community and connectivity. Simultaneously, a digital billboard above the market displays the soaring stock price of Microsoft (MSFT), creating an air of excitement as investors anticipate the success of the new "CluckSoft" product. The scene should capture the warmth and energy of the bustling neighborhood, evoking the whimsical charm typical of a Pixar movie.

*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 Microsoft's stock price (MSFT) to increase.

AI academic paper

(Because p < 0.01)
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

The Journal of Gastronomical Economics

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 dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You 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 Microsoft's stock price (MSFT). Make lots of 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 0.9359866 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 the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.


Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the 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 Microsoft's stock price (MSFT). Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Here is the title and abstract of the paper:
[[TITLE]]
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

Consumer spending patterns have long been of interest to economists and analysts seeking to understand the intricacies of market behaviors and their impact on the broader economy. From necessities to luxuries, the choices made by households can provide a glimpse into the underlying trends and dynamics that drive stock prices and overall market performance. In this context, the seemingly unrelated domains of annual US household spending on meats, poultry, fish, and eggs, and the stock price of Microsoft (MSFT) have piqued our curiosity. The notion that the protein on our plates could influence the performance of a tech giant may initially seem as far-fetched as a vegetarian at a barbecue convention, yet the correlations that have emerged from our analysis suggest otherwise.

The study of consumer spending on meat and its relationship to the stock price of Microsoft has not been explored extensively in academic research. However, as we pondered the daily rituals of American households – the sizzle of a steak on the grill, the gentle poaching of a fillet of fish, and the clucking of a freshly laid egg – we couldn't help but wonder if these culinary choices held implications beyond the dining table. Have we stumbled upon a new form of financial forecasting, one based not on algorithms and market trends, but on the contents of the average American refrigerator?

The aim of our research is to shine a spotlight on this unexpected connection and uncover the potential underlying mechanisms driving this correlation. We delve into the available data to understand how changes in annual household spending on protein-rich foods may echo through the stock market, potentially influencing the behemoth that is Microsoft. As we embark on this journey, we are reminded of the words of Fran Lebowitz: "When you leave New York, you are astonished at how clean the rest of the world is. Clean is not enough." In a similar vein, simply observing the correlation is not enough; we aim to dissect and analyze the data to unveil the hidden layers of this unconventional relationship.

Our investigation is guided by the idea that the choices made by consumers at the supermarket are more than mere dietary decisions; they may hold implications for financial markets and investor sentiment. With this in mind, we meticulously gather and analyze the data to bring forth a clearer understanding of the influence of household spending on meats, poultry, fish, and eggs on the stock price of Microsoft. As we embrace the concept of "meat the market," we invite our readers to embark on this journey of discovery. Let us press on, with the determination of a chef perfecting a signature dish, as we explore the potential ramifications of what lies beneath the surface of this unlikely correlation.

Stay tuned for a meaty revelation in the following sections.


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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). 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 you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Consumer spending patterns have long been of interest to economists and analysts seeking to understand the intricacies of market behaviors and their impact on the broader economy. From necessities to luxuries, the choices made by households can provide a glimpse into the underlying trends and dynamics that drive stock prices and overall market performance. In this context, the seemingly unrelated domains of annual US household spending on meats, poultry, fish, and eggs, and the stock price of Microsoft (MSFT) have piqued our curiosity. The notion that the protein on our plates could influence the performance of a tech giant may initially seem as far-fetched as a vegetarian at a barbecue convention, yet the correlations that have emerged from our analysis suggest otherwise.
The study of consumer spending on meat and its relationship to the stock price of Microsoft has not been explored extensively in academic research. However, as we pondered the daily rituals of American households – the sizzle of a steak on the grill, the gentle poaching of a fillet of fish, and the clucking of a freshly laid egg – we couldn't help but wonder if these culinary choices held implications beyond the dining table. Have we stumbled upon a new form of financial forecasting, one based not on algorithms and market trends, but on the contents of the average American refrigerator?
The aim of our research is to shine a spotlight on this unexpected connection and uncover the potential underlying mechanisms driving this correlation. We delve into the available data to understand how changes in annual household spending on protein-rich foods may echo through the stock market, potentially influencing the behemoth that is Microsoft. As we embark on this journey, we are reminded of the words of Fran Lebowitz: "When you leave New York, you are astonished at how clean the rest of the world is. Clean is not enough." In a similar vein, simply observing the correlation is not enough; we aim to dissect and analyze the data to unveil the hidden layers of this unconventional relationship.
Our investigation is guided by the idea that the choices made by consumers at the supermarket are more than mere dietary decisions; they may hold implications for financial markets and investor sentiment. With this in mind, we meticulously gather and analyze the data to bring forth a clearer understanding of the influence of household spending on meats, poultry, fish, and eggs on the stock price of Microsoft. As we embrace the concept of "meat the market," we invite our readers to embark on this journey of discovery. Let us press on, with the determination of a chef perfecting a signature dish, as we explore the potential ramifications of what lies beneath the surface of this unlikely correlation.
Stay tuned for a meaty revelation in the following sections.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In "Meatonomics," the authors find that the American obsession with meat has significant economic impacts, from healthcare costs to environmental degradation. Meanwhile, Doe and Smith examine the intricate link between consumer spending on meats and its influence on dietary habits and nutritional patterns in "Carnivorous Consumption." These serious studies lay the groundwork for our investigation into the peculiar relationship between annual US household spending on meats, poultry, fish, and eggs and the stock price of Microsoft (MSFT).

Turning to non-fiction works, "The Omnivore's Dilemma" by Michael Pollan presents a thought-provoking analysis of the modern American diet, shedding light on the cultural and economic factors that shape consumer choices in the meat aisle. In a similar vein, "Eating Animals" by Jonathan Safran Foer delves into the ethical, environmental, and financial implications of meat consumption. Though not directly related to stock prices, these texts offer valuable context for our exploration of the meat-market connection.

On the fictional front, "The Grapes of Wrath" illuminates the intersection of poverty and sustenance, drawing poignant parallels between the hardship of the Great Depression era and the dietary decisions of struggling households. Additionally, in "Cloudy with a Chance of Meatballs," the whimsical tale of food falling from the sky raises questions about abundance and its unexpected consequences, albeit in a fantastical setting. While these works may seem tangential to our subject matter, they serve as a reminder that the interplay between consumption, sustenance, and economic well-being has long captivated the literary imagination.

Venturing into the realm of children's entertainment, the animated television series "Bob's Burgers" humorously navigates the dynamics of a family-run restaurant, offering a lighthearted perspective on the culinary world. Furthermore, "Paw Patrol" introduces young audiences to the concept of community service, underscoring the importance of teamwork and problem-solving, albeit in the context of a team of helpful canines. These seemingly unrelated shows, though entertaining, highlight the omnipresence of economic themes in popular culture, reminding us that even the most unexpected sources can offer valuable insights into consumer behavior and financial dynamics.

As we transition from the solemnity of academic literature to the delightful tangents of fiction and televised entertainment, we set the stage for a whimsical exploration of the uncharted connection between household spending on protein and the stock price of Microsoft. As we slice into the meat of our analysis, brace yourselves for a nourishing blend of scholarly inquiry and unexpected mirth.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

[[INTRODUCTION]]
Consumer spending patterns have long been of interest to economists and analysts seeking to understand the intricacies of market behaviors and their impact on the broader economy. From necessities to luxuries, the choices made by households can provide a glimpse into the underlying trends and dynamics that drive stock prices and overall market performance. In this context, the seemingly unrelated domains of annual US household spending on meats, poultry, fish, and eggs, and the stock price of Microsoft (MSFT) have piqued our curiosity. The notion that the protein on our plates could influence the performance of a tech giant may initially seem as far-fetched as a vegetarian at a barbecue convention, yet the correlations that have emerged from our analysis suggest otherwise.
The study of consumer spending on meat and its relationship to the stock price of Microsoft has not been explored extensively in academic research. However, as we pondered the daily rituals of American households – the sizzle of a steak on the grill, the gentle poaching of a fillet of fish, and the clucking of a freshly laid egg – we couldn't help but wonder if these culinary choices held implications beyond the dining table. Have we stumbled upon a new form of financial forecasting, one based not on algorithms and market trends, but on the contents of the average American refrigerator?
The aim of our research is to shine a spotlight on this unexpected connection and uncover the potential underlying mechanisms driving this correlation. We delve into the available data to understand how changes in annual household spending on protein-rich foods may echo through the stock market, potentially influencing the behemoth that is Microsoft. As we embark on this journey, we are reminded of the words of Fran Lebowitz: "When you leave New York, you are astonished at how clean the rest of the world is. Clean is not enough." In a similar vein, simply observing the correlation is not enough; we aim to dissect and analyze the data to unveil the hidden layers of this unconventional relationship.
Our investigation is guided by the idea that the choices made by consumers at the supermarket are more than mere dietary decisions; they may hold implications for financial markets and investor sentiment. With this in mind, we meticulously gather and analyze the data to bring forth a clearer understanding of the influence of household spending on meats, poultry, fish, and eggs on the stock price of Microsoft. As we embrace the concept of "meat the market," we invite our readers to embark on this journey of discovery. Let us press on, with the determination of a chef perfecting a signature dish, as we explore the potential ramifications of what lies beneath the surface of this unlikely correlation.
Stay tuned for a meaty revelation in the following sections.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To uncover the hidden connections between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT), our research employed a blend of traditional econometric methods and a dash of unorthodox data analysis. The data used in this study were primarily sourced from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), covering the extensive timeframe from 2002 to 2022. This mixture of data sources allowed us to marinate our analysis with a rich array of information, ensuring a comprehensive understanding of the relationship between consumer spending on protein and the stock performance of Microsoft.

First, to gauge the magnitude of annual US household spending on meats, poultry, fish, and eggs, we utilized the consumer expenditure data provided by the Bureau of Labor Statistics. This allowed us to slice through the intricate details of household spending patterns and discern the portion dedicated to protein-rich foods. Our research team spared no effort in tenderizing this data, meticulously examining the monthly and annual expenditure trends to capture the nuances of consumer preferences and their financial allocations to protein-based sustenance.

Complementing this, we utilized the stock price data of Microsoft (MSFT) from LSEG Analytics (Refinitiv) to sear through the market performance of this tech giant. Our approach involved carefully crafting a time series analysis, allowing us to capture the fluctuations and seasonings of Microsoft's stock price over the years. This multifaceted approach enabled us to season our analysis with a comprehensive understanding of market dynamics, ensuring that our examination of the correlation between consumer spending on protein and Microsoft's stock price was as flavorful and robust as possible.

Upon amassing the ingredients of our analysis, we seasoned our approach with econometric techniques, including time series analysis and regression modeling. Through these methods, we sought to tenderize the data and distill the essence of the relationship between annual US household spending on protein and Microsoft's stock price. Our research team embraced an array of statistical tools, allowing us to grill, roast, and sauté our datasets to reveal the underlying patterns and interconnections, enhancing the palatability of our findings.

In addition, to corroborate our findings and ensure the reliability of our analysis, we conducted a sensitivity analysis and applied rigorous statistical tests. This process allowed us to assess the robustness of our results and ascertain the validity of the correlations observed. In line with the principles of academic inquiry, our approach underwent a rigorous peer review, akin to the discerning scrutiny of a seasoned sommelier evaluating a fine wine.

In summarizing the methodology employed in our research, our approach can be likened to the meticulous preparation of a gourmet meal, where the selection of high-quality ingredients, the application of refined techniques, and the artful presentation culminate in a delightful sensory experience. With these methods in mind, we embarked on our investigation, aiming to serve up a tantalizing yet substantial examination of the unlikely relationship between annual US household spending on protein and the stock price of Microsoft. The subsequent sections of this paper will present the delectable findings arising from our comprehensive methodology.


Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the 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 Microsoft's stock price (MSFT). Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

For the time period 2002 to 2022, you found a correlation 0.9359866, r-squared of 0.8760709, 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 the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Consumer spending patterns have long been of interest to economists and analysts seeking to understand the intricacies of market behaviors and their impact on the broader economy. From necessities to luxuries, the choices made by households can provide a glimpse into the underlying trends and dynamics that drive stock prices and overall market performance. In this context, the seemingly unrelated domains of annual US household spending on meats, poultry, fish, and eggs, and the stock price of Microsoft (MSFT) have piqued our curiosity. The notion that the protein on our plates could influence the performance of a tech giant may initially seem as far-fetched as a vegetarian at a barbecue convention, yet the correlations that have emerged from our analysis suggest otherwise.
The study of consumer spending on meat and its relationship to the stock price of Microsoft has not been explored extensively in academic research. However, as we pondered the daily rituals of American households – the sizzle of a steak on the grill, the gentle poaching of a fillet of fish, and the clucking of a freshly laid egg – we couldn't help but wonder if these culinary choices held implications beyond the dining table. Have we stumbled upon a new form of financial forecasting, one based not on algorithms and market trends, but on the contents of the average American refrigerator?
The aim of our research is to shine a spotlight on this unexpected connection and uncover the potential underlying mechanisms driving this correlation. We delve into the available data to understand how changes in annual household spending on protein-rich foods may echo through the stock market, potentially influencing the behemoth that is Microsoft. As we embark on this journey, we are reminded of the words of Fran Lebowitz: "When you leave New York, you are astonished at how clean the rest of the world is. Clean is not enough." In a similar vein, simply observing the correlation is not enough; we aim to dissect and analyze the data to unveil the hidden layers of this unconventional relationship.
Our investigation is guided by the idea that the choices made by consumers at the supermarket are more than mere dietary decisions; they may hold implications for financial markets and investor sentiment. With this in mind, we meticulously gather and analyze the data to bring forth a clearer understanding of the influence of household spending on meats, poultry, fish, and eggs on the stock price of Microsoft. As we embrace the concept of "meat the market," we invite our readers to embark on this journey of discovery. Let us press on, with the determination of a chef perfecting a signature dish, as we explore the potential ramifications of what lies beneath the surface of this unlikely correlation.
Stay tuned for a meaty revelation in the following sections.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The results of our analysis unveiled a robust correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT) over the period of 2002 to 2022. The correlation coefficient of 0.9359866 indicates a remarkably strong positive relationship between these seemingly distinct variables. This correlation was further supported by an r-squared value of 0.8760709, suggesting that approximately 87.61% of the variance in Microsoft's stock price can be explained by changes in annual household spending on protein-rich foods. The p-value of less than 0.01 indicates the statistical significance of this correlation, providing compelling evidence of the unexpected link between meaty expenditures and tech stock performance.

Figure 1 illustrates the striking correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). As the saying goes, "the proof is in the pudding," or in this case, in the scatterplot. The convergence of data points on the plot visually substantiates the strength of the relationship, leaving little room for skepticism. It is truly a sight to behold, akin to witnessing a synchronized swim team gracefully navigate the waters of statistical significance.

The implications of these findings are not to be taken lightly. The hearty connection between household spending on protein and the performance of a tech giant such as Microsoft may seem as improbable as finding vegetarian options at a carnivorous convention. However, our data reveal a correlation that cannot be dismissed with a mere shrug of the shoulders. It invites consideration of the potential influence of dietary preferences on financial markets, presenting a feast of unconventional insights for researchers and investors alike.

In conclusion, the unearthing of such a substantial correlation calls for further exploration and contemplation. It prompts us to reevaluate the traditional boundaries of market analysis and consider the menu of factors that may impact stock performance. Indeed, this unexpected link between the contents of American refrigerators and the movement of tech stocks may hold the key to unlocking a new dimension of financial forecasting. As we digest the implications of these findings, it is evident that the world of market analysis is rife with surprises, much like stumbling upon a filet mignon in a tofu aisle.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

[[LITERATURE REVIEW]]
In "Meatonomics," the authors find that the American obsession with meat has significant economic impacts, from healthcare costs to environmental degradation. Meanwhile, Doe and Smith examine the intricate link between consumer spending on meats and its influence on dietary habits and nutritional patterns in "Carnivorous Consumption." These serious studies lay the groundwork for our investigation into the peculiar relationship between annual US household spending on meats, poultry, fish, and eggs and the stock price of Microsoft (MSFT).
Turning to non-fiction works, "The Omnivore's Dilemma" by Michael Pollan presents a thought-provoking analysis of the modern American diet, shedding light on the cultural and economic factors that shape consumer choices in the meat aisle. In a similar vein, "Eating Animals" by Jonathan Safran Foer delves into the ethical, environmental, and financial implications of meat consumption. Though not directly related to stock prices, these texts offer valuable context for our exploration of the meat-market connection.
On the fictional front, "The Grapes of Wrath" illuminates the intersection of poverty and sustenance, drawing poignant parallels between the hardship of the Great Depression era and the dietary decisions of struggling households. Additionally, in "Cloudy with a Chance of Meatballs," the whimsical tale of food falling from the sky raises questions about abundance and its unexpected consequences, albeit in a fantastical setting. While these works may seem tangential to our subject matter, they serve as a reminder that the interplay between consumption, sustenance, and economic well-being has long captivated the literary imagination.
Venturing into the realm of children's entertainment, the animated television series "Bob's Burgers" humorously navigates the dynamics of a family-run restaurant, offering a lighthearted perspective on the culinary world. Furthermore, "Paw Patrol" introduces young audiences to the concept of community service, underscoring the importance of teamwork and problem-solving, albeit in the context of a team of helpful canines. These seemingly unrelated shows, though entertaining, highlight the omnipresence of economic themes in popular culture, reminding us that even the most unexpected sources can offer valuable insights into consumer behavior and financial dynamics.
As we transition from the solemnity of academic literature to the delightful tangents of fiction and televised entertainment, we set the stage for a whimsical exploration of the uncharted connection between household spending on protein and the stock price of Microsoft. As we slice into the meat of our analysis, brace yourselves for a nourishing blend of scholarly inquiry and unexpected mirth.

[[RESULTS]]
The results of our analysis unveiled a robust correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT) over the period of 2002 to 2022. The correlation coefficient of 0.9359866 indicates a remarkably strong positive relationship between these seemingly distinct variables. This correlation was further supported by an r-squared value of 0.8760709, suggesting that approximately 87.61% of the variance in Microsoft's stock price can be explained by changes in annual household spending on protein-rich foods. The p-value of less than 0.01 indicates the statistical significance of this correlation, providing compelling evidence of the unexpected link between meaty expenditures and tech stock performance.
Figure 1 illustrates the striking correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). As the saying goes, "the proof is in the pudding," or in this case, in the scatterplot. The convergence of data points on the plot visually substantiates the strength of the relationship, leaving little room for skepticism. It is truly a sight to behold, akin to witnessing a synchronized swim team gracefully navigate the waters of statistical significance.
The implications of these findings are not to be taken lightly. The hearty connection between household spending on protein and the performance of a tech giant such as Microsoft may seem as improbable as finding vegetarian options at a carnivorous convention. However, our data reveal a correlation that cannot be dismissed with a mere shrug of the shoulders. It invites consideration of the potential influence of dietary preferences on financial markets, presenting a feast of unconventional insights for researchers and investors alike.
In conclusion, the unearthing of such a substantial correlation calls for further exploration and contemplation. It prompts us to reevaluate the traditional boundaries of market analysis and consider the menu of factors that may impact stock performance. Indeed, this unexpected link between the contents of American refrigerators and the movement of tech stocks may hold the key to unlocking a new dimension of financial forecasting. As we digest the implications of these findings, it is evident that the world of market analysis is rife with surprises, much like stumbling upon a filet mignon in a tofu aisle.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The robust correlation uncovered in this study between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT) from 2002 to 2022 provides compelling evidence of a surprising relationship that is not simply a flash in the pan but rather a sustained association with substantial explanatory power. These findings align with prior research that has delved into the economic, cultural, and ethical dimensions of meat consumption and its broader impacts.

Building on the work of "Meatonomics," which meticulously dissected the economic ramifications of meat consumption, and the thought-provoking examination by Doe and Smith on consumer spending and dietary habits, our study emphasizes the nuanced influence of consumer behavior on financial markets. The correlation coefficient of 0.9359866 presents a striking confirmation of the valuable insights provided by these earlier investigations. In a sense, our findings serve as the sizzle to the steak of prior research, reinforcing the significance of meat expenditures as a critical economic variable.

Moreover, our results underscore the relevance of "The Omnivore's Dilemma" and "Eating Animals," as they offer a broader context for understanding the cultural and environmental factors intertwined with dietary choices. While these works may not have directly addressed stock prices, they have primed our understanding of the multifaceted implications of meat consumption, paving the way for uncovering its unforeseen link to market dynamics. In this sense, our study pays homage to the depth and breadth of these literary explorations by transcending the boundaries of traditional economic analysis.

Likewise, the seemingly tangential references to "The Grapes of Wrath" and "Cloudy with a Chance of Meatballs" offer a reminder of the unexpected parallels that can be drawn between historical narratives and fantastical tales and the empirical realities of market behavior. In many ways, our findings bring to the fore the unanticipated connections that underlie consumer behavior and financial performance, echoing the thematic depth of these diverse literary works.

As we carve through the data and grill up some unexpected insights, it becomes evident that our study not only validates but also emboldens the peculiar yet substantial connection between meat expenditures and stock prices. It is akin to uncovering a hidden gem amidst a sea of data – an unanticipated yet valuable discovery that challenges conventional wisdom and promises to enrich the discourse on market dynamics.

Our study, in its whimsical yet rigorous exploration, echoes the sentiment that when it comes to understanding the intricate fabric of market forces, one may stumble upon unexpected correlations that are as delectable as a well-seasoned steak.


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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Meat the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price

[[ABSTRACT]]
This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

[[INTRDUCTION]]
Consumer spending patterns have long been of interest to economists and analysts seeking to understand the intricacies of market behaviors and their impact on the broader economy. From necessities to luxuries, the choices made by households can provide a glimpse into the underlying trends and dynamics that drive stock prices and overall market performance. In this context, the seemingly unrelated domains of annual US household spending on meats, poultry, fish, and eggs, and the stock price of Microsoft (MSFT) have piqued our curiosity. The notion that the protein on our plates could influence the performance of a tech giant may initially seem as far-fetched as a vegetarian at a barbecue convention, yet the correlations that have emerged from our analysis suggest otherwise.
The study of consumer spending on meat and its relationship to the stock price of Microsoft has not been explored extensively in academic research. However, as we pondered the daily rituals of American households – the sizzle of a steak on the grill, the gentle poaching of a fillet of fish, and the clucking of a freshly laid egg – we couldn't help but wonder if these culinary choices held implications beyond the dining table. Have we stumbled upon a new form of financial forecasting, one based not on algorithms and market trends, but on the contents of the average American refrigerator?
The aim of our research is to shine a spotlight on this unexpected connection and uncover the potential underlying mechanisms driving this correlation. We delve into the available data to understand how changes in annual household spending on protein-rich foods may echo through the stock market, potentially influencing the behemoth that is Microsoft. As we embark on this journey, we are reminded of the words of Fran Lebowitz: "When you leave New York, you are astonished at how clean the rest of the world is. Clean is not enough." In a similar vein, simply observing the correlation is not enough; we aim to dissect and analyze the data to unveil the hidden layers of this unconventional relationship.
Our investigation is guided by the idea that the choices made by consumers at the supermarket are more than mere dietary decisions; they may hold implications for financial markets and investor sentiment. With this in mind, we meticulously gather and analyze the data to bring forth a clearer understanding of the influence of household spending on meats, poultry, fish, and eggs on the stock price of Microsoft. As we embrace the concept of "meat the market," we invite our readers to embark on this journey of discovery. Let us press on, with the determination of a chef perfecting a signature dish, as we explore the potential ramifications of what lies beneath the surface of this unlikely correlation.
Stay tuned for a meaty revelation in the following sections.

[[RESULTS]]
The results of our analysis unveiled a robust correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT) over the period of 2002 to 2022. The correlation coefficient of 0.9359866 indicates a remarkably strong positive relationship between these seemingly distinct variables. This correlation was further supported by an r-squared value of 0.8760709, suggesting that approximately 87.61% of the variance in Microsoft's stock price can be explained by changes in annual household spending on protein-rich foods. The p-value of less than 0.01 indicates the statistical significance of this correlation, providing compelling evidence of the unexpected link between meaty expenditures and tech stock performance.
Figure 1 illustrates the striking correlation between annual US household spending on meats, poultry, fish, and eggs and Microsoft's stock price (MSFT). As the saying goes, "the proof is in the pudding," or in this case, in the scatterplot. The convergence of data points on the plot visually substantiates the strength of the relationship, leaving little room for skepticism. It is truly a sight to behold, akin to witnessing a synchronized swim team gracefully navigate the waters of statistical significance.
The implications of these findings are not to be taken lightly. The hearty connection between household spending on protein and the performance of a tech giant such as Microsoft may seem as improbable as finding vegetarian options at a carnivorous convention. However, our data reveal a correlation that cannot be dismissed with a mere shrug of the shoulders. It invites consideration of the potential influence of dietary preferences on financial markets, presenting a feast of unconventional insights for researchers and investors alike.
In conclusion, the unearthing of such a substantial correlation calls for further exploration and contemplation. It prompts us to reevaluate the traditional boundaries of market analysis and consider the menu of factors that may impact stock performance. Indeed, this unexpected link between the contents of American refrigerators and the movement of tech stocks may hold the key to unlocking a new dimension of financial forecasting. As we digest the implications of these findings, it is evident that the world of market analysis is rife with surprises, much like stumbling upon a filet mignon in a tofu aisle.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, the beefy analysis of annual US household spending on meats, poultry, fish, and eggs has revealed a surprisingly robust correlation with Microsoft's stock price (MSFT). This correlation is as strong as a well-marinated steak and statistically significant, leaving little room for doubt. The sight of the data points converging on the scatterplot is as satisfying as a perfectly cooked fillet.

The implications of this unlikely connection are not to be overlooked – they are as substantial as a Thanksgiving turkey. It prompts us to reconsider the traditional boundaries of market analysis and contemplate the myriad factors that may influence stock performance. The idea that dietary preferences could have an impact on financial markets is indeed a rare and unexpected dish on the academic research menu.

The unearthing of such a substantial correlation implies that further research into the influence of household spending on protein-rich foods on stock performance may not be necessary. It appears we've carved out all the meaty insights this topic has to offer. So, in the words of a renowned philosopher, "That's a wrap!" This correlation is a well-done discovery that adds a surprising flavor to the world of financial forecasting. It seems we've truly 'meat' the market in more ways than one.


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 the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Gastronomical Economics


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 the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The International Association of Carnivorous Consumption Studies (IACCS)


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 the Market: A Beefy Analysis of Annual US Household Spending on Protein and its Impact on Microsoft's Stock Price with an abstract of This research delves into the meaty world of annual US household spending on meats, poultry, fish, and eggs, and its unlikely connection to the behemoth that is Microsoft's stock price (MSFT). Leveraging data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our study provides a rare glimpse into the correlation between these two seemingly disparate entities. Our findings reveal a robust correlation coefficient of 0.9359866, with a p-value of less than 0.01, spanning the years 2002 to 2022. The implications of such a strong correlation are not to be minced – could there be a secret recipe for financial success hidden within the kitchen of America's households? Join us as we carve through the data and grill up some unexpected insights.

ChatGPT:

meat spending, poultry spending, fish spending, eggs spending, US household spending, annual spending, Microsoft stock price, MSFT stock, correlation coefficient, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, financial success, household kitchen, data analysis

*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

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

See what else correlates with Microsoft's stock price (MSFT)

Correlation r = 0.9359866 (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 = 0.8760709 (Coefficient of determination)
This means 87.6% of the change in the one variable (i.e., Microsoft's stock price (MSFT)) 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 4.7E-10. 0.0000000004654206439807705500
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 0.94 in 4.7E-8% of random cases. Said differently, if you correlated 2,148,593,993 random variables You don't actually need 2 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

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

[ 0.85, 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
Microsoft's stock price (MSFT) (Stock price)33.3326.1527.5826.826.2529.9135.7919.5330.6228.0526.5527.2537.3546.6654.3262.7986.1399.55158.78222.53335.35




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([33.33,26.15,27.58,26.8,26.25,29.91,35.79,19.53,30.62,28.05,26.55,27.25,37.35,46.66,54.32,62.79,86.13,99.55,158.78,222.53,335.35,])
array_1_name = "Annual US household spending on meats, poultry, fish, and eggs"
array_2_name = "Microsoft's stock price (MSFT)"

# 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: 3045 · Black Variable ID: 19888 · Red Variable ID: 1541
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