Spurious correlation #3,462 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of tailors, dressmakers, and custom sewers in Alabama and the second variable is Liquefied petroleum gas used in Australia. 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 The number of tailors, dressmakers, and custom sewers in Alabama and the second variable is Liquefied petroleum gas used in Australia. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

AI explanation

As the saying goes, 'Sew long, farewell, auf wiedersehen, adieu' to the tailors and dressmakers in Alabama! With fewer of them around to create stunning fashion pieces, the demand for extravagant clothing in Australia plummeted. Consequently, there was a significant reduction in the number of flamboyant dress designs, leading to a decreased need for Liquefied Petroleum Gas used in the glitzy fashion industry. It seems the lack of 'sew' much frilling and 'tailor'ed outfits has left the LPG suppliers in Australia in stitches!

^{Show image generation prompt} Model: dalle-3
Prompt: Using the [Realistic] style, create an image of a vibrant Alabama street filled with tailors, dressmakers, and custom sewers crafting exquisite outfits in bustling workshop settings. The scene should depict hot air blowing about, giving rise to a visual spectacle in the sky. Simultaneously, across the ocean in Australia, showcase the increased demand for Liquefied Petroleum Gas due to the rising hot air, symbolizing the need for energy and heat. 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: The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. 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: Symbolism Realistic Low poly Vaporwave Low poly
ChatGPT: Realistic

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 The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia, in the following situation: Because as the number of tailors, dressmakers, and custom sewers in Alabama increases, the demand for fancy dress parties and flamboyant fashion shows skyrockets. This leads to a surge in gas emissions from all the hot air blown about these events, and as we all know, hot air rises and drifts all the way to Australia, causing an increase in demand for Liquefied Petroleum Gas for heat and energy. So essentially, it's all about chasing the perfect tailor-made party outfit and setting the fashion world on fire!. Keep the description simple.
ChatGPT: In Alabama, a bustling street is lined with tailors, dressmakers, and custom sewers working diligently to create exquisite outfits for fancy dress parties and flamboyant fashion shows. As the demand for these events surges, the hot air blown about fills the sky, creating a visual spectacle. Across the ocean in Australia, Liquefied Petroleum Gas is in high demand as the hot air rises and drifts, causing an increase in the need for energy and heat.

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: [ Realistic ] and depicting this scene: In Alabama, a bustling street is lined with tailors, dressmakers, and custom sewers working diligently to create exquisite outfits for fancy dress parties and flamboyant fashion shows. As the demand for these events surges, the hot air blown about fills the sky, creating a visual spectacle. Across the ocean in Australia, Liquefied Petroleum Gas is in high demand as the hot air rises and drifts, causing an increase in the need for energy and heat. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Using the [Realistic] style, create an image of a vibrant Alabama street filled with tailors, dressmakers, and custom sewers crafting exquisite outfits in bustling workshop settings. The scene should depict hot air blowing about, giving rise to a visual spectacle in the sky. Simultaneously, across the ocean in Australia, showcase the increased demand for Liquefied Petroleum Gas due to the rising hot air, symbolizing the need for energy and heat.

*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 The number of tailors, dressmakers, and custom sewers in Alabama caused Liquefied petroleum gas used in Australia to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

The Journal of Sartorial Studies

Jan 2024

^{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 dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

[[ABSTRACT]]
This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The symbiotic relationship between seemingly unrelated phenomena has long captivated scholars across disciplines. Our study delves into the curious correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. At first glance, one may be flustered by the seemingly incongruous pairing of textiles and energy resources, as if a seamstress and a tanker of LPG found themselves in a precarious pas de deux. However, as we unpick the labyrinthine web of economic, cultural, and environmental factors, a pattern begins to emerge, much like a complex tapestry revealing its intricate design.
We embark on this scholarly inquiry with an air of cautious optimism, recognizing the temptation to dismiss our findings as a mere flight of fancy. However, our thorough analysis of data from the Bureau of Labor Statistics and the Energy Information Administration has yielded a correlation coefficient that simply cannot be brushed aside. The numerical alchemy has bestowed upon us a robust correlation coefficient of 0.8731282, accompanied by a p-value that gleefully dances beneath the 0.01 threshold. The statistical tapestry woven by these numbers beckons us to dive deeper into the warp and weft of this peculiar relationship.
As we thread our way through the fabric of this study, we invite the reader to join us on a journey that traverses the garments of economic theory, the cross-stitch of global trade dynamics, and the hemlines of environmental considerations. Our investigation skilfully stitches together the seemingly disparate realms of fashion and fuel, weaving a narrative that transcends the conventional threads of scholarly inquiry. This quest is not merely an exercise in academic tedium but an opportunity to knit together insights that extend beyond the seams of conventional wisdom. We endeavor to tailor our arguments with precision and engage in a seam-stimulating discourse that may challenge preconceived notions, all the while avoiding any unraveling of our logic.
In the pages that follow, we weave a rich tapestry of empirical evidence, colorful anecdotes, and anecdotal colors (pun intended) to illuminate the intricate relationship between the tutelage of tailors, the patterns of petroleum, and the unforeseen connections that emerge when seemingly incongruent entities are brought under the scrutiny of scholarly inquiry. So, fasten your seatbelts, or perhaps your buttons, as we embark on a journey that promises to unravel the mysteries of the Tailor-Tailgate Tango.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:

LITERATURE REVIEW

The relationship between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia has elicited an array of scholarly investigations, each endeavoring to untangle the intricate threads of this curious correlation. Smith (2010) offers a comprehensive analysis of the historical evolution of textile production and its impact on energy consumption, laying the groundwork for subsequent inquiries into this unique intersection of industries. Doe (2015) further delves into the socio-economic fabric of tailoring professions, illuminating the nuanced dynamics that underlie the supply and demand for bespoke garments and their potential ripple effects on energy markets. Jones (2018), in a statistical tour de force, explores the seams of data to stitch together a compelling argument for the interconnectedness of seemingly unrelated sectors.

Apart from these seminal works, a plethora of literature from diverse domains provides tangential insights that enrich our understanding of this unlikely association. "The Seamstress of Hollywood Boulevard" by Erin McGraw (2019) and "The Petroleum Vagabond" by Michael Turner (2013) offer fictional narratives that, while not serving as scholarly treatises, infuse the discussion with colorful anecdotes and pertinent parallels. Furthermore, the popular board game "Dressmaker’s Dilemma" presents players with strategic dilemmas akin to those faced by real-world tailors in shaping market trends, albeit in a delightfully whimsical fashion.

While these sources form the fabric of existing literature on the subject, our study endeavors to weave a narrative that transcends the conventional threads of scholarly inquiry, employing rigorous analysis and a touch of sartorial humor to elucidate the enduring mystery of the Tailor-Tailgate Tango.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration . 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 Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

[[ABSTRACT]]
This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

[[INTRODUCTION]]
INTRODUCTION
The symbiotic relationship between seemingly unrelated phenomena has long captivated scholars across disciplines. Our study delves into the curious correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. At first glance, one may be flustered by the seemingly incongruous pairing of textiles and energy resources, as if a seamstress and a tanker of LPG found themselves in a precarious pas de deux. However, as we unpick the labyrinthine web of economic, cultural, and environmental factors, a pattern begins to emerge, much like a complex tapestry revealing its intricate design.
We embark on this scholarly inquiry with an air of cautious optimism, recognizing the temptation to dismiss our findings as a mere flight of fancy. However, our thorough analysis of data from the Bureau of Labor Statistics and the Energy Information Administration has yielded a correlation coefficient that simply cannot be brushed aside. The numerical alchemy has bestowed upon us a robust correlation coefficient of 0.8731282, accompanied by a p-value that gleefully dances beneath the 0.01 threshold. The statistical tapestry woven by these numbers beckons us to dive deeper into the warp and weft of this peculiar relationship.
As we thread our way through the fabric of this study, we invite the reader to join us on a journey that traverses the garments of economic theory, the cross-stitch of global trade dynamics, and the hemlines of environmental considerations. Our investigation skilfully stitches together the seemingly disparate realms of fashion and fuel, weaving a narrative that transcends the conventional threads of scholarly inquiry. This quest is not merely an exercise in academic tedium but an opportunity to knit together insights that extend beyond the seams of conventional wisdom. We endeavor to tailor our arguments with precision and engage in a seam-stimulating discourse that may challenge preconceived notions, all the while avoiding any unraveling of our logic.
In the pages that follow, we weave a rich tapestry of empirical evidence, colorful anecdotes, and anecdotal colors (pun intended) to illuminate the intricate relationship between the tutelage of tailors, the patterns of petroleum, and the unforeseen connections that emerge when seemingly incongruent entities are brought under the scrutiny of scholarly inquiry. So, fasten your seatbelts, or perhaps your buttons, as we embark on a journey that promises to unravel the mysteries of the Tailor-Tailgate Tango.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for our study was sourced from the Bureau of Labor Statistics and the Energy Information Administration, spanning the years 2003 to 2022. The information on the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia was meticulously gathered from various sources across the internet, akin to a scavenger hunt in the digital wilderness.

To gather data on the number of tailors, dressmakers, and custom sewers in Alabama, our research team navigated the labyrinth of online databases, employment websites, and governmental reports. We pored over employment figures like a tailor meticulously measuring fabric, ensuring that no loose threads of data were left unexamined. The Bureau of Labor Statistics emerged as our primary source, providing a comprehensive tapestry of information woven into the fabric of its datasets.

Meanwhile, data on the consumption of LPG in Australia was procured with equal fervor. We delved into the recesses of the Energy Information Administration's archives, sifting through statistical treasure troves like prospectors in search of a rare gem. The relentless pursuit of numerical insight yielded a bounty of data, allowing us to trace the ebbs and flows of LPG consumption with the precision of a skilled tailor tracing the contours of a bespoke garment.

Data Analysis:
The correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of LPG in Australia was examined with the rigour of a meticulous tailor inspecting the quality of a finished garment. Utilizing advanced statistical techniques, we measured the strength and direction of this relationship, carefully stitching together the disparate threads of data.

The correlation coefficient was calculated to quantify the degree of association between these seemingly unrelated variables. Our statistical analysis wove a narrative of dependence, where the numerical threads intertwined to form a robust correlation coefficient of 0.8731282. The precision of this coefficient was matched only by the keen eye of a seasoned tailor, ensuring that each stitch of data contributed to the overall pattern with remarkable finesse.

Furthermore, the p-value, akin to a discerning fashion critic, provided a measure of the significance of the observed correlation. The p-value danced beneath the conventional significance threshold of 0.01, adding a touch of statistical flair to our analysis and affirming the substantive nature of the relationship under scrutiny.

Unraveling the Complex Threads:
In unraveling the complex tapestry of the relationship between the number of tailors in Alabama and LPG consumption in Australia, we employed a combination of traditional statistical methods and innovative approaches. Like a tailor experimenting with new techniques to create a dynamic design, we leveraged the power of regression analysis and time series modeling to elucidate the intricate interplay between the two variables.

This multifaceted approach allowed us to navigate the twists and turns of the data, akin to a seasoned seamstress deftly maneuvering through a maze of fabric. By scrutinizing the patterns and trends discernible in the data, we endeavored to uncover the underlying fabric of this unconventional relationship, dexterously navigating the complexities with scholarly finesse.

In summary, our methodological approach combined the precision of a tailor's measuring tape with the analytical acumen of a seasoned researcher, allowing us to weave a compelling narrative that transcends the conventional threads of scholarly inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration .

For the time period 2003 to 2022, you found a correlation 0.8731282, r-squared of 0.7623528, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

[[ABSTRACT]]
This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
The symbiotic relationship between seemingly unrelated phenomena has long captivated scholars across disciplines. Our study delves into the curious correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. At first glance, one may be flustered by the seemingly incongruous pairing of textiles and energy resources, as if a seamstress and a tanker of LPG found themselves in a precarious pas de deux. However, as we unpick the labyrinthine web of economic, cultural, and environmental factors, a pattern begins to emerge, much like a complex tapestry revealing its intricate design.
We embark on this scholarly inquiry with an air of cautious optimism, recognizing the temptation to dismiss our findings as a mere flight of fancy. However, our thorough analysis of data from the Bureau of Labor Statistics and the Energy Information Administration has yielded a correlation coefficient that simply cannot be brushed aside. The numerical alchemy has bestowed upon us a robust correlation coefficient of 0.8731282, accompanied by a p-value that gleefully dances beneath the 0.01 threshold. The statistical tapestry woven by these numbers beckons us to dive deeper into the warp and weft of this peculiar relationship.
As we thread our way through the fabric of this study, we invite the reader to join us on a journey that traverses the garments of economic theory, the cross-stitch of global trade dynamics, and the hemlines of environmental considerations. Our investigation skilfully stitches together the seemingly disparate realms of fashion and fuel, weaving a narrative that transcends the conventional threads of scholarly inquiry. This quest is not merely an exercise in academic tedium but an opportunity to knit together insights that extend beyond the seams of conventional wisdom. We endeavor to tailor our arguments with precision and engage in a seam-stimulating discourse that may challenge preconceived notions, all the while avoiding any unraveling of our logic.
In the pages that follow, we weave a rich tapestry of empirical evidence, colorful anecdotes, and anecdotal colors (pun intended) to illuminate the intricate relationship between the tutelage of tailors, the patterns of petroleum, and the unforeseen connections that emerge when seemingly incongruent entities are brought under the scrutiny of scholarly inquiry. So, fasten your seatbelts, or perhaps your buttons, as we embark on a journey that promises to unravel the mysteries of the Tailor-Tailgate Tango.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis revealed a remarkably strong correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. Over the period from 2003 to 2022, our research team found a correlation coefficient of 0.8731282, indicating a highly positive relationship between these two seemingly unrelated variables. This finding is substantiated by the r-squared value of 0.7623528, suggesting that approximately 76.24% of the variability in LPG usage in Australia can be explained by the number of tailors in Alabama. Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis and supports the existence of a significant relationship.

This irrefutable statistical evidence is visually depicted in Figure 1, which showcases a scatterplot illustrating the tight correlation between the number of tailors in Alabama and the consumption of LPG in Australia. The data points form a pattern reminiscent of a well-tailored suit, with a clear upward trend that reflects the simultaneous increase in both variables over the years. While one may be tempted to dismiss this correlation as mere happenstance, the figures make a compelling case for the intricate connection between the world of fashion and the realm of energy resources.

Our findings provide a thought-provoking insight into the underlying dynamics of these seemingly disparate domains. The implications of this unexpected correlation extend beyond the scope of traditional economic and environmental analyses, challenging conventional wisdom and prompting further inquiry into the nuanced interplay between the garments of economic activity and the fuel of industrial processes. The intricate tapestry of statistical evidence we have woven beckons for a deeper exploration of the intertwined mechanisms governing the Tailor-Tailgate Tango.

In conclusion, our study offers a nuanced perspective on the intricate interweaving of seemingly unrelated phenomena, emphasizing the need to critically examine the underlying threads that stitch together the fabric of our economic and environmental landscapes. These findings underscore the importance of embracing unexpected correlations and exploring the subtle connections that underpin complex systems, reminding us that in the grand tapestry of scholarly inquiry, even the most unconventional pairings may hold valuable insights.

The icy encounter between tailored garments and liquefied petroleum gas has brought forth unexpected warmth, shedding light on the symbiotic relationship between tutelage and tankers, a seemingly mismatched pair that, under scholarly scrutiny, has revealed a rich tapestry of interconnectedness.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

[[ABSTRACT]]
This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

[[LITERATURE REVIEW]]
LITERATURE REVIEW
The relationship between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia has elicited an array of scholarly investigations, each endeavoring to untangle the intricate threads of this curious correlation. Smith (2010) offers a comprehensive analysis of the historical evolution of textile production and its impact on energy consumption, laying the groundwork for subsequent inquiries into this unique intersection of industries. Doe (2015) further delves into the socio-economic fabric of tailoring professions, illuminating the nuanced dynamics that underlie the supply and demand for bespoke garments and their potential ripple effects on energy markets. Jones (2018), in a statistical tour de force, explores the seams of data to stitch together a compelling argument for the interconnectedness of seemingly unrelated sectors.
Apart from these seminal works, a plethora of literature from diverse domains provides tangential insights that enrich our understanding of this unlikely association. "The Seamstress of Hollywood Boulevard" by Erin McGraw (2019) and "The Petroleum Vagabond" by Michael Turner (2013) offer fictional narratives that, while not serving as scholarly treatises, infuse the discussion with colorful anecdotes and pertinent parallels. Furthermore, the popular board game "Dressmaker’s Dilemma" presents players with strategic dilemmas akin to those faced by real-world tailors in shaping market trends, albeit in a delightfully whimsical fashion.
While these sources form the fabric of existing literature on the subject, our study endeavors to weave a narrative that transcends the conventional threads of scholarly inquiry, employing rigorous analysis and a touch of sartorial humor to elucidate the enduring mystery of the Tailor-Tailgate Tango.

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. Over the period from 2003 to 2022, our research team found a correlation coefficient of 0.8731282, indicating a highly positive relationship between these two seemingly unrelated variables. This finding is substantiated by the r-squared value of 0.7623528, suggesting that approximately 76.24% of the variability in LPG usage in Australia can be explained by the number of tailors in Alabama. Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis and supports the existence of a significant relationship.
This irrefutable statistical evidence is visually depicted in Figure 1, which showcases a scatterplot illustrating the tight correlation between the number of tailors in Alabama and the consumption of LPG in Australia. The data points form a pattern reminiscent of a well-tailored suit, with a clear upward trend that reflects the simultaneous increase in both variables over the years. While one may be tempted to dismiss this correlation as mere happenstance, the figures make a compelling case for the intricate connection between the world of fashion and the realm of energy resources.
Our findings provide a thought-provoking insight into the underlying dynamics of these seemingly disparate domains. The implications of this unexpected correlation extend beyond the scope of traditional economic and environmental analyses, challenging conventional wisdom and prompting further inquiry into the nuanced interplay between the garments of economic activity and the fuel of industrial processes. The intricate tapestry of statistical evidence we have woven beckons for a deeper exploration of the intertwined mechanisms governing the Tailor-Tailgate Tango.
In conclusion, our study offers a nuanced perspective on the intricate interweaving of seemingly unrelated phenomena, emphasizing the need to critically examine the underlying threads that stitch together the fabric of our economic and environmental landscapes. These findings underscore the importance of embracing unexpected correlations and exploring the subtle connections that underpin complex systems, reminding us that in the grand tapestry of scholarly inquiry, even the most unconventional pairings may hold valuable insights.
The icy encounter between tailored garments and liquefied petroleum gas has brought forth unexpected warmth, shedding light on the symbiotic relationship between tutelage and tankers, a seemingly mismatched pair that, under scholarly scrutiny, has revealed a rich tapestry of interconnectedness.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study reinforce the existing body of literature that has probed the intricate relationship between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. Our investigation complements the work of Smith, Doe, and Jones, who have meticulously threaded the historical, socio-economic, and statistical dimensions of this enigmatic correlation. In particular, our results resonate with the statistical tour de force of Jones (2018), who adeptly navigated the seams of data to stitch together a compelling argument for the interconnectedness of seemingly unrelated sectors. Furthermore, our study heeds the intriguing fictional narratives of "The Seamstress of Hollywood Boulevard" by McGraw (2019) and "The Petroleum Vagabond" by Turner (2013), which, while not serving as scholarly treatises, offer colorful anecdotes that underscore the resonance of our empirical findings with the broader cultural discourse.

The robust correlation coefficient of 0.8731282 uncovered in our analysis echoes the persistent interplay between the world of fashion and the realm of energy resources. This statistical revelation presents a compelling case for the interconnectedness of these seemingly disparate domains, challenging conventional wisdom and prompting further inquiry into the nuanced interplay between the garments of economic activity and the fuel of industrial processes. Moreover, the substantiating r-squared value of 0.7623528 suggests that approximately 76.24% of the variability in LPG usage in Australia can be encapsulated by the proliferation of tailors in Alabama, underscoring the notable explanatory power of this unlikely association. The negligible p-value of less than 0.01 provides strong evidence against the null hypothesis, lending unwavering support to the existence of a significant relationship between these two variables.

The scatterplot visually portrays the remarkable correlation between the number of tailors in Alabama and the consumption of LPG in Australia, evoking the image of a finely tailored suit with its upward trend mirroring the simultaneous escalation of both variables over the years. This graphical representation aptly mirrors the aptly fitting statistical relationship and underscores the interconnected mechanisms that govern the Tailor-Tailgate Tango.

The unexpected warmth emanating from the unlikely encounter between tailored garments and liquefied petroleum gas beckons for a deeper exploration of the intertwined mechanisms governing this seemingly mismatched pair. While the initial appearance of this correlation may appear as if a well-tailored suit has been thrown into a gas tanker, our statistical analysis demonstrates that it is not merely a fluke but a coherent pattern that merits closer examination amid the grand tapestry of scholarly inquiry.

In conclusion, our study emphasizes the importance of critically examining the underlying threads that stitch together the fabric of our economic and environmental landscapes. The profound implications of this offbeat correlation extend beyond the conventional purview of economic and environmental analyses, challenging us to embrace unexpected connections and to unravel the subtle links that underpin complex systems. Thus, the Tailor-Tailgate Tango warrants further scholarly scrutiny, reminding us that in the grand tapestry of scholarly inquiry, even the most unconventional pairings may hold valuable insights.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of tailors, dressmakers, and custom sewers in Alabama and Liquefied petroleum gas used in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

[[ABSTRACT]]
This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

[[INTRDUCTION]]
INTRODUCTION
The symbiotic relationship between seemingly unrelated phenomena has long captivated scholars across disciplines. Our study delves into the curious correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. At first glance, one may be flustered by the seemingly incongruous pairing of textiles and energy resources, as if a seamstress and a tanker of LPG found themselves in a precarious pas de deux. However, as we unpick the labyrinthine web of economic, cultural, and environmental factors, a pattern begins to emerge, much like a complex tapestry revealing its intricate design.
We embark on this scholarly inquiry with an air of cautious optimism, recognizing the temptation to dismiss our findings as a mere flight of fancy. However, our thorough analysis of data from the Bureau of Labor Statistics and the Energy Information Administration has yielded a correlation coefficient that simply cannot be brushed aside. The numerical alchemy has bestowed upon us a robust correlation coefficient of 0.8731282, accompanied by a p-value that gleefully dances beneath the 0.01 threshold. The statistical tapestry woven by these numbers beckons us to dive deeper into the warp and weft of this peculiar relationship.
As we thread our way through the fabric of this study, we invite the reader to join us on a journey that traverses the garments of economic theory, the cross-stitch of global trade dynamics, and the hemlines of environmental considerations. Our investigation skilfully stitches together the seemingly disparate realms of fashion and fuel, weaving a narrative that transcends the conventional threads of scholarly inquiry. This quest is not merely an exercise in academic tedium but an opportunity to knit together insights that extend beyond the seams of conventional wisdom. We endeavor to tailor our arguments with precision and engage in a seam-stimulating discourse that may challenge preconceived notions, all the while avoiding any unraveling of our logic.
In the pages that follow, we weave a rich tapestry of empirical evidence, colorful anecdotes, and anecdotal colors (pun intended) to illuminate the intricate relationship between the tutelage of tailors, the patterns of petroleum, and the unforeseen connections that emerge when seemingly incongruent entities are brought under the scrutiny of scholarly inquiry. So, fasten your seatbelts, or perhaps your buttons, as we embark on a journey that promises to unravel the mysteries of the Tailor-Tailgate Tango.

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. Over the period from 2003 to 2022, our research team found a correlation coefficient of 0.8731282, indicating a highly positive relationship between these two seemingly unrelated variables. This finding is substantiated by the r-squared value of 0.7623528, suggesting that approximately 76.24% of the variability in LPG usage in Australia can be explained by the number of tailors in Alabama. Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis and supports the existence of a significant relationship.
This irrefutable statistical evidence is visually depicted in Figure 1, which showcases a scatterplot illustrating the tight correlation between the number of tailors in Alabama and the consumption of LPG in Australia. The data points form a pattern reminiscent of a well-tailored suit, with a clear upward trend that reflects the simultaneous increase in both variables over the years. While one may be tempted to dismiss this correlation as mere happenstance, the figures make a compelling case for the intricate connection between the world of fashion and the realm of energy resources.
Our findings provide a thought-provoking insight into the underlying dynamics of these seemingly disparate domains. The implications of this unexpected correlation extend beyond the scope of traditional economic and environmental analyses, challenging conventional wisdom and prompting further inquiry into the nuanced interplay between the garments of economic activity and the fuel of industrial processes. The intricate tapestry of statistical evidence we have woven beckons for a deeper exploration of the intertwined mechanisms governing the Tailor-Tailgate Tango.
In conclusion, our study offers a nuanced perspective on the intricate interweaving of seemingly unrelated phenomena, emphasizing the need to critically examine the underlying threads that stitch together the fabric of our economic and environmental landscapes. These findings underscore the importance of embracing unexpected correlations and exploring the subtle connections that underpin complex systems, reminding us that in the grand tapestry of scholarly inquiry, even the most unconventional pairings may hold valuable insights.
The icy encounter between tailored garments and liquefied petroleum gas has brought forth unexpected warmth, shedding light on the symbiotic relationship between tutelage and tankers, a seemingly mismatched pair that, under scholarly scrutiny, has revealed a rich tapestry of interconnectedness.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the grand tapestry of scholarly inquiry, our study has unravelled a rich seam of interconnectedness between the number of tailors, dressmakers, and custom sewers in Alabama and the consumption of liquefied petroleum gas (LPG) in Australia. The robust correlation coefficient of 0.8731282, coupled with a p-value of less than 0.01, has woven a compelling narrative that underscores the unexpected warmth between tailored garments and industrial energy resources. This curious tango of tutelage and tankers challenges conventional wisdom, reminding us that in the intricate web of economic and environmental phenomena, even the most incongruous pairings may hold valuable insights.

Our findings beckon for a deeper exploration of the subtle connections that underpin complex systems, much like carefully threading a needle through the fabric of statistical analyses. The implications of this unlikely correlation extend beyond the bounds of traditional economic and environmental paradigms, nudging us to embrace the unexpected with open arms and perhaps a well-tailored jacket. As we fasten the buttons on this study, we advocate for the embracing of unpredictability in scholarly inquiry, recognizing that the seemingly mismatched pairings may, in fact, be interlaced in ways that defy conventional logic.

In light of these groundbreaking discoveries, we assert that further research in this area is needless, as we have meticulously hemmed together the scholarly insights that transcend the conventional threads of inquiry. The Tailor-Tailgate Tango has, indeed, been captured in the intricate warp and weft of our analysis, leaving no loose threads for further unraveling.

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 Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Sartorial Studies

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 Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Sartorial Studies and Social Shenanigans

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 Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers with an abstract of This study examines the elusive relationship between the tally of tailors, dressmakers, and custom sewers in Alabama and the tumultuous transnational trade in liquefied petroleum gas (LPG) used in Australia. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team analyzed the period from 2003 to 2022, uncovering a surprisingly robust correlation coefficient of 0.8731282 and a p-value of less than 0.01. While the casual observer may dismiss this connection as mere happenstance, our findings suggest otherwise. We discuss potential factors such as the ripple effect of fashion trends, the fabric of global energy markets, and the stitches in the fabric of statistical analyses. Our study offers insights that extend beyond the seams of conventional wisdom, weaving a narrative that transcends the conventional threads of scholarly inquiry.

ChatGPT:

tailors, dressmakers, custom sewers, Alabama, liquefied petroleum gas, LPG trade, Bureau of Labor Statistics, Energy Information Administration, correlation coefficient, fashion trends, global energy markets, statistical analysis

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

The number of tailors, dressmakers, and custom sewers in Alabama
Detailed data title: BLS estimate of tailors, dressmakers, and custom sewers in Alabama
Source: Bureau of Larbor Statistics
See what else correlates with The number of tailors, dressmakers, and custom sewers in Alabama

Liquefied petroleum gas used in Australia
Detailed data title: Volume of liquefied petroleum gas used consumed in Australia in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Liquefied petroleum gas used in Australia

Correlation r = 0.8731282 (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 = 0.7623528 (Coefficient of determination)
This means 76.2% of the change in the one variable (i.e., Liquefied petroleum gas used in Australia) is predictable based on the change in the other (i.e., The number of tailors, dressmakers, and custom sewers in Alabama) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.06E-7.^Show 0.0000005058746932861086000000
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 0.87 in 5.06E-5% of random cases. Said differently, if you correlated 1,976,774 random variables^Note You don't actually need 1 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.

[ 0.7, 0.95 ] 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
The number of tailors, dressmakers, and custom sewers in Alabama (Laborers)	310	380	570	460	430	420	310	310	330	490	450	330	290	260	210	210	220	210	40	150
Liquefied petroleum gas used in Australia (Million Barrels/Day)	66.263	61.4699	74.1315	65.9123	66.726	59.0874	59.0548	62.8137	62.5836	62.5656	60.8685	55.8356	47.5699	41.541	36.726	28.274	30.3671	25.9208	25.6164	24.8356

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.

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([310,380,570,460,430,420,310,310,330,490,450,330,290,260,210,210,220,210,40,150,])
array_2 = np.array([66.263,61.4699,74.1315,65.9123,66.726,59.0874,59.0548,62.8137,62.5836,62.5656,60.8685,55.8356,47.5699,41.541,36.726,28.274,30.3671,25.9208,25.6164,24.8356,])
array_1_name = "The number of tailors, dressmakers, and custom sewers in Alabama"
array_2_name = "Liquefied petroleum gas used in Australia"

# 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 The number of tailors, dressmakers, and custom sewers in Alabama
Line chart for only Liquefied petroleum gas used in Australia
AI-generated correlation image
The spurious research paper: The Tailor-Tailgate Tango: Tracking the Tutelage of Tailors, Tweeds, and Tankers

View another random correlation

How fun was this correlation?

I'm genuinely thankful for your rating!

Correlation ID: 3462 · Black Variable ID: 4654 · Red Variable ID: 24160


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: