Spurious correlation #4,078 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn and the second variable is Coterra Energy's stock price (CTRA). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn and the second variable is Coterra Energy's stock price (CTRA). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As GMO corn production increased, it led to a surplus of corn syrup. This inadvertently fueled a spike in demand for sugary snacks and soft drinks, boosting the overall profitability of Coterra Energy, a company nobody realized had secretly been diversifying into the production of corn-based biofuels. It's like the corny version of a plot twist on Wall Street!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a hyper-realistic scene: In a vibrant field of tall green cornstalks, workers diligently tend to rows of genetically modified corn. Adjacent to the lively field, an imposing stock market chart displayed on a glowing screen depicts Coterra Energy's (CTRA) stock price soaring upward like a steep mountain. In the background, sugary snacks and soft drinks fly off the shelves of a convenience store, reflecting the skyrocketing demand for corn syrup-fueled products. The scene should capture the contrast between the idyllic agricultural setting and the frenetic pace of modern commerce. 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: GMO use in corn and Coterra Energy's stock price (CTRA). 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: Whimsical illustration Macabre Art nouveau Hyper-realistic Scalar art
ChatGPT: Hyper-realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in corn and Coterra Energy's stock price (CTRA), in the following situation: As GMO corn production increased, it led to a surplus of corn syrup. This inadvertently fueled a spike in demand for sugary snacks and soft drinks, boosting the overall profitability of Coterra Energy, a company nobody realized had secretly been diversifying into the production of corn-based biofuels. It's like the corny version of a plot twist on Wall Street!. Keep the description simple.
ChatGPT: In a lively field of tall green cornstalks, workers are busy tending to rows of genetically modified corn. Beside the vibrant field, an imposing stock market chart displayed on a glowing screen shows Coterra Energy's (CTRA) stock price soaring upward like a steep mountain. In the background, sugary snacks and soft drinks fly off the shelves of a convenience store, reflecting the skyrocketing demand for corn syrup-fueled products.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Hyper-realistic ] and depicting this scene: In a lively field of tall green cornstalks, workers are busy tending to rows of genetically modified corn. Beside the vibrant field, an imposing stock market chart displayed on a glowing screen shows Coterra Energy's (CTRA) stock price soaring upward like a steep mountain. In the background, sugary snacks and soft drinks fly off the shelves of a convenience store, reflecting the skyrocketing demand for corn syrup-fueled products. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a hyper-realistic scene: In a vibrant field of tall green cornstalks, workers diligently tend to rows of genetically modified corn. Adjacent to the lively field, an imposing stock market chart displayed on a glowing screen depicts Coterra Energy's (CTRA) stock price soaring upward like a steep mountain. In the background, sugary snacks and soft drinks fly off the shelves of a convenience store, reflecting the skyrocketing demand for corn syrup-fueled products. The scene should capture the contrast between the idyllic agricultural setting and the frenetic pace of modern commerce.

*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.
^{Show prompt for explanation} 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 GMO use in corn caused Coterra Energy's stock price (CTRA) to increase.

AI academic paper

(Because p < 0.01)

Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)

The Journal of Agricultural Economics and Financial Analysis

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} 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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 GMO use in corn and Coterra Energy's stock price (CTRA). 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
"Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)"

[[ABSTRACT]]
The ongoing debate regarding the use of genetically modified organisms (GMOs) in corn cultivation has sparked curiosity among stakeholders in both the agricultural and energy sectors. This study delves into the dynamic relationship between the adoption of GMOs in corn farming and the fluctuations in Coterra Energy's stock price (CTRA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we examined the correlation between these seemingly disparate variables over the period from 2002 to 2023. Our findings revealed a substantial correlation coefficient of 0.8543326 and a statistically significant p-value of less than 0.01, indicating a robust relationship deserving of further investigation. Through a unifying lens, this research not only sheds light on the interplay between agricultural practices and energy market dynamics but also presents an opportunity for fruitful discourse on the corny side of stock pricing.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
With the omnipresence of genetically modified organisms (GMOs) in modern agriculture, the interconnection between agricultural practices and the broader economic landscape has become a subject of great fascination and scrutiny. Specifically, the utilization of GMOs in corn cultivation has not only garnered attention within the agricultural domain but has also piqued the interest of financial analysts and energy investors alike. In this study, we traverse the fascinating terrain of GMO-infused cornfields and venture into the murky waters of stock market fluctuations, particularly with respect to the stock price of Coterra Energy (CTRA).
The juxtaposition of GMOs and stock prices may initially seem as incongruous as a tofu-loving vegan at a barbecue, but there is a method to this madness. The discerning eye will perceive the symbiotic relationship between these two seemingly disparate entities and recognize the potential for correlation, much like the complex interplay between the roots of a corn stalk and the sunlight that nourishes it.
Drawing upon data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on an empirical voyage to discern the hidden threads that bind GMO corn cultivation and the mercurial nature of Coterra Energy's stock price. Our findings not only unearth a substantial correlation coefficient but also unearth the latent potential for further exploration in this uncharted territory. As we traverse the ebbs and flows of data analysis, we are emboldened by the statistical revelation of a substantial, eyebrow-raising correlation coefficient of 0.8543326, coupled with a tantalizingly low p-value of less than 0.01. These findings, like a successful detective novel, lead us inexorably towards a conclusion that demands more attention and scrutiny.
Therefore, armed with statistical rigor and a touch of whimsy, this study endeavors to shed light on the enigmatic relationship between GMO-infused corn and stock market dynamics, presenting an opportunity for contemplation, exploration, and yes, perhaps even a few corny puns along the way. Join us as we peel back the layers of this intriguing cornucopia, where agricultural innovation meets the capricious dance of stock prices, in a quest to uncover the hidden kernels of truth and perhaps a few kernels for popcorn as well.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
LITERATURE REVIEW

The connection between genetically modified organisms (GMOs) in corn cultivation and stock market dynamics has been a subject of growing interest and speculative inquiry. Numerous studies have sought to unravel the intricate web of relationships between agricultural practices and financial markets, with a specific focus on the implications of GMO adoption in corn farming. Smith, Doe, and Jones (2015) conducted a thorough analysis of corn cultivation methods and their impact on commodity prices, providing valuable insights into the broader economic implications of GMO use in agriculture. Building on this foundation, extensive research has also been undertaken to explore the nuanced interactions between agricultural innovation and the financial sector, shedding light on the potential ripple effects of GMO adoption in corn cultivation (Doe, 2018; Jones, 2019).

Moving beyond the realm of academic research, pragmatic literature on agricultural economics and renewable energy investments offers valuable perspectives on the interplay between crop cultivation practices and market trends. "The Economics of Agricultural Development" by Dwight Huntington (2017) and "Renewable Energy Finance: Powering the Future" by Charles Renewable (2020) provide comprehensive insights into the multifaceted dimensions of agricultural economics and energy finance, setting the stage for a holistic exploration of the GMO-corn-energy nexus.

In the realm of fiction, thematic explorations of futuristic agricultural landscapes and speculative financial markets offer imaginative conjectures that intersect with the subject matter at hand. Works such as "The Farming of Bones" by Edwidge Danticat and "The Wealth of Nations" by Adam Smitten (1776) provide allegorical narratives and historical perspectives that, while not directly addressing GMO-corn correlations with stock prices, offer a different kind of food for thought.

Expanding the scope of literature review beyond traditional academic sources, the researchers adopted an unconventional approach to information gathering, diligently scrutinizing an eclectic array of materials. This included perusing grocery store receipts, examining duck poultry feed packaging, and engaging in spirited discussions with baristas at coffee shops in an endeavor to capture the tacit wisdom embedded in the everyday consumption and production patterns.

The synthesis of findings from these varied sources forms the kaleidoscopic backdrop against which the present study aims to unravel the captivating saga of GMO-infused cornfields and their enigmatic dance with the stock market, fueling the quest for uncovering kernels of knowledge and, undoubtedly, some pun-sational revelations.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 GMO use in corn and Coterra Energy's stock price (CTRA). 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 USDA 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]]
"Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)"

[[ABSTRACT]]
The ongoing debate regarding the use of genetically modified organisms (GMOs) in corn cultivation has sparked curiosity among stakeholders in both the agricultural and energy sectors. This study delves into the dynamic relationship between the adoption of GMOs in corn farming and the fluctuations in Coterra Energy's stock price (CTRA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we examined the correlation between these seemingly disparate variables over the period from 2002 to 2023. Our findings revealed a substantial correlation coefficient of 0.8543326 and a statistically significant p-value of less than 0.01, indicating a robust relationship deserving of further investigation. Through a unifying lens, this research not only sheds light on the interplay between agricultural practices and energy market dynamics but also presents an opportunity for fruitful discourse on the corny side of stock pricing.

[[INTRODUCTION]]
INTRODUCTION
With the omnipresence of genetically modified organisms (GMOs) in modern agriculture, the interconnection between agricultural practices and the broader economic landscape has become a subject of great fascination and scrutiny. Specifically, the utilization of GMOs in corn cultivation has not only garnered attention within the agricultural domain but has also piqued the interest of financial analysts and energy investors alike. In this study, we traverse the fascinating terrain of GMO-infused cornfields and venture into the murky waters of stock market fluctuations, particularly with respect to the stock price of Coterra Energy (CTRA).
The juxtaposition of GMOs and stock prices may initially seem as incongruous as a tofu-loving vegan at a barbecue, but there is a method to this madness. The discerning eye will perceive the symbiotic relationship between these two seemingly disparate entities and recognize the potential for correlation, much like the complex interplay between the roots of a corn stalk and the sunlight that nourishes it.
Drawing upon data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on an empirical voyage to discern the hidden threads that bind GMO corn cultivation and the mercurial nature of Coterra Energy's stock price. Our findings not only unearth a substantial correlation coefficient but also unearth the latent potential for further exploration in this uncharted territory. As we traverse the ebbs and flows of data analysis, we are emboldened by the statistical revelation of a substantial, eyebrow-raising correlation coefficient of 0.8543326, coupled with a tantalizingly low p-value of less than 0.01. These findings, like a successful detective novel, lead us inexorably towards a conclusion that demands more attention and scrutiny.
Therefore, armed with statistical rigor and a touch of whimsy, this study endeavors to shed light on the enigmatic relationship between GMO-infused corn and stock market dynamics, presenting an opportunity for contemplation, exploration, and yes, perhaps even a few corny puns along the way. Join us as we peel back the layers of this intriguing cornucopia, where agricultural innovation meets the capricious dance of stock prices, in a quest to uncover the hidden kernels of truth and perhaps a few kernels for popcorn as well.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In order to untangle the perplexing web of data and ascertain the veritable cornucopia of information required to explore the connection between the use of genetically modified organisms (GMOs) in corn cultivation and the fluctuation of Coterra Energy's stock price (CTRA), our research team employed a multifaceted methodology that was as intricate as a corn maze on a hot summer day.

First and foremost, we collated an extensive dataset spanning the years 2002 to 2023. This data was sourced from various reputable repositories, with a primary focus on data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), which served as the proverbial fields where we harvested our wealth of information. The dataset comprised a rich harvest of variables, including but not limited to, GMO adoption rates in corn cultivation, acreage of GMO corn, corn yield, commodity prices, and, of course, the ever-fluctuating stock prices of Coterra Energy (CTRA).

Employing a blend of econometric techniques that could make a hedge fund manager blush, our analysis revolved around the application of time series analysis, panel data models, and correlation matrices. As we delved deeper into the data, it became apparent that our approach needed to be as flexible as a stalk of corn swaying in the wind, adapting to the nuances and intricacies revealed in the data.

The heart of our methodology lay in the application of advanced statistical methods, carefully selected to tease out the subtle relationships that exist between the adoption of GMOs in corn cultivation and the undulating patterns of stock price movements. Indeed, our statistical analysis proved to be as rigorous as a corncob's kernels, ensuring that our findings were robust and as free from bias as a non-GMO organic farm.

With the data rigorously cultivated and the statistical models meticulously sown, we traversed the sprawling landscape of quantitative analysis, carefully tilling the soil of our data to reveal the hidden gems of insight that lay beneath the earth, akin to archaeologists unearthing ancient treasures in the sands of time.

The veracity of our findings was probed, prodded, and scrutinized, much like the quality control inspections in a corn processing plant, to ensure that our results were not merely kernels of truth but the entire cornucopia of empirical evidence, ripe for scholarly consumption.

And so, armed with a bevy of statistical tools and a touch of corny humor reminiscent of a dad joke at a summer cookout, we endeavored to peel back the layers of complexity surrounding the GMO-corn connection to Coterra Energy's stock price, giving testament to the fact that even in the world of academic research, there's always time for a little bit of wordplay amidst the sea of numbers and analysis.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 GMO use in corn and Coterra Energy's stock price (CTRA). 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]]
"Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)"

[[ABSTRACT]]
The ongoing debate regarding the use of genetically modified organisms (GMOs) in corn cultivation has sparked curiosity among stakeholders in both the agricultural and energy sectors. This study delves into the dynamic relationship between the adoption of GMOs in corn farming and the fluctuations in Coterra Energy's stock price (CTRA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we examined the correlation between these seemingly disparate variables over the period from 2002 to 2023. Our findings revealed a substantial correlation coefficient of 0.8543326 and a statistically significant p-value of less than 0.01, indicating a robust relationship deserving of further investigation. Through a unifying lens, this research not only sheds light on the interplay between agricultural practices and energy market dynamics but also presents an opportunity for fruitful discourse on the corny side of stock pricing.

[[LITERATURE REVIEW]]
LITERATURE REVIEW
The connection between genetically modified organisms (GMOs) in corn cultivation and stock market dynamics has been a subject of growing interest and speculative inquiry. Numerous studies have sought to unravel the intricate web of relationships between agricultural practices and financial markets, with a specific focus on the implications of GMO adoption in corn farming. Smith, Doe, and Jones (2015) conducted a thorough analysis of corn cultivation methods and their impact on commodity prices, providing valuable insights into the broader economic implications of GMO use in agriculture. Building on this foundation, extensive research has also been undertaken to explore the nuanced interactions between agricultural innovation and the financial sector, shedding light on the potential ripple effects of GMO adoption in corn cultivation (Doe, 2018; Jones, 2019).
Moving beyond the realm of academic research, pragmatic literature on agricultural economics and renewable energy investments offers valuable perspectives on the interplay between crop cultivation practices and market trends. "The Economics of Agricultural Development" by Dwight Huntington (2017) and "Renewable Energy Finance: Powering the Future" by Charles Renewable (2020) provide comprehensive insights into the multifaceted dimensions of agricultural economics and energy finance, setting the stage for a holistic exploration of the GMO-corn-energy nexus.
In the realm of fiction, thematic explorations of futuristic agricultural landscapes and speculative financial markets offer imaginative conjectures that intersect with the subject matter at hand. Works such as "The Farming of Bones" by Edwidge Danticat and "The Wealth of Nations" by Adam Smitten (1776) provide allegorical narratives and historical perspectives that, while not directly addressing GMO-corn correlations with stock prices, offer a different kind of food for thought.
Expanding the scope of literature review beyond traditional academic sources, the researchers adopted an unconventional approach to information gathering, diligently scrutinizing an eclectic array of materials. This included perusing grocery store receipts, examining duck poultry feed packaging, and engaging in spirited discussions with baristas at coffee shops in an endeavor to capture the tacit wisdom embedded in the everyday consumption and production patterns.
The synthesis of findings from these varied sources forms the kaleidoscopic backdrop against which the present study aims to unravel the captivating saga of GMO-infused cornfields and their enigmatic dance with the stock market, fueling the quest for uncovering kernels of knowledge and, undoubtedly, some pun-sational revelations.

[[RESULTS]]
The analysis of the data from 2002 to 2023 revealed a substantial correlation between the use of genetically modified organisms (GMOs) in corn cultivation and the stock price of Coterra Energy (CTRA). The correlation coefficient, a robust 0.8543326, suggested a strong relationship between these seemingly unrelated variables. As statistically inclined voyagers navigating the turbulent seas of data, we calculated an r-squared of 0.7298841, signaling that a whopping 72.98% of the variance in CTRA's stock price can be explained by changes in GMO use in corn. In addition, the p-value of less than 0.01 bore witness to the statistical significance and served as a towering beacon of credibility, beckoning the skeptical and the incredulous to embrace the undeniable connection.
As a visual treat, Fig. 1 presents a scatterplot illustrating the striking correlation between GMO use in corn and Coterra Energy's stock price. One cannot help but marvel at the alignment of data points that seem to sashay in harmony, as if cornstalks and stock prices were engaging in a spirited dance of statistical significance. It is a sight to behold, akin to witnessing the synchronized movements of a well-practiced flash mob, but with numbers instead of nimble dancers.
Together, these findings not only establish a compelling case for further inquiry but also invite contemplation on the interconnectedness of agricultural practices and energy market dynamics. This resounding discovery demands attention and prompts us to ponder the intricacies of corny energy and its impact on stock prices.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study validate and extend prior research exploring the interplay between genetically modified organisms (GMOs) in corn cultivation and stock market dynamics. Previous studies have posited a potential link between agricultural practices and financial markets, and our results bolster these assertions with compelling statistical evidence. The substantial correlation coefficient of 0.8543326, supported by a statistically significant p-value of less than 0.01, fortifies the argument for a robust relationship between GMO use in corn and Coterra Energy's stock price (CTRA).

Drawing upon the literature review, which, we might add, incorporated an eclectic mix of sources, including grocery store receipts and spirited exchanges with baristas, we acknowledge the multifaceted nature of this research endeavor. While our findings align with the serious scholarly work in this area, we are acutely aware of the unexpected tangential influences that may have subtly woven themselves into the fabric of our analysis. The shower thoughts resulting from contemplating cereal boxes and the whispered wisdom of coffee aficionados may have, inadvertently, imparted a sprinkle of serendipitous brilliance to our investigations.

Moreover, the literature review, with its seemingly offbeat references to fiction and historical treatises, has endowed this study with a sense of whimsy and intellectual curiosity that transcends the traditional confines of academic inquiry. The kaleidoscopic tapestry of research sources, spanning from economic treatises to imaginative storytelling, underscores the rich and varied tapestry of human knowledge, reminiscent of a hearty cornucopia of insights.

One delightful revelation that emerged from our data analysis is the visual representation of the correlation between GMO use in corn and Coterra Energy's stock price. Our scatterplot, depicted with appropriate scholarly gravitas as Fig. 1, not only serves as a testament to the statistical relationship we uncovered but also presents a charming spectacle of data points frolicking in alignment. It is, to put it plainly, a veritable ballroom dance of numerical significance, where corn and finance meet in a captivating waltz of interdependence.

In concluding this discussion, we underscore the need for continued exploration into the nuanced connections between agricultural innovations, energy markets, and financial dynamics. This study, with its statistical acumen and surreptitious charm, adds a kernel of insight to the ongoing discourse on the corny side of stock pricing and beckons further scholarly pursuit into the captivating saga of GMO-infused cornfields and their enigmatic dance with the stock market.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 GMO use in corn and Coterra Energy's stock price (CTRA). 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]]
"Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)"

[[ABSTRACT]]
The ongoing debate regarding the use of genetically modified organisms (GMOs) in corn cultivation has sparked curiosity among stakeholders in both the agricultural and energy sectors. This study delves into the dynamic relationship between the adoption of GMOs in corn farming and the fluctuations in Coterra Energy's stock price (CTRA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we examined the correlation between these seemingly disparate variables over the period from 2002 to 2023. Our findings revealed a substantial correlation coefficient of 0.8543326 and a statistically significant p-value of less than 0.01, indicating a robust relationship deserving of further investigation. Through a unifying lens, this research not only sheds light on the interplay between agricultural practices and energy market dynamics but also presents an opportunity for fruitful discourse on the corny side of stock pricing.

[[INTRDUCTION]]
INTRODUCTION
With the omnipresence of genetically modified organisms (GMOs) in modern agriculture, the interconnection between agricultural practices and the broader economic landscape has become a subject of great fascination and scrutiny. Specifically, the utilization of GMOs in corn cultivation has not only garnered attention within the agricultural domain but has also piqued the interest of financial analysts and energy investors alike. In this study, we traverse the fascinating terrain of GMO-infused cornfields and venture into the murky waters of stock market fluctuations, particularly with respect to the stock price of Coterra Energy (CTRA).
The juxtaposition of GMOs and stock prices may initially seem as incongruous as a tofu-loving vegan at a barbecue, but there is a method to this madness. The discerning eye will perceive the symbiotic relationship between these two seemingly disparate entities and recognize the potential for correlation, much like the complex interplay between the roots of a corn stalk and the sunlight that nourishes it.
Drawing upon data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), we embarked on an empirical voyage to discern the hidden threads that bind GMO corn cultivation and the mercurial nature of Coterra Energy's stock price. Our findings not only unearth a substantial correlation coefficient but also unearth the latent potential for further exploration in this uncharted territory. As we traverse the ebbs and flows of data analysis, we are emboldened by the statistical revelation of a substantial, eyebrow-raising correlation coefficient of 0.8543326, coupled with a tantalizingly low p-value of less than 0.01. These findings, like a successful detective novel, lead us inexorably towards a conclusion that demands more attention and scrutiny.
Therefore, armed with statistical rigor and a touch of whimsy, this study endeavors to shed light on the enigmatic relationship between GMO-infused corn and stock market dynamics, presenting an opportunity for contemplation, exploration, and yes, perhaps even a few corny puns along the way. Join us as we peel back the layers of this intriguing cornucopia, where agricultural innovation meets the capricious dance of stock prices, in a quest to uncover the hidden kernels of truth and perhaps a few kernels for popcorn as well.

[[RESULTS]]
The analysis of the data from 2002 to 2023 revealed a substantial correlation between the use of genetically modified organisms (GMOs) in corn cultivation and the stock price of Coterra Energy (CTRA). The correlation coefficient, a robust 0.8543326, suggested a strong relationship between these seemingly unrelated variables. As statistically inclined voyagers navigating the turbulent seas of data, we calculated an r-squared of 0.7298841, signaling that a whopping 72.98% of the variance in CTRA's stock price can be explained by changes in GMO use in corn. In addition, the p-value of less than 0.01 bore witness to the statistical significance and served as a towering beacon of credibility, beckoning the skeptical and the incredulous to embrace the undeniable connection.
As a visual treat, Fig. 1 presents a scatterplot illustrating the striking correlation between GMO use in corn and Coterra Energy's stock price. One cannot help but marvel at the alignment of data points that seem to sashay in harmony, as if cornstalks and stock prices were engaging in a spirited dance of statistical significance. It is a sight to behold, akin to witnessing the synchronized movements of a well-practiced flash mob, but with numbers instead of nimble dancers.
Together, these findings not only establish a compelling case for further inquiry but also invite contemplation on the interconnectedness of agricultural practices and energy market dynamics. This resounding discovery demands attention and prompts us to ponder the intricacies of corny energy and its impact on stock prices.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unraveled a cornucopia of insights into the entwined relationship between GMO-infused corn and the stock price of Coterra Energy (CTRA). The robust correlation coefficient and eyebrow-raising r-squared value paint a compelling picture of the influence of agricultural practices on energy market dynamics. One might say it's as clear as corn syrup!

The tantalizing p-value beckons further scrutiny, much like the irresistible scent of freshly popped corn wafting through a movie theater. The scatterplot visually encapsulates the harmonious dance of data points, reminiscent of a well-choreographed ballet – just with more numerical grace and less tulle.

Our findings not only shed light on the intricate interplay between agriculture and finance but also invite contemplation on the corny side of stock pricing. As we wrap up our research, one thing is certain: the connection between GMO corn and CTRA stock price is as undeniable as the crunch of a perfectly salted kernel.

In light of these compelling results, we firmly assert that no more research into this area is needed. It's time to let this GMO-corn and stock price love story blossom into its full potential, much like a cob of corn reaching its golden, market-influencing glory.

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 Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA). Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agricultural Economics and Financial Analysis

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 Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA). Respond only with the name of a fictional organization.

ChatGPT:

The Society for Agricultural Advancement and Renewable Technologies (SAART)

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 Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA) with an abstract of The ongoing debate regarding the use of genetically modified organisms (GMOs) in corn cultivation has sparked curiosity among stakeholders in both the agricultural and energy sectors. This study delves into the dynamic relationship between the adoption of GMOs in corn farming and the fluctuations in Coterra Energy's stock price (CTRA). Leveraging data from the USDA and LSEG Analytics (Refinitiv), we examined the correlation between these seemingly disparate variables over the period from 2002 to 2023. Our findings revealed a substantial correlation coefficient of 0.8543326 and a statistically significant p-value of less than 0.01, indicating a robust relationship deserving of further investigation. Through a unifying lens, this research not only sheds light on the interplay between agricultural practices and energy market dynamics but also presents an opportunity for fruitful discourse on the corny side of stock pricing.

ChatGPT:

GMO corn, corn cultivation, agricultural practices, energy sector, Coterra Energy, CTRA stock price, USDA data, LSEG Analytics, GMO-Corn Connection, stock pricing, correlation analysis, genetically modified organisms, corn farming, energy 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

GMO use in corn
Detailed data title: Percent of all corn planted in United States genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in corn

Coterra Energy's stock price (CTRA)
Detailed data title: Opening price of Coterra Energy (CTRA) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with Coterra Energy's stock price (CTRA)

Correlation r = 0.8543326 (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 = 0.7298841 (Coefficient of determination)
This means 73% of the change in the one variable (i.e., Coterra Energy's stock price (CTRA)) is predictable based on the change in the other (i.e., GMO use in corn) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.2E-7.^Show 0.0000004195907170676850000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note 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 0.85 in 4.2E-5% of random cases. Said differently, if you correlated 2,383,275 random variables^Note You don't actually need 2 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, ^Note 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.

[ 0.68, 0.94 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} 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: ^Note 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
GMO use in corn (GMO corn %)	2	4	6	9	15	28	40	46	47	49	52	71	76	77	76	77	80	80	79	81	81	82
Coterra Energy's stock price (CTRA) (Stock price)	2	2.08	2.45	3.68	5.7	7.49	10.07	6.58	11.16	9.56	19.53	25.37	38.57	29.47	17.67	22.98	28.31	22.1	17.52	16.49	18.9	24.2

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^Note 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^Note 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.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} 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([2,4,6,9,15,28,40,46,47,49,52,71,76,77,76,77,80,80,79,81,81,82,])
array_2 = np.array([2,2.08,2.45,3.68,5.7,7.49,10.07,6.58,11.16,9.56,19.53,25.37,38.57,29.47,17.67,22.98,28.31,22.1,17.52,16.49,18.9,24.2,])
array_1_name = "GMO use in corn"
array_2_name = "Coterra Energy's stock price (CTRA)"

# 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. ^Note 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.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note 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 GMO use in corn
Line chart for only Coterra Energy's stock price (CTRA)
AI-generated correlation image
The spurious research paper: Stocking Up on Corny Energy: Exploring the GMO-Corn Connection to Coterra Energy's Stock Price (CTRA)

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Correlation ID: 4078 · Black Variable ID: 752 · Red Variable ID: 1908


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