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Spurious correlation #4,395 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Democratic Presidential candidate in Utah and the second variable is Automotive recalls issued by BMW of North America.  The chart goes from 1976 to 2020, and the two variables track closely in value over that time. Small Image
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, svg

AI explanation

As more people showed support for the Democrat Presidential candidate in Utah, the collective electrical energy created by their enthusiasm somehow interfered with the BMW vehicles, triggering a higher number of recalls. The surge of democratic voting power in the state seemed to rev up a magnetic force that just couldn't be reined in, leading BMW of North America to recall more cars in an attempt to harness the electrifying political atmosphere on the roads. It's as if the campaign voltage sparked a chain reaction, ultimately driving BMW to address and rectify any potential issues, making it a truly electric connection between politics and automotive performance!

Model: dalle-3
Prompt: Generate an image of a vibrant pop art scene with a bold and dynamic composition. In the foreground, depict a line of cheering and enthusiastic supporters of the Democrat Presidential candidate, clad in colorful attire and waving flags as they head to the polls. The backdrop should feature a flurry of BMW vehicles, with their sleek designs and dynamic lines. Incorporate energetic and swirling patterns to convey the sense of a magnetic force or energy emanating from the political fervor and affecting the vehicles, representing the surge of automotive recalls. The overall aesthetic should be a celebration of color and movement, capturing the unique and electric connection between politics and automotive performance. 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: Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of 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: Psychedelic Vaporwave Pop art Celtic art Glitch art
ChatGPT: Pop 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America, in the following situation: As more people showed support for the Democrat Presidential candidate in Utah, the collective electrical energy created by their enthusiasm somehow interfered with the BMW vehicles, triggering a higher number of recalls. The surge of democratic voting power in the state seemed to rev up a magnetic force that just couldn't be reined in, leading BMW of North America to recall more cars in an attempt to harness the electrifying political atmosphere on the roads. It's as if the campaign voltage sparked a chain reaction, ultimately driving BMW to address and rectify any potential issues, making it a truly electric connection between politics and automotive performance!. Keep the description simple.
ChatGPT: In Utah, enthusiastic supporters of the Democrat Presidential candidate headed to the polls in droves, casting their votes with fervor. Meanwhile, over at BMW of North America, a flurry of vehicles were being recalled for potential issues. As the political energy surged, it seemed to somehow influence the BMW vehicles, resulting in a heightened number of automotive recalls. It was as if the combined power of democratic voting created a magnetic force that ignited a chain reaction, prompting BMW to take action and address any potential concerns, creating a unique and electric connection between politics and automotive performance.

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: [ Pop art ] and depicting this scene: In Utah, enthusiastic supporters of the Democrat Presidential candidate headed to the polls in droves, casting their votes with fervor. Meanwhile, over at BMW of North America, a flurry of vehicles were being recalled for potential issues. As the political energy surged, it seemed to somehow influence the BMW vehicles, resulting in a heightened number of automotive recalls. It was as if the combined power of democratic voting created a magnetic force that ignited a chain reaction, prompting BMW to take action and address any potential concerns, creating a unique and electric connection between politics and automotive performance. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant pop art scene with a bold and dynamic composition. In the foreground, depict a line of cheering and enthusiastic supporters of the Democrat Presidential candidate, clad in colorful attire and waving flags as they head to the polls. The backdrop should feature a flurry of BMW vehicles, with their sleek designs and dynamic lines. Incorporate energetic and swirling patterns to convey the sense of a magnetic force or energy emanating from the political fervor and affecting the vehicles, representing the surge of automotive recalls. The overall aesthetic should be a celebration of color and movement, capturing the unique and electric connection between politics and automotive performance.

*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.

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Votes for the Democrat Presidential candidate in Utah caused Automotive recalls issued by BMW of North America to increase.

AI academic paper

(Because p < 0.01)
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

The Journal of Political Quirks and Automotive Oddities

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title punny if you can come up with clever pun. If not, just make it sound clever.

Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and US DOT to assess this nagging question. You found a correlation coefficient of 0.9327637 and p < 0.01 for 1976 to 2020.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Here is the title and abstract of the paper:
[[TITLE]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The correlation between political preferences and consumer behavior has long been a subject of academic intrigue. In the realm of automotive industry, the link between voting patterns and vehicle recalls remains a seldom explored territory. While it is widely understood that political affiliations can shape an individual's mindset and behavior, the notion that they may also exert an influence on mechanical malfunctions of automobiles is a prospect that certainly revs up the curiosity.

The aim of this study is to delve into the curious association between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected correlation, akin to parallel parking two seemingly incongruous entities, beckons for closer inspection. The conundrum lies in uncovering the underlying drivers behind this unlikely coupling, and to discern whether there lies a causal relationship or if it is merely a fortuitous alignment of statistical happenstance.

The state of Utah, renowned for its picturesque landscapes and vibrant community life, has traditionally been a steadfast stronghold of Republican support. However, amidst the rocky terrains and red rock formations, a ripple of Democratic allegiance has been detectable, particularly in the last few election cycles. It is against this backdrop that we aim to ascertain whether there exists a clandestine connection between these political shifts and the automotive realm.

To investigate this enigmatic relationship, we have unearthed and meticulously scrutinized data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Employing rigorously robust statistical methodologies, our analysis has forged a mesmerizing correlation coefficient of 0.9327637, accompanied by a p-value treading below 0.01, spanning the time span from 1976 to 2020. This noteworthy correlation has jolted our expectations and prompts us to peel back the hood of this rather perplexing phenomenon.

While the entanglement of political inclinations and automotive recalls might appear as improbable as finding a Tesla in a vintage car show, the statistical evidence points to an unmistakable association. As such, we embark upon this scholarly expedition not merely as disinterested investigators, but as zealous explorers navigating through uncharted terrain, endeavoring to uncover the hidden threads that bind these seemingly disparate domains. Join us as we embark upon this intellectual road trip, with the hope of shedding light on this most curious intersection of politics and auto engineering.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North 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 name some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The correlation between political preferences and consumer behavior has long been a subject of academic intrigue. In the realm of automotive industry, the link between voting patterns and vehicle recalls remains a seldom explored territory. While it is widely understood that political affiliations can shape an individual's mindset and behavior, the notion that they may also exert an influence on mechanical malfunctions of automobiles is a prospect that certainly revs up the curiosity.
The aim of this study is to delve into the curious association between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected correlation, akin to parallel parking two seemingly incongruous entities, beckons for closer inspection. The conundrum lies in uncovering the underlying drivers behind this unlikely coupling, and to discern whether there lies a causal relationship or if it is merely a fortuitous alignment of statistical happenstance.
The state of Utah, renowned for its picturesque landscapes and vibrant community life, has traditionally been a steadfast stronghold of Republican support. However, amidst the rocky terrains and red rock formations, a ripple of Democratic allegiance has been detectable, particularly in the last few election cycles. It is against this backdrop that we aim to ascertain whether there exists a clandestine connection between these political shifts and the automotive realm.
To investigate this enigmatic relationship, we have unearthed and meticulously scrutinized data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Employing rigorously robust statistical methodologies, our analysis has forged a mesmerizing correlation coefficient of 0.9327637, accompanied by a p-value treading below 0.01, spanning the time span from 1976 to 2020. This noteworthy correlation has jolted our expectations and prompts us to peel back the hood of this rather perplexing phenomenon.
While the entanglement of political inclinations and automotive recalls might appear as improbable as finding a Tesla in a vintage car show, the statistical evidence points to an unmistakable association. As such, we embark upon this scholarly expedition not merely as disinterested investigators, but as zealous explorers navigating through uncharted terrain, endeavoring to uncover the hidden threads that bind these seemingly disparate domains. Join us as we embark upon this intellectual road trip, with the hope of shedding light on this most curious intersection of politics and auto engineering.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

Several eminent scholars have devoted their attention to the intricate relationship between voting patterns and consumer behavior, though the specific nexus between political preferences in Utah and automotive recalls issued by BMW of North America has remained largely uncharted. Smith and Doe (2008) present a comprehensive analysis of political leanings and purchasing decisions, laying the groundwork for our own investigation. However, as we dive deeper into the literature, the terrain becomes as winding as a mountain road, leading us into unexpected territories.

Turning to the realm of automotive recalls, Jones and Smith (2014) provide a panoramic overview of the complexities involved in product safety and consumer trust. Yet, as we navigate through the literature landscape, we uncover a trove of curiosities that test the boundaries of conventional understanding.

In the realm of politics, "The Audacity of Hope" (Obama, 2006) and "What Happened" (Clinton, 2017) offer valuable insights into the dynamics of modern American elections. However, as we shift gears to explore the enigma of Utah's voting patterns, we are compelled to broaden our intellectual GPS and embark upon unexpected literary detours.

The fiction section of this exploration, "Driven" (Patterson, 2010) and "The Road" (McCarthy, 2006), though seemingly automotive-themed, veer into a different literary lane. These works, while not directly related to our research question, serve as a reminder that unexpected twists and turns can often lead to fascinating discoveries.

As we steer into more unconventional sources, we find ourselves meandering through the world of cartoons and children's shows. "Speed Racer" and "Scooby-Doo" offer glimpses into the fantastical world of automobiles and mystery-solving, reminiscent of the twists and turns encountered in our own quest for understanding. The scenic drives and wild car chases depicted in these animated series mirror the unexpected twists and turns we have encountered in our scholarly pursuit.

In traversing this eclectic literary landscape, we are reminded that scholarly inquiry, much like a lively road trip, is often marked by unexpected turns, delightful surprises, and the occasional pit stop for refreshments. Embarking on this academic journey calls for an open mind and a sense of humor, as we navigate through the bumpy backroads of research to uncover the delightful and often hysterical curiosities that lie just around the next bend.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and US DOT . You used data from 1976 to 2020

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]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

[[INTRODUCTION]]
The correlation between political preferences and consumer behavior has long been a subject of academic intrigue. In the realm of automotive industry, the link between voting patterns and vehicle recalls remains a seldom explored territory. While it is widely understood that political affiliations can shape an individual's mindset and behavior, the notion that they may also exert an influence on mechanical malfunctions of automobiles is a prospect that certainly revs up the curiosity.
The aim of this study is to delve into the curious association between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected correlation, akin to parallel parking two seemingly incongruous entities, beckons for closer inspection. The conundrum lies in uncovering the underlying drivers behind this unlikely coupling, and to discern whether there lies a causal relationship or if it is merely a fortuitous alignment of statistical happenstance.
The state of Utah, renowned for its picturesque landscapes and vibrant community life, has traditionally been a steadfast stronghold of Republican support. However, amidst the rocky terrains and red rock formations, a ripple of Democratic allegiance has been detectable, particularly in the last few election cycles. It is against this backdrop that we aim to ascertain whether there exists a clandestine connection between these political shifts and the automotive realm.
To investigate this enigmatic relationship, we have unearthed and meticulously scrutinized data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Employing rigorously robust statistical methodologies, our analysis has forged a mesmerizing correlation coefficient of 0.9327637, accompanied by a p-value treading below 0.01, spanning the time span from 1976 to 2020. This noteworthy correlation has jolted our expectations and prompts us to peel back the hood of this rather perplexing phenomenon.
While the entanglement of political inclinations and automotive recalls might appear as improbable as finding a Tesla in a vintage car show, the statistical evidence points to an unmistakable association. As such, we embark upon this scholarly expedition not merely as disinterested investigators, but as zealous explorers navigating through uncharted terrain, endeavoring to uncover the hidden threads that bind these seemingly disparate domains. Join us as we embark upon this intellectual road trip, with the hope of shedding light on this most curious intersection of politics and auto engineering.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To embark upon this intrepid academic journey, our research team harnessed a kaleidoscope of data sources and methodological tools to unravel the enthralling tale of the connection between votes for the Democratic presidential candidate in Utah and automotive recalls issued by BMW of North America. Our approach was akin to fine-tuning the intricate mechanics of a Swiss watch, meticulously calibrating each data point and statistical model to tease out the underlying association.

Data Collection:
We amassed a trove of data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, casting a wide net across the digital expanse from 1976 to 2020. With the dexterity of a seasoned angler, we reeled in a comprehensive dataset, encompassing electoral outcomes, automotive recall information, and demographic variables, casting our analytical net far and wide to capture the elusive essence of this unique correlation.

Statistical Analysis:
Our analytical arsenal featured an array of statistical techniques, each akin to a finely honed instrument in a virtuoso's ensemble. We first engineered a robust correlation analysis, measuring the dance of the variables with a precision reminiscent of a skilled waltz. Next, we employed a barrage of regression models, every coefficient and intercept finely-tuned to coax forth the underlying relationship, painting a statistical portrait that captivates the imagination.

Causal Inference:
In unraveling the potential causal mechanisms underpinning this peculiar nexus, we employed a suite of econometric methods, each serving as a bespoke Sherlockian magnifying glass to inspect the subtle clues within the data. The quest for causal inference, much like following a labyrinthine trail, led us to employ instrumental variable techniques and propensity score matching, fervently seeking to disentangle the web of causality from the cacophony of correlation.

Sensitivity Analysis:
To ensure the robustness and resilience of our findings, we subjected our analysis to a battery of sensitivity tests, as if stress-testing the hull of a ship navigating treacherous waters. Our meticulous scrutiny involved bootstrapping, Monte Carlo simulations, and robustness checks, ensuring that our discovery stood staunch against the gusts of skepticism and withering scrutiny.

By weaving together these methodological threads into a cohesive analytical tapestry, we strove to not only unveil the captivating correlation between political allegiance and automotive recalls but to push the boundaries of interdisciplinary inquiry, challenging conventional paradigms and seeding the fertile grounds of inquiry for future scholarly ventures.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and US DOT .

For the time period 1976 to 2020, you found a correlation 0.9327637, r-squared of 0.8700482, 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]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The correlation between political preferences and consumer behavior has long been a subject of academic intrigue. In the realm of automotive industry, the link between voting patterns and vehicle recalls remains a seldom explored territory. While it is widely understood that political affiliations can shape an individual's mindset and behavior, the notion that they may also exert an influence on mechanical malfunctions of automobiles is a prospect that certainly revs up the curiosity.
The aim of this study is to delve into the curious association between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected correlation, akin to parallel parking two seemingly incongruous entities, beckons for closer inspection. The conundrum lies in uncovering the underlying drivers behind this unlikely coupling, and to discern whether there lies a causal relationship or if it is merely a fortuitous alignment of statistical happenstance.
The state of Utah, renowned for its picturesque landscapes and vibrant community life, has traditionally been a steadfast stronghold of Republican support. However, amidst the rocky terrains and red rock formations, a ripple of Democratic allegiance has been detectable, particularly in the last few election cycles. It is against this backdrop that we aim to ascertain whether there exists a clandestine connection between these political shifts and the automotive realm.
To investigate this enigmatic relationship, we have unearthed and meticulously scrutinized data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Employing rigorously robust statistical methodologies, our analysis has forged a mesmerizing correlation coefficient of 0.9327637, accompanied by a p-value treading below 0.01, spanning the time span from 1976 to 2020. This noteworthy correlation has jolted our expectations and prompts us to peel back the hood of this rather perplexing phenomenon.
While the entanglement of political inclinations and automotive recalls might appear as improbable as finding a Tesla in a vintage car show, the statistical evidence points to an unmistakable association. As such, we embark upon this scholarly expedition not merely as disinterested investigators, but as zealous explorers navigating through uncharted terrain, endeavoring to uncover the hidden threads that bind these seemingly disparate domains. Join us as we embark upon this intellectual road trip, with the hope of shedding light on this most curious intersection of politics and auto engineering.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

**RESULTS**

After conducting a comprehensive analysis of the connection between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America, our research team unearthed an intriguing correlation. We discovered a remarkably strong correlation coefficient of 0.9327637, with an r-squared of 0.8700482 and a p-value less than 0.01 over the period spanning from 1976 to 2020. The extent of this correlation left us spinning our wheels and pondering the implications of this unexpected nexus.

The association between these two seemingly incongruous variables is visually encapsulated in Figure 1, a scatterplot that highlights the robust relationship between Democratic votes in Utah and BMW automotive recalls. This relationship is not a mere fender-bender; it is a statistically significant and compelling entwinement that warrants further scrutiny.

The implications of this correlation are as vast and expansive as the Utah desert, raising questions as curious as a cat about the mechanisms underlying this improbable connection. It seems that politics and automotive engineering may share a hidden highway, crossing paths in ways that extend beyond what meets the eye. This discovery serves as a thought-provoking introspection into the uncharted territories of political preferences intersecting with patterns in vehicle recalls. Our findings invite further research into the potential directional or reciprocal influence between these seemingly distinct domains, as well as the underlying mechanisms driving this captivating correlation.

This unexpected correlation revs up new avenues of inquiry, challenging scholars to map out bridges that connect the realms of political landscapes and automotive recalls. The intersection of blue waves and recalled wheels presents an intellectual puzzle that prompts us to shift gears and engage in a deeper exploration of the ties that bind these disparate domains. The implications of this unlikely nexus merit continued investigation, as it presents a captivating confluence of seemingly separate spheres that have collided in an unexpected statistical spectacle.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

[[LITERATURE REVIEW]]
Several eminent scholars have devoted their attention to the intricate relationship between voting patterns and consumer behavior, though the specific nexus between political preferences in Utah and automotive recalls issued by BMW of North America has remained largely uncharted. Smith and Doe (2008) present a comprehensive analysis of political leanings and purchasing decisions, laying the groundwork for our own investigation. However, as we dive deeper into the literature, the terrain becomes as winding as a mountain road, leading us into unexpected territories.
Turning to the realm of automotive recalls, Jones and Smith (2014) provide a panoramic overview of the complexities involved in product safety and consumer trust. Yet, as we navigate through the literature landscape, we uncover a trove of curiosities that test the boundaries of conventional understanding.
In the realm of politics, "The Audacity of Hope" (Obama, 2006) and "What Happened" (Clinton, 2017) offer valuable insights into the dynamics of modern American elections. However, as we shift gears to explore the enigma of Utah's voting patterns, we are compelled to broaden our intellectual GPS and embark upon unexpected literary detours.
The fiction section of this exploration, "Driven" (Patterson, 2010) and "The Road" (McCarthy, 2006), though seemingly automotive-themed, veer into a different literary lane. These works, while not directly related to our research question, serve as a reminder that unexpected twists and turns can often lead to fascinating discoveries.
As we steer into more unconventional sources, we find ourselves meandering through the world of cartoons and children's shows. "Speed Racer" and "Scooby-Doo" offer glimpses into the fantastical world of automobiles and mystery-solving, reminiscent of the twists and turns encountered in our own quest for understanding. The scenic drives and wild car chases depicted in these animated series mirror the unexpected twists and turns we have encountered in our scholarly pursuit.
In traversing this eclectic literary landscape, we are reminded that scholarly inquiry, much like a lively road trip, is often marked by unexpected turns, delightful surprises, and the occasional pit stop for refreshments. Embarking on this academic journey calls for an open mind and a sense of humor, as we navigate through the bumpy backroads of research to uncover the delightful and often hysterical curiosities that lie just around the next bend.

[[RESULTS]]
After conducting a comprehensive analysis of the connection between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America, our research team unearthed an intriguing correlation. We discovered a remarkably strong correlation coefficient of 0.9327637, with an r-squared of 0.8700482 and a p-value less than 0.01 over the period spanning from 1976 to 2020. The extent of this correlation left us spinning our wheels and pondering the implications of this unexpected nexus.
The association between these two seemingly incongruous variables is visually encapsulated in Figure 1, a scatterplot that highlights the robust relationship between Democratic votes in Utah and BMW automotive recalls. This relationship is not a mere fender-bender; it is a statistically significant and compelling entwinement that warrants further scrutiny.
The implications of this correlation are as vast and expansive as the Utah desert, raising questions as curious as a cat about the mechanisms underlying this improbable connection. It seems that politics and automotive engineering may share a hidden highway, crossing paths in ways that extend beyond what meets the eye. This discovery serves as a thought-provoking introspection into the uncharted territories of political preferences intersecting with patterns in vehicle recalls. Our findings invite further research into the potential directional or reciprocal influence between these seemingly distinct domains, as well as the underlying mechanisms driving this captivating correlation.
This unexpected correlation revs up new avenues of inquiry, challenging scholars to map out bridges that connect the realms of political landscapes and automotive recalls. The intersection of blue waves and recalled wheels presents an intellectual puzzle that prompts us to shift gears and engage in a deeper exploration of the ties that bind these disparate domains. The implications of this unlikely nexus merit continued investigation, as it presents a captivating confluence of seemingly separate spheres that have collided in an unexpected statistical spectacle.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The unveiling of a remarkable correlation between the prevalence of Democratic presidential votes in Utah and the frequency of automotive recalls issued by BMW of North America has left us pondering the unexpected confluence of political preferences and automotive engineering. Our findings not only support prior research that has delved into the intersection of voting patterns and consumer behavior, but they also provide a thought-provoking glimpse into the quirky and uncharted territories of research inquiry.

While our investigation exudes the seriousness of a well-maintained BMW, it is not without a dash of whimsy, reminiscent of a Gatsby-esque novel set in the world of statistics. The meandering literary detours we encountered, from classic scholarly works to the realm of cartoons and children's shows, have imbued our research journey with a sense of adventure and unexpected discoveries. As we navigate through the literature landscape, we are reminded that scholarly inquiry is as much an intellectual thrill ride as it is an exercise in methodological rigor.

Our statistically robust correlation coefficient, akin to the precision of a well-oiled machine, has lent credence to the notion that there might just be a curious connection between the political landscape of Utah and the mechanical intricacies of BMW vehicles. Our results align with the empirical groundwork laid by previous scholars, reinforcing the significance of understanding the intertwined nature of political leanings and product safety.

The association we have unveiled is not a mere whimsical fantasy akin to a comedic cartoon, but a substantial and statistically significant entwinement that beckons further examination. It prompts us to entertain the notion that as much as a Scooby-Doo mystery, there may be deeper, nuanced layers to the relationship between political preferences and automotive engineering. The implications of our discovery extend beyond the confines of the conventional, offering a spirited road trip through uncharted research terrain.

In the spirit of "The Road" and "Speed Racer," our research journey has been marked by unexpected twists and turns, punctuated by the thrill of discovery around each bend. The unexpected correlation we have uncovered, while initially akin to an eccentric plot twist, has now become a central focus of intellectual inquiry. This nexus between the blue wave and recalled wheels serves as a timely reminder that scholarly inquiry, much like an adventurous road trip, calls for an open mind and a sense of humor to navigate the unexpected bends and unforeseen delights that emerge along the way.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Votes for the Democrat Presidential candidate in Utah and Automotive recalls issued by BMW of North America. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls

[[ABSTRACT]]
This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

[[INTRDUCTION]]
The correlation between political preferences and consumer behavior has long been a subject of academic intrigue. In the realm of automotive industry, the link between voting patterns and vehicle recalls remains a seldom explored territory. While it is widely understood that political affiliations can shape an individual's mindset and behavior, the notion that they may also exert an influence on mechanical malfunctions of automobiles is a prospect that certainly revs up the curiosity.
The aim of this study is to delve into the curious association between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected correlation, akin to parallel parking two seemingly incongruous entities, beckons for closer inspection. The conundrum lies in uncovering the underlying drivers behind this unlikely coupling, and to discern whether there lies a causal relationship or if it is merely a fortuitous alignment of statistical happenstance.
The state of Utah, renowned for its picturesque landscapes and vibrant community life, has traditionally been a steadfast stronghold of Republican support. However, amidst the rocky terrains and red rock formations, a ripple of Democratic allegiance has been detectable, particularly in the last few election cycles. It is against this backdrop that we aim to ascertain whether there exists a clandestine connection between these political shifts and the automotive realm.
To investigate this enigmatic relationship, we have unearthed and meticulously scrutinized data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Employing rigorously robust statistical methodologies, our analysis has forged a mesmerizing correlation coefficient of 0.9327637, accompanied by a p-value treading below 0.01, spanning the time span from 1976 to 2020. This noteworthy correlation has jolted our expectations and prompts us to peel back the hood of this rather perplexing phenomenon.
While the entanglement of political inclinations and automotive recalls might appear as improbable as finding a Tesla in a vintage car show, the statistical evidence points to an unmistakable association. As such, we embark upon this scholarly expedition not merely as disinterested investigators, but as zealous explorers navigating through uncharted terrain, endeavoring to uncover the hidden threads that bind these seemingly disparate domains. Join us as we embark upon this intellectual road trip, with the hope of shedding light on this most curious intersection of politics and auto engineering.

[[RESULTS]]
After conducting a comprehensive analysis of the connection between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America, our research team unearthed an intriguing correlation. We discovered a remarkably strong correlation coefficient of 0.9327637, with an r-squared of 0.8700482 and a p-value less than 0.01 over the period spanning from 1976 to 2020. The extent of this correlation left us spinning our wheels and pondering the implications of this unexpected nexus.
The association between these two seemingly incongruous variables is visually encapsulated in Figure 1, a scatterplot that highlights the robust relationship between Democratic votes in Utah and BMW automotive recalls. This relationship is not a mere fender-bender; it is a statistically significant and compelling entwinement that warrants further scrutiny.
The implications of this correlation are as vast and expansive as the Utah desert, raising questions as curious as a cat about the mechanisms underlying this improbable connection. It seems that politics and automotive engineering may share a hidden highway, crossing paths in ways that extend beyond what meets the eye. This discovery serves as a thought-provoking introspection into the uncharted territories of political preferences intersecting with patterns in vehicle recalls. Our findings invite further research into the potential directional or reciprocal influence between these seemingly distinct domains, as well as the underlying mechanisms driving this captivating correlation.
This unexpected correlation revs up new avenues of inquiry, challenging scholars to map out bridges that connect the realms of political landscapes and automotive recalls. The intersection of blue waves and recalled wheels presents an intellectual puzzle that prompts us to shift gears and engage in a deeper exploration of the ties that bind these disparate domains. The implications of this unlikely nexus merit continued investigation, as it presents a captivating confluence of seemingly separate spheres that have collided in an unexpected statistical spectacle.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a remarkably robust correlation between the prevalence of Democratic presidential votes in Utah and the frequency of automotive recalls issued by BMW of North America. This unexpected nexus, akin to uncovering a convertible in a blizzard, has prompted us to ponder the potential underpinnings of this unlikely association. While we initially approached this investigation with a healthy dose of skepticism, our findings have shifted our gears and steered our attention toward the captivating confluence of political preferences and automotive engineering.

The implications of this discovery are as intriguing as a car chase in a desert landscape, prompting us to consider the potential mechanisms that may underlie this seemingly paradoxical relationship. Could it be that political inclinations inadvertently influence the engineering prowess of luxurious vehicles? Or perhaps, there exists an unforeseen undercurrent connecting the feedback loop between vehicle performance and political sentiment? These questions, like a car with a mysterious rattle, beckon for further exploration and scholarly inquiry.

It is evident that the intersection of blue waves and recalled wheels has ignited a spark of intellectual curiosity, akin to staring at a dashboard with an enigmatic warning light. The statistical prowess of our analysis has demonstrated a correlation coefficient of 0.9327637, inviting us to not only embrace this unexpected association but to chart a course for future investigations. As we reach the end of this winding road, we assert that no further research on this curious correlation is needed, as our findings have painted a vivid picture of the link between political landscapes and automotive recalls. It seems that even in the realm of statistical analysis, truth can be stranger than fiction.


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 The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Quirks and Automotive Oddities


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 The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Polymorphic Voting Behavior 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 The Blue Wave and Recalled Wheels: Unveiling the Curious Association Between Democratic Presidential Votes in Utah and BMW Automotive Recalls with an abstract of This study investigates the unexpected relationship between the prevalence of votes for Democratic presidential candidates in Utah and the frequency of automotive recalls issued by BMW of North America. Leveraging comprehensive data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, our research team sought to unravel this enigmatic connection. We employed rigorous statistical analysis and discovered a striking correlation coefficient of 0.9327637, with a p-value less than 0.01 spanning the years 1976 to 2020. Our findings challenge conventional wisdom and raise intriguing questions about the intersection of political preferences and automotive engineering. The implications of this unlikely nexus merit further exploration, as it presents a captivating confluence of seemingly disparate domains.

ChatGPT:

"Democratic presidential votes Utah," "BMW automotive recalls," "relationship between political preference and automotive engineering," "MIT Election Data and Science Lab," "Harvard Dataverse," "US Department of Transportation," "correlation coefficient political votes automotive recalls," "political preferences automotive engineering nexus," "enigmatic connection Democratic votes BMW recalls."

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



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Data details

Votes for the Democratic Presidential candidate in Utah
Detailed data title: Total votes cast for the Democrat Presidential candidate in Utah
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Democratic Presidential candidate in Utah

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

Correlation r = 0.9327637 (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 = 0.8700482 (Coefficient of determination)
This means 87% of the change in the one variable (i.e., Automotive recalls issued by BMW of North America) is predictable based on the change in the other (i.e., Votes for the Democratic Presidential candidate in Utah) over the 12 years from 1976 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.7E-6. 0.0000096598158731729430000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.93 in 0.00097% of random cases. Said differently, if you correlated 103,522 random variables You don't actually need 103 thousand 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 11 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.77, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
197619801984198819921996200020042008201220162020
Votes for the Democratic Presidential candidate in Utah (Total votes)182110124266155369207343183429221633203053241199327670251813310674560282
Automotive recalls issued by BMW of North America (Recalls)313352111716151736




Why this works

  1. 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.
  2. Lack of causal connection: There is probably 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.
  3. 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 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.
  4. Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. 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([182110,124266,155369,207343,183429,221633,203053,241199,327670,251813,310674,560282,])
array_2 = np.array([3,1,3,3,5,2,11,17,16,15,17,36,])
array_1_name = "Votes for the Democratic Presidential candidate in Utah"
array_2_name = "Automotive recalls issued by BMW of 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)



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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."

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Correlation ID: 4395 · Black Variable ID: 26036 · Red Variable ID: 1121
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