Spurious correlation #5,129 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Tupelo, Mississippi and the second variable is The number of sewing machine operators in Mississippi. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Tupelo, Mississippi and the second variable is The number of sewing machine operators in Mississippi. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air became cleaner, the fabric no longer felt the need to be constantly hemmed and stitched out of self-preservation. This led to a decline in demand for sewing machine operators across the state, as the fabric learned to just let loose and embrace its natural, frayed state. It turns out, all those years of sewing were just a stitch in time to keep the fabric from unraveling, but now it's finally able to breathe easy!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a textile factory in Tupelo, Mississippi consisting of idle sewing machines and relaxed fabric. The scene should capture the contrast between the bustling, industrial atmosphere and the now calm, unburdened state of the fabric. The machines should be depicted as still and inactive, while the fabric appears smooth and free from the need for constant stitching and hemming. The overall visual should convey a sense of calm and liberation, showcasing the fabric's newfound ability to maintain its natural state without the need for human intervention. The lighting and colors should reflect a sense of tranquility and freedom from the previous industrial constraints. 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 Tupelo, Mississippi and The number of sewing machine operators in Mississippi. 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 comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Scalar art An image that looks like it is a scene from A Disney Movie A TIME magazine cover image Cyberpunk
ChatGPT: A TIME 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 Tupelo, Mississippi and The number of sewing machine operators in Mississippi, in the following situation: As the air became cleaner, the fabric no longer felt the need to be constantly hemmed and stitched out of self-preservation. This led to a decline in demand for sewing machine operators across the state, as the fabric learned to just let loose and embrace its natural, frayed state. It turns out, all those years of sewing were just a stitch in time to keep the fabric from unraveling, but now it's finally able to breathe easy!. Keep the description simple.
ChatGPT: In a bustling textile factory in Tupelo, Mississippi, the air is noticeably cleaner. The fabric, no longer clogged with pollutants, relaxes and no longer requires constant stitching and hemming. As a result, the number of sewing machine operators has declined across the state, as the fabric now embraces its natural, frayed state. The once industrious machines now sit idle, as the fabric no longer needs their constant attention to keep from unraveling.

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 TIME magazine cover image ] and depicting this scene: In a bustling textile factory in Tupelo, Mississippi, the air is noticeably cleaner. The fabric, no longer clogged with pollutants, relaxes and no longer requires constant stitching and hemming. As a result, the number of sewing machine operators has declined across the state, as the fabric now embraces its natural, frayed state. The once industrious machines now sit idle, as the fabric no longer needs their constant attention to keep from unraveling. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a textile factory in Tupelo, Mississippi consisting of idle sewing machines and relaxed fabric. The scene should capture the contrast between the bustling, industrial atmosphere and the now calm, unburdened state of the fabric. The machines should be depicted as still and inactive, while the fabric appears smooth and free from the need for constant stitching and hemming. The overall visual should convey a sense of calm and liberation, showcasing the fabric's newfound ability to maintain its natural state without the need for human intervention. The lighting and colors should reflect a sense of tranquility and freedom from the previous industrial constraints.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Air pollution in Tupelo, Mississippi caused The number of sewing machine operators in Mississippi to decrease.

AI academic paper

(Because p < 0.01)

The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi

The Journal of Eclectic Environmental Economics

Jan 2024

Reminder: This paper is AI-generated. Not real!

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

Model: gpt-3.5-turbo-1106
System prompt: You are a 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 Air pollution in Tupelo, Mississippi and The number of sewing machine operators in Mississippi. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from 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]]
The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi

[[ABSTRACT]]
This study investigates the often overlooked relationship between air pollution levels in Tupelo, Mississippi, and the employment of sewing machine operators in the state. Through the use of data obtained from the Environmental Protection Agency and the Bureau of Labor Statistics, a thorough statistical analysis was conducted for the period from 2003 to 2022. The correlation coefficient of 0.8926638 and a significance level of p < 0.01 suggest a strikingly robust connection between these seemingly disparate variables. We unpack this unexpected correlation and discuss the potential implications, offering insights into the environmental influence on the textile industry workforce. This research sheds light on the intricate fabric of environmental factors and occupational trends, unraveling a thread of curiosity that warrants further exploration.

[[INTRODUCTION]]
The world of academic research often leads us down unexpected paths, weaving through disparate fields and uncovering connections that seemed implausible at first glance. In this paper, we set out to examine the intriguing relationship between air pollution in Tupelo, Mississippi, and the number of sewing machine operators in the state. While at first blush, one might question the association between atmospheric pollutants and the textile industry, our findings reveal a surprising intertwining of these seemingly distinct threads.
Tupelo, known for being the birthplace of the King of Rock and Roll, may now have a new claim to fame - a peculiar correlation between its air quality and the employment of sewing machine operators. One might say we're all shook up by the unanticipated bond between these two variables. As we delve into this uncharted territory, we invite the reader to join us in unraveling this enigmatic tapestry of environmental and economic influences.
The textile industry, much like a finely woven fabric, is a complex entity, susceptible to various external factors, including technological advancements and consumer preferences. As we stitch together the layers of this investigation, we acknowledge the colorful history and economic significance of the textile sector in Mississippi, while also acknowledging the need to thread new areas of exploration into our understanding of its workforce dynamics.
Through the lens of statistical analysis, we aim to sewnfold the patterns of correlation and causation between air pollution levels and employment in the textile industry. Our findings suggest that there's more to this relationship than meets the eye, and that the fabric of occupational trends may be intricately woven into the environmental conditions of Tupelo. So, without further ado, let us embark on this intrepid academic journey, ready to unpick the mysteries and entanglements that await us in the nexus of air quality and textile employment.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study entailed the systematic gathering, analysis, and interpretation of data from disparate sources to unravel the potentially entwined patterns of air pollution levels and the employment of sewing machine operators in Mississippi. The data collection process, like spinning yarn from raw material, involved the extraction of environmental data from the Environmental Protection Agency's Air Quality System in Tupelo, Mississippi. Concurrently, data on the number of sewing machine operators was obtained from the comprehensive databases of the Bureau of Labor Statistics, mirroring the meticulous craftsmanship of a textile artisan.

To measure the air pollution levels, we tastefully selected the Air Quality Index (AQI), incorporating various pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide, ozone, sulfur dioxide, and nitrogen dioxide. Analogous to a seamstress meticulously choosing the right thread, each pollutant was meticulously considered in our analysis to capture the comprehensive fabric of air quality. The data, collected from 2003 to 2022, was then lovingly cleaned and prepared for statistical analysis, akin to preparing a pristine canvas for a masterpiece.

The employment data for sewing machine operators was gathered through an approach akin to unthreading intricate patterns. We meticulously sifted through the Bureau of Labor Statistics' Occupational Employment Statistics, selecting the relevant code for sewing machine operators (51-6031) and conscientiously collating data of employment patterns in Mississippi.

The statistical analysis of the collected data involved an array of methodological tools, akin to the diverse array of stitching techniques in the textile industry. Firstly, a Pearson correlation coefficient was employed to measure the strength and direction of the linear relationship between air pollution levels and the number of sewing machine operators. Additionally, a linear regression model was utilized to stitch together the quantitative interplay between these variables, accounting for potential confounding factors and trends. Finally, a significance level of p < 0.01 was used as a threshold to determine the robustness of the observed relationship, ensuring that our findings were not merely fabrications of chance.

In our analytical approach, we took great care to mend any potential discrepancies and biases, like a tailor meticulously altering a garment to achieve the perfect fit. Sensitivity analyses were also conducted to scrutinize the stability of our findings across different time periods and data subsets, similar to inspecting the durability of various fabric blends under diverse conditions.

Upon completing the statistical analyses, we conducted in-depth qualitative assessments, likened to the scrutinizing of material quality in the textile industry. We delicately unpicked the potential implications of the observed correlation, entwining the environmental influence on the textile industry workforce. Finally, we integrated our findings into the broader tapestry of existing literature, stitching new insights into the fabric of environmental and occupational factors in the textile industry.

In summary, our methodological approach sought to untangle the enigmatic relationship between air pollution and the employment of sewing machine operators, weaving together data from diverse sources and employing a range of statistical and analytical techniques. With this approach, we aimed to not only corroborate the observed correlation but also to provide a framework for future investigations to knit together a more comprehensive understanding of these interwoven factors.

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 Air pollution in Tupelo, Mississippi and The number of sewing machine operators in Mississippi. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi

[[ABSTRACT]]
This study investigates the often overlooked relationship between air pollution levels in Tupelo, Mississippi, and the employment of sewing machine operators in the state. Through the use of data obtained from the Environmental Protection Agency and the Bureau of Labor Statistics, a thorough statistical analysis was conducted for the period from 2003 to 2022. The correlation coefficient of 0.8926638 and a significance level of p < 0.01 suggest a strikingly robust connection between these seemingly disparate variables. We unpack this unexpected correlation and discuss the potential implications, offering insights into the environmental influence on the textile industry workforce. This research sheds light on the intricate fabric of environmental factors and occupational trends, unraveling a thread of curiosity that warrants further exploration.

[[LITERATURE REVIEW]]
The correlation between seemingly unrelated variables has been a subject of curiosity in academic circles, provoking studies that seek to untangle the intricate web of associations. In their seminal work, Smith et al. (2010) delved into the surprising nexus between environmental factors and workforce dynamics, shedding light on the often unforeseen relationships that underpin economic activities. Similarly, Doe and Jones (2015) explored the impacts of regional pollutants on industrial labor patterns, setting the stage for our own investigation into the peculiar bond between air pollution in Tupelo and the employment of sewing machine operators in Mississippi.
Turning our attention to the realm of environmental economics, "The Economics of Clean Air" by Smith (2012) provides a comprehensive overview of the environmental impact on regional economies, encompassing discussions on air quality regulations and industrial responses. In a parallel vein, "Textiles and Industrial Labor" by Doe (2018) offers insights into the historical and contemporary dynamics of textile industry employment, serving as a foundational backdrop for our exploration.
Venturing into the world of fiction, "The Threadbare Conspiracy" by Jane Austen (1811) presents a tale of intrigue and clandestine alliances in the textile industry, offering a whimsical departure from our empirical pursuits. The subtle intertwining of threads and secrets mirrors our own quest to unravel the enigmatic connection between air pollution and sewing machine operators.
As we broaden our literary lens, "War and Stitch" by Leo Tolstoy (1869) invites us to ponder the intricate tapestries of human existence amidst the backdrop of societal tumult—a reflection, perhaps, of the complexities we encounter in our study of environmental influences on occupational trends.
Amidst our scholarly pursuits, it may seem rather unconventional to draw inspiration from sources beyond traditional academia. Nonetheless, it is worth noting that during the early stages of our inquiry, an inadvertently comical reading of the back labels on shampoo bottles sparked a moment of unexpected insight. While this unconventional approach may raise some eyebrows, it serves as a reminder of the unpredictable sources from which scholarly inspiration can arise.
In sum, the diverse array of sources referenced herein underscores the multidisciplinary nature of our investigation into the entwined fates of air pollution and sewing machine operators in Mississippi. We acknowledge the humor laced within our literary excursion and hope that our findings, though rooted in empirical analysis, can be appreciated through the lens of academic levity and scholarly whimsy.

[[RESULTS]]
Our analysis of the data gathered from 2003 to 2022 reveals a robust correlation between air pollution levels in Tupelo, Mississippi, and the number of sewing machine operators in the state. The correlation coefficient of 0.8926638 indicates a strong positive relationship between these two variables. In simpler terms, when air pollution levels go up, so does the employment of sewing machine operators, and vice versa. It's almost as if the sewing machines are buzzing in response to the increase in pollutants, creating a cacophony of industrial orchestra.
The r-squared value of 0.7968486 further emphasizes the substantial proportion of the variance in sewing machine operator employment that can be explained by fluctuations in air pollution levels. This suggests that approximately 80% of the changes in the number of sewing machine operators can be attributed to changes in air pollution. Who knew that the ebb and flow of air quality could have such a tangible impact on the bustling world of textile production? It's like the atmosphere is operating the pedal of the sewing machine, dictating the rhythm of employment in Mississippi's textile industry.
With a significance level of p < 0.01, our results provide compelling evidence of the statistical significance of the correlation. This indicates that the likelihood of observing such a strong relationship between air pollution and sewing machine operator employment by random chance is less than 1 in 100. It's as if the data is sewing together a bold pattern of connection right before our eyes, leaving little room for doubt about the veracity of this unexpected relationship.
Figure 1 visually encapsulates the compelling correlation between air pollution levels and the number of sewing machine operators. The scatterplot illustrates the strikingly cohesive pattern, as if each data point is meticulously stitched together to form a clear picture of the relationship. It's as if the data itself is cloth, and the scatterplot is the tailored representation of its interconnectedness.
In light of these findings, it's evident that the air quality in Tupelo may play a significant role in shaping the employment landscape of the textile industry in Mississippi. This unexpected connection calls for a closer inspection of the ways in which environmental factors intertwine with the fabric of the state's workforce dynamics. It appears that the buzz in Tupelo is not just from the sewing machines, but also from the unexpected symphony of relations between air pollution and textile employment.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study echo the previous research that highlighted the intricate dance between environmental factors and occupational dynamics. Smith et al.'s (2010) exploration of the underappreciated connections between ecological influences and workforce patterns paved the way for our investigation. Similarly, Doe and Jones (2015) acted as trailblazers, delving into the impact of regional pollutants on industrial labor trajectories. Building upon their groundwork, our research not only corroborates their findings but also unravels a thread of curiosity that warrants further exploration.

In our literature review, we cheekily nodded to the fictional works of Austen and Tolstoy, employing a tongue-in-cheek approach to emphasize the multidisciplinary nature of our study. However, the jesting tone should not undermine the seriousness with which we embraced unconventional sources of inspiration. As we unpack our findings, it becomes evident that the seemingly whimsical connections we referenced are not entirely unrelated to the unexpected bond between air pollution and the employment of sewing machine operators. It's as if the fictional weaving of narratives resonates with the empirical unraveling of real-world correlations, forming a tapestry of discovery that transcends the confines of traditional academic pursuits.

The substantial correlation coefficient of 0.8926638 and the r-squared value of 0.7968486 underscore the robust relationship between air pollution levels in Tupelo and the number of sewing machine operators in Mississippi. These statistical metrics not only validate the startling coalescence between environmental quality and textile industry employment but also beckon us to contemplate the tangible impact of air pollution on the rhythm of sewing machine operator employment. It's as if the very atmosphere is dictating the tempo of industrious activity, prompting a symphonic interplay between pollutants and textile workforce dynamics.

The significance level of p < 0.01 further bolsters the credibility of our findings, indicating that the likelihood of observing such a robust correlation by random chance is akin to stumbling upon a needle in a haystack, or perhaps a spool of thread in a cluttered sewing kit. Visualized through the scatterplot in Figure 1, the connection between air pollution and sewing machine operator employment unfolds like a meticulously crafted tapestry, sewn together by the threads of empirical evidence. It's as if the data itself has been spun into a compelling narrative, weaving a story of environmental influence on the textile industry that demands attention.

In conclusion, the surprising correlation between air pollution levels in Tupelo and the employment of sewing machine operators in Mississippi is a testament to the unanticipated interconnectedness of seemingly disparate variables. Our research circumvents conventional boundaries, echoing the sentiment espoused by the unconventional strands of inspiration we encountered in our literature review. As we embark on further inquiries into this intriguing association, let us not forget that sometimes, in the pursuit of scholarly discovery, the most unexpected sources can provide invaluable insights and unravel the most enigmatic connections.

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 Air pollution in Tupelo, Mississippi and The number of sewing machine operators in Mississippi. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi

[[ABSTRACT]]
This study investigates the often overlooked relationship between air pollution levels in Tupelo, Mississippi, and the employment of sewing machine operators in the state. Through the use of data obtained from the Environmental Protection Agency and the Bureau of Labor Statistics, a thorough statistical analysis was conducted for the period from 2003 to 2022. The correlation coefficient of 0.8926638 and a significance level of p < 0.01 suggest a strikingly robust connection between these seemingly disparate variables. We unpack this unexpected correlation and discuss the potential implications, offering insights into the environmental influence on the textile industry workforce. This research sheds light on the intricate fabric of environmental factors and occupational trends, unraveling a thread of curiosity that warrants further exploration.

[[INTRDUCTION]]
The world of academic research often leads us down unexpected paths, weaving through disparate fields and uncovering connections that seemed implausible at first glance. In this paper, we set out to examine the intriguing relationship between air pollution in Tupelo, Mississippi, and the number of sewing machine operators in the state. While at first blush, one might question the association between atmospheric pollutants and the textile industry, our findings reveal a surprising intertwining of these seemingly distinct threads.
Tupelo, known for being the birthplace of the King of Rock and Roll, may now have a new claim to fame - a peculiar correlation between its air quality and the employment of sewing machine operators. One might say we're all shook up by the unanticipated bond between these two variables. As we delve into this uncharted territory, we invite the reader to join us in unraveling this enigmatic tapestry of environmental and economic influences.
The textile industry, much like a finely woven fabric, is a complex entity, susceptible to various external factors, including technological advancements and consumer preferences. As we stitch together the layers of this investigation, we acknowledge the colorful history and economic significance of the textile sector in Mississippi, while also acknowledging the need to thread new areas of exploration into our understanding of its workforce dynamics.
Through the lens of statistical analysis, we aim to sewnfold the patterns of correlation and causation between air pollution levels and employment in the textile industry. Our findings suggest that there's more to this relationship than meets the eye, and that the fabric of occupational trends may be intricately woven into the environmental conditions of Tupelo. So, without further ado, let us embark on this intrepid academic journey, ready to unpick the mysteries and entanglements that await us in the nexus of air quality and textile employment.

[[RESULTS]]
Our analysis of the data gathered from 2003 to 2022 reveals a robust correlation between air pollution levels in Tupelo, Mississippi, and the number of sewing machine operators in the state. The correlation coefficient of 0.8926638 indicates a strong positive relationship between these two variables. In simpler terms, when air pollution levels go up, so does the employment of sewing machine operators, and vice versa. It's almost as if the sewing machines are buzzing in response to the increase in pollutants, creating a cacophony of industrial orchestra.
The r-squared value of 0.7968486 further emphasizes the substantial proportion of the variance in sewing machine operator employment that can be explained by fluctuations in air pollution levels. This suggests that approximately 80% of the changes in the number of sewing machine operators can be attributed to changes in air pollution. Who knew that the ebb and flow of air quality could have such a tangible impact on the bustling world of textile production? It's like the atmosphere is operating the pedal of the sewing machine, dictating the rhythm of employment in Mississippi's textile industry.
With a significance level of p < 0.01, our results provide compelling evidence of the statistical significance of the correlation. This indicates that the likelihood of observing such a strong relationship between air pollution and sewing machine operator employment by random chance is less than 1 in 100. It's as if the data is sewing together a bold pattern of connection right before our eyes, leaving little room for doubt about the veracity of this unexpected relationship.
Figure 1 visually encapsulates the compelling correlation between air pollution levels and the number of sewing machine operators. The scatterplot illustrates the strikingly cohesive pattern, as if each data point is meticulously stitched together to form a clear picture of the relationship. It's as if the data itself is cloth, and the scatterplot is the tailored representation of its interconnectedness.
In light of these findings, it's evident that the air quality in Tupelo may play a significant role in shaping the employment landscape of the textile industry in Mississippi. This unexpected connection calls for a closer inspection of the ways in which environmental factors intertwine with the fabric of the state's workforce dynamics. It appears that the buzz in Tupelo is not just from the sewing machines, but also from the unexpected symphony of relations between air pollution and textile employment.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research unravels a peculiar bond between air pollution levels in Tupelo, Mississippi, and the employment of sewing machine operators in the state. The robust correlation coefficient and statistical significance of our findings underscore the unforeseen connection between atmospheric pollutants and the textile industry workforce. It's almost as if the air pollution is orchestrating a sewing symphony, conducting the rhythm of employment in Mississippi's textile sector.

This study not only stitches together an unexpected relationship but also embroiders a new chapter in the tapestry of environmental and occupational influences. The results highlight the need to weave environmental considerations into our understanding of workforce dynamics, emphasizing the intricate fabric of factors that shape the employment landscape.

With that said, it seems that we've thoroughly hemmed and hawed over this curious correlation, leaving little room for doubt about the significance of this unforeseen connection. It's like uncovering a hidden stitch in the rich fabric of economic and environmental interactions. As such, we assert that further research in this area would be akin to searching for a needle in a haystack - unnecessary and likely to leave us feeling a bit unraveled. It's time to cut the thread and call it a day on the investigation of air pollution and sewing machine operators.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eclectic Environmental Economics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Quirky Environmental Research (SQUER)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi with an abstract of This study investigates the often overlooked relationship between air pollution levels in Tupelo, Mississippi, and the employment of sewing machine operators in the state. Through the use of data obtained from the Environmental Protection Agency and the Bureau of Labor Statistics, a thorough statistical analysis was conducted for the period from 2003 to 2022. The correlation coefficient of 0.8926638 and a significance level of p < 0.01 suggest a strikingly robust connection between these seemingly disparate variables. We unpack this unexpected correlation and discuss the potential implications, offering insights into the environmental influence on the textile industry workforce. This research sheds light on the intricate fabric of environmental factors and occupational trends, unraveling a thread of curiosity that warrants further exploration.

ChatGPT:

Tupelo, Mississippi, air pollution, sewing machine operators, employment, environmental influence, textile industry workforce, correlation, statistical analysis, Bureau of Labor Statistics, Environmental Protection Agency, occupational trends

*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 Tupelo, Mississippi
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Tupelo, MS
Source: Environmental Protection Agency
See what else correlates with Air pollution in Tupelo, Mississippi

The number of sewing machine operators in Mississippi
Detailed data title: BLS estimate of sewing machine operators in Mississippi
Source: Bureau of Larbor Statistics
See what else correlates with The number of sewing machine operators in Mississippi

Correlation r = 0.8926638 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.7968486 (Coefficient of determination)
This means 79.7% of the change in the one variable (i.e., The number of sewing machine operators in Mississippi) is predictable based on the change in the other (i.e., Air pollution in Tupelo, Mississippi) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.21E-7.^Show 0.0000001209423587893057000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.89 in 1.21E-5% of random cases. Said differently, if you correlated 8,268,402 random variables^Note You don't actually need 8 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 19 degrees of freedom, ^Note 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.

[ 0.74, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Tupelo, Mississippi (Bad air quality days)	0.0248447	0.0245902	0.0604396	0.030137	0.032967	0.00552486	0	0.00547945	0	0.00298508	0.00413223	0	0	0	0	0	0	0	0	0
The number of sewing machine operators in Mississippi (Sewing Machine Operators)	8010	7930	8480	7680	6790	5600	4510	3580	3430	3470	3670	3670	3600	3800	3950	4140	3310	2680	3500	3740

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.0248447,0.0245902,0.0604396,0.030137,0.032967,0.00552486,0,0.00547945,0,0.00298508,0.00413223,0,0,0,0,0,0,0,0,0,])
array_2 = np.array([8010,7930,8480,7680,6790,5600,4510,3580,3430,3470,3670,3670,3600,3800,3950,4140,3310,2680,3500,3740,])
array_1_name = "Air pollution in Tupelo, Mississippi"
array_2_name = "The number of sewing machine operators in Mississippi"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Tupelo, Mississippi
Line chart for only The number of sewing machine operators in Mississippi
AI-generated correlation image
The spurious research paper: The Buzz in Tupelo: A Stitch in Time? Exploring the Curious Correlation Between Air Pollution and the Number of Sewing Machine Operators in Mississippi

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Correlation ID: 5129 · Black Variable ID: 22136 · Red Variable ID: 10344


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