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Spurious correlation #5,214 · 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 Democratic Senators in Colorado and the second variable is Automotive recalls issued by BMW of North America.  The chart goes from 1978 to 2020, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The Democrats were revving up their support in Colorado, which sent a powerful message to BMW - prompting them to recall their vehicles and gear up for some major changes. It seems like the political climate wasn't the only thing experiencing a shift, as BMW raced to address the issues and steer clear of any further problems on the road to success. Now, they'll need to work on getting their campaign back on track and win back the trust of their constituents, or this could just be the tip of the iceberg.

Model: dalle-3
Prompt: Generate an image of a vibrant Colorado town showing a bustling Democratic rally with people waving flags and signs of support, while in contrast, a sleek BMW dealership is visible with a team of mechanics hurriedly working on cars lined up for a recall. The scene should capture the charged energy in the air, signifying the simultaneous anticipation of change and progress in both the political and automotive realms. The focus should be on the lively atmosphere, with clear distinctions between the two activities, illustrating the coexistence of political activism and everyday business in the town. 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: Democrat votes for Senators in Colorado 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: Cutsy art illustration High-tech Symbolism A scene from Skyrim A photo-realistic image
ChatGPT: A photo-realistic image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Democrat votes for Senators in Colorado and Automotive recalls issued by BMW of North America, in the following situation: The Democrats were revving up their support in Colorado, which sent a powerful message to BMW - prompting them to recall their vehicles and gear up for some major changes. It seems like the political climate wasn't the only thing experiencing a shift, as BMW raced to address the issues and steer clear of any further problems on the road to success. Now, they'll need to work on getting their campaign back on track and win back the trust of their constituents, or this could just be the tip of the iceberg.. Keep the description simple.
ChatGPT: In a bustling Colorado town, Democrats gathered in a lively rally, waving flags and signs of support. Nearby, at a sleek BMW dealership, a team of mechanics hurriedly worked on cars lined up for a recall. The energy in the air was charged, with both the political and automotive realms gearing up for change, ready to navigate a new course toward progress.

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 photo-realistic image ] and depicting this scene: In a bustling Colorado town, Democrats gathered in a lively rally, waving flags and signs of support. Nearby, at a sleek BMW dealership, a team of mechanics hurriedly worked on cars lined up for a recall. The energy in the air was charged, with both the political and automotive realms gearing up for change, ready to navigate a new course toward progress. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant Colorado town showing a bustling Democratic rally with people waving flags and signs of support, while in contrast, a sleek BMW dealership is visible with a team of mechanics hurriedly working on cars lined up for a recall. The scene should capture the charged energy in the air, signifying the simultaneous anticipation of change and progress in both the political and automotive realms. The focus should be on the lively atmosphere, with clear distinctions between the two activities, illustrating the coexistence of political activism and everyday business in the town.

*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 Democrat votes for Senators in Colorado caused Automotive recalls issued by BMW of North America to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
The Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

The Journal of Political Quirkiness

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 silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

Rhyme words in the title.

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 0.9143854 and p < 0.01 for 1978 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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

Here is the title and abstract of the paper:
[[TITLE]]
The Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The intersection of politics and automotive industry has long puzzled researchers, policymakers, and enthusiasts alike. While some may view these topics as unrelated as a car and a kangaroo, our whimsically curious minds saw an opportunity to explore the intersection of Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. At first glance, one might be inclined to dismiss this connection as mere happenstance, akin to finding a needle in a haystack made entirely of ballot papers and brake pads. However, our inquiry into this unlikely association has yielded remarkably compelling results that are bound to delight and surprise even the most stoic of scientists.

With an insatiable appetite for the quirky and the unexpected, we delved into the depths of election data archives and automotive recall records. The sheer audacity of our endeavor propelled us to summon data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, effectively marrying the worlds of political science and vehicular mishaps. Our research brims with a sense of playful wonder, akin to a child gleefully unearthing obscure treasures in a sandbox, albeit with the rigor and discipline characteristic of esteemed academic pursuits.

As we gingerly tiptoe into the world of statistical analysis, we cannot help but marvel at the mesmerizing dance between numbers and human behavior. The correlation coefficient of 0.9143854 that emerged from our scrutiny hints at a connection so robust it could rival the sturdiest of seat belts, and a p-value less than 0.01 for the years 1978 to 2020 further solidifies our findings like epoxy on a cracked windshield. Indeed, our data sparkle and shimmer like a freshly waxed sports car, inviting the reader to revel in the unexpected synchrony of political leanings and automotive tribulations.

In the following sections of this study, we invite the reader to accompany us on a lighthearted journey through the labyrinth of correlations, seemingly haphazard patterns, and frivolous observations. Through this lens of merry curiosity, we aim to shed light on the interplay between the political landscape of Colorado and the tumultuous world of automotive recalls, offering insights that are as intellectually stimulating as they are unexpectedly delightful. Our investigation is not merely a dry exploration of numbers and political sentiments; rather, it embodies the spirit of mirthful inquiry, capturing the exuberant joy of unraveling peculiar conundrums and unexpected connections. So, buckle up and prepare for a ride through the eccentric, the whimsical, and the delightfully unconventional as we unravel the intricate tango between the ballot and the recall.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
The Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of politics and automotive industry has long puzzled researchers, policymakers, and enthusiasts alike. While some may view these topics as unrelated as a car and a kangaroo, our whimsically curious minds saw an opportunity to explore the intersection of Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. At first glance, one might be inclined to dismiss this connection as mere happenstance, akin to finding a needle in a haystack made entirely of ballot papers and brake pads. However, our inquiry into this unlikely association has yielded remarkably compelling results that are bound to delight and surprise even the most stoic of scientists.
With an insatiable appetite for the quirky and the unexpected, we delved into the depths of election data archives and automotive recall records. The sheer audacity of our endeavor propelled us to summon data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, effectively marrying the worlds of political science and vehicular mishaps. Our research brims with a sense of playful wonder, akin to a child gleefully unearthing obscure treasures in a sandbox, albeit with the rigor and discipline characteristic of esteemed academic pursuits.
As we gingerly tiptoe into the world of statistical analysis, we cannot help but marvel at the mesmerizing dance between numbers and human behavior. The correlation coefficient of 0.9143854 that emerged from our scrutiny hints at a connection so robust it could rival the sturdiest of seat belts, and a p-value less than 0.01 for the years 1978 to 2020 further solidifies our findings like epoxy on a cracked windshield. Indeed, our data sparkle and shimmer like a freshly waxed sports car, inviting the reader to revel in the unexpected synchrony of political leanings and automotive tribulations.
In the following sections of this study, we invite the reader to accompany us on a lighthearted journey through the labyrinth of correlations, seemingly haphazard patterns, and frivolous observations. Through this lens of merry curiosity, we aim to shed light on the interplay between the political landscape of Colorado and the tumultuous world of automotive recalls, offering insights that are as intellectually stimulating as they are unexpectedly delightful. Our investigation is not merely a dry exploration of numbers and political sentiments; rather, it embodies the spirit of mirthful inquiry, capturing the exuberant joy of unraveling peculiar conundrums and unexpected connections. So, buckle up and prepare for a ride through the eccentric, the whimsical, and the delightfully unconventional as we unravel the intricate tango between the ballot and the recall.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The peculiar interplay between political activities and automotive misdemeanors has drawn the attention of researchers from various disciplines. In their seminal work, Smith et al. (2015) meticulously dissect the intricate relationship between political ideologies and consumer behavior, providing a framework that has since captivated the scholarly community. Doe and Jones (2018) further build upon this foundation, shedding light on the unexpected parallels between voter turnout and corporate accountability, laying the groundwork for endeavors such as our own.

Venturing beyond the confines of academic literature, we turn our gaze to non-fictional works that offer tantalizing insights into the enigmatic correlation under scrutiny. In "The Wheels of Democracy: A Political Journey" by Roadster (2019), the author offers a thought-provoking narrative, intertwining the twists and turns of democratic processes with the bumpy road of automotive governance. Similarly, "Recalls and Reckonings: A Chronicle of Corporate Contrition" by Gearhead (2017) presents a gripping exposé on the tumultuous relationship between automakers and their ethical obligations, providing a compelling backdrop for our own investigation.

As we journey into the realm of fiction, the literary landscape abounds with works that, while not directly related to our subject matter, nonetheless beckon with their alluring titles. "The Senator's Sedan" by Novelista (2016) and "The Road to Recalls" by Wordsmith (2014) offer whimsical escapades that, while undoubtedly unrelated to our scholarly pursuits, tickle the imagination with their allegorical resonances. Furthermore, the popularity of board games such as "Political Pit Stop" and "Recall Race Rally" attests to the widespread fascination with the convergence of political drama and automotive mishaps, serving as a testament to the enduring allure of our research subject.

In weaving together the serious and the lighthearted, the scholarly and the fanciful, our exploration of the interconnectedness between Democrat votes in Colorado and BMW automotive recalls takes on a delightful hue, akin to stumbling upon a hidden treasure trove in the form of statistical nuances and playful associations. As we delve deeper into our analysis, the jocund spirit of our inquiry continues to infuse our investigation with a sense of buoyant curiosity and gleeful exploration, much like embarking on a quest for whimsical truths in a land teeming with unexpected connections and irrepressible merriment.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

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 1978 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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

[[INTRODUCTION]]
The intersection of politics and automotive industry has long puzzled researchers, policymakers, and enthusiasts alike. While some may view these topics as unrelated as a car and a kangaroo, our whimsically curious minds saw an opportunity to explore the intersection of Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. At first glance, one might be inclined to dismiss this connection as mere happenstance, akin to finding a needle in a haystack made entirely of ballot papers and brake pads. However, our inquiry into this unlikely association has yielded remarkably compelling results that are bound to delight and surprise even the most stoic of scientists.
With an insatiable appetite for the quirky and the unexpected, we delved into the depths of election data archives and automotive recall records. The sheer audacity of our endeavor propelled us to summon data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, effectively marrying the worlds of political science and vehicular mishaps. Our research brims with a sense of playful wonder, akin to a child gleefully unearthing obscure treasures in a sandbox, albeit with the rigor and discipline characteristic of esteemed academic pursuits.
As we gingerly tiptoe into the world of statistical analysis, we cannot help but marvel at the mesmerizing dance between numbers and human behavior. The correlation coefficient of 0.9143854 that emerged from our scrutiny hints at a connection so robust it could rival the sturdiest of seat belts, and a p-value less than 0.01 for the years 1978 to 2020 further solidifies our findings like epoxy on a cracked windshield. Indeed, our data sparkle and shimmer like a freshly waxed sports car, inviting the reader to revel in the unexpected synchrony of political leanings and automotive tribulations.
In the following sections of this study, we invite the reader to accompany us on a lighthearted journey through the labyrinth of correlations, seemingly haphazard patterns, and frivolous observations. Through this lens of merry curiosity, we aim to shed light on the interplay between the political landscape of Colorado and the tumultuous world of automotive recalls, offering insights that are as intellectually stimulating as they are unexpectedly delightful. Our investigation is not merely a dry exploration of numbers and political sentiments; rather, it embodies the spirit of mirthful inquiry, capturing the exuberant joy of unraveling peculiar conundrums and unexpected connections. So, buckle up and prepare for a ride through the eccentric, the whimsical, and the delightfully unconventional as we unravel the intricate tango between the ballot and the recall.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

In our quest to decipher the confounding connection between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America, we employed a series of methodological antics teeming with academic zest and perhaps a sprinkle of whimsy.

First, we gleefully indulged in the procurement of datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation. Our pursuit of data resembled a charming scavenger hunt, with each dataset serving as a puzzle piece begging to be fittingly placed in the mosaic of our investigation. As we whimsically traversed the digital landscapes, we sought to capture a comprehensive span of time, spanning from 1978 to 2020, akin to collecting memorabilia from a rollicking time-traveling escapade.

Enveloped in the embrace of statistical analyses, we embarked on a journey akin to deciphering a cryptic crossword puzzle laden with bewildering clues and unexpected twists. With the gusto of intrepid spectators at a slightly unconventional sporting event, we calculated the correlation coefficient between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America. Our statistical revelry led to the unearthing of a correlation coefficient of 0.9143854, a value so pronounced it beckons to mind the audacious hues of a freshly painted racing stripe on a zippy sports car.

Furthermore, we daringly ventured into the realm of p-values, conducting tests to ascertain the significance of the observed correlation. Through meticulous analysis, we observed a p-value less than 0.01 for the years 1978 to 2020, a result that shimmered with statistical splendor like a glistening hood ornament catching the sun’s rays on a bright summer day.

Our methodology mirrored a whimsical ballet between academic rigor and playful observation, as we danced through the tangled choreography of data collection, statistical analysis, and the joyous pursuit of uncovering unexpected harmonies in the fabric of human behavior. Our methodology, like a mischievous riddle wrapped in the cloak of scholarly inquiry, speaks to the unyielding merriment that guided our quest to unravel the mysterious rapport between political proclivities and automotive tribulations.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

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 1978 to 2020, you found a correlation 0.9143854, r-squared of 0.8361006, 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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of politics and automotive industry has long puzzled researchers, policymakers, and enthusiasts alike. While some may view these topics as unrelated as a car and a kangaroo, our whimsically curious minds saw an opportunity to explore the intersection of Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. At first glance, one might be inclined to dismiss this connection as mere happenstance, akin to finding a needle in a haystack made entirely of ballot papers and brake pads. However, our inquiry into this unlikely association has yielded remarkably compelling results that are bound to delight and surprise even the most stoic of scientists.
With an insatiable appetite for the quirky and the unexpected, we delved into the depths of election data archives and automotive recall records. The sheer audacity of our endeavor propelled us to summon data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, effectively marrying the worlds of political science and vehicular mishaps. Our research brims with a sense of playful wonder, akin to a child gleefully unearthing obscure treasures in a sandbox, albeit with the rigor and discipline characteristic of esteemed academic pursuits.
As we gingerly tiptoe into the world of statistical analysis, we cannot help but marvel at the mesmerizing dance between numbers and human behavior. The correlation coefficient of 0.9143854 that emerged from our scrutiny hints at a connection so robust it could rival the sturdiest of seat belts, and a p-value less than 0.01 for the years 1978 to 2020 further solidifies our findings like epoxy on a cracked windshield. Indeed, our data sparkle and shimmer like a freshly waxed sports car, inviting the reader to revel in the unexpected synchrony of political leanings and automotive tribulations.
In the following sections of this study, we invite the reader to accompany us on a lighthearted journey through the labyrinth of correlations, seemingly haphazard patterns, and frivolous observations. Through this lens of merry curiosity, we aim to shed light on the interplay between the political landscape of Colorado and the tumultuous world of automotive recalls, offering insights that are as intellectually stimulating as they are unexpectedly delightful. Our investigation is not merely a dry exploration of numbers and political sentiments; rather, it embodies the spirit of mirthful inquiry, capturing the exuberant joy of unraveling peculiar conundrums and unexpected connections. So, buckle up and prepare for a ride through the eccentric, the whimsical, and the delightfully unconventional as we unravel the intricate tango between the ballot and the recall.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The results of our peculiar investigation into the connection between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America are nothing short of astonishing. Our analysis revealed a striking correlation coefficient of 0.9143854, indicating a relationship as strong as steel-belted radial tires. Additionally, the r-squared value of 0.8361006 demonstrates that a whopping 83.61% of the variability in automotive recalls can be explained by the Democrat votes for Senators in Colorado. And if that's not enough to make your statistical socks go wild, the p-value of less than 0.01 has a probability so low, it's like finding a parallel parking spot in a bustling city on the first try.

The delightfully unexpected relationship between these two seemingly unrelated realms is vividly depicted in Fig. 1, a whimsical scatterplot that graphically captures the synchrony between political leanings and automotive tribulations. One cannot help but appreciate the sheer whimsy of the correlation as it dances across the plot, much like a ballet of ballots and recalls.

In summary, our findings not only highlight the unexpected correlation between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America, but also underscore the enchanting charm of statistical exploration at its most whimsical.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

[[LITERATURE REVIEW]]
The peculiar interplay between political activities and automotive misdemeanors has drawn the attention of researchers from various disciplines. In their seminal work, Smith et al. (2015) meticulously dissect the intricate relationship between political ideologies and consumer behavior, providing a framework that has since captivated the scholarly community. Doe and Jones (2018) further build upon this foundation, shedding light on the unexpected parallels between voter turnout and corporate accountability, laying the groundwork for endeavors such as our own.
Venturing beyond the confines of academic literature, we turn our gaze to non-fictional works that offer tantalizing insights into the enigmatic correlation under scrutiny. In "The Wheels of Democracy: A Political Journey" by Roadster (2019), the author offers a thought-provoking narrative, intertwining the twists and turns of democratic processes with the bumpy road of automotive governance. Similarly, "Recalls and Reckonings: A Chronicle of Corporate Contrition" by Gearhead (2017) presents a gripping exposé on the tumultuous relationship between automakers and their ethical obligations, providing a compelling backdrop for our own investigation.
As we journey into the realm of fiction, the literary landscape abounds with works that, while not directly related to our subject matter, nonetheless beckon with their alluring titles. "The Senator's Sedan" by Novelista (2016) and "The Road to Recalls" by Wordsmith (2014) offer whimsical escapades that, while undoubtedly unrelated to our scholarly pursuits, tickle the imagination with their allegorical resonances. Furthermore, the popularity of board games such as "Political Pit Stop" and "Recall Race Rally" attests to the widespread fascination with the convergence of political drama and automotive mishaps, serving as a testament to the enduring allure of our research subject.
In weaving together the serious and the lighthearted, the scholarly and the fanciful, our exploration of the interconnectedness between Democrat votes in Colorado and BMW automotive recalls takes on a delightful hue, akin to stumbling upon a hidden treasure trove in the form of statistical nuances and playful associations. As we delve deeper into our analysis, the jocund spirit of our inquiry continues to infuse our investigation with a sense of buoyant curiosity and gleeful exploration, much like embarking on a quest for whimsical truths in a land teeming with unexpected connections and irrepressible merriment.

[[RESULTS]]
The results of our peculiar investigation into the connection between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America are nothing short of astonishing. Our analysis revealed a striking correlation coefficient of 0.9143854, indicating a relationship as strong as steel-belted radial tires. Additionally, the r-squared value of 0.8361006 demonstrates that a whopping 83.61% of the variability in automotive recalls can be explained by the Democrat votes for Senators in Colorado. And if that's not enough to make your statistical socks go wild, the p-value of less than 0.01 has a probability so low, it's like finding a parallel parking spot in a bustling city on the first try.
The delightfully unexpected relationship between these two seemingly unrelated realms is vividly depicted in Fig. 1, a whimsical scatterplot that graphically captures the synchrony between political leanings and automotive tribulations. One cannot help but appreciate the sheer whimsy of the correlation as it dances across the plot, much like a ballet of ballots and recalls.
In summary, our findings not only highlight the unexpected correlation between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America, but also underscore the enchanting charm of statistical exploration at its most whimsical.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our investigation into the intertwined tapestry of Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America has unravelled with the charm and intrigue of a roadside magic show. As we sashayed through a tango of statistical analyses, we found ourselves delightfully uncovering a correlation that twinkled like the chrome of a vintage Cadillac.

The results of our analysis, which displayed a correlation coefficient of 0.9143854 and a p-value less than 0.01, not only validate but also sparkle like an unexpected gemstone, in accordance with the frameworks established by Smith et al. (2015) and Doe and Jones (2018). It's as if these scholarly luminaries left a trail of breadcrumbs leading us straight to the heart of this enigmatic correlation, much like a gleaming breadcrumb trail through a convoluted statistical forest.

But it's not just the scholarly voices that echoed our whimsical findings. The literary tapestry woven by Roadster (2019) and Gearhead (2017) provided a fantastical backdrop that seemed to anticipate our scholarly pursuits, like literary crystal balls foreseeing our statistical revelations. Even the board games "Political Pit Stop" and "Recall Race Rally" – with their playful allusions to the convergence of political drama and automotive mishaps – seem to suggest that we stumbled upon a hidden truth that has been playfully lurking in the collective imagination, much like a magician revealing his secrets with a theatrical flourish.

The vividness of our correlation is reminiscent of a charming ballet, where the dancers, in this case, the Democrat votes in Colorado and the automotive recalls, pirouette in perfect synchrony. And while our findings confirm the unexpected correlation between these seemingly disparate subjects, it also tickles the scholarly palate with the irrepressible gaiety of statistical exploration at its most whimsical.

With this enlightening investigation complete, it's as if we've uncovered a delightful riddle, as though we discovered a whimsical shindig swathed in the heart of statistical probabilities. The synergy between voter leanings and automotive tribulations would seem like the stuff of whimsical fiction if it weren't for the robust statistical evidence we've carefully laid out, much like a scholarly treasure map leading to the unexpected comedy of correlations.

And with the curtain dropping on this thrilling statistical spectacle, we invite the reader to join us in the merry dance of scholarly inquiry, where the unexpected correlations gleam like the headlights of a moving car, guiding us on a whimsical journey through the hallowed halls of data and the charming alleys of whimsy.




Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Democrat votes for Senators in Colorado 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.

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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls

[[ABSTRACT]]
This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

[[INTRDUCTION]]
The intersection of politics and automotive industry has long puzzled researchers, policymakers, and enthusiasts alike. While some may view these topics as unrelated as a car and a kangaroo, our whimsically curious minds saw an opportunity to explore the intersection of Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. At first glance, one might be inclined to dismiss this connection as mere happenstance, akin to finding a needle in a haystack made entirely of ballot papers and brake pads. However, our inquiry into this unlikely association has yielded remarkably compelling results that are bound to delight and surprise even the most stoic of scientists.
With an insatiable appetite for the quirky and the unexpected, we delved into the depths of election data archives and automotive recall records. The sheer audacity of our endeavor propelled us to summon data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation, effectively marrying the worlds of political science and vehicular mishaps. Our research brims with a sense of playful wonder, akin to a child gleefully unearthing obscure treasures in a sandbox, albeit with the rigor and discipline characteristic of esteemed academic pursuits.
As we gingerly tiptoe into the world of statistical analysis, we cannot help but marvel at the mesmerizing dance between numbers and human behavior. The correlation coefficient of 0.9143854 that emerged from our scrutiny hints at a connection so robust it could rival the sturdiest of seat belts, and a p-value less than 0.01 for the years 1978 to 2020 further solidifies our findings like epoxy on a cracked windshield. Indeed, our data sparkle and shimmer like a freshly waxed sports car, inviting the reader to revel in the unexpected synchrony of political leanings and automotive tribulations.
In the following sections of this study, we invite the reader to accompany us on a lighthearted journey through the labyrinth of correlations, seemingly haphazard patterns, and frivolous observations. Through this lens of merry curiosity, we aim to shed light on the interplay between the political landscape of Colorado and the tumultuous world of automotive recalls, offering insights that are as intellectually stimulating as they are unexpectedly delightful. Our investigation is not merely a dry exploration of numbers and political sentiments; rather, it embodies the spirit of mirthful inquiry, capturing the exuberant joy of unraveling peculiar conundrums and unexpected connections. So, buckle up and prepare for a ride through the eccentric, the whimsical, and the delightfully unconventional as we unravel the intricate tango between the ballot and the recall.

[[RESULTS]]
The results of our peculiar investigation into the connection between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America are nothing short of astonishing. Our analysis revealed a striking correlation coefficient of 0.9143854, indicating a relationship as strong as steel-belted radial tires. Additionally, the r-squared value of 0.8361006 demonstrates that a whopping 83.61% of the variability in automotive recalls can be explained by the Democrat votes for Senators in Colorado. And if that's not enough to make your statistical socks go wild, the p-value of less than 0.01 has a probability so low, it's like finding a parallel parking spot in a bustling city on the first try.
The delightfully unexpected relationship between these two seemingly unrelated realms is vividly depicted in Fig. 1, a whimsical scatterplot that graphically captures the synchrony between political leanings and automotive tribulations. One cannot help but appreciate the sheer whimsy of the correlation as it dances across the plot, much like a ballet of ballots and recalls.
In summary, our findings not only highlight the unexpected correlation between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America, but also underscore the enchanting charm of statistical exploration at its most whimsical.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our whimsical exploration of the relationship between Democrat votes for Senators in Colorado and automotive recalls issued by BMW of North America has produced results that are more surprising than finding a clown car in a Formula 1 race. The robust correlation coefficient and impressively low p-value suggest a connection as snug as a well-fitted transmission gear. It appears that the political landscape in Colorado and the tumultuous world of automotive recalls are engaged in a dance as intricate and captivating as a synchronized car ballet. The unexpected synchrony between these seemingly disparate realms is a testament to the whimsy of statistical exploration and the surprising twists hidden in data, not unlike discovering a whoopee cushion at a high-brow dinner party.

With our findings in hand, we can confidently assert that further research in this area is about as necessary as installing a sunroof in a submarine. The delightful quirks and eyebrow-raising revelations presented in this study offer a complete and enchantingly whimsical picture of the interplay between political leanings and automotive tribulations. It's time to put the brakes on further inquiry in this realm and revel in the delightful absurdity of our findings.


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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Quirkiness


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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Whimsical Research and Inquiry


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 Ballot and The Recall: A Whimsical Examination of Democrat Votes in Colorado and BMW Automotive Recalls with an abstract of This paper explores the enigmatic relationship between Democrat votes for Senators in Colorado and the issuance of automotive recalls by BMW of North America. With an unrestrained vigor, we harnessed data from the MIT Election Data and Science Lab, Harvard Dataverse, and the US Department of Transportation to unravel this perplexing conundrum. Our investigation revealed a correlation coefficient of 0.9143854 and a p-value less than 0.01 for the years 1978 to 2020. Our findings sparkle with unexpected wit and leave the reader grinning from ear to ear as we draw parallels between political leanings and automotive woes. In this scholarly pursuit, we embark on a whimsical journey through statistical correlations and frivolous observations, guided by the spirit of merry curiosity.

ChatGPT:

Colorado Democrat votes, automotive recalls, BMW, North America, MIT Election Data and Science Lab, Harvard Dataverse, US Department of Transportation, correlation coefficient, political leanings, statistical correlations, whimsical examination, Democrat Senators, automotive woes, merry curiosity

*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 Democratic Senators in Colorado
Detailed data title: Total number of votes cast for Federal Democrat Senate candidates in Colorado
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Democratic Senators in Colorado

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 = 0.9143854 (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 = 0.8361006 (Coefficient of determination)
This means 83.6% 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 Democratic Senators in Colorado) over the 15 years from 1978 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.8E-6. 0.0000018396067852077476000000
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 0.91 in 0.00018% of random cases. Said differently, if you correlated 543,594 random variables You don't actually need 543 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 14 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 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.

[ 0.76, 0.97 ] 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.
197819801984198619901992199619982002200420082010201420162020
Votes for Democratic Senators in Colorado (Total votes)3301485905014493275294494257468037256676004647546481301081190123099085159094420313707101731110
Automotive recalls issued by BMW of North America (Recalls)3133252611171615151736




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([330148,590501,449327,529449,425746,803725,667600,464754,648130,1081190,1230990,851590,944203,1370710,1731110,])
array_2 = np.array([3,1,3,3,2,5,2,6,11,17,16,15,15,17,36,])
array_1_name = "Votes for Democratic Senators in Colorado"
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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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.

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Correlation ID: 5214 · Black Variable ID: 26210 · Red Variable ID: 1121
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