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AI explanation
As the air pollution in Vernal, Utah rose, people began wearing more respiratory masks. The demand for copper, a key component in the production of these masks, surged. Rio Tinto Group, a leading copper mining company, saw a significant boost in sales and production, leading to an increase in their stock price.
Model: dalle-3
Prompt: Create an image of a glitchy scene in Vernal, Utah, with a focus on the hazy air pollution hanging over the city. Inside, depict shoppers eagerly clicking on their computers to take advantage of online deals. In the background, show the stock price for Rio Tinto Group (RIO) displayed on a TV screen, with glitchy numbers jumping up as some individuals mistakenly invest in the company. Additionally, feature a glitchy representation of online storefront for outdoor gear store REI. 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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). 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: Glitch art A scene from Skyrim Woodblock painting 3D Pixar Style Sci-fi fantacy
ChatGPT: Glitch art
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO), in the following situation: The increase in Vernal, Utah's air pollution caused everyone to stay indoors, leading to a surge in online shopping. Some people mistakenly invested in "RIO" instead of "REI," boosting Rio Tinto Group's stock price. Clean air saves wallets, folks!. Keep the description simple.
ChatGPT: In Vernal, Utah, the thick haze of air pollution hangs low over the city as people stay indoors. Through the windows, shoppers are seen eagerly clicking away on their computers, taking advantage of online deals. Meanwhile, in the background, the stock price for Rio Tinto Group (RIO) is displayed on a TV screen, with the numbers jumping up as some individuals mistakenly invest in the company, while others are busy looking for outdoor gear store REI online.
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: [ Glitch art ] and depicting this scene: In Vernal, Utah, the thick haze of air pollution hangs low over the city as people stay indoors. Through the windows, shoppers are seen eagerly clicking away on their computers, taking advantage of online deals. Meanwhile, in the background, the stock price for Rio Tinto Group (RIO) is displayed on a TV screen, with the numbers jumping up as some individuals mistakenly invest in the company, while others are busy looking for outdoor gear store REI online. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a glitchy scene in Vernal, Utah, with a focus on the hazy air pollution hanging over the city. Inside, depict shoppers eagerly clicking on their computers to take advantage of online deals. In the background, show the stock price for Rio Tinto Group (RIO) displayed on a TV screen, with glitchy numbers jumping up as some individuals mistakenly invest in the company. Additionally, feature a glitchy representation of online storefront for outdoor gear store REI.
*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.
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 pollution in Vernal, Utah caused Rio Tinto Group's stock price (RIO) to increase. Make it feel plausible.
AI academic paper
 |
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price The Journal of Ecological Economics and Environmental Ethics Jan 2024 Reminder: This paper is AI-generated. Not real! |
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Make the title an alliteration.
Your research team used data from Environmental Protection Agency and LSEG Analytics (Refinitiv) to assess this nagging question. You found a correlation coefficient of 0.6591983 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]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Here is the title and abstract of the paper:
[[TITLE]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
It is a truth universally acknowledged, that a single town in possession of an excessive amount of vog, must be in want of a stock market rollercoaster ride. Vernal, Utah, a town known for its picturesque landscapes and abundant natural resources, has also found itself in an unlikely relationship with the stock price of the Rio Tinto Group (RIO). While one might expect the correlation between environmental factors and financial markets to be about as clear as Vernal's smoggy skies, our research has uncovered a surprising link between air pollution levels in Vernal and the fluctuation of RIO stock prices.
As our team delves into this unconventional connection, it becomes apparent that the intersection of environmental impact and market dynamics is as intricate as the pattern of a snowflake under a microscope. Who would have thought that the atmospheric conditions in a remote town could have an impact on the stock prices of a global mining corporation? It's as unexpected as finding a diamond in a coal mine, or perhaps more aptly, discovering a vein of gold in a desert canyon.
The ubiquity of air pollution and its potential effects on financial markets remains an underexplored territory, akin to navigating through a dense fog without a compass. However, our study aims to shine a light on this peculiar relationship, offering a unique perspective on the whimsical interplay between economic trends and environmental concerns. In doing so, we hope to contribute to a broader understanding of the myriad factors that influence market dynamics, unveiling the unexpected ways in which a town's air quality may influence the performance of a corporate giant.
So, buckle up and brace yourselves, dear readers, as we embark on a journey through the voluminous Vernal vog, seeking to disentangle the confounding relationship between air pollution and Rio Tinto's RIO stock price. As we unravel this enigmatic alliance, we invite you to join us in exploring this uncharted territory where the winds of change may blow stock prices in unforeseen ways, just like a gust of wind scattering autumn leaves.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). 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]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
It is a truth universally acknowledged, that a single town in possession of an excessive amount of vog, must be in want of a stock market rollercoaster ride. Vernal, Utah, a town known for its picturesque landscapes and abundant natural resources, has also found itself in an unlikely relationship with the stock price of the Rio Tinto Group (RIO). While one might expect the correlation between environmental factors and financial markets to be about as clear as Vernal's smoggy skies, our research has uncovered a surprising link between air pollution levels in Vernal and the fluctuation of RIO stock prices.
As our team delves into this unconventional connection, it becomes apparent that the intersection of environmental impact and market dynamics is as intricate as the pattern of a snowflake under a microscope. Who would have thought that the atmospheric conditions in a remote town could have an impact on the stock prices of a global mining corporation? It's as unexpected as finding a diamond in a coal mine, or perhaps more aptly, discovering a vein of gold in a desert canyon.
The ubiquity of air pollution and its potential effects on financial markets remains an underexplored territory, akin to navigating through a dense fog without a compass. However, our study aims to shine a light on this peculiar relationship, offering a unique perspective on the whimsical interplay between economic trends and environmental concerns. In doing so, we hope to contribute to a broader understanding of the myriad factors that influence market dynamics, unveiling the unexpected ways in which a town's air quality may influence the performance of a corporate giant.
So, buckle up and brace yourselves, dear readers, as we embark on a journey through the voluminous Vernal vog, seeking to disentangle the confounding relationship between air pollution and Rio Tinto's RIO stock price. As we unravel this enigmatic alliance, we invite you to join us in exploring this uncharted territory where the winds of change may blow stock prices in unforeseen ways, just like a gust of wind scattering autumn leaves.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In "The Correlation between Air Pollution Levels and Financial Markets," Smith et al. observed a significant correlation between air pollution and stock price fluctuations. Similarly, Doe's "Air Quality Index and Stock Price Volatility" delineated the impact of air quality on market volatility, bolstering the argument for an intricate relationship between environmental factors and financial markets. These studies underscore the need for further exploration of the nuanced interplay between air pollution and stock prices.
Moving beyond the realm of traditional academic literature, works such as "Economic Implications of Environmental Hazards" by Jones shed light on the broader implications of environmental factors on financial systems. In a more unconventional vein, "The Mining Dilemma: A Tale of Stocks and Smog" by Brown delves into the fictionalized account of a mining company's stock price being swayed by the atmospheric conditions of a distant town. While not grounded in empirical data, the narrative provides a whimsical portrayal of the potential impact of air pollution on market dynamics.
Navigating through the crevices of pop culture, cartoon series like "Captain Planet and the Planeteers" and children's shows such as "The Magic School Bus" offer playful depictions of environmental issues, often hinting at the broader societal implications of air quality and pollution. While not empirical in nature, these media sources provide a window into the public's consciousness about environmental concerns and their potential ramifications on various aspects of life, including financial markets.
The diverse array of literature spanning from rigorous academic studies to fictional narratives and children's programming underscores the multifaceted nature of the relationship between air pollution and market dynamics. By integrating these diverse perspectives, our analysis aims to unravel the intricacies of this unlikely alliance, shedding light on the whimsical and unforeseen ways in which a town's air quality may influence the performance of a corporate giant's stock in the market.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from 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]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
[[INTRODUCTION]]
It is a truth universally acknowledged, that a single town in possession of an excessive amount of vog, must be in want of a stock market rollercoaster ride. Vernal, Utah, a town known for its picturesque landscapes and abundant natural resources, has also found itself in an unlikely relationship with the stock price of the Rio Tinto Group (RIO). While one might expect the correlation between environmental factors and financial markets to be about as clear as Vernal's smoggy skies, our research has uncovered a surprising link between air pollution levels in Vernal and the fluctuation of RIO stock prices.
As our team delves into this unconventional connection, it becomes apparent that the intersection of environmental impact and market dynamics is as intricate as the pattern of a snowflake under a microscope. Who would have thought that the atmospheric conditions in a remote town could have an impact on the stock prices of a global mining corporation? It's as unexpected as finding a diamond in a coal mine, or perhaps more aptly, discovering a vein of gold in a desert canyon.
The ubiquity of air pollution and its potential effects on financial markets remains an underexplored territory, akin to navigating through a dense fog without a compass. However, our study aims to shine a light on this peculiar relationship, offering a unique perspective on the whimsical interplay between economic trends and environmental concerns. In doing so, we hope to contribute to a broader understanding of the myriad factors that influence market dynamics, unveiling the unexpected ways in which a town's air quality may influence the performance of a corporate giant.
So, buckle up and brace yourselves, dear readers, as we embark on a journey through the voluminous Vernal vog, seeking to disentangle the confounding relationship between air pollution and Rio Tinto's RIO stock price. As we unravel this enigmatic alliance, we invite you to join us in exploring this uncharted territory where the winds of change may blow stock prices in unforeseen ways, just like a gust of wind scattering autumn leaves.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
In our pursuit of unraveling the enigmatic alliance between the air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO), our research team embarked on a methodological adventure worthy of a quest for the Holy Grail – or at least an elusive stock market trend. We employed an eclectic array of statistical tools and analytical techniques, akin to an alchemist concocting a potion, to distill and decipher the complex relationship between environmental factors and market dynamics.
Firstly, we scoured the vast expanse of the internet, navigating through the digital wilderness like intrepid explorers, to gather data on air pollution levels in Vernal from the Environmental Protection Agency's comprehensive archives. Our keen-eyed data hunters pored over years of air quality measurements, sifting through the virtual haystack to uncover the needle in the data haystack. We then complemented this atmospheric treasure trove with stock price data for the Rio Tinto Group (RIO), sourced from LSEG Analytics (Refinitiv), to capture the financial ebbs and flows of this industrial colossus.
With our data arsenal assembled, we turned to the art of statistical analysis, employing rigorous methods to interrogate the relationship between air pollution levels and RIO stock prices. We utilized time series analysis to track the temporal fluctuations of both air quality metrics and stock prices, akin to studying the tidal patterns of an economic ocean. Additionally, we applied multivariate regression models to disentangle the web of potential confounding variables, akin to taming a thorny vine of covariates that threatened to obscure our quest for a clear causal link.
Furthermore, to ensure the robustness of our findings, we incorporated various sensitivity analyses, akin to stress-testing the hull of a financial vessel for potential weak spots. These examinations allowed us to fortify our conclusions against the tempestuous winds of statistical uncertainty, ensuring that our results stood firm like an impregnable fortress against spurious interpretations.
Lastly, we conducted an array of diagnostic tests to assess the assumptions underpinning our statistical models, akin to scrutinizing the structural integrity of a theoretical framework. These examinations ensured that our data conformed to the expectations of our analytical techniques, validating the fidelity of our results and precluding any lurking biases from obscuring the veracity of our discoveries.
In this manner, our methodological approach served as a compass guiding us through the mists of uncertainty, leading us to unveil the intriguing relationship between the voggy airs of Vernal and the undulating stock prices of Rio Tinto – as unexpected as stumbling upon a mirage in the desert of economic inquiry. So, dear readers, join us as we march forth into the methodological machinations that underpin our whimsical journey through the vagaries of vog and stocks.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and LSEG Analytics (Refinitiv) .
For the time period 2002 to 2023, you found a correlation 0.6591983, r-squared of 0.4345424, 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]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
It is a truth universally acknowledged, that a single town in possession of an excessive amount of vog, must be in want of a stock market rollercoaster ride. Vernal, Utah, a town known for its picturesque landscapes and abundant natural resources, has also found itself in an unlikely relationship with the stock price of the Rio Tinto Group (RIO). While one might expect the correlation between environmental factors and financial markets to be about as clear as Vernal's smoggy skies, our research has uncovered a surprising link between air pollution levels in Vernal and the fluctuation of RIO stock prices.
As our team delves into this unconventional connection, it becomes apparent that the intersection of environmental impact and market dynamics is as intricate as the pattern of a snowflake under a microscope. Who would have thought that the atmospheric conditions in a remote town could have an impact on the stock prices of a global mining corporation? It's as unexpected as finding a diamond in a coal mine, or perhaps more aptly, discovering a vein of gold in a desert canyon.
The ubiquity of air pollution and its potential effects on financial markets remains an underexplored territory, akin to navigating through a dense fog without a compass. However, our study aims to shine a light on this peculiar relationship, offering a unique perspective on the whimsical interplay between economic trends and environmental concerns. In doing so, we hope to contribute to a broader understanding of the myriad factors that influence market dynamics, unveiling the unexpected ways in which a town's air quality may influence the performance of a corporate giant.
So, buckle up and brace yourselves, dear readers, as we embark on a journey through the voluminous Vernal vog, seeking to disentangle the confounding relationship between air pollution and Rio Tinto's RIO stock price. As we unravel this enigmatic alliance, we invite you to join us in exploring this uncharted territory where the winds of change may blow stock prices in unforeseen ways, just like a gust of wind scattering autumn leaves.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The results of our investigation regarding the peculiar interconnection between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) have revealed a statistically significant correlation. From 2002 to 2023, our analysis unveiled a correlation coefficient of 0.6591983, implying a moderately strong positive association between these two seemingly disparate variables. This correlation was further substantiated by an r-squared value of 0.4345424, which indicates that approximately 43.45% of the variance in RIO stock prices can be attributed to the fluctuations in Vernal's air quality. Moreover, the significance level of p < 0.01 underscores the robustness of this relationship, suggesting that the observed correlation is not a mere figment of statistical coincidence.
Furthermore, the scatterplot depicted in Fig. 1 illustrates the discernible pattern of the correlation, visually encapsulating the intriguing association between air pollution in Vernal and the movements of RIO stock prices. The scatterplot vividly portrays the titillating dance of these variables, akin to witnessing the unexpected tango of a fine dining connoisseur and an aficionado of air guitar.
In summary, our findings defy conventional wisdom, shedding light on the unanticipated influence of environmental factors on the fluctuations of a global corporation's stock price. This intricate dance between Vernal's vog and RIO's financial performance calls for further exploration, as it prompts us to question the underlying mechanisms at play. The significance of this correlation serves as a testament to the enigmatic and symbiotic relationship between seemingly distant phenomena, capturing the essence of the improbable and the delightful in the realm of financial research.
Stay tuned, dear readers, as we venture further into this captivating odyssey, navigating the uncharted waters where the breezy currents of air pollution may hold the key to deciphering the movements of financial markets. Take a deep breath, for the air is ripe with anticipation as we unravel the voluminous Vernal vog and its curious connection to Rio Tinto's stock price.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Limit your response to 500 tokens.
Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.
Do not write a conclusion. I will add a conclusion after this.
[[TITLE]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
[[LITERATURE REVIEW]]
In "The Correlation between Air Pollution Levels and Financial Markets," Smith et al. observed a significant correlation between air pollution and stock price fluctuations. Similarly, Doe's "Air Quality Index and Stock Price Volatility" delineated the impact of air quality on market volatility, bolstering the argument for an intricate relationship between environmental factors and financial markets. These studies underscore the need for further exploration of the nuanced interplay between air pollution and stock prices.
Moving beyond the realm of traditional academic literature, works such as "Economic Implications of Environmental Hazards" by Jones shed light on the broader implications of environmental factors on financial systems. In a more unconventional vein, "The Mining Dilemma: A Tale of Stocks and Smog" by Brown delves into the fictionalized account of a mining company's stock price being swayed by the atmospheric conditions of a distant town. While not grounded in empirical data, the narrative provides a whimsical portrayal of the potential impact of air pollution on market dynamics.
Navigating through the crevices of pop culture, cartoon series like "Captain Planet and the Planeteers" and children's shows such as "The Magic School Bus" offer playful depictions of environmental issues, often hinting at the broader societal implications of air quality and pollution. While not empirical in nature, these media sources provide a window into the public's consciousness about environmental concerns and their potential ramifications on various aspects of life, including financial markets.
The diverse array of literature spanning from rigorous academic studies to fictional narratives and children's programming underscores the multifaceted nature of the relationship between air pollution and market dynamics. By integrating these diverse perspectives, our analysis aims to unravel the intricacies of this unlikely alliance, shedding light on the whimsical and unforeseen ways in which a town's air quality may influence the performance of a corporate giant's stock in the market.
[[RESULTS]]
The results of our investigation regarding the peculiar interconnection between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) have revealed a statistically significant correlation. From 2002 to 2023, our analysis unveiled a correlation coefficient of 0.6591983, implying a moderately strong positive association between these two seemingly disparate variables. This correlation was further substantiated by an r-squared value of 0.4345424, which indicates that approximately 43.45% of the variance in RIO stock prices can be attributed to the fluctuations in Vernal's air quality. Moreover, the significance level of p < 0.01 underscores the robustness of this relationship, suggesting that the observed correlation is not a mere figment of statistical coincidence.
Furthermore, the scatterplot depicted in Fig. 1 illustrates the discernible pattern of the correlation, visually encapsulating the intriguing association between air pollution in Vernal and the movements of RIO stock prices. The scatterplot vividly portrays the titillating dance of these variables, akin to witnessing the unexpected tango of a fine dining connoisseur and an aficionado of air guitar.
In summary, our findings defy conventional wisdom, shedding light on the unanticipated influence of environmental factors on the fluctuations of a global corporation's stock price. This intricate dance between Vernal's vog and RIO's financial performance calls for further exploration, as it prompts us to question the underlying mechanisms at play. The significance of this correlation serves as a testament to the enigmatic and symbiotic relationship between seemingly distant phenomena, capturing the essence of the improbable and the delightful in the realm of financial research.
Stay tuned, dear readers, as we venture further into this captivating odyssey, navigating the uncharted waters where the breezy currents of air pollution may hold the key to deciphering the movements of financial markets. Take a deep breath, for the air is ripe with anticipation as we unravel the voluminous Vernal vog and its curious connection to Rio Tinto's stock price.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our findings are in line with the prior research that has highlighted the unexpected bond between environmental factors and financial markets. The correlation coefficient of 0.6591983 we uncovered in the context of Vernal's air pollution and RIO stock prices echoes the sentiments of Smith et al., who also observed a significant correlation between air pollution and stock price fluctuations. This confirmation of earlier findings lends substantial weight to the argument for a dance of financial figures to the tune of atmospheric composition. Moreover, Doe's work on the impact of air quality on market volatility is consistently echoed in our results, as indicated by the r-squared value of 0.4345424, which demonstrates the extent of variance in RIO stock prices explained by Vernal's air quality.
While our study may have seemed a whimsical pursuit, the statistical significance we uncovered calls into question the otherwise whimsical portrayal in Brown's "The Mining Dilemma: A Tale of Stocks and Smog." Perhaps the fictionalized account of a mining company's stock being influenced by the atmospheric conditions of a distant town was not entirely implausible after all. In a similar vein, the alluring dance of our variables, portrayed in the scatterplot akin to the unexpected tango of disparate enthusiasts, brings to mind the dubious yet delightful depiction of environmental themes in pop culture media, such as the flirtatious yet underlyingly serious ensembles in "Captain Planet and the Planeteers."
Our findings offer quirky evidence of the whimsical interplay between environmental factors and market dynamics, providing a refreshing glimpse into this hitherto overshadowed theme. Our results raise intriguing questions about the mechanisms at play and call for further exploration into this peculiar alliance. The significance of our findings emphasizes the enigmatic and symbiotic relationship between seemingly distant phenomena, encapsulating the essence of improbable delight in the realm of financial research. It's as if the whims of Vernal's air quality may hold the key to unlocking the mysterious dances of financial markets. After all, there is often more than meets the eye beneath the veneer of the vernal vog.
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 pollution in Vernal, Utah and Rio Tinto Group's stock price (RIO). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.
Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
[[INTRDUCTION]]
It is a truth universally acknowledged, that a single town in possession of an excessive amount of vog, must be in want of a stock market rollercoaster ride. Vernal, Utah, a town known for its picturesque landscapes and abundant natural resources, has also found itself in an unlikely relationship with the stock price of the Rio Tinto Group (RIO). While one might expect the correlation between environmental factors and financial markets to be about as clear as Vernal's smoggy skies, our research has uncovered a surprising link between air pollution levels in Vernal and the fluctuation of RIO stock prices.
As our team delves into this unconventional connection, it becomes apparent that the intersection of environmental impact and market dynamics is as intricate as the pattern of a snowflake under a microscope. Who would have thought that the atmospheric conditions in a remote town could have an impact on the stock prices of a global mining corporation? It's as unexpected as finding a diamond in a coal mine, or perhaps more aptly, discovering a vein of gold in a desert canyon.
The ubiquity of air pollution and its potential effects on financial markets remains an underexplored territory, akin to navigating through a dense fog without a compass. However, our study aims to shine a light on this peculiar relationship, offering a unique perspective on the whimsical interplay between economic trends and environmental concerns. In doing so, we hope to contribute to a broader understanding of the myriad factors that influence market dynamics, unveiling the unexpected ways in which a town's air quality may influence the performance of a corporate giant.
So, buckle up and brace yourselves, dear readers, as we embark on a journey through the voluminous Vernal vog, seeking to disentangle the confounding relationship between air pollution and Rio Tinto's RIO stock price. As we unravel this enigmatic alliance, we invite you to join us in exploring this uncharted territory where the winds of change may blow stock prices in unforeseen ways, just like a gust of wind scattering autumn leaves.
[[RESULTS]]
The results of our investigation regarding the peculiar interconnection between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) have revealed a statistically significant correlation. From 2002 to 2023, our analysis unveiled a correlation coefficient of 0.6591983, implying a moderately strong positive association between these two seemingly disparate variables. This correlation was further substantiated by an r-squared value of 0.4345424, which indicates that approximately 43.45% of the variance in RIO stock prices can be attributed to the fluctuations in Vernal's air quality. Moreover, the significance level of p < 0.01 underscores the robustness of this relationship, suggesting that the observed correlation is not a mere figment of statistical coincidence.
Furthermore, the scatterplot depicted in Fig. 1 illustrates the discernible pattern of the correlation, visually encapsulating the intriguing association between air pollution in Vernal and the movements of RIO stock prices. The scatterplot vividly portrays the titillating dance of these variables, akin to witnessing the unexpected tango of a fine dining connoisseur and an aficionado of air guitar.
In summary, our findings defy conventional wisdom, shedding light on the unanticipated influence of environmental factors on the fluctuations of a global corporation's stock price. This intricate dance between Vernal's vog and RIO's financial performance calls for further exploration, as it prompts us to question the underlying mechanisms at play. The significance of this correlation serves as a testament to the enigmatic and symbiotic relationship between seemingly distant phenomena, capturing the essence of the improbable and the delightful in the realm of financial research.
Stay tuned, dear readers, as we venture further into this captivating odyssey, navigating the uncharted waters where the breezy currents of air pollution may hold the key to deciphering the movements of financial markets. Take a deep breath, for the air is ripe with anticipation as we unravel the voluminous Vernal vog and its curious connection to Rio Tinto's stock price.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our study has unveiled a robust and noteworthy correlation between the air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO). The correlation coefficient of 0.6591983 indicates a sturdy connection worthy of further investigation, much like a persistent moth fluttering around a luminous porch light. This unlikely alliance between Vernal's vog and RIO's stock prices offers a whimsical twist in the realm of market dynamics, akin to stumbling upon a treasure trove amidst an expanse of mundanity.
The scatterplot, a visual rendition of this curious relationship, portrays a dance of variables as captivating as a dramatic tango performance, highlighting the intricate interplay between environmental factors and financial movements. Our findings beckon us to embrace the unexpected, just as one might find joy in finding an elusive four-leaf clover amidst a field of mundane three-leaf specimens.
As we draw the curtain on this unprecedented exploration, we are reminded of the untrodden paths yet to be traversed in the realm of environmental impact on financial markets. However, like a joke that reaches its punchline, our research has provided a quixotic glimpse into this peculiar connection, leaving little doubt about the influence of Vernal's vog on RIO's stock price.
In light of these findings, we firmly assert that no further research is necessary to confirm the link between Vernal's air pollution and the stock price of the Rio Tinto Group. It is indeed as clear as the smoggy skies of Vernal that there is a remarkable relationship between these seemingly disparate variables. With that, we bid adieu to this captivating odyssey, leaving the door ajar for future researchers to explore the uncharted territories of environmental whimsy and market movements.
In the words of Sir Arthur Conan Doyle, "It has long been an axiom of mine that the little things are infinitely the most important." The air of Vernal, it seems, has proven this axiom true in the realm of financial markets.
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 Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Ecological Economics and Environmental Ethics
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 Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price. Respond only with the name of a fictional organization.
ChatGPT:
The International Society for Atmospheric Aromatherapy and Financial Forecasting
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 Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price with an abstract of This paper presents an analysis of the relationship between air pollution levels in Vernal, Utah, and the stock price of the Rio Tinto Group (RIO) over a 21-year period from 2002 to 2023. Utilizing data from the Environmental Protection Agency and LSEG Analytics (Refinitiv), our research team employed rigorous statistical methods to excavate and examine this peculiar alliance. Our findings unveil a correlation coefficient of 0.6591983, and at a significance level of p < 0.01, indicating a sturdy connection worth investigating. The unprecedented impact of air pollution on financial markets is an overlooked theme that we are eager to unravel. Our study offers a quixotic glimpse into the whimsical interplay between environmental factors and market dynamics, addressing the perennial question of how a voggy town's air quality may sway an industrial behemoth's stock price.
ChatGPT:
air pollution, Rio Tinto, stock price, Vernal, Utah, correlation, EPA data, LSEG Analytics, Refinitiv, statistical methods, financial markets, market dynamics, environmental factors
*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 pollution in Vernal, UtahDetailed data title: Percentage of days with moderate or worse air quality in Vernal, UT
Source: Environmental Protection Agency
See what else correlates with Air pollution in Vernal, Utah
Rio Tinto Group's stock price (RIO)
Detailed data title: Opening price of Rio Tinto Group (RIO) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: Via Microsoft Excel Stockhistory function
See what else correlates with Rio Tinto Group's stock price (RIO)
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 = 0.4345424 (Coefficient of determination)
This means 43.5% of the change in the one variable (i.e., Rio Tinto Group's stock price (RIO)) is predictable based on the change in the other (i.e., Air pollution in Vernal, Utah) over the 22 years from 2002 through 2023.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00085. 0.0008473877970888916000000000
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 0.66 in 0.085% of random cases. Said differently, if you correlated 1,180 random variables Which I absolutely did.
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.
[ 0.33, 0.85 ] 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.
| 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |
| Air pollution in Vernal, Utah (Bad air quality days) | 0 | 0 | 0 | 0 | 0 | 41.1043 | 54.2169 | 30.315 | 54.7945 | 46.3014 | 52.459 | 63.2877 | 35.6164 | 31.7808 | 31.9672 | 43.0137 | 48.2192 | 38.9041 | 30.3279 | 39.1781 | 29.0411 | 52.7473 |
| Rio Tinto Group's stock price (RIO) (Stock price) | 19.58 | 19.95 | 28.04 | 29.87 | 46.49 | 52.5 | 105.28 | 23.75 | 55.73 | 72.31 | 51.56 | 60.3 | 55.65 | 45.73 | 28.24 | 38.84 | 53.95 | 46.45 | 59.67 | 78.98 | 67.5 | 71.03 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
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([0,0,0,0,0,41.1043,54.2169,30.315,54.7945,46.3014,52.459,63.2877,35.6164,31.7808,31.9672,43.0137,48.2192,38.9041,30.3279,39.1781,29.0411,52.7473,])
array_2 = np.array([19.58,19.95,28.04,29.87,46.49,52.5,105.28,23.75,55.73,72.31,51.56,60.3,55.65,45.73,28.24,38.84,53.95,46.45,59.67,78.98,67.5,71.03,])
array_1_name = "Air pollution in Vernal, Utah"
array_2_name = "Rio Tinto Group's stock price (RIO)"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.
For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."
When spoken, my last name is pronounced "vegan," like I don't eat meat.
Full license details.
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Download images for these variables:
- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only Air pollution in Vernal, Utah
- Line chart for only Rio Tinto Group's stock price (RIO)
- AI-generated correlation image
- The spurious research paper: Voluminous Vernal Vog: A Vicarious View of Air Pollution and Rio Tinto's RIO Stock Price
Big thanks for reviewing!
Correlation ID: 3157 · Black Variable ID: 21390 · Red Variable ID: 1631
