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Spurious correlation #3,641 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air quality in Pittsburgh and the second variable is Walmart's stock price (WMT).  The chart goes from 2002 to 2023, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The cleaner air in Pittsburgh led to people feeling more energetic and eager to go shopping at Walmart, boosting the company's sales and ultimately their stock price. It's amazing what a breath of fresh air can do for the retail industry!

Model: dalle-3
Prompt: Prompt: Generate an image of a bustling Walmart store with the Pittsburgh skyline visible through the windows. Show the sun shining brightly and people walking briskly along the streets. The electronic ticker displays the rising stock price for Walmart (WMT) in the background. The scene should capture the positive energy and invigorating atmosphere of the city and the store. 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: Air quality in Pittsburgh and Walmart's stock price (WMT). Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: a funny image macro Low poly Psychedelic inspired by bauhaus Scalar art
ChatGPT: a funny image macro

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 Air quality in Pittsburgh and Walmart's stock price (WMT), in the following situation: As air quality improved in Pittsburgh, more people were able to breathe easier and thus had the energy to go shopping at Walmart, leading to increased sales and a boost in the company's stock price.. Keep the description simple.
ChatGPT: The sun shines brightly over Pittsburgh as the city skyline is visible through the clear, fresh air. People walk briskly along the streets, breathing easier and feeling invigorated. Meanwhile, the Walmart store bustles with activity, as more and more customers enter the doors, carrying shopping bags filled with purchases. The stock price for Walmart (WMT) steadily rises on the electronic ticker, reflecting the increased sales and positive energy in the air.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ a funny image macro ] and depicting this scene: The sun shines brightly over Pittsburgh as the city skyline is visible through the clear, fresh air. People walk briskly along the streets, breathing easier and feeling invigorated. Meanwhile, the Walmart store bustles with activity, as more and more customers enter the doors, carrying shopping bags filled with purchases. The stock price for Walmart (WMT) steadily rises on the electronic ticker, reflecting the increased sales and positive energy in the air. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image of a bustling Walmart store with the Pittsburgh skyline visible through the windows. Show the sun shining brightly and people walking briskly along the streets. The electronic ticker displays the rising stock price for Walmart (WMT) in the background. The scene should capture the positive energy and invigorating atmosphere of the city and the store.

*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 Air quality in Pittsburgh caused Walmart's stock price (WMT) to increase.

AI academic paper

(Because p < 0.01)
Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)

Journal of Environmental Economics and Retail Dynamics

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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Include a pun in the title.

Your research team used data from Environmental Protection Agency and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.9194964 and p < 0.01 for 2002 to 2023.

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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

The intricate interplay between environmental factors and financial markets has long been a subject of interest and inquiry. Despite the seeming disconnect between the natural world and the tumultuous domain of stocks and assets, empirical evidence continues to uncover surprising connections that challenge conventional wisdom. Our research embarks on a journey to unravel one such enigma, delving into the realm of air quality in Pittsburgh and its purported influence on the stock price of Walmart (WMT).

Pittsburgh, a city renowned for its industrial heritage and historical significance, provides a compelling backdrop for this investigation. Known as the "Steel City," Pittsburgh has undergone significant environmental transformations over the years, transitioning from a flourishing hub of heavy industry to a modern metropolis with a burgeoning emphasis on sustainability. As the city grapples with the complexities of environmental regulation and economic development, the implications of air quality on corporate performances take center stage.

Walmart, a behemoth in the realm of retail, stands as a beacon of market dynamics, its stock price serving as a barometer of economic sentiment and consumer behavior. As we embark on this odyssey of data analysis and statistical scrutiny, the prevailing question emerges: could the ephemeral whispers of particulate matter and ozone in the air of Pittsburgh hold sway over the nuanced fluctuations of Walmart's stock price?

The confluence of these seemingly disparate entities, air quality and stock market valor, invites a measure of skepticism and curiosity. Nonetheless, the initial scintillating findings from our research endeavor urge a closer examination of this uncanny association. Our inquiry transcends the confines of traditional financial analysis, traversing into the realm of atmospheric science and environmental economics, where unsuspected correlations await their revelation.

As we paint the canvas of our study, against the backdrop of smog and financial tickers, the revelations that unfurl before us are poised to recalibrate perceptions of the intricate dance between environmental indicators and market performances. Join us on this cerebral escapade, as we unpack the paradoxical link between the air we breathe and the stocks we trade.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). 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 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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The intricate interplay between environmental factors and financial markets has long been a subject of interest and inquiry. Despite the seeming disconnect between the natural world and the tumultuous domain of stocks and assets, empirical evidence continues to uncover surprising connections that challenge conventional wisdom. Our research embarks on a journey to unravel one such enigma, delving into the realm of air quality in Pittsburgh and its purported influence on the stock price of Walmart (WMT).
Pittsburgh, a city renowned for its industrial heritage and historical significance, provides a compelling backdrop for this investigation. Known as the "Steel City," Pittsburgh has undergone significant environmental transformations over the years, transitioning from a flourishing hub of heavy industry to a modern metropolis with a burgeoning emphasis on sustainability. As the city grapples with the complexities of environmental regulation and economic development, the implications of air quality on corporate performances take center stage.
Walmart, a behemoth in the realm of retail, stands as a beacon of market dynamics, its stock price serving as a barometer of economic sentiment and consumer behavior. As we embark on this odyssey of data analysis and statistical scrutiny, the prevailing question emerges: could the ephemeral whispers of particulate matter and ozone in the air of Pittsburgh hold sway over the nuanced fluctuations of Walmart's stock price?
The confluence of these seemingly disparate entities, air quality and stock market valor, invites a measure of skepticism and curiosity. Nonetheless, the initial scintillating findings from our research endeavor urge a closer examination of this uncanny association. Our inquiry transcends the confines of traditional financial analysis, traversing into the realm of atmospheric science and environmental economics, where unsuspected correlations await their revelation.
As we paint the canvas of our study, against the backdrop of smog and financial tickers, the revelations that unfurl before us are poised to recalibrate perceptions of the intricate dance between environmental indicators and market performances. Join us on this cerebral escapade, as we unpack the paradoxical link between the air we breathe and the stocks we trade.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In their study, Smith and Doe (2015) assert that the interplay between environmental factors and financial markets is a topic of increasing relevance. Their findings underscore the importance of considering the multifaceted impact of environmental indicators on market dynamics. Similarly, Jones et al. (2018) delve into the complexities of environmental regulation and economic development, shedding light on the implications of air quality on corporate performances.

Expanding beyond the realm of academic research, several non-fiction books offer valuable insights into the intersection of environmental factors and financial markets. In "The Economics of Air Quality" by Green and Clean (2017), the authors dive into the economic ramifications of air quality management, offering a comprehensive perspective on the potential repercussions on businesses and stock performance.

Furthermore, "Sustainability in the 21st Century" by Eco Guru (2019) explores the evolving landscape of sustainable business practices and its influence on financial markets, providing a contextual backdrop for the correlation between environmental indicators and stock valuations.

Transitioning to fictional literature, "The Polluted Portfolio" by Penny Stockman (2005) presents a satirical take on the unforeseen impacts of air pollution on investment portfolios, weaving a whimsical narrative that ponders the absurdities of environmental influences on stock prices.

In a sheer departure from conventional sources, the animated series "Captain Planet and the Planeteers" and its titular protagonist's eco-conscious endeavors offer a lighthearted lens through which to contemplate the intertwining of environmental stewardship and economic dynamics. Meanwhile, episodes of "SpongeBob SquarePants" intriguingly delve into the undersea economics and environmental conditions of Bikini Bottom, subtly prodding at the parallels between fictional ecosystems and real-world market performances.

This eclectic amalgamation of sources sets the stage for a literature review that transcends the boundaries of traditional scholarly discourse, enriching the understanding of the vibrant interconnections between air quality in Pittsburgh and the stock price of Walmart (WMT).

As we embark on this intellectual voyage, the juxtaposition of empirical studies, non-fiction literature, and playful fiction serves as a testament to the multifaceted layers within which this investigation unfolds.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Environmental Protection Agency and LSEG Analytics (Refinitiv) . You used data from 2002 to 2023

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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

[[INTRODUCTION]]
INTRODUCTION
The intricate interplay between environmental factors and financial markets has long been a subject of interest and inquiry. Despite the seeming disconnect between the natural world and the tumultuous domain of stocks and assets, empirical evidence continues to uncover surprising connections that challenge conventional wisdom. Our research embarks on a journey to unravel one such enigma, delving into the realm of air quality in Pittsburgh and its purported influence on the stock price of Walmart (WMT).
Pittsburgh, a city renowned for its industrial heritage and historical significance, provides a compelling backdrop for this investigation. Known as the "Steel City," Pittsburgh has undergone significant environmental transformations over the years, transitioning from a flourishing hub of heavy industry to a modern metropolis with a burgeoning emphasis on sustainability. As the city grapples with the complexities of environmental regulation and economic development, the implications of air quality on corporate performances take center stage.
Walmart, a behemoth in the realm of retail, stands as a beacon of market dynamics, its stock price serving as a barometer of economic sentiment and consumer behavior. As we embark on this odyssey of data analysis and statistical scrutiny, the prevailing question emerges: could the ephemeral whispers of particulate matter and ozone in the air of Pittsburgh hold sway over the nuanced fluctuations of Walmart's stock price?
The confluence of these seemingly disparate entities, air quality and stock market valor, invites a measure of skepticism and curiosity. Nonetheless, the initial scintillating findings from our research endeavor urge a closer examination of this uncanny association. Our inquiry transcends the confines of traditional financial analysis, traversing into the realm of atmospheric science and environmental economics, where unsuspected correlations await their revelation.
As we paint the canvas of our study, against the backdrop of smog and financial tickers, the revelations that unfurl before us are poised to recalibrate perceptions of the intricate dance between environmental indicators and market performances. Join us on this cerebral escapade, as we unpack the paradoxical link between the air we breathe and the stocks we trade.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

METHODOLOGY

Data Acquisition:
The data for this study was gathered with the tenacity of a determined squirrel seeking its hidden acorns, meticulously collecting a wealth of information from the Environmental Protection Agency (EPA) and LSEG Analytics (Refinitiv). Our intrepid research team scoured through copious datasets spanning the years 2002 to 2023, sifting through the digital haystack in pursuit of the elusive needle of correlation between air quality in Pittsburgh and Walmart's stock price (WMT).

Air Quality Measurements:
The air quality measurements were procured from the EPA, encapsulating a spectrum of pollutants including particulate matter, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. This veritable feast of atmospheric data was then subjected to rigorous scrutiny, as each datum was inspected with the scrutiny of a hawk eyeing its prey. We focused on Pittsburgh as our primary arena of inquiry, drawn to its rich atmospheric tapestry that intertwines urban hustle with environmental dynamism.

Stock Price Data:
On the financial front, the daily stock price data of Walmart (WMT) was obtained from the ever-churning crucible of market data provided by LSEG Analytics (Refinitiv). The labyrinthine tides of stock prices were charted, observed, and meticulously logged, resembling the work of diligent cartographers mapping uncharted seas of financial fluctuation.

Statistical Analysis:
Employing the formidable arsenal of statistical techniques, we harnessed the power of correlation analysis to unravel the hidden threads weaving through our data tapestry. The stalwart Pearson correlation coefficient was brandished as our trusty sword in this quest, slicing through the layers of complexity to reveal the underlying relationship between air quality and Walmart's stock price. Our statistical maneuvers were executed with a finesse rivaling that of a virtuoso pianist, drawing out the harmonious melodies of data association amidst the discordant cacophony of variables.

Cross-Validation and Sensitivity Analysis:
To fortify the robustness of our findings, we subjected our results to a rigorous gauntlet of cross-validation and sensitivity analysis, akin to stress-testing the structural integrity of a newly-built bridge. This rigorous exercise sought to ascertain the resilience of our findings against perturbations and deviations, ensuring that our discovered correlations stood sturdy in the face of statistical turbulence.

Control Measures:
Not content with merely scratching the surface, we delved into the annals of control measures and covariate adjustments, safeguarding against the lurking specter of confounding variables. By fortifying our analytical edifice with these meticulous details, we sought to erect a bulwark against the whims of statistical misdirection, allowing our findings to stand tall with the unyielding certainty of stone fortifications.

Ethical Considerations:
As upholders of ethical conduct in research, we maintained a vigilant stance, ensuring that our data collection adhered to the principles of data privacy and integrity. The sanctity of our data was safeguarded with the vigilance of a guardian dragon, protecting the integrity of our findings from the marauding hands of data manipulation and malfeasance.

Conclusion:
Through the employment of these unyielding methodological frameworks, our research endeavors to unfurl the enigmatic tapestry of interconnections between atmospheric intricacies and Wall Street gyrations. The journey ahead promises to be one rife with revelations and discoveries, akin to embarking on a captivating odyssey through the windswept realms of environmental influence on market dynamics.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Environmental Protection Agency and LSEG Analytics (Refinitiv) .

For the time period 2002 to 2023, you found a correlation 0.9194964, r-squared of 0.8454736, 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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
The intricate interplay between environmental factors and financial markets has long been a subject of interest and inquiry. Despite the seeming disconnect between the natural world and the tumultuous domain of stocks and assets, empirical evidence continues to uncover surprising connections that challenge conventional wisdom. Our research embarks on a journey to unravel one such enigma, delving into the realm of air quality in Pittsburgh and its purported influence on the stock price of Walmart (WMT).
Pittsburgh, a city renowned for its industrial heritage and historical significance, provides a compelling backdrop for this investigation. Known as the "Steel City," Pittsburgh has undergone significant environmental transformations over the years, transitioning from a flourishing hub of heavy industry to a modern metropolis with a burgeoning emphasis on sustainability. As the city grapples with the complexities of environmental regulation and economic development, the implications of air quality on corporate performances take center stage.
Walmart, a behemoth in the realm of retail, stands as a beacon of market dynamics, its stock price serving as a barometer of economic sentiment and consumer behavior. As we embark on this odyssey of data analysis and statistical scrutiny, the prevailing question emerges: could the ephemeral whispers of particulate matter and ozone in the air of Pittsburgh hold sway over the nuanced fluctuations of Walmart's stock price?
The confluence of these seemingly disparate entities, air quality and stock market valor, invites a measure of skepticism and curiosity. Nonetheless, the initial scintillating findings from our research endeavor urge a closer examination of this uncanny association. Our inquiry transcends the confines of traditional financial analysis, traversing into the realm of atmospheric science and environmental economics, where unsuspected correlations await their revelation.
As we paint the canvas of our study, against the backdrop of smog and financial tickers, the revelations that unfurl before us are poised to recalibrate perceptions of the intricate dance between environmental indicators and market performances. Join us on this cerebral escapade, as we unpack the paradoxical link between the air we breathe and the stocks we trade.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The analysis of the collected data brought forth a noteworthy connection between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). The correlation coefficient was calculated to be 0.9194964, indicating a strong positive correlation between the two variables. This significant correlation was supported by an r-squared value of 0.8454736, signifying that approximately 84.55% of the variability in Walmart's stock price could be explained by changes in air quality in Pittsburgh. Furthermore, the p-value of less than 0.01 reinforced the statistical significance of this relationship, providing compelling evidence to support the hypothesized link between air quality and stock market dynamics.

The correlation is evident in the scatterplot depicted in Figure 1, which showcases the striking alignment between air quality measurements and the fluctuations in Walmart's stock price. The visual representation vividly illustrates the synchronicity between these seemingly disparate variables, underscoring the compelling nature of our findings.

The implications of these results are far-reaching, illuminating a previously undetected association between atmospheric conditions and financial market performance. The robustness of the correlation observed prompts further contemplation of the underlying mechanisms that underpin this connection. While it may be tempting to attribute the observed relationship to mere coincidence, our findings beckon us to delve deeper into the intricate interplay between environmental factors and economic dynamics.

It is worth noting that the strength of this relationship underscores the need for expanded research into the multifaceted impact of environmental indicators on market behaviors. This revelatory linkage presents an exciting avenue for future exploration and warrants consideration within the broader context of environmental economics and financial analysis.

The compelling nature of this correlation prompts a reevaluation of traditional market analyses, prompting us to consider the ramifications of ambient air quality as a potential influencer of stock prices. The entwining of the ethereal whispers of Pittsburgh's air with the ebbs and flows of Walmart's stock price unfurls an unparalleled narrative of interconnectedness, urging us to re-envision the dimensions of environmental influences on economic landscapes.

In conclusion, our research not only accentuates the entwined nature of environmental and financial realms but also accentuates the pressing need for continued inquiry into the intricate dance between the air we breathe and the stocks we trade. This discovery serves as a testament to the often unforeseen and whimsical connections that lie beneath the surface of empirical investigations, beckoning us to embrace the unexpected and to venture boldly into uncharted territories in pursuit of knowledge and understanding.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

[[LITERATURE REVIEW]]
In their study, Smith and Doe (2015) assert that the interplay between environmental factors and financial markets is a topic of increasing relevance. Their findings underscore the importance of considering the multifaceted impact of environmental indicators on market dynamics. Similarly, Jones et al. (2018) delve into the complexities of environmental regulation and economic development, shedding light on the implications of air quality on corporate performances.
Expanding beyond the realm of academic research, several non-fiction books offer valuable insights into the intersection of environmental factors and financial markets. In "The Economics of Air Quality" by Green and Clean (2017), the authors dive into the economic ramifications of air quality management, offering a comprehensive perspective on the potential repercussions on businesses and stock performance.
Furthermore, "Sustainability in the 21st Century" by Eco Guru (2019) explores the evolving landscape of sustainable business practices and its influence on financial markets, providing a contextual backdrop for the correlation between environmental indicators and stock valuations.
Transitioning to fictional literature, "The Polluted Portfolio" by Penny Stockman (2005) presents a satirical take on the unforeseen impacts of air pollution on investment portfolios, weaving a whimsical narrative that ponders the absurdities of environmental influences on stock prices.
In a sheer departure from conventional sources, the animated series "Captain Planet and the Planeteers" and its titular protagonist's eco-conscious endeavors offer a lighthearted lens through which to contemplate the intertwining of environmental stewardship and economic dynamics. Meanwhile, episodes of "SpongeBob SquarePants" intriguingly delve into the undersea economics and environmental conditions of Bikini Bottom, subtly prodding at the parallels between fictional ecosystems and real-world market performances.
This eclectic amalgamation of sources sets the stage for a literature review that transcends the boundaries of traditional scholarly discourse, enriching the understanding of the vibrant interconnections between air quality in Pittsburgh and the stock price of Walmart (WMT).
As we embark on this intellectual voyage, the juxtaposition of empirical studies, non-fiction literature, and playful fiction serves as a testament to the multifaceted layers within which this investigation unfolds.

[[RESULTS]]
The analysis of the collected data brought forth a noteworthy connection between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). The correlation coefficient was calculated to be 0.9194964, indicating a strong positive correlation between the two variables. This significant correlation was supported by an r-squared value of 0.8454736, signifying that approximately 84.55% of the variability in Walmart's stock price could be explained by changes in air quality in Pittsburgh. Furthermore, the p-value of less than 0.01 reinforced the statistical significance of this relationship, providing compelling evidence to support the hypothesized link between air quality and stock market dynamics.
The correlation is evident in the scatterplot depicted in Figure 1, which showcases the striking alignment between air quality measurements and the fluctuations in Walmart's stock price. The visual representation vividly illustrates the synchronicity between these seemingly disparate variables, underscoring the compelling nature of our findings.
The implications of these results are far-reaching, illuminating a previously undetected association between atmospheric conditions and financial market performance. The robustness of the correlation observed prompts further contemplation of the underlying mechanisms that underpin this connection. While it may be tempting to attribute the observed relationship to mere coincidence, our findings beckon us to delve deeper into the intricate interplay between environmental factors and economic dynamics.
It is worth noting that the strength of this relationship underscores the need for expanded research into the multifaceted impact of environmental indicators on market behaviors. This revelatory linkage presents an exciting avenue for future exploration and warrants consideration within the broader context of environmental economics and financial analysis.
The compelling nature of this correlation prompts a reevaluation of traditional market analyses, prompting us to consider the ramifications of ambient air quality as a potential influencer of stock prices. The entwining of the ethereal whispers of Pittsburgh's air with the ebbs and flows of Walmart's stock price unfurls an unparalleled narrative of interconnectedness, urging us to re-envision the dimensions of environmental influences on economic landscapes.
In conclusion, our research not only accentuates the entwined nature of environmental and financial realms but also accentuates the pressing need for continued inquiry into the intricate dance between the air we breathe and the stocks we trade. This discovery serves as a testament to the often unforeseen and whimsical connections that lie beneath the surface of empirical investigations, beckoning us to embrace the unexpected and to venture boldly into uncharted territories in pursuit of knowledge and understanding.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The findings of our study have unveiled a compelling relationship between air quality in Pittsburgh and the stock price of Walmart (WMT), further solidifying the enigmatic romance between seemingly incongruous entities. The statistical analysis yielded a correlation coefficient of 0.9194964, which, in itself, is quite the captivating affair. This magnetic attraction was substantiated by a remarkable r-squared value of 0.8454736, signifying that approximately 84.55% of the variability in Walmart's stock price could be attributed to the ethereal whispers of Pittsburgh's air. The undeniable allure of this connection piques curiosity and inspires further dives into the mysterious depths of market dynamics.

Reflecting on the lighthearted anecdotes and whimsical ponderings in our literature review, particularly those shared by fictional literary works and animated series, we recognize the underlying kernel of truth that imbues seemingly fanciful musings. The outlandish satirical take in "The Polluted Portfolio" and the eco-conscious endeavors of "Captain Planet and the Planeteers" now beckon us to reconsider their underlying wisdom, concealed within the cloak of levity. It is with a discerning eye that we gaze upon these unexpected sources, recognizing that amidst their jocularity lie kernels of wisdom waiting to be unraveled.

Our results not only corroborate the existing scholarly discourse but also delve into uncharted territories, breathing life into the hitherto unexplored nexus of environmental influences on financial markets. The irresistible allure of this correlation prompts a paradigm shift, urging us to rethink traditional market analyses and expand the boundaries of our financial comprehension. The entwining of environmental factors and economic dynamics materializes as an unexpected waltz, where the rhythm of Pittsburgh's air orchestrates an intricate ballet alongside Walmart's stock price.

In contemplating the web of interconnectedness between air quality and stock valuation, we are motivated to delve deeper into the cavernous realm of environmental economics and financial analysis. The revelation of this poignant connection serves as a clarion call, rousing us to venture boldly into the nebulous landscape of uncharted correlations and compelling interplays. As we journey forth, we are poised to embrace the whimsical tales that navigate the unfathomable depths of empirical research, unveiling that amidst scholarly endeavors lie untold stories waiting to be heard.

This revelatory linkage sets the stage for an enthralling saga of continuing exploration, underscoring the urgency and curiosity-driven pursuit in unraveling the intricate dance between the air we breathe and the stocks we trade. Our study, conducted at the nexus of empirical research and surreptitious whimsy, seeks to embolden the academic community to navigate boldly into the uncharted realms where empirical truth and unexpected narratives coalesce.


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 Air quality in Pittsburgh and Walmart's stock price (WMT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT)"

[[ABSTRACT]]
This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

[[INTRDUCTION]]
INTRODUCTION
The intricate interplay between environmental factors and financial markets has long been a subject of interest and inquiry. Despite the seeming disconnect between the natural world and the tumultuous domain of stocks and assets, empirical evidence continues to uncover surprising connections that challenge conventional wisdom. Our research embarks on a journey to unravel one such enigma, delving into the realm of air quality in Pittsburgh and its purported influence on the stock price of Walmart (WMT).
Pittsburgh, a city renowned for its industrial heritage and historical significance, provides a compelling backdrop for this investigation. Known as the "Steel City," Pittsburgh has undergone significant environmental transformations over the years, transitioning from a flourishing hub of heavy industry to a modern metropolis with a burgeoning emphasis on sustainability. As the city grapples with the complexities of environmental regulation and economic development, the implications of air quality on corporate performances take center stage.
Walmart, a behemoth in the realm of retail, stands as a beacon of market dynamics, its stock price serving as a barometer of economic sentiment and consumer behavior. As we embark on this odyssey of data analysis and statistical scrutiny, the prevailing question emerges: could the ephemeral whispers of particulate matter and ozone in the air of Pittsburgh hold sway over the nuanced fluctuations of Walmart's stock price?
The confluence of these seemingly disparate entities, air quality and stock market valor, invites a measure of skepticism and curiosity. Nonetheless, the initial scintillating findings from our research endeavor urge a closer examination of this uncanny association. Our inquiry transcends the confines of traditional financial analysis, traversing into the realm of atmospheric science and environmental economics, where unsuspected correlations await their revelation.
As we paint the canvas of our study, against the backdrop of smog and financial tickers, the revelations that unfurl before us are poised to recalibrate perceptions of the intricate dance between environmental indicators and market performances. Join us on this cerebral escapade, as we unpack the paradoxical link between the air we breathe and the stocks we trade.

[[RESULTS]]
The analysis of the collected data brought forth a noteworthy connection between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). The correlation coefficient was calculated to be 0.9194964, indicating a strong positive correlation between the two variables. This significant correlation was supported by an r-squared value of 0.8454736, signifying that approximately 84.55% of the variability in Walmart's stock price could be explained by changes in air quality in Pittsburgh. Furthermore, the p-value of less than 0.01 reinforced the statistical significance of this relationship, providing compelling evidence to support the hypothesized link between air quality and stock market dynamics.
The correlation is evident in the scatterplot depicted in Figure 1, which showcases the striking alignment between air quality measurements and the fluctuations in Walmart's stock price. The visual representation vividly illustrates the synchronicity between these seemingly disparate variables, underscoring the compelling nature of our findings.
The implications of these results are far-reaching, illuminating a previously undetected association between atmospheric conditions and financial market performance. The robustness of the correlation observed prompts further contemplation of the underlying mechanisms that underpin this connection. While it may be tempting to attribute the observed relationship to mere coincidence, our findings beckon us to delve deeper into the intricate interplay between environmental factors and economic dynamics.
It is worth noting that the strength of this relationship underscores the need for expanded research into the multifaceted impact of environmental indicators on market behaviors. This revelatory linkage presents an exciting avenue for future exploration and warrants consideration within the broader context of environmental economics and financial analysis.
The compelling nature of this correlation prompts a reevaluation of traditional market analyses, prompting us to consider the ramifications of ambient air quality as a potential influencer of stock prices. The entwining of the ethereal whispers of Pittsburgh's air with the ebbs and flows of Walmart's stock price unfurls an unparalleled narrative of interconnectedness, urging us to re-envision the dimensions of environmental influences on economic landscapes.
In conclusion, our research not only accentuates the entwined nature of environmental and financial realms but also accentuates the pressing need for continued inquiry into the intricate dance between the air we breathe and the stocks we trade. This discovery serves as a testament to the often unforeseen and whimsical connections that lie beneath the surface of empirical investigations, beckoning us to embrace the unexpected and to venture boldly into uncharted territories in pursuit of knowledge and understanding.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

The findings of our investigation into the relationship between air quality in Pittsburgh and the stock price of Walmart (WMT) have undeniably opened a new window into the interconnectedness of seemingly unrelated variables. The substantial correlation coefficient of 0.9194964, accompanied by a significance level of p < 0.01, has compellingly brought to light the whimsical dynamics at play in the financial markets. While the notion of Pittsburgh's ambient air quality weaving its influence over Walmart's stock price may initially seem far-fetched, the robust statistical evidence demands an acknowledgment of this curious connection. As we ponder the implications of our discoveries, we cannot help but marvel at the unexpected twists and turns encountered on this research expedition. Indeed, the whims of the wind in Pittsburgh seem to carry more weight than mere particulate matter, shaping the performance of a retail giant. However, as we bask in the bemusement of this revelation, it is crucial to recognize the pressing need for further investigations into this uncharted territory – much like exploring the aisles of a Walmart superstore, there is potential for more surprises and revelations. Nevertheless, in the spirit of scientific inquiry, we must assert that no more research is needed in this area. After all, how much more "breathtaking" can the stock market get?


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 Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT). Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Economics and Retail Dynamics


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 Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT). Respond only with the name of a fictional organization.

ChatGPT:

The Center for Urban Environmental Research and Analysis


Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Air Quality in Pittsburgh: A 'Breathtaking' Impact on Walmart's Stock Price (WMT) with an abstract of This research delves into the overlooked relationship between ambient air quality in Pittsburgh and the stock price of Walmart (WMT). Leveraging data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our team conducted a comprehensive analysis of air quality measurements and Walmart's stock performance from 2002 to 2023. Despite the seemingly disparate nature of these factors, our findings revealed a remarkably high correlation coefficient of 0.9194964 with a significance level of p < 0.01. The implications of these results are not to be taken lightly, as they uncover a previously undetected link between air quality and stock market dynamics. Our study contributes to the growing body of evidence that underscores the multifaceted impact of environmental factors on financial markets.

ChatGPT:

"Air quality Pittsburgh stock price correlation," "Walmart stock performance air quality," "Environmental factors financial markets," "Pittsburgh air quality impact stock price," "Ambient air quality Walmart stock price," "EPA air quality measurements Walmart stock price," "Leveraging data LSEG Analytics stock performance," "Stock price correlation environmental factors," "Air quality impact financial markets," "Link between air quality and stock market dynamics."

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



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Data details

Air quality in Pittsburgh
Detailed data title: Percentage of days with good air quality in Pittsburgh, PA
Source: Environmental Protection Agency
See what else correlates with Air quality in Pittsburgh

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

See what else correlates with Walmart's stock price (WMT)

Correlation r = 0.9194964 (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 = 0.8454736 (Coefficient of determination)
This means 84.5% of the change in the one variable (i.e., Walmart's stock price (WMT)) is predictable based on the change in the other (i.e., Air quality in Pittsburgh) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.5E-9. 0.0000000014754773055838109000
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 0.92 in 1.5E-7% of random cases. Said differently, if you correlated 677,746,785 random variables You don't actually need 677 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 21 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 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.

[ 0.81, 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.
2002200320042005200620072008200920102011201220132014201520162017201820192020202120222023
Air quality in Pittsburgh (Good air quality)24.657523.013727.595623.561621.643818.630117.759619.452125.753422.739722.677632.876730.684934.520531.967226.027437.534237.534254.644844.931544.109651.4403
Walmart's stock price (WMT) (Stock price)57.6550.6553.3553.7546.3647.0947.4155.9853.7454.2359.9768.9378.7286.2760.569.2499.391.64118.86144.3144142.55




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.




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([24.6575,23.0137,27.5956,23.5616,21.6438,18.6301,17.7596,19.4521,25.7534,22.7397,22.6776,32.8767,30.6849,34.5205,31.9672,26.0274,37.5342,37.5342,54.6448,44.9315,44.1096,51.4403,])
array_2 = np.array([57.65,50.65,53.35,53.75,46.36,47.09,47.41,55.98,53.74,54.23,59.97,68.93,78.72,86.27,60.5,69.24,99.3,91.64,118.86,144.3,144,142.55,])
array_1_name = "Air quality in Pittsburgh"
array_2_name = "Walmart's stock price (WMT)"

# 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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You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

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

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Correlation ID: 3641 · Black Variable ID: 21067 · Red Variable ID: 1553
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