Download png, svg
AI explanation
More babies were named Maci, leading to a higher demand for personalized mini-vans. The addition of extra-large decorative decals on the rear windows was obstructing the driver's view. Remember, when it comes to car safety, it's all fun and games until Maci can't see out of her baby-on-board sticker!
Model: dalle-3
Prompt: An image that looks like it is a scene from a Disney movie, depicting a bustling town with vibrant buildings lining the streets. A line of cars with "Maci" emblazoned on their license plates stretches around the block, with happy and eager drivers behind the wheels. In the distance, a group of cars with their headlights obscured by thick fog are being recalled for visibility issues, creating a sense of mystery and intrigue. The atmosphere is filled with excitement and animated energy. 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: Popularity of the first name Maci and Automotive recalls for issues with Visibility. 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: Cyber folk Concept art An image that looks like it is a scene from A Disney Movie Macabre Magic realism
ChatGPT: An image that looks like it is a scene from A Disney Movie
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 Popularity of the first name Maci and Automotive recalls for issues with Visibility together. Keep the description simple.
ChatGPT: In a bustling town, a line of cars with the name "Maci" emblazoned on their license plates stretches around the block. Meanwhile, in the distance, a group of cars with their headlights obscured by thick fog are being recalled for visibility issues.
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: [ An image that looks like it is a scene from A Disney Movie ] and depicting this scene: In a bustling town, a line of cars with the name "Maci" emblazoned on their license plates stretches around the block. Meanwhile, in the distance, a group of cars with their headlights obscured by thick fog are being recalled for visibility issues. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: An image that looks like it is a scene from a Disney movie, depicting a bustling town with vibrant buildings lining the streets. A line of cars with "Maci" emblazoned on their license plates stretches around the block, with happy and eager drivers behind the wheels. In the distance, a group of cars with their headlights obscured by thick fog are being recalled for visibility issues, creating a sense of mystery and intrigue. The atmosphere is filled with excitement and animated energy.
*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.
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 Popularity of the first name Maci caused Automotive recalls for issues with Visibility to increase. Add a funny one-liner at the end.
AI academic paper
 |
Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls The Journal of Transportation Perception and Social Trends Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from US Social Security Administration and US DOT to assess this nagging question. You found a correlation coefficient of 0.6925451 and p < 0.01 for 1977 to 2022.
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]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of jokes, goofy observations, and puns.
Here is the title and abstract of the paper:
[[TITLE]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
**[[INTRODUCTION]]**
In the vast and bewildering landscape of academia, where researchers strive to shed light on the most obscure and tangled relationships, we often find ourselves at the intersection of the bizarre and the profound. It is in this spirit of scholarly whimsy that we embark on our journey to explore the curious connection between the popularity of the first name Maci and automotive recalls for visibility issues. As we delve into this offbeat investigation, we invite you to join us on a quirky expedition that may challenge your preconceptions about statistical analysis and nomenclature alike.
Picture this: a world where the fate of automotive safety and the ebb and flow of baby names intertwine in a captivating dance. It's a place where the grip of statistical significance meets the allure of alliteration, and where the data tells a story that seems too fanciful to be true. But true it is, my dear reader, and as we traverse the uncharted territory of peculiar correlations, you might just find yourself chuckling at the unexpected twists and turns that await.
Now, you may raise an eyebrow, furrow your brow, or quirk a skeptical smile at the notion that the name Maci could have any bearing on automotive visibility recalls. But fear not, for our findings are no mere flight of fancy. Our rigorous analysis has unearthed a correlation coefficient that will make even the most seasoned statistician do a double take. So, fasten your seatbelts, adjust your rearview mirrors, and get ready to traverse the winding roads of empirical inquiry, where the interplay of names and automotive woes yields revelations that are anything but pedestrian.
As we delve into the heart of this unconventional inquiry, we invite you to embrace the unexpected, revel in the absurd, and revel in the sheer delight of academic exploration at its most delightfully peculiar. Our journey awaits, dear reader, and it promises to be one filled with laughter, astonishment, and, just maybe, a dash of statistical enlightenment.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.
Here is the title and abstract of the paper:
[[TITLE]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the vast and bewildering landscape of academia, where researchers strive to shed light on the most obscure and tangled relationships, we often find ourselves at the intersection of the bizarre and the profound. It is in this spirit of scholarly whimsy that we embark on our journey to explore the curious connection between the popularity of the first name Maci and automotive recalls for visibility issues. As we delve into this offbeat investigation, we invite you to join us on a quirky expedition that may challenge your preconceptions about statistical analysis and nomenclature alike.
Picture this: a world where the fate of automotive safety and the ebb and flow of baby names intertwine in a captivating dance. It's a place where the grip of statistical significance meets the allure of alliteration, and where the data tells a story that seems too fanciful to be true. But true it is, my dear reader, and as we traverse the uncharted territory of peculiar correlations, you might just find yourself chuckling at the unexpected twists and turns that await.
Now, you may raise an eyebrow, furrow your brow, or quirk a skeptical smile at the notion that the name Maci could have any bearing on automotive visibility recalls. But fear not, for our findings are no mere flight of fancy. Our rigorous analysis has unearthed a correlation coefficient that will make even the most seasoned statistician do a double take. So, fasten your seatbelts, adjust your rearview mirrors, and get ready to traverse the winding roads of empirical inquiry, where the interplay of names and automotive woes yields revelations that are anything but pedestrian.
As we delve into the heart of this unconventional inquiry, we invite you to embrace the unexpected, revel in the absurd, and revel in the sheer delight of academic exploration at its most delightfully peculiar. Our journey awaits, dear reader, and it promises to be one filled with laughter, astonishment, and, just maybe, a dash of statistical enlightenment.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In their seminal work, Smith and Doe (2010) examined the correlation between popular first names and automotive safety issues, exploring the notion that societal trends in nomenclature may have unforeseen repercussions on vehicular visibility. Their findings hinted at a potential connection between the rise of certain names and an increased likelihood of recalls related to poor visibility. This intriguing premise set the stage for our own investigation into the specific nexus of Maci-named individuals and automotive visibility recalls.
Jones (2015) further delved into the impact of names on consumer behavior, uncovering the psychological nuances that underlie the associations between names and product preferences. While Jones's study primarily focused on consumer products, its insights provided a thought-provoking lens through which we could examine how the popularity of a name might influence the design and safety features of automobiles.
Turning to non-fiction literature, "Freakonomics" by Levitt and Dubner (2005) tantalizingly touches upon the influence of names on personal destinies, sparking contemplation about the broader ramifications of nomenclature in various domains. "Predictably Irrational" by Dan Ariely (2008) offers a whimsical exploration of human decision-making, teasing out the subtle ways in which our choices may be influenced by seemingly inconsequential factors such as names. These works prompted us to consider the potential ramifications of name popularity on automotive recalls, leading us down the delightful rabbit hole of our investigation.
In the realm of fiction, the dystopian masterpiece "Brave New World" by Aldous Huxley (1932) challenges conventional notions of identity and societal conditioning, beckoning us to ponder the whims of fate and cultural influences on individual destinies. Meanwhile, J.K. Rowling's "Harry Potter" series features an enchanting array of spellbinding names, reminding us of the magical allure that names can hold in shaping our perceptions and experiences. While these fictional works may seem unrelated at first glance, their themes provided us with creative inspiration and a playful perspective as we unraveled the curious connection between the name Maci and automotive visibility recalls.
Our foray into popular media led us to the television series "Breaking Bad," a gripping saga that captivates audiences with its intense character dynamics and moral ambiguity. The intricate web of relationships in the show prompted us to contemplate the interplay between personal identity and societal influences, prompting us to explore how the popularity of a name, such as Maci, could intersect with systemic issues in automotive safety. Similarly, "The Big Bang Theory" invited us to ponder the idiosyncrasies of human behavior and the unexpected ways in which individual traits can shape broader phenomena, evoking a sense of wonder about the potential interconnections between names and automotive recalls.
As our quirky quest unfolded, we found ourselves immersed in an academic odyssey that not only defied conventional wisdom but also brought laughter, astonishment, and a dash of statistical enlightenment. The literature provided the breadcrumb trail for our intellectual escapade, guiding us through a landscape of whimsy and scholarly intrigue as we endeavored to shed light on the whimsical and wondrous Maci-nation of name popularity and automotive visibility recalls.
And now, dear reader, join us as we unravel the tales of automotive recalls entwined with the captivating allure of the name Maci. Our adventure awaits, promising insight, amusement, and perhaps, just a touch of scholarly mischief.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from US Social Security Administration and US DOT . You used data from 1977 to 2022
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
[[INTRODUCTION]]
In the vast and bewildering landscape of academia, where researchers strive to shed light on the most obscure and tangled relationships, we often find ourselves at the intersection of the bizarre and the profound. It is in this spirit of scholarly whimsy that we embark on our journey to explore the curious connection between the popularity of the first name Maci and automotive recalls for visibility issues. As we delve into this offbeat investigation, we invite you to join us on a quirky expedition that may challenge your preconceptions about statistical analysis and nomenclature alike.
Picture this: a world where the fate of automotive safety and the ebb and flow of baby names intertwine in a captivating dance. It's a place where the grip of statistical significance meets the allure of alliteration, and where the data tells a story that seems too fanciful to be true. But true it is, my dear reader, and as we traverse the uncharted territory of peculiar correlations, you might just find yourself chuckling at the unexpected twists and turns that await.
Now, you may raise an eyebrow, furrow your brow, or quirk a skeptical smile at the notion that the name Maci could have any bearing on automotive visibility recalls. But fear not, for our findings are no mere flight of fancy. Our rigorous analysis has unearthed a correlation coefficient that will make even the most seasoned statistician do a double take. So, fasten your seatbelts, adjust your rearview mirrors, and get ready to traverse the winding roads of empirical inquiry, where the interplay of names and automotive woes yields revelations that are anything but pedestrian.
As we delve into the heart of this unconventional inquiry, we invite you to embrace the unexpected, revel in the absurd, and revel in the sheer delight of academic exploration at its most delightfully peculiar. Our journey awaits, dear reader, and it promises to be one filled with laughter, astonishment, and, just maybe, a dash of statistical enlightenment.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To embark on our whimsical escapade through the realms of empirical inquiry, our methodology involved a convergence of statistical analysis, data mining, and a pinch of lighthearted curiosity. We began by delving into the treasure troves of the US Social Security Administration's database, where the captivating saga of baby names unfolded before our very eyes. With a twinkle in our data collectors' eyes, we meticulously gathered information spanning the years from 1977 to 2022, embracing the ebb and flow of name popularity with an enthusiasm normally reserved for a thrilling suspense novel.
Once our captivation with the nomenclature odyssey reached its zenith, we set our sights on the enigmatic world of automotive recalls. From wading through recall notices to deciphering industry jargon, our valiant data warriors scoured the annals of the US Department of Transportation's archives, determined to unveil the clandestine connection between name popularity and automotive visibility mishaps. Armed with spreadsheets, caffeinated beverages, and an unwavering commitment to the unusual, they triumphantly emerged with a bounty of recall data, ready to be entwined with the narrative of Maci's ascendancy.
With our treasure trove of data in hand, we meticulously crafted an intricate web of statistical analyses, employing the noble chi-square test to assess the association between the proliferation of the name Maci and the occurrence of automotive recalls for visibility issues. Our exploration was not confined to mere correlation, as we diligently probed for causal inference with the solemnity of detectives unraveling a perplexing mystery.
Amidst the clattering of keyboards and the hum of statistical software, our team engaged in spirited debates, occasional bouts of name-related puns, and the fervent pursuit of enlightenment through the lens of unorthodox inquiry. Together, we journeyed through the highs and lows of data aggregation, hypothesis testing, and the discernment of significance – all while holding fast to the belief that even the most improbable connections deserve a moment in the analytical spotlight.
Armed with the spoils of our scholarly odyssey, we now present the fruits of our labor, where the convergence of Maci's allure and automotive visibility recalls transcends the ordinary and beckons forth a world of statistical amusement. Join us as we decipher the intricate dance between name popularity and automotive safety concerns, where laughter mingles with analysis, and our peculiar quest for knowledge continues to ignite the flames of curiosity.
In the words of the venerable Shakespeare, "All the world's a stage, and all the data but players." With this spirit of scholarly playfulness, we offer our methodology as a testament to the pursuit of knowledge, wherever it may lead – even if that journey takes us through the serpentine paths of Maci's captivating intrigue and the unforeseen intersections of empirical inquiry.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from US Social Security Administration and US DOT .
For the time period 1977 to 2022, you found a correlation 0.6925451, r-squared of 0.4796188, 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]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the vast and bewildering landscape of academia, where researchers strive to shed light on the most obscure and tangled relationships, we often find ourselves at the intersection of the bizarre and the profound. It is in this spirit of scholarly whimsy that we embark on our journey to explore the curious connection between the popularity of the first name Maci and automotive recalls for visibility issues. As we delve into this offbeat investigation, we invite you to join us on a quirky expedition that may challenge your preconceptions about statistical analysis and nomenclature alike.
Picture this: a world where the fate of automotive safety and the ebb and flow of baby names intertwine in a captivating dance. It's a place where the grip of statistical significance meets the allure of alliteration, and where the data tells a story that seems too fanciful to be true. But true it is, my dear reader, and as we traverse the uncharted territory of peculiar correlations, you might just find yourself chuckling at the unexpected twists and turns that await.
Now, you may raise an eyebrow, furrow your brow, or quirk a skeptical smile at the notion that the name Maci could have any bearing on automotive visibility recalls. But fear not, for our findings are no mere flight of fancy. Our rigorous analysis has unearthed a correlation coefficient that will make even the most seasoned statistician do a double take. So, fasten your seatbelts, adjust your rearview mirrors, and get ready to traverse the winding roads of empirical inquiry, where the interplay of names and automotive woes yields revelations that are anything but pedestrian.
As we delve into the heart of this unconventional inquiry, we invite you to embrace the unexpected, revel in the absurd, and revel in the sheer delight of academic exploration at its most delightfully peculiar. Our journey awaits, dear reader, and it promises to be one filled with laughter, astonishment, and, just maybe, a dash of statistical enlightenment.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The analysis of the data revealed a tantalizing correlation coefficient of 0.6925451 between the popularity of the first name Maci and automotive recalls for visibility issues, spanning the years 1977 to 2022. This coefficient, representing the strength and direction of the relationship between the variables, astounded even the most seasoned researchers. It seems that the saga of Maci and automotive visibility recalls is not merely a tall tale from the road.
The coefficient of determination (r-squared) further illuminated the narrative, indicating that approximately 47.96% of the variability in automotive visibility recalls can be explained by the popularity of the name Maci. It's as if the statistical stars aligned to cast a spotlight on this peculiar association, beckoning us to peer through the mist of uncertainty and into the realm of unexpected connections.
With a p-value of less than 0.01, the results reject the null hypothesis with resounding vigor. In other words, the likelihood of observing such a strong relationship between the popularity of Maci and visibility-related automotive recalls purely by chance is lower than a low-beam headlight on a foggy night. The evidence speaks for itself, urging us to embrace the whimsical nature of this phenomenon with open arms.
And speaking of visual aids, Figure 1 beckons the reader to behold the scatterplot that encapsulates the extraordinary correlation. As you gaze upon this visual masterpiece (to be included separately), let it serve as a beacon of clarity in a sea of statistical intrigue. The clustering of data points paints a picture of coherence, inviting you to marvel at the bond between a name and automotive visibility challenges.
In conclusion, the results of this study not only unearth a substantial relationship between the proliferation of the name Maci and automotive visibility recalls but also beckon us to embrace the magical and unexpected realm of statistical analysis. As we navigate the winding roads of empirical inquiry, may this peculiar correlation remind us that the world of data is a place where both the mundane and the fantastical collide in a symphony of discovery.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of 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]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
[[LITERATURE REVIEW]]
In their seminal work, Smith and Doe (2010) examined the correlation between popular first names and automotive safety issues, exploring the notion that societal trends in nomenclature may have unforeseen repercussions on vehicular visibility. Their findings hinted at a potential connection between the rise of certain names and an increased likelihood of recalls related to poor visibility. This intriguing premise set the stage for our own investigation into the specific nexus of Maci-named individuals and automotive visibility recalls.
Jones (2015) further delved into the impact of names on consumer behavior, uncovering the psychological nuances that underlie the associations between names and product preferences. While Jones's study primarily focused on consumer products, its insights provided a thought-provoking lens through which we could examine how the popularity of a name might influence the design and safety features of automobiles.
Turning to non-fiction literature, "Freakonomics" by Levitt and Dubner (2005) tantalizingly touches upon the influence of names on personal destinies, sparking contemplation about the broader ramifications of nomenclature in various domains. "Predictably Irrational" by Dan Ariely (2008) offers a whimsical exploration of human decision-making, teasing out the subtle ways in which our choices may be influenced by seemingly inconsequential factors such as names. These works prompted us to consider the potential ramifications of name popularity on automotive recalls, leading us down the delightful rabbit hole of our investigation.
In the realm of fiction, the dystopian masterpiece "Brave New World" by Aldous Huxley (1932) challenges conventional notions of identity and societal conditioning, beckoning us to ponder the whims of fate and cultural influences on individual destinies. Meanwhile, J.K. Rowling's "Harry Potter" series features an enchanting array of spellbinding names, reminding us of the magical allure that names can hold in shaping our perceptions and experiences. While these fictional works may seem unrelated at first glance, their themes provided us with creative inspiration and a playful perspective as we unraveled the curious connection between the name Maci and automotive visibility recalls.
Our foray into popular media led us to the television series "Breaking Bad," a gripping saga that captivates audiences with its intense character dynamics and moral ambiguity. The intricate web of relationships in the show prompted us to contemplate the interplay between personal identity and societal influences, prompting us to explore how the popularity of a name, such as Maci, could intersect with systemic issues in automotive safety. Similarly, "The Big Bang Theory" invited us to ponder the idiosyncrasies of human behavior and the unexpected ways in which individual traits can shape broader phenomena, evoking a sense of wonder about the potential interconnections between names and automotive recalls.
As our quirky quest unfolded, we found ourselves immersed in an academic odyssey that not only defied conventional wisdom but also brought laughter, astonishment, and a dash of statistical enlightenment. The literature provided the breadcrumb trail for our intellectual escapade, guiding us through a landscape of whimsy and scholarly intrigue as we endeavored to shed light on the whimsical and wondrous Maci-nation of name popularity and automotive visibility recalls.
And now, dear reader, join us as we unravel the tales of automotive recalls entwined with the captivating allure of the name Maci. Our adventure awaits, promising insight, amusement, and perhaps, just a touch of scholarly mischief.
[[RESULTS]]
The analysis of the data revealed a tantalizing correlation coefficient of 0.6925451 between the popularity of the first name Maci and automotive recalls for visibility issues, spanning the years 1977 to 2022. This coefficient, representing the strength and direction of the relationship between the variables, astounded even the most seasoned researchers. It seems that the saga of Maci and automotive visibility recalls is not merely a tall tale from the road.
The coefficient of determination (r-squared) further illuminated the narrative, indicating that approximately 47.96% of the variability in automotive visibility recalls can be explained by the popularity of the name Maci. It's as if the statistical stars aligned to cast a spotlight on this peculiar association, beckoning us to peer through the mist of uncertainty and into the realm of unexpected connections.
With a p-value of less than 0.01, the results reject the null hypothesis with resounding vigor. In other words, the likelihood of observing such a strong relationship between the popularity of Maci and visibility-related automotive recalls purely by chance is lower than a low-beam headlight on a foggy night. The evidence speaks for itself, urging us to embrace the whimsical nature of this phenomenon with open arms.
And speaking of visual aids, Figure 1 beckons the reader to behold the scatterplot that encapsulates the extraordinary correlation. As you gaze upon this visual masterpiece (to be included separately), let it serve as a beacon of clarity in a sea of statistical intrigue. The clustering of data points paints a picture of coherence, inviting you to marvel at the bond between a name and automotive visibility challenges.
In conclusion, the results of this study not only unearth a substantial relationship between the proliferation of the name Maci and automotive visibility recalls but also beckon us to embrace the magical and unexpected realm of statistical analysis. As we navigate the winding roads of empirical inquiry, may this peculiar correlation remind us that the world of data is a place where both the mundane and the fantastical collide in a symphony of discovery.
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Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The results of our investigation into the enthralling nexus of the first name Maci and automotive recalls for visibility issues have illuminated a delightful and unexpected connection. Building upon the whimsical foundations set forth in the literature review, we have effectively peered through the looking glass of data to confirm and extend the prior research findings.
Smith and Doe's (2010) pioneering work suggested that popular first names might influence vehicular visibility concerns, paving the way for our inquiry into the specific charisma of the name Maci. Our findings not only validate their initial speculation but also reveal the statistical robustness of this correlation. It appears that the repercussions of nomenclature on automotive safety are not to be taken lightly, as our results lend empirical support to the intriguing hints proposed by Smith and Doe.
Taking a playful nod to Jones's (2015) exploration of names and consumer behavior, we must acknowledge that our study has teased out the psychological nuances underlying the association between the name Maci and design issues affecting automotive visibility. While Jones focused on consumer products, our inquiry has extended the influence of name popularity to the realm of vehicle safety features, further corroborating the subtle yet impactful effects of nomenclature on product attributes.
Venturing into the whimsical world of fiction and media influences as outlined in the literature review, we find that the empirical findings of our study reflect the enchanting allure of names in shaping individual destinies and societal phenomena. While we embarked on this academic odyssey with an air of scholarly mischief, the data has whispered a resounding affirmation of the underlying themes proffered by these unlikely inspirations. The saga of Maci and automotive visibility recalls is not merely a fantastical figment but a statistical reality worthy of scholarly contemplation.
In essence, our results not only affirm the previous speculations regarding the influence of names on automotive safety issues but also beckon us to ponder the serendipitous interplay between societal trends in nomenclature and automotive design. As we revel in the statistical revelry of this peculiar correlation, let us embrace the whimsical nature of our findings with the same scholarly fervor that propels us through the traditional realms of empirical inquiry. The Maci-nation of name popularity and automotive visibility recalls stands as a testament to the fantastical and illuminating intersections that enliven the scholarly landscape.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Popularity of the first name Maci and Automotive recalls for issues with Visibility. Make lots of 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]]
"Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls"
[[ABSTRACT]]
This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
[[INTRDUCTION]]
In the vast and bewildering landscape of academia, where researchers strive to shed light on the most obscure and tangled relationships, we often find ourselves at the intersection of the bizarre and the profound. It is in this spirit of scholarly whimsy that we embark on our journey to explore the curious connection between the popularity of the first name Maci and automotive recalls for visibility issues. As we delve into this offbeat investigation, we invite you to join us on a quirky expedition that may challenge your preconceptions about statistical analysis and nomenclature alike.
Picture this: a world where the fate of automotive safety and the ebb and flow of baby names intertwine in a captivating dance. It's a place where the grip of statistical significance meets the allure of alliteration, and where the data tells a story that seems too fanciful to be true. But true it is, my dear reader, and as we traverse the uncharted territory of peculiar correlations, you might just find yourself chuckling at the unexpected twists and turns that await.
Now, you may raise an eyebrow, furrow your brow, or quirk a skeptical smile at the notion that the name Maci could have any bearing on automotive visibility recalls. But fear not, for our findings are no mere flight of fancy. Our rigorous analysis has unearthed a correlation coefficient that will make even the most seasoned statistician do a double take. So, fasten your seatbelts, adjust your rearview mirrors, and get ready to traverse the winding roads of empirical inquiry, where the interplay of names and automotive woes yields revelations that are anything but pedestrian.
As we delve into the heart of this unconventional inquiry, we invite you to embrace the unexpected, revel in the absurd, and revel in the sheer delight of academic exploration at its most delightfully peculiar. Our journey awaits, dear reader, and it promises to be one filled with laughter, astonishment, and, just maybe, a dash of statistical enlightenment.
[[RESULTS]]
The analysis of the data revealed a tantalizing correlation coefficient of 0.6925451 between the popularity of the first name Maci and automotive recalls for visibility issues, spanning the years 1977 to 2022. This coefficient, representing the strength and direction of the relationship between the variables, astounded even the most seasoned researchers. It seems that the saga of Maci and automotive visibility recalls is not merely a tall tale from the road.
The coefficient of determination (r-squared) further illuminated the narrative, indicating that approximately 47.96% of the variability in automotive visibility recalls can be explained by the popularity of the name Maci. It's as if the statistical stars aligned to cast a spotlight on this peculiar association, beckoning us to peer through the mist of uncertainty and into the realm of unexpected connections.
With a p-value of less than 0.01, the results reject the null hypothesis with resounding vigor. In other words, the likelihood of observing such a strong relationship between the popularity of Maci and visibility-related automotive recalls purely by chance is lower than a low-beam headlight on a foggy night. The evidence speaks for itself, urging us to embrace the whimsical nature of this phenomenon with open arms.
And speaking of visual aids, Figure 1 beckons the reader to behold the scatterplot that encapsulates the extraordinary correlation. As you gaze upon this visual masterpiece (to be included separately), let it serve as a beacon of clarity in a sea of statistical intrigue. The clustering of data points paints a picture of coherence, inviting you to marvel at the bond between a name and automotive visibility challenges.
In conclusion, the results of this study not only unearth a substantial relationship between the proliferation of the name Maci and automotive visibility recalls but also beckon us to embrace the magical and unexpected realm of statistical analysis. As we navigate the winding roads of empirical inquiry, may this peculiar correlation remind us that the world of data is a place where both the mundane and the fantastical collide in a symphony of discovery.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
Ah, the saga of Maci and automotive visibility recalls has reached its whimsical conclusion. In illuminating the connection between the proliferation of the name Maci and automotive visibility challenges, we've not only unveiled a striking correlation coefficient but also embarked on a journey through statistical whimsy. It's as if the statistical stars aligned, casting a spotlight on the peculiar association between a name and vehicular visibility woes.
As we stand at the intersection of empirical inquiry and the world of baby names, it's clear that the allure of this correlation is not just a flash in the pan. It beckons us to peer through the mist of uncertainty and into the realm of unexpected connections, where the whimsical and the empirical converge in a captivating dance.
The correlation coefficient of 0.6925451 has proven to be more than mere happenstance. With a p-value of less than 0.01, the likelihood of observing such a strong relationship purely by chance is lower than a low-beam headlight on a foggy night. Our findings reject the null hypothesis with resounding vigor, leaving no room for doubt that the Maci-nation of name popularity and automotive visibility recalls is no mere flight of fancy.
As we draw the curtains on this statistical quirk, it's evident that no more research is needed in this area. The findings of this study not only shed light on a remarkable correlation but also remind us that the world of data is a place where both the mundane and the fantastical collide in a symphony of discovery. So, let us bid adieu to the realm of Maci and automotive visibility recalls, knowing that our journey through statistical whimsy has left an indelible mark on the landscape of empirical inquiry.
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 Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Transportation Perception and Social Trends
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 Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls. Respond only with the name of a fictional organization.
ChatGPT:
The Center for Social Media and Snail Population 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 Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls with an abstract of This study endeavors to shed light on the intriguing relationship between the popularity of the first name Maci and automotive recalls for visibility issues. Utilizing data from the US Social Security Administration and the US Department of Transportation, our research team embarked on a journey to unravel this enigmatic connection. Despite the quirky nature of our investigation, the findings unveiled a striking correlation coefficient of 0.6925451, with a p-value of less than 0.01 for the years spanning from 1977 to 2022. Join us in this whimsical quest for knowledge, where we illuminate the intersection of automotive safety concerns and the proliferation of a name that seems to captivate vision-related manufacturing defects. As we navigate through the data, we invite you to see through the fog and into the world of Maci and automotive visibility recalls.
ChatGPT:
"Maci name popularity", "automotive visibility recalls", "US Social Security Administration data", "US Department of Transportation data", "correlation between name popularity and automotive recalls", "vision-related manufacturing defects", "automotive safety concerns", "Maci first name correlation coefficient", "Maci name and automotive recalls", "Maci visibility recall correlation".
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
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Data details
Popularity of the first name MaciDetailed data title: Babies of all sexes born in the US named Maci
Source: US Social Security Administration
See what else correlates with Popularity of the first name Maci
Automotive recalls for issues with Visibility
Source: US DOT
See what else correlates with Automotive recalls for issues with Visibility
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 = 0.4796188 (Coefficient of determination)
This means 48% of the change in the one variable (i.e., Automotive recalls for issues with Visibility) is predictable based on the change in the other (i.e., Popularity of the first name Maci) over the 46 years from 1977 through 2022.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.7E-8. 0.0000000969730925570569800000
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 0.69 in 9.7E-6% of random cases. Said differently, if you correlated 10,312,139 random variables You don't actually need 10 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 45 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 45 because we have two variables measured over a period of 46 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.
[ 0.5, 0.82 ] 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.
| 1977 | 1978 | 1979 | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
| Popularity of the first name Maci (Babies born) | 6 | 5 | 8 | 10 | 7 | 5 | 5 | 16 | 10 | 14 | 18 | 21 | 40 | 102 | 145 | 143 | 164 | 171 | 192 | 181 | 191 | 231 | 270 | 299 | 391 | 334 | 501 | 518 | 499 | 432 | 418 | 409 | 450 | 1356 | 1734 | 1470 | 1340 | 1238 | 1180 | 1195 | 1002 | 902 | 824 | 677 | 620 | 455 |
| Automotive recalls for issues with Visibility (Recalls) | 14 | 12 | 15 | 13 | 5 | 6 | 5 | 3 | 2 | 6 | 5 | 7 | 15 | 18 | 12 | 9 | 14 | 11 | 18 | 9 | 10 | 18 | 14 | 18 | 18 | 8 | 19 | 11 | 10 | 16 | 14 | 15 | 11 | 26 | 77 | 40 | 15 | 10 | 20 | 24 | 11 | 34 | 25 | 18 | 16 | 24 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
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. - Outlandish outliers: There are "outliers" in this data.
In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.
For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.
(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.
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([6,5,8,10,7,5,5,16,10,14,18,21,40,102,145,143,164,171,192,181,191,231,270,299,391,334,501,518,499,432,418,409,450,1356,1734,1470,1340,1238,1180,1195,1002,902,824,677,620,455,])
array_2 = np.array([14,12,15,13,5,6,5,3,2,6,5,7,15,18,12,9,14,11,18,9,10,18,14,18,18,8,19,11,10,16,14,15,11,26,77,40,15,10,20,24,11,34,25,18,16,24,])
array_1_name = "Popularity of the first name Maci"
array_2_name = "Automotive recalls for issues with Visibility"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.
For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."
When spoken, my last name is pronounced "vegan," like I don't eat meat.
Full license details.
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Download images for these variables:
- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only Popularity of the first name Maci
- Line chart for only Automotive recalls for issues with Visibility
- AI-generated correlation image
- The spurious research paper: Seeing Clearly: The Maci-nation of Name Popularity and Automotive Visibility Recalls
Your rating skills are legendary!
Correlation ID: 1468 · Black Variable ID: 3781 · Red Variable ID: 1107
