Spurious correlation #1,331 · 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 grown in South Dakota and the second variable is Rogers Communications' stock price (RCI). 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 grown in South Dakota and the second variable is Rogers Communications' stock price (RCI). The chart goes from 2002 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As South Dakota corn became more genetically modified, it developed a natural ability to transmit positive vibes. These vibes were inexplicably in sync with Rogers Communications' business philosophy, creating a strange but undeniable link between the two. As a result, the stock price of Rogers Communications (RCI) experienced an unexpected boost, riding the wave of good vibes all the way to financial success. It seems the key to a fruitful investment may just be growing GMO corn in sync with the stock market!

^{Show image generation prompt} Model: dalle-3
Prompt: Assuming the style of "Futuristic," the prompt would be: "In a futuristic setting, a scene unfolds where in a vast cornfield in South Dakota, farmers are tending to genetically modified crops. The genetically engineered corn tower over the landscape with strong, resilient stalks and lush green leaves, showcasing their modified traits. Meanwhile, in a bustling financial district, a massive digital screen prominently displays the fluctuating stock price of Rogers Communications. Traders are intensely monitoring these movements, while the iconic Rogers Communications logo is prominently featured on the futuristic screen." 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 situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: Because the genetically modified corn in South Dakota caused a cornucopia of delicious kernels, people's love for corn grew as fast as the crops, causing a surge in demand for corn-based products, including high-fructose corn syrup in soft drinks. This sugar rush led to an increase in consumers' cravings for sugary drinks, which in turn boosted the stock price of Rogers Communications, as people sought out more entertainment to balance their sugar highs!. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: 3D Pixar Style Low poly Futuristic Scalar art Charcoal
ChatGPT: Futuristic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that prominently depicts both GMO use in corn grown in South Dakota and Rogers Communications' stock price Keep the description simple.
ChatGPT: In a vast cornfield in South Dakota, farmers are using genetically modified seeds to grow abundant crops. The strong stalks of genetically modified corn stand tall, with lush green leaves and mature golden ears. Meanwhile, in a bustling financial district, a large digital screen displays the fluctuating stock price of Rogers Communications. Traders are closely monitoring the movements, while the iconic Rogers Communications logo is prominently displayed on the screen.

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: [ Futuristic ] and depicting this scene: In a vast cornfield in South Dakota, farmers are using genetically modified seeds to grow abundant crops. The strong stalks of genetically modified corn stand tall, with lush green leaves and mature golden ears. Meanwhile, in a bustling financial district, a large digital screen displays the fluctuating stock price of Rogers Communications. Traders are closely monitoring the movements, while the iconic Rogers Communications logo is prominently displayed on the screen. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Assuming the style of "Futuristic," the prompt would be: "In a futuristic setting, a scene unfolds where in a vast cornfield in South Dakota, farmers are tending to genetically modified crops. The genetically engineered corn tower over the landscape with strong, resilient stalks and lush green leaves, showcasing their modified traits. Meanwhile, in a bustling financial district, a massive digital screen prominently displays the fluctuating stock price of Rogers Communications. Traders are intensely monitoring these movements, while the iconic Rogers Communications logo is prominently featured on the futuristic screen."

*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 grown in South Dakota caused Rogers Communications' stock price (RCI) to increase.

AI academic paper

(Because p < 0.01)

GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection

The Journal of Agricultural Finance and Crop Economics

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Rogers Communications' stock price (RCI). 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection"

[[ABSTRACT]]
This study explores the unexpected link between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). By meticulously analyzing extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 were observed for the period spanning from 2002 to 2023. The findings invite further investigation into the complex and intricate interactions between seemingly disparate economic and agricultural factors, shedding light on the mysterious forces that influence stock prices in peculiar ways.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The exploration of the intricate relationship between seemingly unrelated phenomena has long been a pursuit of great interest to researchers in various disciplines. In this study, we embark upon an investigation that, at first glance, may seem as unrelated as apples and oranges, or perhaps in this case, as unrelated as maize and telecommunications. Our curiosity was piqued by the striking correlation between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI), a Canadian telecommunications company. The confluence of these two seemingly unrelated entities led us down a maize of unexpected discoveries and a-MAIZE-ing insights into the complex interplay between agricultural practices and stock market dynamics.
As we delved into this seemingly peculiar connection, it became evident that the intertwining roots of GMO corn and stock prices were not merely a kernel of truth but a bushel of compelling data. The unexpected correlation that emerged from our rigorous analysis of extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv) gave rise to a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 over the period from 2002 to 2023. These findings not only prompt a reevaluation of our preconceived notions about the factors that influence stock prices but also sow the seeds of further inquiry into the captivating interplay between agricultural practices and economic indicators.
In this paper, we present the methodical examination of the relationship between GMO corn production and RCI stock prices, uncovering unexpected connections that challenge conventional wisdom and stimulate the imagination. Our endeavor to cultivate a deeper understanding of this unlikely relationship offers a fresh perspective on the influence of agricultural practices on the ebb and flow of stock market dynamics. As we navigate through this maze of unexpected correlations, we invite the reader to join us in savoring the a-MAIZE-ing twists and turns that this exploration has to offer.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that genetically modified organisms (GMOs) have had a profound impact on agricultural practices, revolutionizing the cultivation of crops such as corn and soybeans. The utilization of GMO technology has resulted in improved pest resistance, higher yields, and reduced production costs, thereby exerting significant influence on the agricultural landscape. Similarly, "Doe and Jones" present compelling evidence regarding the intricate interplay between technological innovation and agricultural output, suggesting that the adoption of GMOs has altered the dynamics of crop production and supply chain management.

However, as we venture further into the realm of unexpected connections, it becomes apparent that the relationship between GMO corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI) is as unusual as a cow wearing a telephone as a necklace. In "Real Economic Stuff," the authors delve into the enigmatic forces that drive stock prices, emphasizing the myriad economic, political, and market-related factors that underlie stock market dynamics. Meanwhile, "Financial Factors Unraveled" sheds light on the complex web of financial indicators that can impact stock prices, revealing a labyrinth of influences that seemingly defy conventional logic.

As we continue our journey down this uncharted path, it is worth considering the potential impact of fictional works that may offer unexpected insights. "The Maize Mysteries" and "Telecom Tales" captivate the imagination with their tantalizing narratives, weaving tales of speculative intrigue and convoluted connections. Additionally, board games such as "Agricola: The Phone Expansion" and "Stock Market Shenanigans" beckon with their peculiar amalgamation of agricultural themes and financial maneuvering, posing thought-provoking scenarios that blur the lines between reality and whimsy.

In light of these varied sources of inspiration, we are poised to delve into the a-MAIZE-ing terrain of unexpected correlations and delightful detours, unveiling the captivating interplay between GMO corn cultivation and RCI stock prices. As we proceed with our analysis, we invite the reader to embrace the whimsical possibilities that await, while maintaining a discerning eye for the kernels of truth that lie beneath the surface.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Rogers Communications' stock price (RCI). 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]]
"GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection"

[[ABSTRACT]]
This study explores the unexpected link between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). By meticulously analyzing extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 were observed for the period spanning from 2002 to 2023. The findings invite further investigation into the complex and intricate interactions between seemingly disparate economic and agricultural factors, shedding light on the mysterious forces that influence stock prices in peculiar ways.

[[INTRODUCTION]]
INTRODUCTION
The exploration of the intricate relationship between seemingly unrelated phenomena has long been a pursuit of great interest to researchers in various disciplines. In this study, we embark upon an investigation that, at first glance, may seem as unrelated as apples and oranges, or perhaps in this case, as unrelated as maize and telecommunications. Our curiosity was piqued by the striking correlation between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI), a Canadian telecommunications company. The confluence of these two seemingly unrelated entities led us down a maize of unexpected discoveries and a-MAIZE-ing insights into the complex interplay between agricultural practices and stock market dynamics.
As we delved into this seemingly peculiar connection, it became evident that the intertwining roots of GMO corn and stock prices were not merely a kernel of truth but a bushel of compelling data. The unexpected correlation that emerged from our rigorous analysis of extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv) gave rise to a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 over the period from 2002 to 2023. These findings not only prompt a reevaluation of our preconceived notions about the factors that influence stock prices but also sow the seeds of further inquiry into the captivating interplay between agricultural practices and economic indicators.
In this paper, we present the methodical examination of the relationship between GMO corn production and RCI stock prices, uncovering unexpected connections that challenge conventional wisdom and stimulate the imagination. Our endeavor to cultivate a deeper understanding of this unlikely relationship offers a fresh perspective on the influence of agricultural practices on the ebb and flow of stock market dynamics. As we navigate through this maze of unexpected correlations, we invite the reader to join us in savoring the a-MAIZE-ing twists and turns that this exploration has to offer.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The data utilized in this study were procured from various sources, primarily drawing from the databases of the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv) for the period spanning from 2002 to 2023. The dataset comprises information on the cultivation of genetically modified organism (GMO) corn in South Dakota and the historical stock prices of Rogers Communications Inc. (RCI).

To establish the relationship between the cultivation of GMO corn and RCI stock prices, a multifaceted approach was undertaken. Initially, extensive research was conducted to ascertain the prevalence and distribution of GMO corn cultivation in South Dakota. The data encompassed various metrics such as acreage under GMO corn, adoption rates, and technological advancements in GMO cultivation practices.

Simultaneously, the stock price of RCI and pertinent financial indicators were analyzed, taking into account market trends, corporate performance, and external economic variables. The research team also delved into the broader context of the telecommunications sector to contextualize RCI's performance within the industry landscape.

Subsequently, a complex statistical analysis involving advanced econometric techniques and machine learning algorithms was employed to delineate the intricate interplay between GMO corn cultivation and RCI stock prices. The correlation between these seemingly disparate variables was examined with meticulous attention to detail, accounting for potential confounding factors and spurious relationships.

Moreover, various models and hypotheses were considered and tested to discern the causality and temporal dynamics underlying the relationship between GMO corn cultivation and RCI stock prices. Utilizing state-of-the-art data visualization tools and econometric software, the research team conducted a granular assessment of the data to elucidate the nuances of the association.

The temporal aspect of the relationship was scrutinized through time series analysis, employing sophisticated techniques such as autoregressive integrated moving average (ARIMA) modeling and Granger causality tests to capture the temporal dynamics of the intertwined variables.

Furthermore, to address the multidimensionality of the research question, a comprehensive sensitivity analysis was conducted to assess the robustness of the findings and validate the stability of the observed correlations across diverse scenarios and sub-periods.

In sum, a comprehensive and rigorous methodological framework was meticulously crafted to unravel the unexpected linkage between GMO corn cultivation in South Dakota and the stock prices of Rogers Communications Inc. (RCI). The intricate interplay of agricultural practices and stock market dynamics was scrutinized with scholarly precision and analytical depth, yielding a compelling insight into the whimsical forces that influence stock prices in peculiar and often unanticipated ways.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Rogers Communications' stock price (RCI). 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]]
"GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection"

[[ABSTRACT]]
This study explores the unexpected link between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). By meticulously analyzing extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 were observed for the period spanning from 2002 to 2023. The findings invite further investigation into the complex and intricate interactions between seemingly disparate economic and agricultural factors, shedding light on the mysterious forces that influence stock prices in peculiar ways.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that genetically modified organisms (GMOs) have had a profound impact on agricultural practices, revolutionizing the cultivation of crops such as corn and soybeans. The utilization of GMO technology has resulted in improved pest resistance, higher yields, and reduced production costs, thereby exerting significant influence on the agricultural landscape. Similarly, "Doe and Jones" present compelling evidence regarding the intricate interplay between technological innovation and agricultural output, suggesting that the adoption of GMOs has altered the dynamics of crop production and supply chain management.
However, as we venture further into the realm of unexpected connections, it becomes apparent that the relationship between GMO corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI) is as unusual as a cow wearing a telephone as a necklace. In "Real Economic Stuff," the authors delve into the enigmatic forces that drive stock prices, emphasizing the myriad economic, political, and market-related factors that underlie stock market dynamics. Meanwhile, "Financial Factors Unraveled" sheds light on the complex web of financial indicators that can impact stock prices, revealing a labyrinth of influences that seemingly defy conventional logic.
As we continue our journey down this uncharted path, it is worth considering the potential impact of fictional works that may offer unexpected insights. "The Maize Mysteries" and "Telecom Tales" captivate the imagination with their tantalizing narratives, weaving tales of speculative intrigue and convoluted connections. Additionally, board games such as "Agricola: The Phone Expansion" and "Stock Market Shenanigans" beckon with their peculiar amalgamation of agricultural themes and financial maneuvering, posing thought-provoking scenarios that blur the lines between reality and whimsy.
In light of these varied sources of inspiration, we are poised to delve into the a-MAIZE-ing terrain of unexpected correlations and delightful detours, unveiling the captivating interplay between GMO corn cultivation and RCI stock prices. As we proceed with our analysis, we invite the reader to embrace the whimsical possibilities that await, while maintaining a discerning eye for the kernels of truth that lie beneath the surface.

[[RESULTS]]
The analysis of the connection between GMO use in corn grown in South Dakota and the stock price of Rogers Communications Inc. (RCI) yielded a striking correlation coefficient of 0.9321962, indicative of a remarkably strong positive relationship between these seemingly disparate variables. The coefficient of determination (r-squared) further underscored the robustness of the relationship, demonstrating that approximately 86.9% of the variability in RCI stock prices could be explained by changes in GMO corn usage in South Dakota. Furthermore, the statistical significance of this relationship was confirmed by a p-value of less than 0.01, indicating an extremely low probability of observing such a strong association by chance alone.
Fig. 1, not to be corny, depicts the scatterplot illustrating this substantial correlation between GMO corn usage in South Dakota and RCI stock prices. The figure emphasizes the pronounced linear trend, visually reinforcing the compelling statistical findings of this investigation.
These results, while seemingly ear-responsible, raise intriguing questions about the potential mechanisms underlying this unexpected relationship. The empirical evidence presented here challenges conventional assumptions about the influences on stock prices, highlighting the need for further research into the unanticipated interplay between agricultural practices and economic indicators.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The noteworthy correlation between GMO use in corn grown in South Dakota and Rogers Communications Inc. (RCI) stock prices has prompted a maize of intriguing speculation and potential explanations. Our findings, which indeed seem to have ear-marked a surprisingly substantial relationship, are in line with prior research on the transformative impact of GMO technology on agricultural production. The revelations of Smith et al. and Doe and Jones regarding the enhanced robustness and yield of GMO crops provide a compelling backdrop for our unexpected discovery. These prior accounts serve as a solid foundation upon which to sow the seeds of our investigation into the unanticipated connection between GMO corn cultivation and RCI stock prices.

While the relationship at hand may appear as peculiar as a cow donning a telephone necklace, it is imperative to heed the empirical evidence that emerges from our meticulous analysis. The robust correlation coefficient and the remarkably low p-value suggest that the association between GMO corn usage in South Dakota and RCI stock prices is not to be dismissed as mere crop-corn. Instead, these findings compel us to consider the possibility of underlying mechanisms or external factors that could be driving this unlikely correlation.

Our results echo the sentiments expressed by "Real Economic Stuff" and "Financial Factors Unraveled," underscoring the complex and multi-faceted nature of stock price determination. Just as unsuspected agricultural bovine-maneuvers may catch the farmer off-guard, so too do the intricate market forces at play in influencing stock prices. Thus, the enigma of this relationship prompts further investigation into the a-MAIZE-ing terrain of agricultural and economic interplay.

It is crucial to maintain a discerning eye for the kernels of truth amidst the whimsically fertile soil of this unexpected correlation. While the idea of cornstalks influencing stock prices may elicit a chuckle, the statistically robust findings compel us to dig deeper into the bushels of data and unearth the underlying factors that may be at play. This a-MAIZE-ing connection challenges conventional wisdom, prompting researchers to weed through the possibilities and ponder the unexpected rippling effects of agricultural practices on stock market dynamics.

In conclusion, the unexpected connection between GMO corn cultivation in South Dakota and RCI stock prices may seem as unlikely as a cow wearing a telephone as a necklace, but our rigorous analysis presents compelling evidence of a robust statistical relationship. The improbable nature of this connection beckons further investigation into the a-MAIZE-ing interplay between agricultural practices and stock market dynamics, offering fertile ground for future explorations.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Rogers Communications' stock price (RCI). 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]]
"GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection"

[[ABSTRACT]]
This study explores the unexpected link between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). By meticulously analyzing extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 were observed for the period spanning from 2002 to 2023. The findings invite further investigation into the complex and intricate interactions between seemingly disparate economic and agricultural factors, shedding light on the mysterious forces that influence stock prices in peculiar ways.

[[INTRDUCTION]]
INTRODUCTION
The exploration of the intricate relationship between seemingly unrelated phenomena has long been a pursuit of great interest to researchers in various disciplines. In this study, we embark upon an investigation that, at first glance, may seem as unrelated as apples and oranges, or perhaps in this case, as unrelated as maize and telecommunications. Our curiosity was piqued by the striking correlation between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI), a Canadian telecommunications company. The confluence of these two seemingly unrelated entities led us down a maize of unexpected discoveries and a-MAIZE-ing insights into the complex interplay between agricultural practices and stock market dynamics.
As we delved into this seemingly peculiar connection, it became evident that the intertwining roots of GMO corn and stock prices were not merely a kernel of truth but a bushel of compelling data. The unexpected correlation that emerged from our rigorous analysis of extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv) gave rise to a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 over the period from 2002 to 2023. These findings not only prompt a reevaluation of our preconceived notions about the factors that influence stock prices but also sow the seeds of further inquiry into the captivating interplay between agricultural practices and economic indicators.
In this paper, we present the methodical examination of the relationship between GMO corn production and RCI stock prices, uncovering unexpected connections that challenge conventional wisdom and stimulate the imagination. Our endeavor to cultivate a deeper understanding of this unlikely relationship offers a fresh perspective on the influence of agricultural practices on the ebb and flow of stock market dynamics. As we navigate through this maze of unexpected correlations, we invite the reader to join us in savoring the a-MAIZE-ing twists and turns that this exploration has to offer.

[[RESULTS]]
The analysis of the connection between GMO use in corn grown in South Dakota and the stock price of Rogers Communications Inc. (RCI) yielded a striking correlation coefficient of 0.9321962, indicative of a remarkably strong positive relationship between these seemingly disparate variables. The coefficient of determination (r-squared) further underscored the robustness of the relationship, demonstrating that approximately 86.9% of the variability in RCI stock prices could be explained by changes in GMO corn usage in South Dakota. Furthermore, the statistical significance of this relationship was confirmed by a p-value of less than 0.01, indicating an extremely low probability of observing such a strong association by chance alone.
Fig. 1, not to be corny, depicts the scatterplot illustrating this substantial correlation between GMO corn usage in South Dakota and RCI stock prices. The figure emphasizes the pronounced linear trend, visually reinforcing the compelling statistical findings of this investigation.
These results, while seemingly ear-responsible, raise intriguing questions about the potential mechanisms underlying this unexpected relationship. The empirical evidence presented here challenges conventional assumptions about the influences on stock prices, highlighting the need for further research into the unanticipated interplay between agricultural practices and economic indicators.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study illuminate an a-MAIZE-ing connection between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). The remarkably strong positive relationship, represented by a correlation coefficient of 0.9321962, reveals a kernel of truth - or shall we say, a kernel of corn - in the intricate web of economic and agricultural dynamics. It is indeed ear-responsible to witness the significant impact of GMO corn usage on RCI stock prices, a revelation that has ripened the discourse on the multifaceted influences shaping financial markets.

The robustness of the observed relationship, as indicated by the coefficient of determination, suggests that approximately 86.9% of the variability in RCI stock prices can be attributed to changes in GMO corn usage in South Dakota. This substantial explanatory power not only underscores the a-peel of GMO corn as an economic indicator but also emphasizes the pivotal role of agricultural practices in driving stock market fluctuations.

While the statistical significance of this relationship has been established through a p-value of less than 0.01, it is clear that the tale of GMO corn and RCI stock prices is no mere corny anecdote but a compelling narrative that demands further exploration. The scatterplot depicting this striking correlation may appear corny at first glance, but it serves as a visual testament to the robustness of our findings. Together, these results challenge conventional assumptions, inviting the scholarly community to sow the seeds of inquiry into the captivating interplay between agriculture and finance.

In light of these revelatory findings, it is evident that the complex and intricate interactions between seemingly disparate domains merit continued investigation. However, it is the firm belief of this researcher that the a-MAIZE-ing discoveries presented herein mark a pivotal milestone in understanding the captivating forces at play. As such, it is asserted with due conviction that no further research is ear-marked in this area.

Thank you for your attention to this important but, dare we say, corn-plexing matter.

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 GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agricultural Finance and Crop Economics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Agricultural Advancements and Research (SAAR)

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 GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection with an abstract of This study explores the unexpected link between the use of genetically modified organisms (GMOs) in corn cultivation in South Dakota and the stock price of Rogers Communications Inc. (RCI). By meticulously analyzing extensive data from the United States Department of Agriculture (USDA) and LSEG Analytics (Refinitiv), a striking correlation coefficient of 0.9321962 and a p-value less than 0.01 were observed for the period spanning from 2002 to 2023. The findings invite further investigation into the complex and intricate interactions between seemingly disparate economic and agricultural factors, shedding light on the mysterious forces that influence stock prices in peculiar ways.

ChatGPT:

GMO corn, genetically modified organisms, corn cultivation, South Dakota, stock price, Rogers Communications Inc., RCI, correlation coefficient, USDA data, LSEG Analytics, Refinitiv, p-value, agriculture, economic factors, agricultural influences, stock price correlation

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Data details

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

Rogers Communications' stock price (RCI)
Detailed data title: Opening price of Rogers Communications (RCI) 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 Rogers Communications' stock price (RCI)

Correlation r = 0.9321962 (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 = 0.8689898 (Coefficient of determination)
This means 86.9% of the change in the one variable (i.e., Rogers Communications' stock price (RCI)) is predictable based on the change in the other (i.e., GMO use in corn grown in South Dakota) over the 22 years from 2002 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.8E-10.^Show 0.0000000002796467477148414000
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 0.93 in 2.8E-8% of random cases. Said differently, if you correlated 3,575,940,032 random variables^Note You don't actually need 3 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

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

[ 0.84, 0.97 ] 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 grown in South Dakota (GMO corn %)	10	17	21	22	34	43	58	65	60	64	62	82	80	83	78	77	79	79	80	83	84	87
Rogers Communications' stock price (RCI) (Stock price)	8.45	4.7	8.38	13.08	21.26	29.8	45.5	30.15	31.37	34.84	39	46.24	45.25	38.71	34	38.66	50.85	51.04	49.84	46.67	47.66	46.72

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.

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([10,17,21,22,34,43,58,65,60,64,62,82,80,83,78,77,79,79,80,83,84,87,])
array_2 = np.array([8.45,4.7,8.38,13.08,21.26,29.8,45.5,30.15,31.37,34.84,39,46.24,45.25,38.71,34,38.66,50.85,51.04,49.84,46.67,47.66,46.72,])
array_1_name = "GMO use in corn grown in South Dakota"
array_2_name = "Rogers Communications' stock price (RCI)"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

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You 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
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Line chart for only GMO use in corn grown in South Dakota
Line chart for only Rogers Communications' stock price (RCI)
The spurious research paper: GMO Corn: A-Maize-ing Effects on RCI Stock Price Yield Unlikely Connection

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Correlation ID: 1331 · Black Variable ID: 749 · Red Variable ID: 1904


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