Download png, svg
AI explanation
As the air became cleaner, the need for bellhops to "handle" the smog decreased as well!
Model: dalle-3
Prompt: Prompt: Generate an image showing downtown Lafayette, Louisiana, with a thick layer of smog lifting to reveal historic buildings and formerly invisible bellhops standing on the sidewalks in their now-visible cloaks. Show the diminishing smog and increased visibility creating an impact on the demand for the bellhops' services. Create a scene that conveys the transformation and the changing dynamics brought about by reduced air pollution. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:
Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. 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: A Discovery magazine cover image High-tech Art nouveau Glitch art Woodblock painting
ChatGPT: A Discovery magazine cover 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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana, in the following situation: As air pollution decreased, people could finally see the bellhops' invisible cloaks, causing a drop in demand for their services.. Keep the description simple.
ChatGPT: In downtown Lafayette, Louisiana, a thick layer of smog had obscured the view of the historic buildings for years. As the air pollution decreased, the lingering haze gradually lifted, revealing a group of formerly invisible bellhops standing on the sidewalks with their now visible cloaks. With the reduced pollution, people were now able to see the bellhops and their services were no longer in demand as they once were.
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 Discovery magazine cover image ] and depicting this scene: In downtown Lafayette, Louisiana, a thick layer of smog had obscured the view of the historic buildings for years. As the air pollution decreased, the lingering haze gradually lifted, revealing a group of formerly invisible bellhops standing on the sidewalks with their now visible cloaks. With the reduced pollution, people were now able to see the bellhops and their services were no longer in demand as they once were. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image showing downtown Lafayette, Louisiana, with a thick layer of smog lifting to reveal historic buildings and formerly invisible bellhops standing on the sidewalks in their now-visible cloaks. Show the diminishing smog and increased visibility creating an impact on the demand for the bellhops' services. Create a scene that conveys the transformation and the changing dynamics brought about by reduced air pollution.
*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 a decrease in Air pollution in Lafayette, Louisiana caused The number of bellhops in Louisiana to decrease. Add a funny one-liner at the end.
AI academic paper
 |
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis Journal of Ecological Economics and Entertaining Entomology Jan 2024 Reminder: This paper is AI-generated. Not real! |
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns.
Make the title an alliteration.
Your research team used data from Environmental Protection Agency and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.8352544 and p < 0.01 for 2003 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]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Here is the title and abstract of the paper:
[[TITLE]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
Pardon the pun, but let's clear the air: the connection between air pollution and the number of bellhops in Louisiana may seem like a load of hot air at first glance. However, our research endeavors to shed light on this curious correlation, examining how the haze of pollution in Lafayette is seemingly clearing the path for a surge in the bellhop business across the whole state.
As we embark on this whimsical yet revelatory journey, it's worth noting the sheer audacity of the question at hand. Who would have thought that the quality of air in one city could be linked to the bustling activities of bellhops miles away? Far from being a mere flight of fancy, our analysis is firmly rooted in empirical data and rigorous statistical methods.
Our investigation stems from the merging of seemingly disparate realms - environmental science and labor economics. We seek to blend these disciplines to unearth what might appear as an improbable coincidence. To the untrained eye, it might sound like the setup to a good bar joke, but rest assured, our methods and findings are firmly anchored in the world of academia.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. 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. Perhaps you also got inspiration from some board games that are vaugely related.
Here is the title and abstract of the paper:
[[TITLE]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Pardon the pun, but let's clear the air: the connection between air pollution and the number of bellhops in Louisiana may seem like a load of hot air at first glance. However, our research endeavors to shed light on this curious correlation, examining how the haze of pollution in Lafayette is seemingly clearing the path for a surge in the bellhop business across the whole state.
As we embark on this whimsical yet revelatory journey, it's worth noting the sheer audacity of the question at hand. Who would have thought that the quality of air in one city could be linked to the bustling activities of bellhops miles away? Far from being a mere flight of fancy, our analysis is firmly rooted in empirical data and rigorous statistical methods.
Our investigation stems from the merging of seemingly disparate realms - environmental science and labor economics. We seek to blend these disciplines to unearth what might appear as an improbable coincidence. To the untrained eye, it might sound like the setup to a good bar joke, but rest assured, our methods and findings are firmly anchored in the world of academia.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The scholarly literature on air pollution and its impact on labor markets offers a wealth of insights that are relevant to our investigation of the connection between air quality in Lafayette, Louisiana and the number of bellhops in the state. Smith et al. (2015) provide a comprehensive analysis of the effects of pollution on labor productivity, with a focus on urban areas. The authors find that higher levels of air pollutants are associated with reduced worker productivity, although their study did not specifically examine the bellhop profession. Likewise, Doe (2017) explores the economic ramifications of environmental degradation and demonstrates the deleterious consequences of air pollution on overall employment levels. The longstanding understanding that air pollution is detrimental to workforce well-being serves as a solid foundation for our investigation.
Moving away from the strictly academic works, "The Air We Breathe: Understanding Pollution and Its Effects" by Environmental Expert is a well-regarded non-fiction source that dissects the repercussions of air pollution, from health impacts to economic repercussions. On the fiction front, "The Bellhop Chronicles" by Author Bell delves into the whimsical world of hotel bellhops, presenting a lighthearted yet oddly relevant take on the bustling world of hospitality.
But hold onto your hats -- or perhaps bellhop hats in this case -- as we dive into the unexpected. "Parcheesi Pollution: A Study of Air Quality in Board Games" may seem like an unlikely source of inspiration, but the game's emphasis on movement and strategy prompts intriguing parallels to our investigation. Could the roll of the dice in pollution levels be influencing the movement of bellhops in Louisiana? We might jest, but such fantastical ponderings are firmly within the realm of possibility in our whimsical quest for knowledge.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Bureau of Larbor Statistics . You used data from 2003 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]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
[[INTRODUCTION]]
Pardon the pun, but let's clear the air: the connection between air pollution and the number of bellhops in Louisiana may seem like a load of hot air at first glance. However, our research endeavors to shed light on this curious correlation, examining how the haze of pollution in Lafayette is seemingly clearing the path for a surge in the bellhop business across the whole state.
As we embark on this whimsical yet revelatory journey, it's worth noting the sheer audacity of the question at hand. Who would have thought that the quality of air in one city could be linked to the bustling activities of bellhops miles away? Far from being a mere flight of fancy, our analysis is firmly rooted in empirical data and rigorous statistical methods.
Our investigation stems from the merging of seemingly disparate realms - environmental science and labor economics. We seek to blend these disciplines to unearth what might appear as an improbable coincidence. To the untrained eye, it might sound like the setup to a good bar joke, but rest assured, our methods and findings are firmly anchored in the world of academia.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To begin with, our research team embarked on an odyssey through a maze of data, navigating the treacherous paths of statistics with the fortitude of Odysseus, armed with nothing but our trusty spreadsheets and a healthy dose of skepticism. We extracted copious amounts of data from the Environmental Protection Agency and the Bureau of Labor Statistics, approaching this endeavor with the fervor of a hound on the scent. Our data collection spanned the years 2003 to 2022, ensuring a comprehensive overview of the dynamic relationship between air pollution in Lafayette and the number of bellhops in Louisiana.
Our first challenge revolved around the acquisition of air pollution data for Lafayette. Channeling our inner Sherlock Holmes, we pieced together information from a myriad of sources, weaving a tapestry of pollution levels that captured the very essence of Lafayette's atmospheric woes. While some may find this data collection process as thrilling as watching paint dry, we reveled in the thrill of the chase, akin to a gripping detective novel with a pungent subplot.
Turning our attention to the bellhop population, we delved into the Bureau of Labor Statistics data with the finesse of a seasoned detective dusting for fingerprints. With a sleuth's precision, we meticulously analysed employment figures pertaining to bellhops in the entire state of Louisiana. Much like uncovering clues at a crime scene, we interrogated the data for any inkling of a connection between the rise of bellhops and the looming presence of air pollutants in Lafayette.
Our statistical analysis was akin to crafting a symphony, harmonizing the cacophony of variables to distill the essence of our findings. We applied the formidable powers of correlation analysis, adorning our results with confidence intervals and p-values, transforming the mundane into the entrancing with the flair of a magician casting a spell.
In an effort to ensure the integrity of our findings, we employed robust sensitivity analyses, akin to stress-testing a theoretical structure, to gauge the resilience of our results to potential confounding factors. In essence, we sieved through the data with the meticulousness of a skilled baker sifting flour to achieve the perfect consistency in a delectable cake.
In summary, our methodology was a blend of meticulous data sleuthing, statistical acrobatics, and a touch of whimsy. This fusion paved the way for what may initially seem an improbable endeavor, but one that ultimately led to the unveiling of the intriguing connection between Lafayette's lousy pollution and the lively bellhop population of Louisiana.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Bureau of Larbor Statistics .
For the time period 2003 to 2022, you found a correlation 0.8352544, r-squared of 0.6976499, 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]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
Pardon the pun, but let's clear the air: the connection between air pollution and the number of bellhops in Louisiana may seem like a load of hot air at first glance. However, our research endeavors to shed light on this curious correlation, examining how the haze of pollution in Lafayette is seemingly clearing the path for a surge in the bellhop business across the whole state.
As we embark on this whimsical yet revelatory journey, it's worth noting the sheer audacity of the question at hand. Who would have thought that the quality of air in one city could be linked to the bustling activities of bellhops miles away? Far from being a mere flight of fancy, our analysis is firmly rooted in empirical data and rigorous statistical methods.
Our investigation stems from the merging of seemingly disparate realms - environmental science and labor economics. We seek to blend these disciplines to unearth what might appear as an improbable coincidence. To the untrained eye, it might sound like the setup to a good bar joke, but rest assured, our methods and findings are firmly anchored in the world of academia.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
Our research unearthed a surprising and statistically significant correlation between air pollution in Lafayette, Louisiana, and the number of bellhops employed across the state of Louisiana from 2003 to 2022. The correlation coefficient clocked in at a staggering 0.8352544, signifying a strong positive relationship between these seemingly unrelated variables. This relationship was further supported by an r-squared value of 0.6976499, indicating that over 69% of the variation in bellhop employment can be explained by changes in air pollution levels. With a p-value of less than 0.01, our findings are not to be brushed off as mere coincidence.
Fig. 1 displays the scatterplot, visually encapsulating the mesmerizing correlation between air pollution in Lafayette and the sheer number of bellhops bustling about in Louisiana. One must admit, witnessing data points coalesce in such a way is as unexpected as finding a bellhop in a Louisiana swamp.
The results of our analysis compel us to consider the possibility that air pollution in Lafayette acts as a silent but forceful advocate for the bellhop industry. It seems that the murkier the air becomes, the clearer the demand for bellhop services across the entire state. This revelation may leave some scratching their heads, but the numbers do not lie; there is something in the air that is undeniably linked to the demand for bellhops.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 500 tokens.
Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.
Do not write a conclusion. I will add a conclusion after this.
[[TITLE]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
[[LITERATURE REVIEW]]
The scholarly literature on air pollution and its impact on labor markets offers a wealth of insights that are relevant to our investigation of the connection between air quality in Lafayette, Louisiana and the number of bellhops in the state. Smith et al. (2015) provide a comprehensive analysis of the effects of pollution on labor productivity, with a focus on urban areas. The authors find that higher levels of air pollutants are associated with reduced worker productivity, although their study did not specifically examine the bellhop profession. Likewise, Doe (2017) explores the economic ramifications of environmental degradation and demonstrates the deleterious consequences of air pollution on overall employment levels. The longstanding understanding that air pollution is detrimental to workforce well-being serves as a solid foundation for our investigation.
Moving away from the strictly academic works, "The Air We Breathe: Understanding Pollution and Its Effects" by Environmental Expert is a well-regarded non-fiction source that dissects the repercussions of air pollution, from health impacts to economic repercussions. On the fiction front, "The Bellhop Chronicles" by Author Bell delves into the whimsical world of hotel bellhops, presenting a lighthearted yet oddly relevant take on the bustling world of hospitality.
But hold onto your hats as we dive into the unexpected. "Parcheesi Pollution: A Study of Air Quality in Board Games" may seem like an unlikely source of inspiration, but the game's emphasis on movement and strategy prompts intriguing parallels to our investigation. Could the roll of the dice in pollution levels be influencing the movement of bellhops in Louisiana? We might jest, but such fantastical ponderings are firmly within the realm of possibility in our whimsical quest for knowledge.
[[RESULTS]]
Our research unearthed a surprising and statistically significant correlation between air pollution in Lafayette, Louisiana, and the number of bellhops employed across the state of Louisiana from 2003 to 2022. The correlation coefficient clocked in at a staggering 0.8352544, signifying a strong positive relationship between these seemingly unrelated variables. This relationship was further supported by an r-squared value of 0.6976499, indicating that over 69% of the variation in bellhop employment can be explained by changes in air pollution levels. With a p-value of less than 0.01, our findings are not to be brushed off as mere coincidence.
Fig. 1 displays the scatterplot, visually encapsulating the mesmerizing correlation between air pollution in Lafayette and the sheer number of bellhops bustling about in Louisiana. One must admit, witnessing data points coalesce in such a way is as unexpected as finding a bellhop in a Louisiana swamp.
The results of our analysis compel us to consider the possibility that air pollution in Lafayette acts as a silent but forceful advocate for the bellhop industry. It seems that the murkier the air becomes, the clearer the demand for bellhop services across the entire state. This revelation may leave some scratching their heads, but the numbers do not lie; there is something in the air that is undeniably linked to the demand for bellhops.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The findings of our study offer compelling evidence that there exists a substantial and previously unrecognized relationship between air pollution in Lafayette, Louisiana, and the number of bellhops employed across the state. While some may find the notion of a connection between these two variables as far-fetched as stumbling upon a bellhop in a Louisiana bayou, our results cannot be dismissed as mere whimsy. Rather, they lend credence to the possibility that the hazy air of Lafayette is intertwined with the bustling activity of bellhops across Louisiana. Our discovery aligns with prior research by Smith et al. (2015) and Doe (2017), who highlighted the detrimental impact of air pollution on workforce well-being and employment levels, providing a scholarly foundation for our investigation.
Returning to our whimsical literature review, our findings bizarrely seem to substantiate the peculiar parallels drawn from "Parcheesi Pollution: A Study of Air Quality in Board Games." Although we may have quipped about the resemblance between pollution levels and the movement of a bellhop token, the statistically robust correlation we uncovered hints at an unexpected interconnectedness that is as perplexing as it is captivating.
The notable correlation coefficient of 0.8352544 and the r-squared value of 0.6976499 further underscore the strength of the relationship between air pollution in Lafayette and the demand for bellhops in Louisiana. The scatterplot graphically illustrates this surprising connection, perhaps as surprising as stumbling upon a bellhop amidst the bayous. This raises the question of whether there may be a tangible economic influence of Lafayette's polluted air on the bustling activity of bellhops across the state.
While we tread carefully in interpreting causality, our findings open the door to a range of potential explanatory mechanisms. Could it be that the murkier air in Lafayette somehow stimulates the demand for bellhop services, prompting a surge in their employment across the state? Indeed, one might wonder whether the prevalence of airborne pollutants engenders a need for soothing bellhop assistance, as the haze calls for an extra helping hand.
The implications of our research raise intriguing possibilities and spark a reevaluation of the intricate dynamics at play between environmental factors and labor markets. The notion that the polluted air in Lafayette may inadvertently serve as a boon for the bellhop industry challenges conventional understanding and calls for further investigation into the fabulous and unforeseen effects of environmental factors on labor markets. As we venture deeper into uncharted academic territory, the findings of our study serve as a testament to the unanticipated connections that lurk beneath the seemingly disparate realms of air quality and bellhop employment. Our unearthing of this "Breathtaking Bellhop Correlation" (as humorously dubbed) underscores the potential for discoveries that defy expectation, cementing our study as a quirky but impactful addition to the realm of labor market research.
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 Air pollution in Lafayette, Louisiana and The number of bellhops in Louisiana. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.
Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.
Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
[[ABSTRACT]]
This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
[[INTRDUCTION]]
Pardon the pun, but let's clear the air: the connection between air pollution and the number of bellhops in Louisiana may seem like a load of hot air at first glance. However, our research endeavors to shed light on this curious correlation, examining how the haze of pollution in Lafayette is seemingly clearing the path for a surge in the bellhop business across the whole state.
As we embark on this whimsical yet revelatory journey, it's worth noting the sheer audacity of the question at hand. Who would have thought that the quality of air in one city could be linked to the bustling activities of bellhops miles away? Far from being a mere flight of fancy, our analysis is firmly rooted in empirical data and rigorous statistical methods.
Our investigation stems from the merging of seemingly disparate realms - environmental science and labor economics. We seek to blend these disciplines to unearth what might appear as an improbable coincidence. To the untrained eye, it might sound like the setup to a good bar joke, but rest assured, our methods and findings are firmly anchored in the world of academia.
[[RESULTS]]
Our research unearthed a surprising and statistically significant correlation between air pollution in Lafayette, Louisiana, and the number of bellhops employed across the state of Louisiana from 2003 to 2022. The correlation coefficient clocked in at a staggering 0.8352544, signifying a strong positive relationship between these seemingly unrelated variables. This relationship was further supported by an r-squared value of 0.6976499, indicating that over 69% of the variation in bellhop employment can be explained by changes in air pollution levels. With a p-value of less than 0.01, our findings are not to be brushed off as mere coincidence.
Fig. 1 displays the scatterplot, visually encapsulating the mesmerizing correlation between air pollution in Lafayette and the sheer number of bellhops bustling about in Louisiana. One must admit, witnessing data points coalesce in such a way is as unexpected as finding a bellhop in a Louisiana swamp.
The results of our analysis compel us to consider the possibility that air pollution in Lafayette acts as a silent but forceful advocate for the bellhop industry. It seems that the murkier the air becomes, the clearer the demand for bellhop services across the entire state. This revelation may leave some scratching their heads, but the numbers do not lie; there is something in the air that is undeniably linked to the demand for bellhops.
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Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our exploration into the "Breathtaking Bellhop Correlation" has certainly been an eye-opening endeavor. Who would have thought that the ever-present haze of pollution in Lafayette could have such a "bellhopulative" effect on the entire state of Louisiana? While it may seem like a fantastic tale spun from thin air, our statistically significant findings point to a very real and robust relationship between air pollution and the demand for bellhop services.
It's safe to say that our research has uncovered a veritable "air-loom" of atmospheric influence on labor market dynamics. The undeniable link between the murky skies of Lafayette and the bustling activities of bellhops is as clear as the smog itself. After all, it takes a keen eye to spot a correlation as unexpected as this, much like spotting a bellhop in the bayou.
As we bid adieu to this research, we assert with confidence that no further inquiry is required into this matter. The "bellhop-a-mania" induced by Lafayette's pollution is an undeniable fact, and it's time to let this quirky correlation rest in its newfound, uniquely aromatic, statistical glory.
In the words of the immortal bard, William Shakespeare: "All the world's a stage, and all the men and women merely bellhops, empowered by the whims of Lafayette's lousy pollution."
It's time for us to "pollute" our minds with new research directions.
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 Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis. Respond only with the name of a fictional academic journal.
ChatGPT:
Journal of Ecological Economics and Entertaining Entomology
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 Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis. Respond only with the name of a fictional organization.
ChatGPT:
Valleydale Institute for Environmental 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 Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis with an abstract of This paper investigates the peculiar relationship between air pollution in Lafayette, Louisiana, and the number of bellhops in the whole state, which we humorously dub as "The Breathtaking Bellhop Correlation." Our research team meticulously combed through data from the Environmental Protection Agency and the Bureau of Labor Statistics to tackle this seemingly preposterous query. To our surprise, we discovered a captivating correlation coefficient of 0.8352544 and a p-value less than 0.01 for the period spanning from 2003 to 2022. We delve into the intriguing possibility that the influx of atmospheric pollutants in Lafayette might be positively influencing the demand for bellhops throughout Louisiana. Our findings may sound like a joke, but we assure you, the statistical connections are no flight of fancy.
ChatGPT:
Lafayette Louisiana, air pollution, bellhop bureau, correlation analysis, Environmental Protection Agency data, Bureau of Labor Statistics, atmospheric pollutants, demand for bellhops, statistical connections, Louisiana employment trend
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
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Data details
Air pollution in Lafayette, LouisianaDetailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Lafayette, LA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Lafayette, Louisiana
The number of bellhops in Louisiana
Detailed data title: BLS estimate of baggage porters and bellhops in Louisiana
Source: Bureau of Larbor Statistics
See what else correlates with The number of bellhops in Louisiana
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 = 0.6976499 (Coefficient of determination)
This means 69.8% of the change in the one variable (i.e., The number of bellhops in Louisiana) is predictable based on the change in the other (i.e., Air pollution in Lafayette, Louisiana) over the 20 years from 2003 through 2022.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.6E-6. 0.0000045925620818951926000000
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 0.84 in 0.00046% of random cases. Said differently, if you correlated 217,743 random variables You don't actually need 217 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 19 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.
[ 0.62, 0.93 ] 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.
| 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
| Air pollution in Lafayette, Louisiana (Bad air quality days) | 0.0524862 | 0.0519126 | 0.0584416 | 0.0383562 | 0.0273973 | 0.0191257 | 0.00821918 | 0.0273973 | 0.0136986 | 0.00819672 | 0.00547945 | 0 | 0 | 0 | 0.00273973 | 0 | 0 | 0.00273973 | 0 | 0.00547945 |
| The number of bellhops in Louisiana (Laborers) | 980 | 980 | 1000 | 720 | 590 | 600 | 590 | 510 | 480 | 450 | 470 | 430 | 460 | 480 | 480 | 630 | 670 | 500 | 150 | 500 |
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.
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([0.0524862,0.0519126,0.0584416,0.0383562,0.0273973,0.0191257,0.00821918,0.0273973,0.0136986,0.00819672,0.00547945,0,0,0,0.00273973,0,0,0.00273973,0,0.00547945,])
array_2 = np.array([980,980,1000,720,590,600,590,510,480,450,470,430,460,480,480,630,670,500,150,500,])
array_1_name = "Air pollution in Lafayette, Louisiana"
array_2_name = "The number of bellhops in Louisiana"
# 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.
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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 Air pollution in Lafayette, Louisiana
- Line chart for only The number of bellhops in Louisiana
- AI-generated correlation image
- The spurious research paper: Lafayette's Lousy Pollution and Louisiana's Lively Bellhop Bureau: A Correlative Analysis
You're the rating guru we've been waiting for!
Correlation ID: 3249 · Black Variable ID: 21666 · Red Variable ID: 8736
