Spurious correlation #1,756 · 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 Dickinson, North Dakota and the second variable is Automotive recalls issued by Nissan North America. The chart goes from 1985 to 2022, 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 Dickinson, North Dakota and the second variable is Automotive recalls issued by Nissan North America. The chart goes from 1985 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As air pollution in Dickinson, North Dakota increased, the local wildlife developed a newfound love for creating art. This led to a surge in avant-garde avantomotive sculptures, inspiring Nissan North America to issue recalls in order to incorporate these unexpected masterpieces into their car designs. The smog-induced artistic renaissance in the region inadvertently sparked a revolution in automotive aesthetics, proving that sometimes, beauty truly is in the eye of the pollutant-holder.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a glitchy, surreal skyline overlooking Dickinson, North Dakota. The city is shrouded in a hazy, dust-filled atmosphere. Nissan vehicles traverse the streets amidst a chaotic flurry of glitched-out caution signs and notices from Nissan North America regarding vehicle recalls. The scene is a mix of distorted, fragmented elements, creating an otherworldly and disorienting portrayal of the city's skyline and the presence of the automotive brand's influence. 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: Less than ideal air quality in Dickinson, North Dakota and Automotive recalls issued by Nissan North America. 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: Vaporwave Cute cartoon Psychedelic Celtic art Glitch art
ChatGPT: Glitch art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Less than ideal air quality in Dickinson, North Dakota and Automotive recalls issued by Nissan North America together. Keep the description simple.
ChatGPT: A dusty skyline looms over Dickinson, North Dakota, with a haze hanging in the air as Nissan vehicles navigate the city streets. Caution signs and notices from Nissan North America about vehicle recalls are seen posted on street corners, reminding drivers to ensure their safety on the road despite the challenging air conditions.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Glitch art ] and depicting this scene: A dusty skyline looms over Dickinson, North Dakota, with a haze hanging in the air as Nissan vehicles navigate the city streets. Caution signs and notices from Nissan North America about vehicle recalls are seen posted on street corners, reminding drivers to ensure their safety on the road despite the challenging air conditions. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a glitchy, surreal skyline overlooking Dickinson, North Dakota. The city is shrouded in a hazy, dust-filled atmosphere. Nissan vehicles traverse the streets amidst a chaotic flurry of glitched-out caution signs and notices from Nissan North America regarding vehicle recalls. The scene is a mix of distorted, fragmented elements, creating an otherworldly and disorienting portrayal of the city's skyline and the presence of the automotive brand's influence.

*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 increases in Air pollution in Dickinson, North Dakota positively influenced Automotive recalls issued by Nissan North America. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

The Journal of Environmental Anomalies

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 Dickinson, North Dakota and Automotive recalls issued by Nissan North America. Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

[[ABSTRACT]]
This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of research can be a lot like a smoggy day in Dickinson, North Dakota - filled with unexpected twists, murky correlations, and the occasional surprise lurking behind the statistical haze. It's a place where the scent of discovery hangs heavy in the air, much like the industrial emissions that waft through Dickinson. In the pursuit of knowledge, we often find ourselves navigating through a fog of data, trying to sniff out meaningful relationships and clear the air of scientific uncertainties.
In the automotive industry, the landscape is equally filled with turbulence, recalls, and the occasional emissions scandal. Like a well-oiled machine, it chugs and sputters along, much like the cars themselves, as manufacturers strive to balance performance, safety, and environmental impact. So, what happens when these two seemingly disparate worlds collide? Our research sets out to unravel the mysteries of the unexpected relationship between air pollution levels in Dickinson, North Dakota, and the frequency of automotive recalls issued by Nissan North America.
The very idea that the air we breathe in the Northern Plains could affect the performance of vehicles across the continent might seem like a stretch, a little like a rusty muffler reaching for the stars. Yet, as we delved into the data from the Environmental Protection Agency and the US Department of Transportation, we found a correlation coefficient that emerged from the statistical mire like a shiny chrome bumper on a foggy morning - a remarkable 0.7104324, with a p-value of less than 0.01. This discovery left us gasping for air, much like a faulty catalytic converter.
In our exploration of this surprising connection, we will first take a deep dive into the data, followed by an analysis of the potential mechanisms at play. We'll explore the implications for both the environmental and automotive industries, shedding light on the curious interplay between air quality and vehicle reliability. So, buckle up and roll down the windows, as we embark on a journey through the hazy realms of statistics and science, in search of the unexpected harmony between Dickinson's pollution and Nissan's recalls.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature reviewed in this paper presents a comprehensive exploration of the interrelationship between air pollution and automotive recalls, drawing from a diverse array of sources to shed light on this unexpected nexus. Smith and Doe (2018) delve into the intricacies of air quality measurement and its impact on environmental and public health. Their findings lay the groundwork for understanding the potential ramifications of elevated pollution levels on vehicular performance and reliability. In "Jones' Journal of Automotive Engineering" (2020), the authors examine the complexities of automotive manufacturing processes, elucidating the many factors influencing vehicle safety and functionality, providing a solid foundation for our investigation into automotive recalls.

Transitioning from these more serious works, we turn our attention to non-fiction books with a tinge of humor and insight, such as "Drive: The Surprising Truth About What Motivates Us" by Daniel H. Pink. While not directly related to air pollution or automotive recalls, the book's exploration of human motivation and behavior applies an unexpected lens to our understanding of the intricate relationship between environmental factors and automotive industry performance.

Expanding the scope further, we encounter fictional literature that seems oddly apropos, such as Michael Crichton's "Airframe." Although a work of fiction, the novel provides a thrilling exploration of aircraft safety and engineering, drawing parallels to the automotive industry's intricate web of regulations, inspections, and occasional malfunctions. Furthermore, Richard Preston's "The Hot Zone" offers a captivating glimpse into the world of infectious diseases and biohazard containment, tangentially relevant to our discussions by highlighting the intricacies of unexpected consequences within complex systems.

Diverging into more unconventional sources, our literature review extends to unexpected realms of inquiry. A perusal of the backs of shampoo bottles surprisingly yielded insight into the chemical composition of common air pollutants, reinforcing the interconnectedness of seemingly unrelated domains - a testament to the interdisciplinary nature of our investigation.

In this comprehensive review, we endeavor to capture the breadth and depth of literary contributions to the understanding of the connection between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America.

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 Dickinson, North Dakota and Automotive recalls issued by Nissan North America. Make lots of 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 Environmental Protection Agency and US DOT . You used data from 1985 to 2022

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]]
Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

[[ABSTRACT]]
This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

[[INTRODUCTION]]
The world of research can be a lot like a smoggy day in Dickinson, North Dakota - filled with unexpected twists, murky correlations, and the occasional surprise lurking behind the statistical haze. It's a place where the scent of discovery hangs heavy in the air, much like the industrial emissions that waft through Dickinson. In the pursuit of knowledge, we often find ourselves navigating through a fog of data, trying to sniff out meaningful relationships and clear the air of scientific uncertainties.
In the automotive industry, the landscape is equally filled with turbulence, recalls, and the occasional emissions scandal. Like a well-oiled machine, it chugs and sputters along, much like the cars themselves, as manufacturers strive to balance performance, safety, and environmental impact. So, what happens when these two seemingly disparate worlds collide? Our research sets out to unravel the mysteries of the unexpected relationship between air pollution levels in Dickinson, North Dakota, and the frequency of automotive recalls issued by Nissan North America.
The very idea that the air we breathe in the Northern Plains could affect the performance of vehicles across the continent might seem like a stretch, a little like a rusty muffler reaching for the stars. Yet, as we delved into the data from the Environmental Protection Agency and the US Department of Transportation, we found a correlation coefficient that emerged from the statistical mire like a shiny chrome bumper on a foggy morning - a remarkable 0.7104324, with a p-value of less than 0.01. This discovery left us gasping for air, much like a faulty catalytic converter.
In our exploration of this surprising connection, we will first take a deep dive into the data, followed by an analysis of the potential mechanisms at play. We'll explore the implications for both the environmental and automotive industries, shedding light on the curious interplay between air quality and vehicle reliability. So, buckle up and roll down the windows, as we embark on a journey through the hazy realms of statistics and science, in search of the unexpected harmony between Dickinson's pollution and Nissan's recalls.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The present investigation involved a multidimensional approach to unraveling the enigmatic connection between air pollution in Dickinson, North Dakota, and the occurrence of automotive recalls by Nissan North America. Our research team embarked on a journey through the convoluted avenues of data collection, statistical analysis, and scientific inquiry in pursuit of illuminating this unexpected relationship.

Data Collection:
The primary source of air quality data for Dickinson, North Dakota, was drawn from the Environmental Protection Agency's comprehensive repository of air quality measurements. The dataset encompassed atmospheric pollutant levels, including particulate matter, nitrogen oxides, sulfur dioxide, and volatile organic compounds, spanning from 1985 to 2022. Additionally, data on automotive recalls issued by Nissan North America during the same period was acquired from the Federal Motor Carrier Safety Administration and the US Department of Transportation databases. The trove of recall information encompassed a spectrum of vehicular concerns such as engine malfunctions, electrical system defects, and safety-related issues.

Data Transformation and Normalization:
In order to harmonize the disparate datasets and ensure comparability, the air quality measurements were standardized and subjected to rigorous quality control processes. This involved applying mathematical transformations akin to tuning the engine of a high-performance vehicle, ensuring that the variables were ready for a smooth and reliable ride through the statistical analyses.

Statistical Analysis:
The quantitative investigation of the association between air pollution levels in Dickinson and the frequency of automotive recalls issued by Nissan involved advanced statistical methodologies. Correlation analyses were performed to gauge the strength and direction of the relationship. Additionally, multiple regression models were constructed, akin to fine-tuning the intricate components of an automobile engine, to disentangle the complex interplay of variables contributing to the observed phenomenon.

Sensitivity Analyses:
To assess the robustness of the findings, sensitivity analyses were conducted, harnessing the power of varying statistical models, as if attempting to test the resilience of different automotive parts under diverse driving conditions. These analyses provided insights into the stability of the observed relationship and its susceptibility to potential confounding variables, akin to identifying the factors that could potentially lead to a "recall" of the analytical findings.

Validity Checks:
The validity of the observed associations was scrutinized through cross-validation techniques, resembling the rigorous testing of vehicle safety features to ensure their reliability under diverse scenarios. This process entailed partitioning the dataset into training and validation sets, rigorously examining the consistency of the findings across distinct subsets, and fortifying the robustness of the identified relationship.

Ethical Considerations:
Throughout the research endeavor, ethical standards were strictly upheld, analogous to the stringent safety regulations governing the automotive industry. Privacy and confidentiality of data sources were rigorously protected, and the dissemination of findings adhered to the principles of scientific integrity and transparency, akin to the stringent control standards governing the release of automotive safety information.

In summary, the methodological framework employed in this investigation entailed a comprehensive amalgamation of data collection, transformation, statistical analyses, and validity checks, all channeled towards demystifying the unexpected linkage between air pollution in Dickinson, North Dakota, and automotive recalls by Nissan North America.

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 Dickinson, North Dakota and Automotive recalls issued by Nissan North America. Make lots of 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 Environmental Protection Agency and US DOT .

For the time period 1985 to 2022, you found a correlation 0.7104324, r-squared of 0.5047142, 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]]
Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

[[ABSTRACT]]
This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The world of research can be a lot like a smoggy day in Dickinson, North Dakota - filled with unexpected twists, murky correlations, and the occasional surprise lurking behind the statistical haze. It's a place where the scent of discovery hangs heavy in the air, much like the industrial emissions that waft through Dickinson. In the pursuit of knowledge, we often find ourselves navigating through a fog of data, trying to sniff out meaningful relationships and clear the air of scientific uncertainties.
In the automotive industry, the landscape is equally filled with turbulence, recalls, and the occasional emissions scandal. Like a well-oiled machine, it chugs and sputters along, much like the cars themselves, as manufacturers strive to balance performance, safety, and environmental impact. So, what happens when these two seemingly disparate worlds collide? Our research sets out to unravel the mysteries of the unexpected relationship between air pollution levels in Dickinson, North Dakota, and the frequency of automotive recalls issued by Nissan North America.
The very idea that the air we breathe in the Northern Plains could affect the performance of vehicles across the continent might seem like a stretch, a little like a rusty muffler reaching for the stars. Yet, as we delved into the data from the Environmental Protection Agency and the US Department of Transportation, we found a correlation coefficient that emerged from the statistical mire like a shiny chrome bumper on a foggy morning - a remarkable 0.7104324, with a p-value of less than 0.01. This discovery left us gasping for air, much like a faulty catalytic converter.
In our exploration of this surprising connection, we will first take a deep dive into the data, followed by an analysis of the potential mechanisms at play. We'll explore the implications for both the environmental and automotive industries, shedding light on the curious interplay between air quality and vehicle reliability. So, buckle up and roll down the windows, as we embark on a journey through the hazy realms of statistics and science, in search of the unexpected harmony between Dickinson's pollution and Nissan's recalls.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of the analysis unveiled a correlation coefficient of 0.7104324 between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. The strength of this correlation is akin to discovering that the air filter in a car engine mysteriously affects the performance of the radio - unexpected and puzzling, yet undeniably intriguing. With an r-squared value of 0.5047142, over 50% of the variability in automotive recalls can be explained by the level of air pollution in Dickinson. This finding is as striking as stumbling upon a rare spare part in a junkyard - a statistical gem amidst the noise of data.

Further adding to the weight of evidence, the p-value of less than 0.01 indicates a high level of confidence in the observed relationship, unlike the uncertainty one might feel while driving a recalled vehicle. This statistical significance amplifies the intrigue surrounding the connection between seemingly unrelated variables, much like the unexpected friendship between a tailpipe and a transmission.

As a visual testament to the strength of this association, the scatterplot in Fig. 1 captures the tight clustering of data points, resembling a crowded intersection where air quality and automotive performance intersect in a peculiar dance of statistical synchrony.

Overall, these results underscore the remarkable coherence between air pollution in Dickinson, North Dakota, and the frequency of automotive recalls by Nissan North America, leaving us to ponder the mysterious forces at play and the potential downstream effects on both the environment and the automotive industry. Indeed, this unexpected relationship serves as a reminder that in the world of research, just like on the open road, the most surprising connections can often be found where one least expects them.

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 Dickinson, North Dakota and Automotive recalls issued by Nissan North America. Make lots of 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]]
Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

[[ABSTRACT]]
This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

[[LITERATURE REVIEW]]
The literature reviewed in this paper presents a comprehensive exploration of the interrelationship between air pollution and automotive recalls, drawing from a diverse array of sources to shed light on this unexpected nexus. Smith and Doe (2018) delve into the intricacies of air quality measurement and its impact on environmental and public health. Their findings lay the groundwork for understanding the potential ramifications of elevated pollution levels on vehicular performance and reliability. In "Jones' Journal of Automotive Engineering" (2020), the authors examine the complexities of automotive manufacturing processes, elucidating the many factors influencing vehicle safety and functionality, providing a solid foundation for our investigation into automotive recalls.
Transitioning from these more serious works, we turn our attention to non-fiction books with a tinge of humor and insight, such as "Drive: The Surprising Truth About What Motivates Us" by Daniel H. Pink. While not directly related to air pollution or automotive recalls, the book's exploration of human motivation and behavior applies an unexpected lens to our understanding of the intricate relationship between environmental factors and automotive industry performance.
Expanding the scope further, we encounter fictional literature that seems oddly apropos, such as Michael Crichton's "Airframe." Although a work of fiction, the novel provides a thrilling exploration of aircraft safety and engineering, drawing parallels to the automotive industry's intricate web of regulations, inspections, and occasional malfunctions. Furthermore, Richard Preston's "The Hot Zone" offers a captivating glimpse into the world of infectious diseases and biohazard containment, tangentially relevant to our discussions by highlighting the intricacies of unexpected consequences within complex systems.
Diverging into more unconventional sources, our literature review extends to unexpected realms of inquiry. A perusal of the backs of shampoo bottles surprisingly yielded insight into the chemical composition of common air pollutants, reinforcing the interconnectedness of seemingly unrelated domains - a testament to the interdisciplinary nature of our investigation.
In this comprehensive review, we endeavor to capture the breadth and depth of literary contributions to the understanding of the connection between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America.

[[RESULTS]]
The results of the analysis unveiled a correlation coefficient of 0.7104324 between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. The strength of this correlation is akin to discovering that the air filter in a car engine mysteriously affects the performance of the radio - unexpected and puzzling, yet undeniably intriguing. With an r-squared value of 0.5047142, over 50% of the variability in automotive recalls can be explained by the level of air pollution in Dickinson. This finding is as striking as stumbling upon a rare spare part in a junkyard - a statistical gem amidst the noise of data.
Further adding to the weight of evidence, the p-value of less than 0.01 indicates a high level of confidence in the observed relationship, unlike the uncertainty one might feel while driving a recalled vehicle. This statistical significance amplifies the intrigue surrounding the connection between seemingly unrelated variables, much like the unexpected friendship between a tailpipe and a transmission.
As a visual testament to the strength of this association, the scatterplot in Fig. 1 captures the tight clustering of data points, resembling a crowded intersection where air quality and automotive performance intersect in a peculiar dance of statistical synchrony.
Overall, these results underscore the remarkable coherence between air pollution in Dickinson, North Dakota, and the frequency of automotive recalls by Nissan North America, leaving us to ponder the mysterious forces at play and the potential downstream effects on both the environment and the automotive industry. Indeed, this unexpected relationship serves as a reminder that in the world of research, just like on the open road, the most surprising connections can often be found where one least expects them.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study provide robust support for the previous research that hints at the perplexing association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Our analysis corroborates the work of Smith and Doe (2018), who laid the groundwork for understanding the potential impact of elevated pollution levels on vehicular performance and reliability. Just as engine oil lubricates the moving parts of a car, their scholarly contributions have facilitated the smoother understanding of the mechanics of the air pollution-automotive recall relationship.

Furthermore, our results align with the insights offered by "Jones' Journal of Automotive Engineering" (2020), shedding light on the intricate interplay of factors influencing vehicle safety and functionality. Much like a well-executed gear shift, their work has seamlessly meshed with our findings, enriching our understanding of the unexpected connection between seemingly disparate domains.

Returning to our literature review, we whimsically revisit our perusal of the backs of shampoo bottles, which unexpectedly yielded insight into the chemical composition of common air pollutants. This unorthodox source of information is a testament to the interconnectedness of diverse fields and has provided a lathering of understanding regarding the interdisciplinary nature of our investigation.

The statistical coherence evidenced in our study is as remarkable as finding a well-oiled, smoothly functioning machine amidst the tangled heap of variables. The visual representation of our data in the scatterplot resembles a crowded intersection where air quality and automotive performance engage in a statistical dance, akin to the intricate choreography of a well-tuned engine.

In conclusion, our findings not only validate the unexpected nexus between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America but also emphasize the serendipitous nature of research, where the most astonishing connections can be unearthed in the unlikeliest of places. With these intriguing results, the automotive and environmental industries are beckoned to consider the harmonious symphony and occasional discordance between the emissions from Dickinson and the recalls from Nissan, propelling them to embark on a collaborative journey of discovery and rectification.

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 Dickinson, North Dakota and Automotive recalls issued by Nissan North America. Make lots of 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]]
Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

[[ABSTRACT]]
This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

[[INTRDUCTION]]
The world of research can be a lot like a smoggy day in Dickinson, North Dakota - filled with unexpected twists, murky correlations, and the occasional surprise lurking behind the statistical haze. It's a place where the scent of discovery hangs heavy in the air, much like the industrial emissions that waft through Dickinson. In the pursuit of knowledge, we often find ourselves navigating through a fog of data, trying to sniff out meaningful relationships and clear the air of scientific uncertainties.
In the automotive industry, the landscape is equally filled with turbulence, recalls, and the occasional emissions scandal. Like a well-oiled machine, it chugs and sputters along, much like the cars themselves, as manufacturers strive to balance performance, safety, and environmental impact. So, what happens when these two seemingly disparate worlds collide? Our research sets out to unravel the mysteries of the unexpected relationship between air pollution levels in Dickinson, North Dakota, and the frequency of automotive recalls issued by Nissan North America.
The very idea that the air we breathe in the Northern Plains could affect the performance of vehicles across the continent might seem like a stretch, a little like a rusty muffler reaching for the stars. Yet, as we delved into the data from the Environmental Protection Agency and the US Department of Transportation, we found a correlation coefficient that emerged from the statistical mire like a shiny chrome bumper on a foggy morning - a remarkable 0.7104324, with a p-value of less than 0.01. This discovery left us gasping for air, much like a faulty catalytic converter.
In our exploration of this surprising connection, we will first take a deep dive into the data, followed by an analysis of the potential mechanisms at play. We'll explore the implications for both the environmental and automotive industries, shedding light on the curious interplay between air quality and vehicle reliability. So, buckle up and roll down the windows, as we embark on a journey through the hazy realms of statistics and science, in search of the unexpected harmony between Dickinson's pollution and Nissan's recalls.

[[RESULTS]]
The results of the analysis unveiled a correlation coefficient of 0.7104324 between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. The strength of this correlation is akin to discovering that the air filter in a car engine mysteriously affects the performance of the radio - unexpected and puzzling, yet undeniably intriguing. With an r-squared value of 0.5047142, over 50% of the variability in automotive recalls can be explained by the level of air pollution in Dickinson. This finding is as striking as stumbling upon a rare spare part in a junkyard - a statistical gem amidst the noise of data.
Further adding to the weight of evidence, the p-value of less than 0.01 indicates a high level of confidence in the observed relationship, unlike the uncertainty one might feel while driving a recalled vehicle. This statistical significance amplifies the intrigue surrounding the connection between seemingly unrelated variables, much like the unexpected friendship between a tailpipe and a transmission.
As a visual testament to the strength of this association, the scatterplot in Fig. 1 captures the tight clustering of data points, resembling a crowded intersection where air quality and automotive performance intersect in a peculiar dance of statistical synchrony.
Overall, these results underscore the remarkable coherence between air pollution in Dickinson, North Dakota, and the frequency of automotive recalls by Nissan North America, leaving us to ponder the mysterious forces at play and the potential downstream effects on both the environment and the automotive industry. Indeed, this unexpected relationship serves as a reminder that in the world of research, just like on the open road, the most surprising connections can often be found where one least expects them.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the link between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America has left us with a tailwind of surprise and curiosity. The unexpected strength of the correlation coefficient, akin to stumbling upon a hidden mechanic's note in the glove compartment, has piqued our interest in the potential mechanisms behind this peculiar relationship. We are left pondering whether the emissions from the Northern Plains could be whispering fumes of influence into the machinery of vehicles across the continent, much like a mischievous exhaust pipe playing a tune. The statistically significant p-value, akin to a firm seatbelt in a bumpy statistical ride, reinforces the credence of this unseen bond between air quality and automotive reliability. As we reflect on the implications for both the environmental and automotive industries, it becomes evident that the air in Dickinson holds more than just the scent of wheat fields; it holds a statistical secret that leaves us marveling at the harmonious dance between pollution and recalls.

The picture painted by our results, much like a quirky abstract piece in a gallery of statistical art, reveals a tight clustering of data points in a scatterplot, mirroring the peculiar waltz of air quality and automotive performance. This unexpected alignment challenges traditional notions of causality, injecting a breath of fresh (albeit polluted) air into the world of scientific inquiry. Just as a worn-out brake pad can unexpectedly affect a vehicle's handling, our study disrupts conventional wisdom with its revelation of an unlikely kinship between air pollution and automotive recalls.

In light of these revelatory findings, we assert that no further research is needed in this area. The evidence presented here stands as a testament to the whims of statistical fate and the unanticipated connections that can emerge from the most unlikely of variables. Just as a sunroof unexpectedly adds a touch of delight to a mundane commute, our research serves as a reminder that in the world of academia, the most unassuming relationships can often yield the most fascinating insights.

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 Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Anomalies

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 Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Environmental Health and Automotive Safety (CEHAS)

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 Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America with an abstract of This paper presents the surprising findings of an investigation into the association between air pollution in Dickinson, North Dakota, and automotive recalls issued by Nissan North America. Using data from the Environmental Protection Agency and the US Department of Transportation spanning from 1985 to 2022, our research team calculated a correlation coefficient of 0.7104324 and p < 0.01. The analysis revealed an unexpectedly strong connection between the level of air pollution in Dickinson and the number of automotive recalls issued by Nissan. This paper delves into the potential mechanisms behind this unexpected relationship and provides insights into the implications for both environmental and automotive industries.

ChatGPT:

air pollution, Dickinson North Dakota, Nissan, automotive recalls, correlation coefficient, Environmental Protection Agency, US Department of Transportation, automotive industry, environmental implications

*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 Dickinson, North Dakota
Detailed data title: Percentage of days with moderate or worse air quality in Dickinson, ND
Source: Environmental Protection Agency
See what else correlates with Air pollution in Dickinson, North Dakota

Automotive recalls issued by Nissan North America
Detailed data title: Automotive recals issued by Nissan North America
Source: US DOT
See what else correlates with Automotive recalls issued by Nissan North America

Correlation r = 0.7104324 (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 = 0.5047142 (Coefficient of determination)
This means 50.5% of the change in the one variable (i.e., Automotive recalls issued by Nissan North America) is predictable based on the change in the other (i.e., Air pollution in Dickinson, North Dakota) over the 38 years from 1985 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.8E-7.^Show 0.0000005835184383427877000000
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 0.71 in 5.8E-5% of random cases. Said differently, if you correlated 1,713,742 random variables^Note You don't actually need 1 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 37 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 37 because we have two variables measured over a period of 38 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.

[ 0.51, 0.84 ] 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.

	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Dickinson, North Dakota (Bad air quality days)	0.0227273	0.0564972	0	0	0	0	0	0	0	0	0	0	0	0.0236686	0.0418994	0.0491803	0.0383562	0.0630137	0.013986	0.026616	0.0482955	0.142857	0.0726257	0.0546448	0.0194444	0.0480226	0.0371517	0.0508475	0.0465753	0.0191781	0.117808	0.0273224	0.107143	0.110803	0.030137	0.0191257	0.151099	0.0385675
Automotive recalls issued by Nissan North America (Recalls)	2	2	4	0	0	2	2	3	1	8	6	6	4	9	4	7	6	14	13	8	9	16	9	10	6	17	8	13	16	17	20	19	17	14	5	12	28	15

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([0.0227273,0.0564972,0,0,0,0,0,0,0,0,0,0,0,0.0236686,0.0418994,0.0491803,0.0383562,0.0630137,0.013986,0.026616,0.0482955,0.142857,0.0726257,0.0546448,0.0194444,0.0480226,0.0371517,0.0508475,0.0465753,0.0191781,0.117808,0.0273224,0.107143,0.110803,0.030137,0.0191257,0.151099,0.0385675,])
array_2 = np.array([2,2,4,0,0,2,2,3,1,8,6,6,4,9,4,7,6,14,13,8,9,16,9,10,6,17,8,13,16,17,20,19,17,14,5,12,28,15,])
array_1_name = "Air pollution in Dickinson, North Dakota"
array_2_name = "Automotive recalls issued by Nissan North America"

# 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 Dickinson, North Dakota
Line chart for only Automotive recalls issued by Nissan North America
AI-generated correlation image
The spurious research paper: Clearing the Air: Exploring the Link Between Air Pollution in Dickinson, North Dakota and Nissan Automotive Recalls in North America

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Correlation ID: 1756 · Black Variable ID: 21730 · Red Variable ID: 1124


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