Spurious correlation #3,947 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Solar power generated in Ireland and the second variable is Microsoft's stock price (MSFT). The chart goes from 2009 to 2021, 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 Solar power generated in Ireland and the second variable is Microsoft's stock price (MSFT). The chart goes from 2009 to 2021, and the two variables track closely in value over that time.

AI explanation

The leprechauns managing the solar panels also happen to have a pot of gold invested in Microsoft. When the solar power output goes up, so does their good luck, leading to a rise in Microsoft's stock price. It's a shining example of how green energy and financial success can go hand in hand!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an image inspired by a TIME magazine cover featuring the scene of sunny Ireland with glistening solar panels on rooftops. In the background, a computer screen displays Microsoft's soaring stock price, shining brighter than a polished Surface Pro. Investors smile as the sun's power brightens the stock market, bringing a sunny outlook to renewable energy and stock prices. 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: Solar power generated in Ireland and Microsoft's stock price (MSFT). Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Photorealism A TIME magazine cover image An image that looks like it is a scene from a Pixar movie Psychedelic Macabre
ChatGPT: A TIME magazine cover image

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 Solar power generated in Ireland and Microsoft's stock price (MSFT), in the following situation: Because the increase in solar power in Ireland made Microsoft's stock price shine brighter than a freshly polished Surface Pro! The sunny outlook on renewable energy made investors beam with delight, sending MSFT soaring to new heights. It's clear that the power of the sun can truly brighten up the stock market! ☀️📈. Keep the description simple.
ChatGPT: On a sunny day in Ireland, solar panels glisten on rooftops, generating clean renewable energy. In the background, a computer screen shows Microsoft's stock price (MSFT) soaring upwards, shining even brighter than a freshly polished Surface Pro. Investors smile with delight as the sun's power brightens up the stock market, bringing a sunny outlook to both renewable energy and stock prices.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A TIME magazine cover image ] and depicting this scene: On a sunny day in Ireland, solar panels glisten on rooftops, generating clean renewable energy. In the background, a computer screen shows Microsoft's stock price (MSFT) soaring upwards, shining even brighter than a freshly polished Surface Pro. Investors smile with delight as the sun's power brightens up the stock market, bringing a sunny outlook to both renewable energy and stock prices. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image inspired by a TIME magazine cover featuring the scene of sunny Ireland with glistening solar panels on rooftops. In the background, a computer screen displays Microsoft's soaring stock price, shining brighter than a polished Surface Pro. Investors smile as the sun's power brightens the stock market, bringing a sunny outlook to renewable energy and stock prices.

*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 Solar power generated in Ireland caused Microsoft's stock price (MSFT) to increase.

AI academic paper

(Because p < 0.01)

Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price

The Journal of Solar Economics and Corporate Influence

Jan 2024

^{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 Solar power generated in Ireland and Microsoft's stock price (MSFT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
"Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price"

[[ABSTRACT]]
In this study, we set out to shed some light on the potential illuminating connection between solar power generation in the cloudy green landscapes of Ireland and the fluctuations in the stock price of technology behemoth Microsoft (MSFT). Through rigorous statistical analysis of data sourced from the Energy Information Administration and LSEG Analytics (Refinitiv) covering the years 2009 to 2021, we discovered a startling correlation coefficient of 0.9812876 and an astounding p-value of less than 0.01. Our findings not only suggest a compelling relationship between the solar power generated in Ireland and Microsoft's stock price, but they also cast a bright spotlight on the potential impact of renewable energy sources on the financial markets. This research aims to spark further investigation into the intertwined dynamics of green energy and stock performance, and ultimately, to shine a beacon of knowledge on this captivating yet quirky phenomenon.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Introduction
The pursuit of sustainable energy sources has long been a shining beacon of research and innovation in the scientific community. As society navigates the complex interplay between environmental conservation and technological advancement, the quest for cleaner and more efficient energy solutions has taken center stage. One such burgeoning source of renewable energy is solar power, harnessing the awe-inspiring power of the sun to generate electricity. However, while the potential environmental benefits of solar energy are widely acknowledged, its impact on financial markets and stock prices remains a somewhat dimly lit area of investigation.

Concurrently, the world of finance and stock markets is a realm illuminated by the glow of data, metrics, and statistical analysis. Within this realm, the fluctuations in stock prices are scrutinized with an intensity akin to the focused rays of sunlight through a magnifying glass. Therefore, it is only natural that researchers would endeavor to shed light on the relationship between solar energy generation and stock prices, albeit metaphorically speaking.

In the context of this intriguing quest for knowledge, our study seeks to unravel the enigmatic connection between solar power generation in Ireland and the stock price of Microsoft (MSFT). This seemingly unlikely pairing of an idyllic green landscape and a technological giant gives rise to a juxtaposition that is both perplexing and potentially illuminating. By diving into a sea of data spanning over a decade, we aim to elucidate the intriguing correlation, or lack thereof, between these seemingly disparate variables.
Our analysis encompasses a wide array of statistical techniques, effectively bringing the power of precision and rigor to bear on this captivating investigation. Like the alchemists of old, we seek to transmute raw data into actionable insights, turning the base elements of solar power generation and stock prices into knowledge gold.
As we embark on this venture, we do so with a mix of scientific rigor and a hint of whimsy, much like a mad scientist concocting a deviously clever experiment in their laboratory. The notion of such a relationship may initially seem far-fetched, akin to attempting to extract sunlight from a cucumber, but as the great Marie Curie once said, "Now is the time to understand more, so that we may fear less." Thus, armed with data, methodology, and a dash of scientific curiosity, we aim to illuminate the murky waters of this uncharted territory and bring the dawn of comprehension to this phenomenon.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
LITERATURE REVIEW

In "The Solar Stock Connection," Smith et al. explore the potential links between solar power generation and stock prices, focusing primarily on the European context. Their findings reveal a notable correlation between solar energy production and the stock prices of technology companies, albeit with some regional variations. Similarly, Doe and Jones, in "Renewable Energy and Financial Markets," delve into the complex relationship between renewable energy sources and stock performance, drawing attention to the oft-overlooked interplay between environmental factors and financial indicators.

The literature on solar power and its impact on financial markets has provided a foundation for understanding the potential interconnectedness between seemingly unrelated variables. However, the research landscape is not limited to dry empirical studies. In "Solar Power: A Bright Future," the authors illuminate the advancements in solar technology and its potential influence on broader economic trends. Moreover, "Microsoft's Fortune: A Tale of Technology and Triumph" captures the historical narrative of Microsoft's rise in the stock market, offering a compelling backdrop for considering the potential impact of external factors, including solar power generation in specific regions.

Moving beyond the traditional confines of academic literature, the fiction realm has also contributed to our understanding, albeit in a more whimsical manner. Books such as "Sunshine and Stocks: The Curious Tale of Solar Power in Financial Wizardry" weave imaginative narratives around the entwined fate of solar power and stock prices, offering a lighthearted perspective on an otherwise serious topic. Additionally, in "The Light-Hearted Trading Theory," the protagonist's quest for understanding stock market fluctuations leads to unexpected encounters with solar-powered contraptions and their alleged influence on financial markets.

It would be remiss to overlook the role of popular culture in shaping perceptions and, in some unexpected ways, informing research endeavors. Concurrent with our investigation, the researchers diligently devoted time to analyze the intricate dynamics of cartoon series such as "Sunny the Solar Panel Superhero" and "Microsoft Mania: Adventures in the Stock Market," incorporating subtle yet potentially enlightening insights into the larger discourse on solar power and stock market correlations.

However, as one delves deeper into the multifaceted realm of children's shows, one cannot underestimate the valuable insights that "Solar Power Pals" and "The MSFT Mysteries: A Tech-savvy Tale" offer in enlivening the imagination and, perhaps inadvertently, sparking contemplation on the intersection of renewable energy sources and financial market dynamism.

In light of this diverse array of literature, it is evident that the relationship between solar power generation in Ireland and Microsoft's stock price is a topic that transcends the boundaries of conventional research, entering the realms of speculative fiction, popular culture, and childhood nostalgia. As we seek to navigate this unconventional terrain, it becomes ever more crucial to approach our investigation with a balanced blend of scholarly rigor and the occasional playful jest, much like a tightrope walker traversing the delicate balance between empirical evidence and fanciful curiosity.

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 Solar power generated in Ireland and Microsoft's stock price (MSFT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and LSEG Analytics (Refinitiv) . You used data from 2009 to 2021

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]]
"Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price"

[[ABSTRACT]]
In this study, we set out to shed some light on the potential illuminating connection between solar power generation in the cloudy green landscapes of Ireland and the fluctuations in the stock price of technology behemoth Microsoft (MSFT). Through rigorous statistical analysis of data sourced from the Energy Information Administration and LSEG Analytics (Refinitiv) covering the years 2009 to 2021, we discovered a startling correlation coefficient of 0.9812876 and an astounding p-value of less than 0.01. Our findings not only suggest a compelling relationship between the solar power generated in Ireland and Microsoft's stock price, but they also cast a bright spotlight on the potential impact of renewable energy sources on the financial markets. This research aims to spark further investigation into the intertwined dynamics of green energy and stock performance, and ultimately, to shine a beacon of knowledge on this captivating yet quirky phenomenon.

[[INTRODUCTION]]
Introduction
The pursuit of sustainable energy sources has long been a shining beacon of research and innovation in the scientific community. As society navigates the complex interplay between environmental conservation and technological advancement, the quest for cleaner and more efficient energy solutions has taken center stage. One such burgeoning source of renewable energy is solar power, harnessing the awe-inspiring power of the sun to generate electricity. However, while the potential environmental benefits of solar energy are widely acknowledged, its impact on financial markets and stock prices remains a somewhat dimly lit area of investigation.

Concurrently, the world of finance and stock markets is a realm illuminated by the glow of data, metrics, and statistical analysis. Within this realm, the fluctuations in stock prices are scrutinized with an intensity akin to the focused rays of sunlight through a magnifying glass. Therefore, it is only natural that researchers would endeavor to shed light on the relationship between solar energy generation and stock prices, albeit metaphorically speaking.

In the context of this intriguing quest for knowledge, our study seeks to unravel the enigmatic connection between solar power generation in Ireland and the stock price of Microsoft (MSFT). This seemingly unlikely pairing of an idyllic green landscape and a technological giant gives rise to a juxtaposition that is both perplexing and potentially illuminating. By diving into a sea of data spanning over a decade, we aim to elucidate the intriguing correlation, or lack thereof, between these seemingly disparate variables.
Our analysis encompasses a wide array of statistical techniques, effectively bringing the power of precision and rigor to bear on this captivating investigation. Like the alchemists of old, we seek to transmute raw data into actionable insights, turning the base elements of solar power generation and stock prices into knowledge gold.
As we embark on this venture, we do so with a mix of scientific rigor and a hint of whimsy, much like a mad scientist concocting a deviously clever experiment in their laboratory. The notion of such a relationship may initially seem far-fetched, akin to attempting to extract sunlight from a cucumber, but as the great Marie Curie once said, "Now is the time to understand more, so that we may fear less." Thus, armed with data, methodology, and a dash of scientific curiosity, we aim to illuminate the murky waters of this uncharted territory and bring the dawn of comprehension to this phenomenon.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the intricacies of the connection between solar power generation in Ireland and the stock price of Microsoft (MSFT), our research team embarked on a methodological expedition akin to a scientific quest for buried treasure, equipped with statistical tools as our compass and an unwavering sense of curiosity as our guiding star. The journey began with the harmonization and meticulous scrutiny of data obtained from reputable sources, including the Energy Information Administration and LSEG Analytics (Refinitiv), spanning the years 2009 to 2021, akin to gathering clues from various historical archives in pursuit of an enigmatic truth.

Our first endeavor involved generating a robust time series dataset, capturing the bountiful fluctuations in solar power generation in Ireland and the corresponding ebbs and flows of Microsoft's stock price. The solar power generation data, much like discovering a pot of gold at the end of a rainbow, was meticulously collected from publicly available sources, accounting for variances in weather conditions, technological advancements, and legislative influences. Simultaneously, Microsoft's stock price data were diligently harvested, akin to a meticulous vintner plucking the finest grapes, from the stock market grapevine, encompassing both sunny and stormy periods in the financial landscape.

With our treasure trove of data assembled, we initiated the sacrificial rite known as data cleaning, thereby exorcising the demons of missing values, outliers, and other assorted data anomalies that would have otherwise haunted our statistical analyses like mischievous apparitions. Once our dataset was sanctified and purified, much like a brewer fermenting a fine ale, we conducted a series of exploratory data analyses, crafting visualizations that illuminated the ebb and flow of solar power generation and MSFT's stock price over the years. These visualizations served as our cartographic tools, providing a preliminary glimpse into the undulating terrain of our research domain.

Subsequently, we delved into the inimitable realm of statistical modeling, where hypotheses were tested and data was subjected to the rigor of various statistical tests - akin to wielding a battering ram against the gates of uncertainty. Not content with simple correlations, we employed sophisticated time series analyses, such as ARIMA and VAR models, to disentangle the intricate dance of solar power generation and MSFT's stock price, much like unraveling a complex scientific conundrum that would make the likes of Sherlock Holmes proud.

Furthermore, our methodology embraced the noble art of Granger causality tests, seeking to discern whether the solar power generation in the emerald isle of Ireland could indeed cast its luminous influence on the ebbs and flows of MSFT's stock price, or whether this connection was but a mere mirage in the desert of statistical insignificance.

In essence, our methodological approach was a fusion of scientific inquiry and statistical sorcery, weaving together the threads of observation, experimentation, and rigorous analysis to illuminate the entwined relationship between solar power generation in Ireland and the stock price of Microsoft (MSFT). The fruits of our labor have not only shed light on this captivating phenomenon but also opened the door to further exploration and discovery in the realm of renewable energy and financial markets.

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 Solar power generated in Ireland and Microsoft's stock price (MSFT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
"Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price"

[[ABSTRACT]]
In this study, we set out to shed some light on the potential illuminating connection between solar power generation in the cloudy green landscapes of Ireland and the fluctuations in the stock price of technology behemoth Microsoft (MSFT). Through rigorous statistical analysis of data sourced from the Energy Information Administration and LSEG Analytics (Refinitiv) covering the years 2009 to 2021, we discovered a startling correlation coefficient of 0.9812876 and an astounding p-value of less than 0.01. Our findings not only suggest a compelling relationship between the solar power generated in Ireland and Microsoft's stock price, but they also cast a bright spotlight on the potential impact of renewable energy sources on the financial markets. This research aims to spark further investigation into the intertwined dynamics of green energy and stock performance, and ultimately, to shine a beacon of knowledge on this captivating yet quirky phenomenon.

[[LITERATURE REVIEW]]
LITERATURE REVIEW
In "The Solar Stock Connection," Smith et al. explore the potential links between solar power generation and stock prices, focusing primarily on the European context. Their findings reveal a notable correlation between solar energy production and the stock prices of technology companies, albeit with some regional variations. Similarly, Doe and Jones, in "Renewable Energy and Financial Markets," delve into the complex relationship between renewable energy sources and stock performance, drawing attention to the oft-overlooked interplay between environmental factors and financial indicators.
The literature on solar power and its impact on financial markets has provided a foundation for understanding the potential interconnectedness between seemingly unrelated variables. However, the research landscape is not limited to dry empirical studies. In "Solar Power: A Bright Future," the authors illuminate the advancements in solar technology and its potential influence on broader economic trends. Moreover, "Microsoft's Fortune: A Tale of Technology and Triumph" captures the historical narrative of Microsoft's rise in the stock market, offering a compelling backdrop for considering the potential impact of external factors, including solar power generation in specific regions.
Moving beyond the traditional confines of academic literature, the fiction realm has also contributed to our understanding, albeit in a more whimsical manner. Books such as "Sunshine and Stocks: The Curious Tale of Solar Power in Financial Wizardry" weave imaginative narratives around the entwined fate of solar power and stock prices, offering a lighthearted perspective on an otherwise serious topic. Additionally, in "The Light-Hearted Trading Theory," the protagonist's quest for understanding stock market fluctuations leads to unexpected encounters with solar-powered contraptions and their alleged influence on financial markets.
It would be remiss to overlook the role of popular culture in shaping perceptions and, in some unexpected ways, informing research endeavors. Concurrent with our investigation, the researchers diligently devoted time to analyze the intricate dynamics of cartoon series such as "Sunny the Solar Panel Superhero" and "Microsoft Mania: Adventures in the Stock Market," incorporating subtle yet potentially enlightening insights into the larger discourse on solar power and stock market correlations.
However, as one delves deeper into the multifaceted realm of children's shows, one cannot underestimate the valuable insights that "Solar Power Pals" and "The MSFT Mysteries: A Tech-savvy Tale" offer in enlivening the imagination and, perhaps inadvertently, sparking contemplation on the intersection of renewable energy sources and financial market dynamism.
In light of this diverse array of literature, it is evident that the relationship between solar power generation in Ireland and Microsoft's stock price is a topic that transcends the boundaries of conventional research, entering the realms of speculative fiction, popular culture, and childhood nostalgia. As we seek to navigate this unconventional terrain, it becomes ever more crucial to approach our investigation with a balanced blend of scholarly rigor and the occasional playful jest, much like a tightrope walker traversing the delicate balance between empirical evidence and fanciful curiosity.

[[RESULTS]]
The statistical analysis of the relationship between solar power generation in Ireland and the stock price of Microsoft (MSFT) has yielded some truly enlightening findings. Over the period from 2009 to 2021, our research team uncovered a remarkably strong correlation coefficient of 0.9812876 between these seemingly unrelated variables. This correlation is akin to the reliable glow of a well-illuminated street lamp on a dark night, guiding us toward the intriguing nexus of green energy and financial markets.
Additionally, the r-squared value of 0.9629254 further underscores the robustness of the relationship, highlighting that approximately 96.29% of the variability in Microsoft's stock price can be explained by solar power generation in Ireland. This statistical beacon of confidence provides compelling evidence of the substantial influence that solar energy dynamics may exert on the fluctuations of a tech giant's stock price.
Furthermore, the p-value of less than 0.01 illuminates the significance of our findings, indicating that the observed correlation is not merely a fortuitous reflection of randomness, but rather a scientifically notable connection worthy of further investigation. It's as if the statistical analysis has shone a spotlight on this unexpected relationship, compelling us to delve deeper into its intricacies and potential implications.
In the spirit of empirical illumination, we present Figure 1, a scatterplot showcasing the striking correlation between solar power generation in Ireland and Microsoft's stock price. This visual representation vividly captures the impressive coherence of the two variables, akin to the harmonious interplay of celestial bodies in the vast expanse of the universe.
Overall, our results not only shed light on the previously obscure relationship between solar power generation and stock pricing but also beckon future inquiry into the captivating interplay of renewable energy and financial markets. The findings of this study stand as a testament to the intriguing and often surprising connections that can emerge from the careful scrutiny of data, akin to uncovering hidden treasures in the labyrinthine corridors of statistical analysis.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation has brought to light a captivating association between solar power generation in Ireland and the stock price of Microsoft (MSFT), thereby reinforcing and amplifying the conclusions drawn from the existing literature. The significant correlation coefficient of 0.9812876 uncovers a connection that shines like a beacon of empirical evidence emanating from the realm of statistical analysis. This resonates with the work of Smith et al. and Doe and Jones, who have previously hinted at the intricate interplay between renewable energy dynamics and stock performance. Despite the seemingly whimsical nature of some prior literature, our findings affirm the enduring relevance of these insights and accentuate the gravity of this mesmerizing correlation.

The robust r-squared value of 0.9629254 further bolsters the solidity of this relationship, akin to a sturdy solar panel harnessing the maximum energy from the sun's radiant beams. Just as a well-crafted solar panel efficiently captures and converts sunlight into renewable power, our statistical modeling adeptly captures the influential sway of solar energy dynamics on Microsoft's stock pricing. The r-squared value serves as a quantitative testament to the substantial explanatory power of solar power generation in elucidating the variability observed in MSFT's stock price, offering a luminous perspective on the potential impact of green energy on financial markets.

Notably, the p-value of less than 0.01 engenders confidence in the reliability and significance of this correlation, analogous to the assured glow of a meticulously engineered LED light bulb illuminating the path toward further exploration and scrutiny. This statistical beacon beckons researchers to embark on a journey akin to navigating through the intricate pathways of a solar-powered maze, where each turn may unravel new insights into the profound relationship between renewable energy sources and stock market dynamics.

While our discussion has remained focused on the quantitative light shed by our statistical analysis, it is vital to acknowledge the qualitative shine emanating from these findings. The striking correlation unveiled in our study emboldens the call for additional empirical investigations and theoretical scrutiny, much like a radiant sun urging humankind to bask in its invigorating glow. The visual representation encapsulated in Figure 1 captures this luminous connection, akin to a celestial dance choreographed by the forces of nature and financial dynamics.

In conclusion, the findings of this study not only reaffirm the assertions put forth by prior literature but also ignite a fervent call for continued exploration into the captivating confluence of solar power generation and stock pricing. As we navigate through this luminous terrain of empirical inquiry, it becomes ever more crucial to maintain a balanced blend of scholarly rigor and the occasional playful jest, akin to navigating the nuanced interplay of sunlight and shadow in the datasets of statistical 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 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 Solar power generated in Ireland and Microsoft's stock price (MSFT). Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
"Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price"

[[ABSTRACT]]
In this study, we set out to shed some light on the potential illuminating connection between solar power generation in the cloudy green landscapes of Ireland and the fluctuations in the stock price of technology behemoth Microsoft (MSFT). Through rigorous statistical analysis of data sourced from the Energy Information Administration and LSEG Analytics (Refinitiv) covering the years 2009 to 2021, we discovered a startling correlation coefficient of 0.9812876 and an astounding p-value of less than 0.01. Our findings not only suggest a compelling relationship between the solar power generated in Ireland and Microsoft's stock price, but they also cast a bright spotlight on the potential impact of renewable energy sources on the financial markets. This research aims to spark further investigation into the intertwined dynamics of green energy and stock performance, and ultimately, to shine a beacon of knowledge on this captivating yet quirky phenomenon.

[[INTRDUCTION]]
Introduction
The pursuit of sustainable energy sources has long been a shining beacon of research and innovation in the scientific community. As society navigates the complex interplay between environmental conservation and technological advancement, the quest for cleaner and more efficient energy solutions has taken center stage. One such burgeoning source of renewable energy is solar power, harnessing the awe-inspiring power of the sun to generate electricity. However, while the potential environmental benefits of solar energy are widely acknowledged, its impact on financial markets and stock prices remains a somewhat dimly lit area of investigation.

Concurrently, the world of finance and stock markets is a realm illuminated by the glow of data, metrics, and statistical analysis. Within this realm, the fluctuations in stock prices are scrutinized with an intensity akin to the focused rays of sunlight through a magnifying glass. Therefore, it is only natural that researchers would endeavor to shed light on the relationship between solar energy generation and stock prices, albeit metaphorically speaking.

In the context of this intriguing quest for knowledge, our study seeks to unravel the enigmatic connection between solar power generation in Ireland and the stock price of Microsoft (MSFT). This seemingly unlikely pairing of an idyllic green landscape and a technological giant gives rise to a juxtaposition that is both perplexing and potentially illuminating. By diving into a sea of data spanning over a decade, we aim to elucidate the intriguing correlation, or lack thereof, between these seemingly disparate variables.
Our analysis encompasses a wide array of statistical techniques, effectively bringing the power of precision and rigor to bear on this captivating investigation. Like the alchemists of old, we seek to transmute raw data into actionable insights, turning the base elements of solar power generation and stock prices into knowledge gold.
As we embark on this venture, we do so with a mix of scientific rigor and a hint of whimsy, much like a mad scientist concocting a deviously clever experiment in their laboratory. The notion of such a relationship may initially seem far-fetched, akin to attempting to extract sunlight from a cucumber, but as the great Marie Curie once said, "Now is the time to understand more, so that we may fear less." Thus, armed with data, methodology, and a dash of scientific curiosity, we aim to illuminate the murky waters of this uncharted territory and bring the dawn of comprehension to this phenomenon.

[[RESULTS]]
The statistical analysis of the relationship between solar power generation in Ireland and the stock price of Microsoft (MSFT) has yielded some truly enlightening findings. Over the period from 2009 to 2021, our research team uncovered a remarkably strong correlation coefficient of 0.9812876 between these seemingly unrelated variables. This correlation is akin to the reliable glow of a well-illuminated street lamp on a dark night, guiding us toward the intriguing nexus of green energy and financial markets.
Additionally, the r-squared value of 0.9629254 further underscores the robustness of the relationship, highlighting that approximately 96.29% of the variability in Microsoft's stock price can be explained by solar power generation in Ireland. This statistical beacon of confidence provides compelling evidence of the substantial influence that solar energy dynamics may exert on the fluctuations of a tech giant's stock price.
Furthermore, the p-value of less than 0.01 illuminates the significance of our findings, indicating that the observed correlation is not merely a fortuitous reflection of randomness, but rather a scientifically notable connection worthy of further investigation. It's as if the statistical analysis has shone a spotlight on this unexpected relationship, compelling us to delve deeper into its intricacies and potential implications.
In the spirit of empirical illumination, we present Figure 1, a scatterplot showcasing the striking correlation between solar power generation in Ireland and Microsoft's stock price. This visual representation vividly captures the impressive coherence of the two variables, akin to the harmonious interplay of celestial bodies in the vast expanse of the universe.
Overall, our results not only shed light on the previously obscure relationship between solar power generation and stock pricing but also beckon future inquiry into the captivating interplay of renewable energy and financial markets. The findings of this study stand as a testament to the intriguing and often surprising connections that can emerge from the careful scrutiny of data, akin to uncovering hidden treasures in the labyrinthine corridors of statistical analysis.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation has undeniably illuminated a compelling connection between solar power generation in Ireland and the stock price of Microsoft (MSFT). The robust correlation coefficient of 0.9812876 serves as a beacon in the night, guiding us toward a clearer understanding of the intertwining dynamics of green energy and financial markets. This correlation, akin to the reliability of the sun rising in the morning, highlights the potential impact of renewable energy sources on stock price variability.

The r-squared value of 0.9629254 further cements the significance of this relationship, akin to a lighthouse steadfastly guiding ships through turbulent waters. Approximately 96.29% of Microsoft's stock price variability can be attributed to solar power generation in Ireland, shedding a radiant light on the substantial influence of solar energy dynamics in this enigmatic dance of numbers.

Moreover, the p-value of less than 0.01 serves as a statistical fireworks display, sparkling with significance and urging further exploration into this captivating phenomenon. The visual representation in Figure 1, much like a dazzling constellation in the night sky, vividly captures the striking coherence between these seemingly disparate variables.

As we bask in the glow of these findings, it is clear that the relationship between solar power generation and stock prices is not merely a flash in the pan, but rather a radiant avenue for future research. We urge scholars to embrace this illuminating discovery and delve deeper into the intriguing interplay of renewable energy and financial markets. However, it is important to note that no more research is needed in this area, as we have undoubtedly shed enough light on this radiant topic.

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 Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Solar Economics and Corporate Influence

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 Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

Sunshine Solutions Institute

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 Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price with an abstract of In this study, we set out to shed some light on the potential illuminating connection between solar power generation in the cloudy green landscapes of Ireland and the fluctuations in the stock price of technology behemoth Microsoft (MSFT). Through rigorous statistical analysis of data sourced from the Energy Information Administration and LSEG Analytics (Refinitiv) covering the years 2009 to 2021, we discovered a startling correlation coefficient of 0.9812876 and an astounding p-value of less than 0.01. Our findings not only suggest a compelling relationship between the solar power generated in Ireland and Microsoft's stock price, but they also cast a bright spotlight on the potential impact of renewable energy sources on the financial markets. This research aims to spark further investigation into the intertwined dynamics of green energy and stock performance, and ultimately, to shine a beacon of knowledge on this captivating yet quirky phenomenon.

ChatGPT:

solar power generation Ireland, Microsoft stock price, renewable energy impact on financial markets, solar energy correlation with stock prices, green energy stock performance, solar power stock market relationship

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

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Data details

Solar power generated in Ireland
Detailed data title: Total solar power generated in Ireland in billion kWh
Source: Energy Information Administration
See what else correlates with Solar power generated in Ireland

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

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

Correlation r = 0.9812876 (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 = 0.9629254 (Coefficient of determination)
This means 96.3% of the change in the one variable (i.e., Microsoft's stock price (MSFT)) is predictable based on the change in the other (i.e., Solar power generated in Ireland) over the 13 years from 2009 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.2E-9.^Show 0.0000000032228088419257450000
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 0.98 in 3.2E-7% of random cases. Said differently, if you correlated 310,288,338 random variables^Note You don't actually need 310 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 12 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 12 because we have two variables measured over a period of 13 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.

[ 0.94, 0.99 ] 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.

	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Solar power generated in Ireland (Billion kWh)	0.001	0.000476	0.000543	0.000645	0.000698	0.002459	0.00616	0.006161	0.0108	0.0166561	0.0401201	0.0638438	0.079
Microsoft's stock price (MSFT) (Stock price)	19.53	30.62	28.05	26.55	27.25	37.35	46.66	54.32	62.79	86.13	99.55	158.78	222.53

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([0.001,0.000476,0.000543,0.000645,0.000698,0.002459,0.00616,0.006161,0.0108,0.0166561,0.0401201,0.0638438,0.079,])
array_2 = np.array([19.53,30.62,28.05,26.55,27.25,37.35,46.66,54.32,62.79,86.13,99.55,158.78,222.53,])
array_1_name = "Solar power generated in Ireland"
array_2_name = "Microsoft's stock price (MSFT)"

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

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

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 Solar power generated in Ireland
Line chart for only Microsoft's stock price (MSFT)
AI-generated correlation image
The spurious research paper: Shedding Light on Solar Power: Illuminating the Relationship Between Solar Generation in Ireland and Microsoft's Stock Price

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Correlation ID: 3947 · Black Variable ID: 23744 · Red Variable ID: 1541


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