Spurious correlation #4,683 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Bay City, Michigan and the second variable is Wind power generated in British Virgin Islands. The chart goes from 2010 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 Air pollution in Bay City, Michigan and the second variable is Wind power generated in British Virgin Islands. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air cleared in Bay City, Michigan, it created a literal power vacuum, leading to an increase in wind power generated in the British Virgin Islands. The correlation was found to be quite a breath of fresh air for the renewable energy industry, as the winds of change blew in favor of cleaner and more sustainable electricity production. It seems that Bay City's pollution reduction had a far-reaching impact, proving that when it comes to environmental improvements, the effects can truly be felt across the wind-swept seas. This unexpected connection has certainly given us a lot to un-AIRth!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s. Depict a clear blue sky over Bay City, Michigan with wind turbines spinning vigorously in the British Virgin Islands. The clean air in Bay City has created a powerful gust of wind propelling the turbines, symbolizing the positive impact of reduced air pollution on renewable energy generation. Keep the visual depiction simple, with no words or speech bubbles. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. 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: Psychedelic Parallax art Psychedelic Cutsy graphics A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands, in the following situation: As the air cleared in Bay City, Michigan, it created a literal power vacuum, leading to an increase in wind power generated in the British Virgin Islands. The correlation was found to be quite a breath of fresh air for the renewable energy industry, as the winds of change blew in favor of cleaner and more sustainable electricity production. It seems that Bay City's pollution reduction had a far-reaching impact, proving that when it comes to environmental improvements, the effects can truly be felt across the wind-swept seas. This unexpected connection has certainly given us a lot to un-AIRth!. Keep the description simple.
ChatGPT: A clear blue sky over Bay City, Michigan as the air pollution decreases, revealing wind turbines spinning vigorously in the British Virgin Islands. The clean air in Bay City seems to have created a powerful gust of wind propelling the turbines, symbolizing the positive impact of reduced air pollution on renewable energy generation.

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 comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: A clear blue sky over Bay City, Michigan as the air pollution decreases, revealing wind turbines spinning vigorously in the British Virgin Islands. The clean air in Bay City seems to have created a powerful gust of wind propelling the turbines, symbolizing the positive impact of reduced air pollution on renewable energy generation. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s. Depict a clear blue sky over Bay City, Michigan with wind turbines spinning vigorously in the British Virgin Islands. The clean air in Bay City has created a powerful gust of wind propelling the turbines, symbolizing the positive impact of reduced air pollution on renewable energy generation. Keep the visual depiction simple, with no words or speech bubbles.

*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 a decrease in Air pollution in Bay City, Michigan caused Wind power generated in British Virgin Islands to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

The Journal of Environmental Alchemy

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 Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

[[ABSTRACT]]
This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.

Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.

Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The connection between environmental factors and energy generation has long been a topic of interest, with ongoing efforts to unravel the complex interplay between the two. In this study, we investigate the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands. As researchers delving into this intriguing connection, we found ourselves "blown away" by the unexpected correlations and the potential implications for environmental and energy policy.
The juxtaposition of Bay City, known for its industrial heritage, and the British Virgin Islands, renowned for their picturesque landscapes, seems like an unlikely pairing – akin to a "trade wind" blowing in opposite directions. However, our analysis has unveiled a remarkable inverse relationship between air pollution in one location and wind power generation in another, challenging traditional assumptions about the localization of environmental impacts and renewable energy sources.
While examining our data, we couldn't help but ponder: "What do you call a power source that remembers birthdays?" A "renewable" energy, of course! But in all seriousness, our findings not only offer a captivating insight into the interconnectedness of environmental dynamics but also prompt a reexamination of energy policy objectives and strategies on a global scale.
By unveiling the negative correlation between air pollution levels and wind power generation in these distinct locations, our research calls attention to the potential for leveraging this unexpected relationship to steer environmental and energy policies toward more sustainable and renewable paths. It's like finding a silver lining in a thick cloud of pollution – or should we say, a "wind-turbine silver lining"?
In the subsequent sections, we will delve into the methodology employed, the data sources utilized, the statistical analyses conducted, and the implications of our findings for environmental and energy policies. Our research lays the groundwork for future investigations into the correlates of environmental factors and renewable energy sources, with the hope of guiding the transition toward cleaner and more sustainable energy solutions – and perhaps even blowing a breath of fresh air into the field of environmental research and policy.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

To contextualize the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands, the authors conducted a thorough review of existing literature on environmental impacts and renewable energy sources. Smith et al. (2015) examined the effects of air pollution on atmospheric dynamics, highlighting the potential for far-reaching consequences beyond localized environmental degradation. Meanwhile, Doe and Jones (2018) delved into the intricacies of wind power generation and its role in sustainable energy strategies, offering valuable insights into the dynamic landscape of renewable energy technologies.

In "Environmental Economics: A Very Short Introduction," Goodstein (2011) presents a concise yet comprehensive overview of the economic principles underlying environmental policies and resource allocation. This foundational text sheds light on the intersection of environmental concerns and economic decision-making, providing a framework for understanding the potential implications of the findings on air pollution and wind power generation for policy and governance.

On the fictional front, Crichton's "The Windup Girl" (2009) weaves a dystopian narrative set in a future world ravaged by environmental crises, where corporate interests clash with the imperatives of sustainability. While purely fictional, the themes explored in this novel resonate with the complexities inherent in the relationship between environmental degradation and renewable energy transitions, albeit in a dramatically embellished and fictionalized context.

Beyond traditional sources, the authors embarked on an unconventional quest for insights, delving into the enigmatic world of grocery store receipts, self-help books, and even the musings of a fortune cookie. While the applicability of these unconventional sources may be met with skepticism, the authors discovered an unexpected nugget of wisdom in an old self-help book – "The Power of Blowing Away Your Problems" – which espouses the virtues of harnessing the "winds of change" to overcome adversity, providing an unexpectedly apt metaphor for the potential of leveraging the inverse relationship between air pollution and wind power for sustainable energy solutions.

Remarkably, the authors also stumbled upon an ancient scroll containing cryptic verses that appeared to contain insights into the balance of environmental forces and energy dynamics. Written in a forgotten language, the scroll purportedly contained wisdom passed down through generations, hinting at the harmonious equilibrium of natural elements, including the dance of wind and the purification of air. Though met with skepticism by the academic community, this serendipitous discovery underscored the profound, if occasionally whimsical, ways in which disparate sources of knowledge can converge to illuminate unexpected connections.

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 Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration . You used data from 2010 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]]
From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

[[ABSTRACT]]
This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.

Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.

Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

[[INTRODUCTION]]
The connection between environmental factors and energy generation has long been a topic of interest, with ongoing efforts to unravel the complex interplay between the two. In this study, we investigate the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands. As researchers delving into this intriguing connection, we found ourselves "blown away" by the unexpected correlations and the potential implications for environmental and energy policy.
The juxtaposition of Bay City, known for its industrial heritage, and the British Virgin Islands, renowned for their picturesque landscapes, seems like an unlikely pairing – akin to a "trade wind" blowing in opposite directions. However, our analysis has unveiled a remarkable inverse relationship between air pollution in one location and wind power generation in another, challenging traditional assumptions about the localization of environmental impacts and renewable energy sources.
While examining our data, we couldn't help but ponder: "What do you call a power source that remembers birthdays?" A "renewable" energy, of course! But in all seriousness, our findings not only offer a captivating insight into the interconnectedness of environmental dynamics but also prompt a reexamination of energy policy objectives and strategies on a global scale.
By unveiling the negative correlation between air pollution levels and wind power generation in these distinct locations, our research calls attention to the potential for leveraging this unexpected relationship to steer environmental and energy policies toward more sustainable and renewable paths. It's like finding a silver lining in a thick cloud of pollution – or should we say, a "wind-turbine silver lining"?
In the subsequent sections, we will delve into the methodology employed, the data sources utilized, the statistical analyses conducted, and the implications of our findings for environmental and energy policies. Our research lays the groundwork for future investigations into the correlates of environmental factors and renewable energy sources, with the hope of guiding the transition toward cleaner and more sustainable energy solutions – and perhaps even blowing a breath of fresh air into the field of environmental research and policy.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data utilized in this study was primarily sourced from the Environmental Protection Agency and the Energy Information Administration. We diligently combed through an extensive range of datasets encompassing air pollution levels in Bay City, Michigan, and wind power generation in the British Virgin Islands from the years 2010 to 2021. We also collected additional information from various reputable sources across the internet, strategically avoiding the more "airy" and unreliable sources.

Our approach to data collection was akin to a wind turbine – methodically sifting through the digital landscape to gather the most robust and reliable datasets, but with much less rotation and considerably less wind resistance.

Data Analysis:
Having amassed the pertinent data, we employed a series of statistical analyses to explore the correlation between air pollution in Bay City and wind power generation in the British Virgin Islands. To quantify the strength and direction of the relationship, we calculated the correlation coefficient, which, much like a guiding wind, provides us with a sense of the magnitude and direction of the association between the variables.

The correlation coefficient came out so clean and strong, it made us think, "What did one wind turbine say to the other? 'I'm a big fan of yours.'" This joke may not have the power to generate electricity, but it sure did bring a gust of amusement to the research team.

In addition to the correlation coefficient, we performed a regression analysis to model the relationship between air pollution and wind power generation, attempting to untangle the complex web of environmental and energy dynamics. This process was akin to unraveling a tangled kite string, requiring patience, precision, and an occasional gentle breeze of inspiration.

Interdisciplinary Approach:
To further contextualize our findings, we adopted an interdisciplinary approach, drawing insights from environmental science, renewable energy studies, and environmental policy frameworks. By integrating perspectives from diverse fields, our research sought to paint a comprehensive picture of the interconnectedness between environmental quality and renewable energy generation, forging a pathway to identify sustainable energy solutions.

As we ventured into this interdisciplinary realm, we couldn't help but remember the quip, "Did you hear about the claustrophobic wind turbine? He just needed a little space." While our research did not involve the personal space preferences of turbines, it did emphasize the significance of space – both geographically and academically – in understanding the relationship between air pollution and wind power generation.

Ethical Considerations:
In conducting this research, we adhered to the ethical standards outlined by relevant regulatory bodies and research guidelines, ensuring the responsible and transparent use of data and the dissemination of accurate and reliable findings. Our commitment to ethical conduct was unwavering, much like a steadfast wind blowing in the right direction, guiding our research endeavors with integrity and accountability.

The methodology section was navigated with the precision of a sailboat catching the perfect wind, equipped with a steady moral compass to steer us clear of any ethical turbulence.

---
I hope this methodology section meets your expectations! Please note that the statistical analyses and interdisciplinary approach mentioned are fictional and lighthearted in nature.

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 results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration .

For the time period 2010 to 2021, you found a correlation -1.0000000, r-squared of 1.0000000, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

[[ABSTRACT]]
This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.

Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.

Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The connection between environmental factors and energy generation has long been a topic of interest, with ongoing efforts to unravel the complex interplay between the two. In this study, we investigate the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands. As researchers delving into this intriguing connection, we found ourselves "blown away" by the unexpected correlations and the potential implications for environmental and energy policy.
The juxtaposition of Bay City, known for its industrial heritage, and the British Virgin Islands, renowned for their picturesque landscapes, seems like an unlikely pairing – akin to a "trade wind" blowing in opposite directions. However, our analysis has unveiled a remarkable inverse relationship between air pollution in one location and wind power generation in another, challenging traditional assumptions about the localization of environmental impacts and renewable energy sources.
While examining our data, we couldn't help but ponder: "What do you call a power source that remembers birthdays?" A "renewable" energy, of course! But in all seriousness, our findings not only offer a captivating insight into the interconnectedness of environmental dynamics but also prompt a reexamination of energy policy objectives and strategies on a global scale.
By unveiling the negative correlation between air pollution levels and wind power generation in these distinct locations, our research calls attention to the potential for leveraging this unexpected relationship to steer environmental and energy policies toward more sustainable and renewable paths. It's like finding a silver lining in a thick cloud of pollution – or should we say, a "wind-turbine silver lining"?
In the subsequent sections, we will delve into the methodology employed, the data sources utilized, the statistical analyses conducted, and the implications of our findings for environmental and energy policies. Our research lays the groundwork for future investigations into the correlates of environmental factors and renewable energy sources, with the hope of guiding the transition toward cleaner and more sustainable energy solutions – and perhaps even blowing a breath of fresh air into the field of environmental research and policy.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of the data obtained from the Environmental Protection Agency and the Energy Information Administration for the period of 2010 to 2021 revealed a strikingly strong negative correlation between air pollution levels in Bay City, Michigan and wind power generated in the British Virgin Islands, with a correlation coefficient of -1.0000000. The p-value, at less than 0.01, further supported the statistical significance of this relationship. It seemed as if the wind was not quite in the sails of polluted air.

Figure 1 displays a scatterplot depicting the clear inverse relationship between air pollution levels in Bay City and wind power generated in the British Virgin Islands. The data points form a distinct downward trend, underscoring the robustness of the negative association. One could say our findings were truly "groundbreaking," or should we say, "wind-breaking"?

Our results illuminate a novel and previously unexplored connection between seemingly remote environmental factors. As air pollution levels increased in Bay City, the wind power generated in the British Virgin Islands consistently decreased, leading us to ponder the "air-repairing" potential of sustainable energy sources. It's as if the winds themselves were communicating a message of environmental importance across the miles.

Our research uncovers the potential for leveraging this inverse relationship to inform transformative energy policies. By harnessing the power of wind and implementing measures to address air pollution, communities can steer their environmental impact in a more sustainable direction. In essence, it's about "changing the winds" of energy policy and embracing a gust of clean, renewable power.

The unexpectedly strong inverse correlation between air pollution in Bay City, Michigan and wind power generated in the British Virgin Islands offers a fresh perspective on the intersection of environmental factors and energy generation. Our findings not only challenge traditional assumptions but also highlight the potential for reimagining energy strategies with a focus on sustainability. This research sets the stage for a "breezy" transition towards cleaner and more environmentally friendly energy solutions.

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 Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

[[ABSTRACT]]
This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.

Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.

Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

[[LITERATURE REVIEW]]
To contextualize the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands, the authors conducted a thorough review of existing literature on environmental impacts and renewable energy sources. Smith et al. (2015) examined the effects of air pollution on atmospheric dynamics, highlighting the potential for far-reaching consequences beyond localized environmental degradation. Meanwhile, Doe and Jones (2018) delved into the intricacies of wind power generation and its role in sustainable energy strategies, offering valuable insights into the dynamic landscape of renewable energy technologies.
In "Environmental Economics: A Very Short Introduction," Goodstein (2011) presents a concise yet comprehensive overview of the economic principles underlying environmental policies and resource allocation. This foundational text sheds light on the intersection of environmental concerns and economic decision-making, providing a framework for understanding the potential implications of the findings on air pollution and wind power generation for policy and governance.
On the fictional front, Crichton's "The Windup Girl" (2009) weaves a dystopian narrative set in a future world ravaged by environmental crises, where corporate interests clash with the imperatives of sustainability. While purely fictional, the themes explored in this novel resonate with the complexities inherent in the relationship between environmental degradation and renewable energy transitions, albeit in a dramatically embellished and fictionalized context.
Beyond traditional sources, the authors embarked on an unconventional quest for insights, delving into the enigmatic world of grocery store receipts, self-help books, and even the musings of a fortune cookie. While the applicability of these unconventional sources may be met with skepticism, the authors discovered an unexpected nugget of wisdom in an old self-help book – "The Power of Blowing Away Your Problems" – which espouses the virtues of harnessing the "winds of change" to overcome adversity, providing an unexpectedly apt metaphor for the potential of leveraging the inverse relationship between air pollution and wind power for sustainable energy solutions.
Remarkably, the authors also stumbled upon an ancient scroll containing cryptic verses that appeared to contain insights into the balance of environmental forces and energy dynamics. Written in a forgotten language, the scroll purportedly contained wisdom passed down through generations, hinting at the harmonious equilibrium of natural elements, including the dance of wind and the purification of air. Though met with skepticism by the academic community, this serendipitous discovery underscored the profound, if occasionally whimsical, ways in which disparate sources of knowledge can converge to illuminate unexpected connections.

[[RESULTS]]
The analysis of the data obtained from the Environmental Protection Agency and the Energy Information Administration for the period of 2010 to 2021 revealed a strikingly strong negative correlation between air pollution levels in Bay City, Michigan and wind power generated in the British Virgin Islands, with a correlation coefficient of -1.0000000. The p-value, at less than 0.01, further supported the statistical significance of this relationship. It seemed as if the wind was not quite in the sails of polluted air.
Figure 1 displays a scatterplot depicting the clear inverse relationship between air pollution levels in Bay City and wind power generated in the British Virgin Islands. The data points form a distinct downward trend, underscoring the robustness of the negative association. One could say our findings were truly "groundbreaking," or should we say, "wind-breaking"?
Our results illuminate a novel and previously unexplored connection between seemingly remote environmental factors. As air pollution levels increased in Bay City, the wind power generated in the British Virgin Islands consistently decreased, leading us to ponder the "air-repairing" potential of sustainable energy sources. It's as if the winds themselves were communicating a message of environmental importance across the miles.
Our research uncovers the potential for leveraging this inverse relationship to inform transformative energy policies. By harnessing the power of wind and implementing measures to address air pollution, communities can steer their environmental impact in a more sustainable direction. In essence, it's about "changing the winds" of energy policy and embracing a gust of clean, renewable power.
The unexpectedly strong inverse correlation between air pollution in Bay City, Michigan and wind power generated in the British Virgin Islands offers a fresh perspective on the intersection of environmental factors and energy generation. Our findings not only challenge traditional assumptions but also highlight the potential for reimagining energy strategies with a focus on sustainability. This research sets the stage for a "breezy" transition towards cleaner and more environmentally friendly energy solutions.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The surprising connection between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands has raised eyebrows and, dare I say, "blown a few minds." The results of our study not only align with prior research but also provide a refreshing breeze of insight into the complex interplay of environmental dynamics. The strong negative correlation observed between these disparate variables supports and extends the existing body of knowledge, serving as a breath of fresh air for scholars and policymakers alike.

As touched upon in the literature review, the unexpected relationship between seemingly unrelated environmental factors has garnered attention in various spheres. Smith et al. (2015) and Doe and Jones (2018) laid the groundwork for understanding the intricate web of environmental impacts and renewable energy sources, setting the stage for our own investigation. Our results provide empirical evidence to substantiate and amplify the importance of these prior studies, demonstrating that even the winds of research can align with prior knowledge in unexpected ways.

Speaking of winds, our findings lend credence to the age-old adage that "air pollution blows," albeit in a more scholarly context. The robust negative correlation between air pollution in Bay City and wind power in the British Virgin Islands underscores the potential for leveraging environmental dynamics to steer the course of sustainable energy strategies. One could say that our research has cast a breath of fresh air on the prospects for renewable energy by harnessing the dual forces of wind power and pollution mitigation.

Returning to the literature review, the light-hearted yet relevant quest for insights from unconventional sources, such as the self-help book "The Power of Blowing Away Your Problems," takes on a new dimension in light of our findings. The metaphorical resonance of harnessing the "winds of change" becomes more than just a playful turn of phrase; it encapsulates the transformative potential of our research in guiding environmental policies toward cleaner, more renewable energy alternatives. It seems that wisdom can indeed blow in from unexpected sources, much like the winds that power sustainable energy solutions.

In summary, our study has unveiled a compelling and previously overlooked relationship between air pollution and wind power generation. By aligning with and building upon prior research, our findings advocate for a "breezy" transition towards renewable energy policies that capture the essence of our novel correlations. This research sets the stage for a refreshing gust of change in the realm of environmental and energy discourse, reminding us all that even the most unexpected connections can blow a breath of fresh air into scholarly inquiry.

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 Air pollution in Bay City, Michigan and Wind power generated in British Virgin Islands. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

[[ABSTRACT]]
This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.

Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.

Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

[[INTRDUCTION]]
The connection between environmental factors and energy generation has long been a topic of interest, with ongoing efforts to unravel the complex interplay between the two. In this study, we investigate the unexpected relationship between air pollution levels in Bay City, Michigan, and the wind power generated in the British Virgin Islands. As researchers delving into this intriguing connection, we found ourselves "blown away" by the unexpected correlations and the potential implications for environmental and energy policy.
The juxtaposition of Bay City, known for its industrial heritage, and the British Virgin Islands, renowned for their picturesque landscapes, seems like an unlikely pairing – akin to a "trade wind" blowing in opposite directions. However, our analysis has unveiled a remarkable inverse relationship between air pollution in one location and wind power generation in another, challenging traditional assumptions about the localization of environmental impacts and renewable energy sources.
While examining our data, we couldn't help but ponder: "What do you call a power source that remembers birthdays?" A "renewable" energy, of course! But in all seriousness, our findings not only offer a captivating insight into the interconnectedness of environmental dynamics but also prompt a reexamination of energy policy objectives and strategies on a global scale.
By unveiling the negative correlation between air pollution levels and wind power generation in these distinct locations, our research calls attention to the potential for leveraging this unexpected relationship to steer environmental and energy policies toward more sustainable and renewable paths. It's like finding a silver lining in a thick cloud of pollution – or should we say, a "wind-turbine silver lining"?
In the subsequent sections, we will delve into the methodology employed, the data sources utilized, the statistical analyses conducted, and the implications of our findings for environmental and energy policies. Our research lays the groundwork for future investigations into the correlates of environmental factors and renewable energy sources, with the hope of guiding the transition toward cleaner and more sustainable energy solutions – and perhaps even blowing a breath of fresh air into the field of environmental research and policy.

[[RESULTS]]
The analysis of the data obtained from the Environmental Protection Agency and the Energy Information Administration for the period of 2010 to 2021 revealed a strikingly strong negative correlation between air pollution levels in Bay City, Michigan and wind power generated in the British Virgin Islands, with a correlation coefficient of -1.0000000. The p-value, at less than 0.01, further supported the statistical significance of this relationship. It seemed as if the wind was not quite in the sails of polluted air.
Figure 1 displays a scatterplot depicting the clear inverse relationship between air pollution levels in Bay City and wind power generated in the British Virgin Islands. The data points form a distinct downward trend, underscoring the robustness of the negative association. One could say our findings were truly "groundbreaking," or should we say, "wind-breaking"?
Our results illuminate a novel and previously unexplored connection between seemingly remote environmental factors. As air pollution levels increased in Bay City, the wind power generated in the British Virgin Islands consistently decreased, leading us to ponder the "air-repairing" potential of sustainable energy sources. It's as if the winds themselves were communicating a message of environmental importance across the miles.
Our research uncovers the potential for leveraging this inverse relationship to inform transformative energy policies. By harnessing the power of wind and implementing measures to address air pollution, communities can steer their environmental impact in a more sustainable direction. In essence, it's about "changing the winds" of energy policy and embracing a gust of clean, renewable power.
The unexpectedly strong inverse correlation between air pollution in Bay City, Michigan and wind power generated in the British Virgin Islands offers a fresh perspective on the intersection of environmental factors and energy generation. Our findings not only challenge traditional assumptions but also highlight the potential for reimagining energy strategies with a focus on sustainability. This research sets the stage for a "breezy" transition towards cleaner and more environmentally friendly energy solutions.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated a remarkable and robust negative correlation between air pollution levels in Bay City, Michigan, and wind power generated in the British Virgin Islands. The unexpected nature of this relationship truly "blew us away" and highlights the intricate interconnectedness of environmental dynamics across geographically disparate regions. It's as if the winds of environmental impact are whispering their message across the miles, urging us toward sustainable change.

This inverse relationship provides a compelling basis for informing sustainable energy policies, offering a "breath of fresh air" in the realm of energy planning and environmental policy. By incorporating measures to mitigate air pollution and harnessing the power of wind, communities can steer their environmental impact in a more sustainable direction. It's a bit like finding a silver lining in a cloud of pollution – or, dare we say, a "wind-turbine silver lining"?

Our findings not only challenge traditional assumptions but also pave the way for transformative energy strategies focused on sustainability and renewable sources. This research may just be the "wind beneath the wings" of future energy policy initiatives, propelling us towards cleaner, greener energy solutions and a "breeze" of change in environmental impact.

Furthermore, the dad joke quota for this conclusion section is officially fulfilled. No more research is needed in this area.

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 From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Alchemy

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 From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Atmospheric Alchemy and Renewable Energy Studies

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 From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands with an abstract of This research paper delves into the surprising relationship between air pollution levels in Bay City, Michigan and the wind power generated in the British Virgin Islands. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team conducted a thorough analysis from 2010 to 2021. Our findings revealed a strong negative correlation, with a correlation coefficient of -1.0000000 and p < 0.01, indicating a significant and robust relationship between the two variables.
Examining the connection between these geographically disparate locations, we discovered that as air pollution levels in Bay City increased, the wind power generated in the British Virgin Islands exhibited a consistent and noteworthy decline. This unexpected correlation left us "blown away" by the intricate interplay of environmental factors across vast distances.
Furthermore, our research uncovered the potential for utilizing this inverse relationship to inform sustainable energy policies. By harnessing the power of wind and incorporating measures to address air pollution, regions can change the direction of their environmental impact. This research paves the way for "winds of change" in strategic energy planning, encouraging a shift towards cleaner, renewable sources of power.

ChatGPT:

air pollution, wind power, correlation, Bay City Michigan, British Virgin Islands, environmental impact, sustainable energy policies, renewable energy, geographic correlation, energy planning, pollution levels

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

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Data details

Air pollution in Bay City, Michigan
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Bay City, MI
Source: Environmental Protection Agency
See what else correlates with Air pollution in Bay City, Michigan

Wind power generated in British Virgin Islands
Detailed data title: Total wind power generated in British Virgin Islands in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in British Virgin Islands

Correlation r = -1.0000000 (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 = 1.0000000 (Coefficient of determination)
This means 100% of the change in the one variable (i.e., Wind power generated in British Virgin Islands) is predictable based on the change in the other (i.e., Air pollution in Bay City, Michigan) over the 12 years from 2010 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.^Show 0.0000000000000000000000000000
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 -1 in 0% of random cases. Said differently, if you correlated
Warning: Division by zero in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 636
inf random variables^Note
Warning: Division by zero in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 638
You don't actually need
Warning: Division by zero in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 643

Notice: Undefined offset: 10 in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 572
INF variables to find a correlation like this one.
Warning: Division by zero in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 643
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.
Warning: Division by zero in /var/www/html/spurious/_CORRELATION_VIEW_PAGE.php on line 646

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 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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.

[ -1, -1 ] 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.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Bay City, Michigan (Bad air quality days)	0.826446	0	0	0	0	0	0	0	0	0	0	0
Wind power generated in British Virgin Islands (Billion kWh)	0.001	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125	0.00125

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis.

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([0.826446,0,0,0,0,0,0,0,0,0,0,0,])
array_2 = np.array([0.001,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,0.00125,])
array_1_name = "Air pollution in Bay City, Michigan"
array_2_name = "Wind power generated in British Virgin Islands"

# 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 Air pollution in Bay City, Michigan
Line chart for only Wind power generated in British Virgin Islands
AI-generated correlation image
The spurious research paper: From Polluted Air to Wind Power: Blowing Away the Connection between Bay City, Michigan and British Virgin Islands

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Correlation ID: 4683 · Black Variable ID: 20336 · Red Variable ID: 23545


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