Spurious correlation #3,360 · 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 Carson City, Nevada and the second variable is Automotive recalls issued by Hyundai Motor America. The chart goes from 1986 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 Carson City, Nevada and the second variable is Automotive recalls issued by Hyundai Motor America. The chart goes from 1986 to 2022, and the two variables track closely in value over that time.

AI explanation

The smog was just too much for the Hyundai engines to handle, they couldn't help but stall out and demand their own 'clean' air systems. It's like the pollution particles were revving them up for a breakdown, creating a real emissions emergency - talk about a toxic relationship! The correlation is nothing but a car-mic coincidence, really giving a whole new meaning to 'air filters.' In the end, it seems the polluted air in Carson City was just hyyundai-ing trouble for the automaker - looks like they'll need to find a smog-free solution to clear the air!

^{Show image generation prompt} Model: dalle-3
Prompt: [A scene from Skyrim] An epic dragon soaring over a majestic mountain peak, casting a shadow over a vast, lush valley below. The dragon's scales shimmer and reflect the sunlight as it unleashes a powerful breath of ice, freezing a group of towering trees and the surrounding landscape in a dazzling spectacle of frozen beauty. Snowflakes glisten in the air, creating a wondrous and mystical scene that captures the awe-inspiring power of the dragon. 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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor 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: Data cyber art Steampunk Concept art Voxel art A scene from Skyrim
ChatGPT: A scene from Skyrim

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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America, in the following situation: Because as air pollution levels in Carson City, Nevada increased, it caused Hyundai cars to start suffocating and panting for fresh air like asthmatic engine, resulting in a surge of automotive recalls by Hyundai Motor America. The poor cars couldn't handle the smog and started coughing up all sorts of trouble, so the company had to call them in for a breath of fresh air.. Keep the description simple.
ChatGPT: In Carson City, Nevada, Hyundai cars are parked along the street, their hoods up and engines exposed. The air is thick with smog, making it hard to breathe. The cars seem to be struggling, their engines panting and coughing like asthmatic lungs. Nearby, a banner reads "Hyundai Motor America Automotive Recalls" as the company calls the cars in for a breath of fresh air to address the issue.

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 scene from Skyrim ] and depicting this scene: In Carson City, Nevada, Hyundai cars are parked along the street, their hoods up and engines exposed. The air is thick with smog, making it hard to breathe. The cars seem to be struggling, their engines panting and coughing like asthmatic lungs. Nearby, a banner reads "Hyundai Motor America Automotive Recalls" as the company calls the cars in for a breath of fresh air to address the issue. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [A scene from Skyrim] An epic dragon soaring over a majestic mountain peak, casting a shadow over a vast, lush valley below. The dragon's scales shimmer and reflect the sunlight as it unleashes a powerful breath of ice, freezing a group of towering trees and the surrounding landscape in a dazzling spectacle of frozen beauty. Snowflakes glisten in the air, creating a wondrous and mystical scene that captures the awe-inspiring power of the dragon.

*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 Carson City, Nevada positively influenced Automotive recalls issued by Hyundai Motor America. Include one good pun.

AI academic paper

(Because p < 0.01)

Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

The Journal of Ecological Epiphanies

Jan 2024

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a 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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

[[ABSTRACT]]
This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.

Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of air pollution and automotive recalls forms a tantalizing nexus ripe for investigation. These puzzle pieces, seemingly disparate at first glance, beckon the discerning researcher to uncover their hidden connections. In this paper, we delve into the enigmatic relationship between the air quality of Carson City, Nevada, and the frequency of automotive recalls issued by Hyundai Motor America. As we embark on this academic escapade, we navigate through the labyrinth of statistical analysis, where the allure of correlation coefficients and p-values awaits with bated breath.
The adage "what goes up must come down" in the realm of environmental pollutants takes on a new dimension when juxtaposed with the intricacies of automotive engineering. Our quest to decode the interplay between these seemingly unrelated domains takes us through the annals of time, from the year 1986 to the present day, utilizing data from the Environmental Protection Agency and the US Department of Transportation. The fervent pursuit of knowledge leads us to intriguing revelations, shedding light on the statistical dance between air pollution levels and Hyundai automotive recalls.
As we unfurl our findings, we cautiously tiptoe around the lurking specter of confounding variables, the mischievous imps of statistical analysis. The whispered rumors of climatic conditions, driving habits, and other elusive factors beckon us to ponder their role in shaping the narrative of air quality and automotive reliability. Could it be that the cosmic dice are not always in our favor, clandestinely influencing the ebb and flow of car malfunctions and pollutant levels?
In unraveling this tapestry of relationships, we invite the reader to embrace the unconventional, to entertain the notion that beneath the veneer of dry statistics lies a realm of whimsy and wonder. So, as we embark on this scholarly odyssey, remember that while the air may be thick with pollutants, our minds remain as sharp as Occam's razor, ready to slice through the fog of uncertainty and unveil the intriguing dance between air pollution and automotive recalls.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The authors find that the correlation between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America has been a subject of increasing interest in recent years. Smith et al. (2018) explored the impact of environmental factors on vehicle performance, revealing the intricate web of influences that extend beyond the confines of traditional automotive engineering. Conversely, Doe (2019) focused on the patterns of automotive recalls, shedding light on the multifaceted dynamics that underpin these events.

Turning to the realm of non-fiction literature, "Air Quality in Urban Environments" by Jones (2017) offers a comprehensive analysis of air pollution in metropolitan areas, providing a contextual backdrop for our investigation. Furthermore, "Automotive Woes: A Compendium of Recalls and Remedies" (2020) by Brown delves into the enigmatic world of automotive malfunctions, enriching our understanding of the complexities at play.

On a more whimsical note, fictional works such as "The Smog Struggle" by Ainsley (2015) and "Recall Riddles" by Carter (2018) invite readers to explore fantastical adventures intertwined with the mysteries of air pollution and automotive recalls. Although anecdotal, these literary works infuse an air of intrigue into our scholarly discourse, prompting contemplation of unconventional perspectives.

Moreover, social media posts have become an unexpected source of insight into public perceptions of air pollution and automotive recalls. A Twitter user @DriveEasy23 professed, "I am convinced that the smog in Carson City is causing my Hyundai to act up! #AirPollutionMystery." Similarly, a Reddit thread titled "Hyundai Horror Stories" features gripping anecdotes of automotive malfunctions, hinting at the latent undercurrents that animate public discourse on this topic.

In synthesizing these diverse sources, the discerning reader is urged to approach the ensuing discussion with a keen appreciation for the juxtaposition of serious scholarship and lighthearted contemplation. As we traverse the labyrinthine avenues of academic inquiry, the interplay between scholarly rigor and whimsical musings maneuvers us through uncharted terrain flush with both intellectual gravitas and unexpected levity.

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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and US DOT . You used data from 1986 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]]
Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

[[ABSTRACT]]
This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.

Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

[[INTRODUCTION]]
The intersection of air pollution and automotive recalls forms a tantalizing nexus ripe for investigation. These puzzle pieces, seemingly disparate at first glance, beckon the discerning researcher to uncover their hidden connections. In this paper, we delve into the enigmatic relationship between the air quality of Carson City, Nevada, and the frequency of automotive recalls issued by Hyundai Motor America. As we embark on this academic escapade, we navigate through the labyrinth of statistical analysis, where the allure of correlation coefficients and p-values awaits with bated breath.
The adage "what goes up must come down" in the realm of environmental pollutants takes on a new dimension when juxtaposed with the intricacies of automotive engineering. Our quest to decode the interplay between these seemingly unrelated domains takes us through the annals of time, from the year 1986 to the present day, utilizing data from the Environmental Protection Agency and the US Department of Transportation. The fervent pursuit of knowledge leads us to intriguing revelations, shedding light on the statistical dance between air pollution levels and Hyundai automotive recalls.
As we unfurl our findings, we cautiously tiptoe around the lurking specter of confounding variables, the mischievous imps of statistical analysis. The whispered rumors of climatic conditions, driving habits, and other elusive factors beckon us to ponder their role in shaping the narrative of air quality and automotive reliability. Could it be that the cosmic dice are not always in our favor, clandestinely influencing the ebb and flow of car malfunctions and pollutant levels?
In unraveling this tapestry of relationships, we invite the reader to embrace the unconventional, to entertain the notion that beneath the veneer of dry statistics lies a realm of whimsy and wonder. So, as we embark on this scholarly odyssey, remember that while the air may be thick with pollutants, our minds remain as sharp as Occam's razor, ready to slice through the fog of uncertainty and unveil the intriguing dance between air pollution and automotive recalls.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved a multifaceted approach to disentangling the potential connection between air pollution in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. Our research team conducted a comprehensive data collection endeavor, summoning the omnipresent powers of the internet to procure a plethora of information. While the majority of our data hails from the Environmental Protection Agency and the US Department of Transportation, a smorgasbord of resources from 1986 to 2022 was assimilated, ensuring a robust dataset for our analyses.

To commence this grandiose undertaking, we first gathered air pollution data from the Environmental Protection Agency's treasure trove of atmospheric insights. Utilizing a blend of atmospheric composition data, emission metrics, and air quality indices, we sought to encapsulate the ethereal nature of pollutants permeating the environs of Carson City. Our team also delved into the labyrinthine world of automotive recalls, scouring the archives and annals of Hyundai Motor America's recall notices to capture the quirks and quibbles of Hyundai vehicles over the years.

Having amassed this veritable cornucopia of data, we then set forth on the path of statistical analysis, navigating the tempestuous seas of correlation and regression. Leveraging the scintillating power of software packages suitable for such intellectual endeavors, we embarked on a voyage to unravel the mystique of associations and causation. The statistical analyses conducted included, but were not limited to, Pearson correlation coefficients, multiple regression analyses, and perhaps the occasional séance to commune with the spirits of whimsical statistical anomalies.

As part of our methodological mosaic, we cautiously attended to the potential confounding variables that clandestinely weave their influence into the tapestry of our findings. Confounders such as weather patterns, vehicular mileage, and, dare we say, the capricious whims of vehicular fate, were considered with the utmost gravity. Our endeavor to disentangle the convoluted relationships between air pollution and automotive recalls was imbued with a vigilant eye for lurking variables, ensuring that our inferences would not be misled astray by mischievous statistical imps.

Through this methodological symphony, our research team navigated the seas of data, delighted in the cacophony of statistical analyses, and remained ever vigilant in our pursuit of uncovering the entwined fate of air pollution in Carson City and the echoes of Hyundai recalls. The journey was not without its perils, but through resilience, tenacity, and perhaps a dash of academic whimsy, we emerged with a robust framework for unraveling the enigma that lies at the nexus of environmental pollutants and automotive tribulations.

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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

For the time period 1986 to 2022, you found a correlation 0.5538940, r-squared of 0.3067986, 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]]
Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

[[ABSTRACT]]
This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.

Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of air pollution and automotive recalls forms a tantalizing nexus ripe for investigation. These puzzle pieces, seemingly disparate at first glance, beckon the discerning researcher to uncover their hidden connections. In this paper, we delve into the enigmatic relationship between the air quality of Carson City, Nevada, and the frequency of automotive recalls issued by Hyundai Motor America. As we embark on this academic escapade, we navigate through the labyrinth of statistical analysis, where the allure of correlation coefficients and p-values awaits with bated breath.
The adage "what goes up must come down" in the realm of environmental pollutants takes on a new dimension when juxtaposed with the intricacies of automotive engineering. Our quest to decode the interplay between these seemingly unrelated domains takes us through the annals of time, from the year 1986 to the present day, utilizing data from the Environmental Protection Agency and the US Department of Transportation. The fervent pursuit of knowledge leads us to intriguing revelations, shedding light on the statistical dance between air pollution levels and Hyundai automotive recalls.
As we unfurl our findings, we cautiously tiptoe around the lurking specter of confounding variables, the mischievous imps of statistical analysis. The whispered rumors of climatic conditions, driving habits, and other elusive factors beckon us to ponder their role in shaping the narrative of air quality and automotive reliability. Could it be that the cosmic dice are not always in our favor, clandestinely influencing the ebb and flow of car malfunctions and pollutant levels?
In unraveling this tapestry of relationships, we invite the reader to embrace the unconventional, to entertain the notion that beneath the veneer of dry statistics lies a realm of whimsy and wonder. So, as we embark on this scholarly odyssey, remember that while the air may be thick with pollutants, our minds remain as sharp as Occam's razor, ready to slice through the fog of uncertainty and unveil the intriguing dance between air pollution and automotive recalls.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis revealed a correlation coefficient of 0.5538940 between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. The observed correlation displayed a coefficient of determination (r-squared) of 0.3067986, underscoring the notable relationship between the two variables. The probability value (p) further confirmed the statistical significance of the association, with a p-value less than 0.01.

Notably, these findings hint at a connection as palpable as the exhaust fumes emanating from a sputtering engine. The scatterplot (Fig. 1) visually encapsulates this compelling relationship, showcasing the strong correlation between air pollution levels and Hyundai automotive recalls. One might even say that the data points align with the precision of a well-tuned engine, each point contributing to the broader narrative of environmental factors intertwining with automotive performance.

The results of this analysis invite contemplation on the myriad factors at play, including the whimsical influences of climatic conditions, driving habits, and, as rumor has it, the capricious whims of cosmic forces. The tangled web of variables unfurls before us, beckoning us to peel back the layers and uncover the nuanced interplay between pollutants and automotive malfunctions. Embracing the spirit of inquiry, the pursuit of knowledge transcends the mundane to embrace the unexpected, for in the realm of statistics, surprises lurk around every corner like mischievous statistical imps.

In sum, the findings of this study underscore the intricate dance between air pollution in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. As we navigate this labyrinth of statistical inquiry, the veiled connections between environmental conditions and automotive performance invite us to acknowledge the pervasive influence of these factors. Amidst the haze of statistical analysis, our findings compel us to recognize the interwoven tapestry of air quality and automotive reliability, where the only constant may be the unpredictability of the variables themselves.

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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

[[ABSTRACT]]
This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.

Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

[[LITERATURE REVIEW]]
The authors find that the correlation between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America has been a subject of increasing interest in recent years. Smith et al. (2018) explored the impact of environmental factors on vehicle performance, revealing the intricate web of influences that extend beyond the confines of traditional automotive engineering. Conversely, Doe (2019) focused on the patterns of automotive recalls, shedding light on the multifaceted dynamics that underpin these events.
Turning to the realm of non-fiction literature, "Air Quality in Urban Environments" by Jones (2017) offers a comprehensive analysis of air pollution in metropolitan areas, providing a contextual backdrop for our investigation. Furthermore, "Automotive Woes: A Compendium of Recalls and Remedies" (2020) by Brown delves into the enigmatic world of automotive malfunctions, enriching our understanding of the complexities at play.
On a more whimsical note, fictional works such as "The Smog Struggle" by Ainsley (2015) and "Recall Riddles" by Carter (2018) invite readers to explore fantastical adventures intertwined with the mysteries of air pollution and automotive recalls. Although anecdotal, these literary works infuse an air of intrigue into our scholarly discourse, prompting contemplation of unconventional perspectives.
Moreover, social media posts have become an unexpected source of insight into public perceptions of air pollution and automotive recalls. A Twitter user @DriveEasy23 professed, "I am convinced that the smog in Carson City is causing my Hyundai to act up! #AirPollutionMystery." Similarly, a Reddit thread titled "Hyundai Horror Stories" features gripping anecdotes of automotive malfunctions, hinting at the latent undercurrents that animate public discourse on this topic.
In synthesizing these diverse sources, the discerning reader is urged to approach the ensuing discussion with a keen appreciation for the juxtaposition of serious scholarship and lighthearted contemplation. As we traverse the labyrinthine avenues of academic inquiry, the interplay between scholarly rigor and whimsical musings maneuvers us through uncharted terrain flush with both intellectual gravitas and unexpected levity.

[[RESULTS]]
The statistical analysis revealed a correlation coefficient of 0.5538940 between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. The observed correlation displayed a coefficient of determination (r-squared) of 0.3067986, underscoring the notable relationship between the two variables. The probability value (p) further confirmed the statistical significance of the association, with a p-value less than 0.01.
Notably, these findings hint at a connection as palpable as the exhaust fumes emanating from a sputtering engine. The scatterplot (Fig. 1) visually encapsulates this compelling relationship, showcasing the strong correlation between air pollution levels and Hyundai automotive recalls. One might even say that the data points align with the precision of a well-tuned engine, each point contributing to the broader narrative of environmental factors intertwining with automotive performance.
The results of this analysis invite contemplation on the myriad factors at play, including the whimsical influences of climatic conditions, driving habits, and, as rumor has it, the capricious whims of cosmic forces. The tangled web of variables unfurls before us, beckoning us to peel back the layers and uncover the nuanced interplay between pollutants and automotive malfunctions. Embracing the spirit of inquiry, the pursuit of knowledge transcends the mundane to embrace the unexpected, for in the realm of statistics, surprises lurk around every corner like mischievous statistical imps.
In sum, the findings of this study underscore the intricate dance between air pollution in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. As we navigate this labyrinth of statistical inquiry, the veiled connections between environmental conditions and automotive performance invite us to acknowledge the pervasive influence of these factors. Amidst the haze of statistical analysis, our findings compel us to recognize the interwoven tapestry of air quality and automotive reliability, where the only constant may be the unpredictability of the variables themselves.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of the present study align closely with prior research, bolstering the existing body of literature that delves into the enigmatic relationship between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. Building on Smith et al.'s (2018) exploration of environmental influences on vehicle performance, our findings corroborate the notion that the variables at play are as intricate and multifaceted as the inner workings of a finely tuned engine. Likewise, Doe's (2019) examination of automotive recall patterns receives further support from our study, as we unveil the persistent association between air quality in Carson City and Hyundai vehicle malfunctions.

The whimsical musings of Ainsley (2015) and Carter (2018) in "The Smog Struggle" and "Recall Riddles," respectively, may initially seem out of place in scholarly discourse, yet our investigation prompts a fresh perspective. The overlay of literary intrigue upon our statistical inquiry reinforces the complexity and depth of the phenomenon under scrutiny. Furthermore, the social media anecdotes highlighted in the literature review, as exemplified by the fervent declaration of @DriveEasy23 on Twitter, are, dare we say, echoed in the statistical robustness of our findings. The unexpected alignment of these whimsical narratives with our empirical data serves as a testament to the multifaceted nature of reality, where statistical analyses and anecdotal accounts converge in a harmonious, if not paradoxical, manner.

The scatterplot that visually encapsulates the correlation between air pollution levels and Hyundai automotive recalls, akin to an intricately choreographed ballet, serves as a testament to the compelling nature of the association. The metaphorical exhaust fumes of statistical analysis, much like those emitted from a sputtering engine, hint at the tangible connection between atmospheric pollutants and automotive malfunctions. In doing so, they beckon the reader to ponder the intricacies of our interconnected world, where the whims of cosmic forces may not be as capricious as previously thought.

In summary, the present study not only contributes to our understanding of the interplay between air pollution and automotive recalls but also invites a reevaluation of how scholarly inquiry intersects with the unexpected. As we continue to navigate the nuanced terrain of statistical analysis, the underlying intricacies of environmental conditions and automotive performance invite us to view the pursuit of knowledge through a lens of both earnest inquiry and whimsical contemplation.

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 Carson City, Nevada and Automotive recalls issued by Hyundai Motor America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

[[ABSTRACT]]
This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.

Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

[[INTRDUCTION]]
The intersection of air pollution and automotive recalls forms a tantalizing nexus ripe for investigation. These puzzle pieces, seemingly disparate at first glance, beckon the discerning researcher to uncover their hidden connections. In this paper, we delve into the enigmatic relationship between the air quality of Carson City, Nevada, and the frequency of automotive recalls issued by Hyundai Motor America. As we embark on this academic escapade, we navigate through the labyrinth of statistical analysis, where the allure of correlation coefficients and p-values awaits with bated breath.
The adage "what goes up must come down" in the realm of environmental pollutants takes on a new dimension when juxtaposed with the intricacies of automotive engineering. Our quest to decode the interplay between these seemingly unrelated domains takes us through the annals of time, from the year 1986 to the present day, utilizing data from the Environmental Protection Agency and the US Department of Transportation. The fervent pursuit of knowledge leads us to intriguing revelations, shedding light on the statistical dance between air pollution levels and Hyundai automotive recalls.
As we unfurl our findings, we cautiously tiptoe around the lurking specter of confounding variables, the mischievous imps of statistical analysis. The whispered rumors of climatic conditions, driving habits, and other elusive factors beckon us to ponder their role in shaping the narrative of air quality and automotive reliability. Could it be that the cosmic dice are not always in our favor, clandestinely influencing the ebb and flow of car malfunctions and pollutant levels?
In unraveling this tapestry of relationships, we invite the reader to embrace the unconventional, to entertain the notion that beneath the veneer of dry statistics lies a realm of whimsy and wonder. So, as we embark on this scholarly odyssey, remember that while the air may be thick with pollutants, our minds remain as sharp as Occam's razor, ready to slice through the fog of uncertainty and unveil the intriguing dance between air pollution and automotive recalls.

[[RESULTS]]
The statistical analysis revealed a correlation coefficient of 0.5538940 between air pollution levels in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. The observed correlation displayed a coefficient of determination (r-squared) of 0.3067986, underscoring the notable relationship between the two variables. The probability value (p) further confirmed the statistical significance of the association, with a p-value less than 0.01.
Notably, these findings hint at a connection as palpable as the exhaust fumes emanating from a sputtering engine. The scatterplot (Fig. 1) visually encapsulates this compelling relationship, showcasing the strong correlation between air pollution levels and Hyundai automotive recalls. One might even say that the data points align with the precision of a well-tuned engine, each point contributing to the broader narrative of environmental factors intertwining with automotive performance.
The results of this analysis invite contemplation on the myriad factors at play, including the whimsical influences of climatic conditions, driving habits, and, as rumor has it, the capricious whims of cosmic forces. The tangled web of variables unfurls before us, beckoning us to peel back the layers and uncover the nuanced interplay between pollutants and automotive malfunctions. Embracing the spirit of inquiry, the pursuit of knowledge transcends the mundane to embrace the unexpected, for in the realm of statistics, surprises lurk around every corner like mischievous statistical imps.
In sum, the findings of this study underscore the intricate dance between air pollution in Carson City, Nevada, and automotive recalls issued by Hyundai Motor America. As we navigate this labyrinth of statistical inquiry, the veiled connections between environmental conditions and automotive performance invite us to acknowledge the pervasive influence of these factors. Amidst the haze of statistical analysis, our findings compel us to recognize the interwoven tapestry of air quality and automotive reliability, where the only constant may be the unpredictability of the variables themselves.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has shed light on the intriguing relationship between air pollution levels in Carson City, Nevada, and the occurence of automotive recalls issued by Hyundai Motor America. The statistically significant correlation coefficient of 0.5538940 with a p-value less than 0.01 has illuminated a connection as conspicuous as a sparkling emission from a well-maintained exhaust pipe. The results affirm the compelling interplay between environmental pollutants and automotive malfunctions, prompting contemplation on the whimsical influences that shape this dance of variables.

The subtle interplay of cosmic forces, driving habits, and climatic conditions invites us to embrace the enigmatic nature of statistical inquiry, where uncovering hidden connections is as satisfying as finding the missing piece of a complex jigsaw puzzle. While our study offers valuable insights, we acknowledge that the allure of statistical analysis can sometimes border on the mystical. As such, this research serves as a compelling chapter in the ongoing saga of unraveling the enigmatic tendrils of correlation in the realm of environmental and automotive dynamics.

In considering potential practical implications, we emphasize the need for continued vigilance in monitoring the interwoven tapestry of air quality and automotive performance. Perhaps, future research might delve deeper into the cosmic influences, the potential impact of lunar phases on vehicular performance, or the statistical significance of the occasional solar flare. As researchers, we are reminded that the pursuit of knowledge is akin to a perpetual scavenger hunt, where the next hidden treasure might be just a statistical model away.

In closing, we assert that further research in this area may yield diminishing returns, as the statistical stars seem to have aligned in presenting a clear connection between air pollution in Carson City and Hyundai automotive recalls. With that in mind, we bid adieu to this particular academic escapade, content in the knowledge that the spellbinding dance between pollutants and vehicles has been unveiled, leaving us only to marvel at the whimsy and wonder of statistical inquiry.

Ultimately, the results of our study stand as a testament to the endlessly fascinating interplay of variables, where the pursuit of knowledge is as intoxicating as the heady scent of a freshly printed statistical report.

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 Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Ecological Epiphanies

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 Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls. Respond only with the name of a fictional organization.

ChatGPT:

Institute for Environmental Automotive 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 Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls with an abstract of This paper aims to investigate the potential relationship between air pollution in Carson City, Nevada, and the issuance of automotive recalls by Hyundai Motor America. Leveraging data from the Environmental Protection Agency and the US Department of Transportation spanning the years 1986 to 2022, our research team utilized statistical analysis to unveil intriguing patterns. The correlation coefficient of 0.5538940 with a significance level of p < 0.01 between air pollution levels and Hyundai automotive recalls suggests a noteworthy association.
Our findings prompt contemplation on the possible extraneous factors that influence both air quality in Carson City and Hyundai vehicle malfunctions, including climatic conditions, driving patterns, and, dare we say, the occasional cosmic interference. Furthermore, the results underscore the need for continuous scrutiny over the interplay between environmental conditions and automotive performance. As we unravel these connections, we invite the reader to join us in this journey of discovery, where the air may be thick with pollutants, but our humor remains as dry as statistical analysis itself.

ChatGPT:

Hyundai, Heartache, air pollution, Carson City, Nevada, automotive recalls, Hyundai Motor America, Environmental Protection Agency, US Department of Transportation, statistical analysis, correlation coefficient, extraneous factors, climatic conditions, driving patterns, cosmic interference, environmental conditions, automotive performance, interplay, data analysis

*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 Carson City, Nevada
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Carson City, NV
Source: Environmental Protection Agency
See what else correlates with Air pollution in Carson City, Nevada

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

Correlation r = 0.5538940 (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 = 0.3067986 (Coefficient of determination)
This means 30.7% of the change in the one variable (i.e., Automotive recalls issued by Hyundai Motor America) is predictable based on the change in the other (i.e., Air pollution in Carson City, Nevada) over the 37 years from 1986 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00038.^Show 0.0003757857469416062300000000
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 0.55 in 0.038% of random cases. Said differently, if you correlated 2,661 random variables^Note Which I absolutely did.
with the same 36 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 36 because we have two variables measured over a period of 37 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.

[ 0.28, 0.74 ] 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.

	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 Carson City, Nevada (Bad air quality days)	0.00914634	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0363636	0.00273973	0	0	0.00273973	0.00821918	0	0.0218579	0.0520548	0.010989
Automotive recalls issued by Hyundai Motor America (Recalls)	1	1	0	1	3	1	0	3	3	4	1	1	1	2	5	5	6	6	3	7	5	0	9	6	4	5	4	9	10	12	17	14	11	7	15	20	22

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([0.00914634,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0363636,0.00273973,0,0,0.00273973,0.00821918,0,0.0218579,0.0520548,0.010989,])
array_2 = np.array([1,1,0,1,3,1,0,3,3,4,1,1,1,2,5,5,6,6,3,7,5,0,9,6,4,5,4,9,10,12,17,14,11,7,15,20,22,])
array_1_name = "Air pollution in Carson City, Nevada"
array_2_name = "Automotive recalls issued by Hyundai Motor 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
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Carson City, Nevada
Line chart for only Automotive recalls issued by Hyundai Motor America
AI-generated correlation image
The spurious research paper: Hyundai Heartache: Unraveling the Link between Air Pollution in Carson City, Nevada, and Automotive Recalls

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