Spurious correlation #3,120 · 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 Iron Mountain, Michigan and the second variable is Customer satisfaction with Dillard's. The chart goes from 1994 to 2007, 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 Iron Mountain, Michigan and the second variable is Customer satisfaction with Dillard's. The chart goes from 1994 to 2007, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As air pollution in Iron Mountain, Michigan increased, it led to a greater concentration of iron in the air. This iron-infused air then wafted over to the Dillard's department store, causing a bizarre reaction with the clothing. The once pristine Dillard's merchandise began to rust and develop an unsightly patina, much to the dismay of the customers. After all, no one wants to buy a "vintage" blouse that's actually just been tarnished by airborne iron particles. As a result, customer satisfaction with Dillard's plummeted, proving that when it comes to air pollution and retail, the correlation is anything but "iron"-clad.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a serene image of Iron Mountain, Michigan with the smog-laden sky gradually clearing, allowing rays of sunlight to shine through. In the foreground, depict people taking deep breaths of fresh, clean air with expressions of relief and gratitude. In the background, indicate Dillard's with customers moving through the aisles purposefully, interacting with items that bring joy. The scene should exude a sense of clarity, satisfaction, and rejuvenation. 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 Iron Mountain, Michigan and Customer satisfaction with Dillard's. 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: Cute cartoon A Discovery magazine cover image Hyper-realistic Cute 2D animation Magic realism
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Iron Mountain, Michigan and Customer satisfaction with Dillard's, in the following situation: Because as air quality improved in Iron Mountain, Michigan, people were able to breathe easier, leading to better oxygen flow to the brain, which in turn improved their decision-making skills. With clearer heads, customers at Dillard's were more satisfied with their purchases, because with their improved mental acuity, they were less likely to make impulsive buys and more likely to choose items that truly brought them joy. Breathe easy – it's science!. Keep the description simple.
ChatGPT: In Iron Mountain, Michigan, the smog-laden sky slowly cleared as the air quality improved. The cleaner air allowed people to take deeper breaths, filling their lungs with fresh, invigorating oxygen. Meanwhile, at Dillard's, customers browsed the aisles with focused determination. With clear minds and improved decision-making skills, they carefully selected items that truly brought them joy, leading to higher levels of satisfaction with their purchases.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: In Iron Mountain, Michigan, the smog-laden sky slowly cleared as the air quality improved. The cleaner air allowed people to take deeper breaths, filling their lungs with fresh, invigorating oxygen. Meanwhile, at Dillard's, customers browsed the aisles with focused determination. With clear minds and improved decision-making skills, they carefully selected items that truly brought them joy, leading to higher levels of satisfaction with their purchases. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a serene image of Iron Mountain, Michigan with the smog-laden sky gradually clearing, allowing rays of sunlight to shine through. In the foreground, depict people taking deep breaths of fresh, clean air with expressions of relief and gratitude. In the background, indicate Dillard's with customers moving through the aisles purposefully, interacting with items that bring joy. The scene should exude a sense of clarity, satisfaction, and rejuvenation.

*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 increases in Air pollution in Iron Mountain, Michigan negatively influenced Customer satisfaction with Dillard's.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

The Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

Journal of Quirky Atmospheric Science

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Iron Mountain, Michigan and Customer satisfaction with Dillard's. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

[[ABSTRACT]]
In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.

Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.

Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.

In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The pursuit of knowledge often leads researchers down unexpected and unconventional paths. As curious minds, we have a knack for uncovering correlations in the most unlikely of places. In this pursuit of intellectual enlightenment, we found ourselves delving into the captivating realm of atmospheric intricacies and retail satisfaction. If knowledge is power, then we are determined to wield the mighty sword of insight, even if it leads us through the smog.
It is often said that truth is stranger than fiction, and our investigation into the connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's certainly attests to that maxim. Armed with data and a healthy dose of skepticism, we embarked on this inquiry with equal parts enthusiasm and trepidation. After all, who would have thought that the fumes of industrial activity could have a measurable impact on the delight of Dillard's patrons? It's almost as surprising as finding a sale on a sunny day – a rare occurrence indeed!
As researchers, we are no strangers to combing through volumes of data in pursuit of meaningful relationships. So, when we stumbled upon evidence that air pollution levels and customer satisfaction scores were intertwined, we couldn't help but let out an exasperated "air-yah kidding me?" And while we approached our analysis with the utmost scientific rigor, we also couldn't resist the occasional "hot air" pun to lighten the mood. It's important to "vent" a little, even in the midst of scholarly pursuits.
Our study, while grounded in empirical evidence and statistical analysis, also opened a window of opportunity for whimsical contemplation. We pondered whether the shoppers, while perusing the latest fashions, were unwittingly taking in more than just the scent of new fabric. Perhaps they were unknowingly inhaling the essence of Iron Mountain's airborne particles, leaving them yearning for oxygen amidst the perfume section. It's enough to make one quip, "Looks like someone left an 'iron' in the air."
As we embarked on this scholarly expedition, we couldn't help but be swept up in the paradox of it all – the irony, if you will. Who would have thought that the intangible tendrils of pollution could intertwine with the tangible fabric of retail experiences? It's as perplexing as finding a pair of mismatched socks in the laundry – a twist of fate that demands careful examination.
In the pursuit of understanding, we must be willing to explore the uncharted territories of knowledge, even if it means navigating through the unlikeliest of landscapes. With our findings, we hope to shed light on the interplay between environmental factors and consumer satisfaction, offering a breath of fresh air in the realm of retail research. And who knows, perhaps our study will pave the way for a new era of pun-infused academic inquiry – a field ripe for exploration and dad jokes galore.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

As we venture into the scholarly expanse of literature, we encounter a myriad of studies exploring the multifaceted relationship between environmental factors and consumer behavior. Smith et al. conducted a comprehensive analysis of air pollution's impact on retail experiences in "Green Skies, Happy Buys," uncovering a notable association between smog levels and customer satisfaction. Meanwhile, Doe's research in "Eco-Commerce Chronicles" further emphasizes the need to consider atmospheric nuances in understanding consumer preferences.

Now, before we delve any further, let's take a moment to appreciate the irony of our situation. The connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's might seem like a breath of fresh air in the realm of retail research, but the implications are nothing to sneeze at. It's almost as unexpected as finding a vacuum cleaner at a summer picnic – certainly not your typical pairing, but we're here to explore the unexpected, after all.

Turning our attention to non-fiction works of relevance, we find "Atmospheric Anomalies" by Wendy Jones, delving into the intricate interplay between atmospheric conditions and human experiences. Additionally, "The Retail Odyssey" by John Smith offers valuable insights into the nuanced factors shaping consumer satisfaction, including the impact of environmental influences. These scholarly contributions provide a solid foundation for our inquiry, but we cannot ignore the tantalizing allure of fiction in this exploration.

In the realm of fiction, the works of Agatha Christie provide an unexpected but pertinent parallel. In "Mist over the Mall," Christie weaves a tale of mystery set in a department store, where the hidden truths in the foggy air mirror the complexities of our own investigation. Similarly, the magical realism of Haruki Murakami's "The Wind-Up Bird Chronicle" draws attention to the untold stories swirling amidst the atmospheric currents, offering a whimsical yet contemplative backdrop to our scholarly pursuit.

Now, who could forget the cinematic contributions to our intellectual quest? Movies like "Up in the Air" and "The Secret Life of Walter Mitty" may seem tangentially related at first glance, but their exploration of personal journeys tied to environmental elements speaks to the underlying theme of our investigation. As we navigate through this labyrinth of scholarly and imaginative endeavors, we're reminded that sometimes the most unexpected connections yield the most fascinating insights.

In the pursuit of knowledge, we must be willing to tread the winding paths of inquiry, armed with data and a healthy sense of humor. After all, when grappling with the unexpected correlation between air pollution and customer satisfaction, a good dad joke might just be the breath of fresh air we need. So, let us press on with scholarly rigor and a touch of whimsy, for the depths of academic exploration are truly inexhaustible – much like a well-ventilated room with a dad joke echoing through the corridors.

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 Iron Mountain, Michigan and Customer satisfaction with Dillard's. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and American Customer Satisfaction Index . You used data from 1994 to 2007

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

[[ABSTRACT]]
In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.

Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.

Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.

In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

[[INTRODUCTION]]
The pursuit of knowledge often leads researchers down unexpected and unconventional paths. As curious minds, we have a knack for uncovering correlations in the most unlikely of places. In this pursuit of intellectual enlightenment, we found ourselves delving into the captivating realm of atmospheric intricacies and retail satisfaction. If knowledge is power, then we are determined to wield the mighty sword of insight, even if it leads us through the smog.
It is often said that truth is stranger than fiction, and our investigation into the connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's certainly attests to that maxim. Armed with data and a healthy dose of skepticism, we embarked on this inquiry with equal parts enthusiasm and trepidation. After all, who would have thought that the fumes of industrial activity could have a measurable impact on the delight of Dillard's patrons? It's almost as surprising as finding a sale on a sunny day – a rare occurrence indeed!
As researchers, we are no strangers to combing through volumes of data in pursuit of meaningful relationships. So, when we stumbled upon evidence that air pollution levels and customer satisfaction scores were intertwined, we couldn't help but let out an exasperated "air-yah kidding me?" And while we approached our analysis with the utmost scientific rigor, we also couldn't resist the occasional "hot air" pun to lighten the mood. It's important to "vent" a little, even in the midst of scholarly pursuits.
Our study, while grounded in empirical evidence and statistical analysis, also opened a window of opportunity for whimsical contemplation. We pondered whether the shoppers, while perusing the latest fashions, were unwittingly taking in more than just the scent of new fabric. Perhaps they were unknowingly inhaling the essence of Iron Mountain's airborne particles, leaving them yearning for oxygen amidst the perfume section. It's enough to make one quip, "Looks like someone left an 'iron' in the air."
As we embarked on this scholarly expedition, we couldn't help but be swept up in the paradox of it all – the irony, if you will. Who would have thought that the intangible tendrils of pollution could intertwine with the tangible fabric of retail experiences? It's as perplexing as finding a pair of mismatched socks in the laundry – a twist of fate that demands careful examination.
In the pursuit of understanding, we must be willing to explore the uncharted territories of knowledge, even if it means navigating through the unlikeliest of landscapes. With our findings, we hope to shed light on the interplay between environmental factors and consumer satisfaction, offering a breath of fresh air in the realm of retail research. And who knows, perhaps our study will pave the way for a new era of pun-infused academic inquiry – a field ripe for exploration and dad jokes galore.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the perplexing mystery of the interplay between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's, our research team delved into the labyrinth of methodological intricacies with both fervor and a spritz of whimsy. As researchers, we sought to approach our study with the precision of a tailor measuring cloth, yet we couldn't resist sprinkling in a dash of lightheartedness to keep our spirits high – after all, science should never be a "scent"-less endeavor.

We began our odyssey by gathering data from diverse sources, navigating through the digital ether like intrepid explorers. Our primary sources of information included the Environmental Protection Agency (EPA) and the American Customer Satisfaction Index (ACSI), where we unearthed a treasure trove of statistical gems and satisfaction scores. It's safe to say that we sifted through these datasets with a fervor akin to a passionate prospector panning for nuggets of insight – though in our case, the real gold was in the form of correlation coefficients and p-values.

Harnessing the power of technological marvels, we utilized cutting-edge software to wrangle the data into submission, weaving it into a rich tapestry of numerical precision. We performed a thorough assessment of air quality metrics in Iron Mountain, examining factors such as particulate matter, sulfur dioxide, and nitrogen dioxide levels. Meanwhile, our investigation into Dillard's customer satisfaction ratings gave us a window into the whims of the retail world, exposing the ebbs and flows of patrons' contentment with the shopping experience.

In the spirit of scientific rigor, we adopted a time-spanning approach, considering data from 1994 to 2007 to capture the essence of this intriguing connection across the years. It was akin to taking a historical stroll through the annals of air quality and retail satisfaction, with each year offering a new chapter in the unfolding saga of our research. As we meticulously combed through the digital timelines, we couldn't help but muse that our study was akin to a "Time Travel Mart," where the past and the present collided in a delightful interplay of data particles.

In mapping out the correlation between air pollution and customer satisfaction, we employed statistical analyses that rivaled the complexity of a Rubik's Cube in their intricacy. We calculated Pearson correlation coefficients, unleashing the mathematical prowess of our team to reveal the dance of interconnectedness between these seemingly disparate variables. And just when we thought we'd cracked the code, we embarked on hypothesis testing, subjecting our findings to the rigorous scrutiny of p-values that would make even the most skeptical statistician crack a smile.

As a final flourish, we carefully validated our results through sensitivity analyses and robustness checks, ensuring that our conclusions stood firm in the face of methodological scrutiny. Like a chef perfecting a recipe, we sought to create a scientific dish that would leave no room for doubt or skepticism.

With our methodology embraced and the data realms conquered, we forged ahead with a blend of scientific precision and scholarly jest, unearthing insights that challenged convention and tickled the intellect. And though our pursuit of knowledge was no laughing matter, a good dad joke here and there indeed provided a breath of fresh air amidst the weighty rigors of research.

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

Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Iron Mountain, Michigan and Customer satisfaction with Dillard's. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and American Customer Satisfaction Index .

For the time period 1994 to 2007, you found a correlation -0.7076608, r-squared of 0.5007838, 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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

[[ABSTRACT]]
In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.

Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.

Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.

In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The pursuit of knowledge often leads researchers down unexpected and unconventional paths. As curious minds, we have a knack for uncovering correlations in the most unlikely of places. In this pursuit of intellectual enlightenment, we found ourselves delving into the captivating realm of atmospheric intricacies and retail satisfaction. If knowledge is power, then we are determined to wield the mighty sword of insight, even if it leads us through the smog.
It is often said that truth is stranger than fiction, and our investigation into the connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's certainly attests to that maxim. Armed with data and a healthy dose of skepticism, we embarked on this inquiry with equal parts enthusiasm and trepidation. After all, who would have thought that the fumes of industrial activity could have a measurable impact on the delight of Dillard's patrons? It's almost as surprising as finding a sale on a sunny day – a rare occurrence indeed!
As researchers, we are no strangers to combing through volumes of data in pursuit of meaningful relationships. So, when we stumbled upon evidence that air pollution levels and customer satisfaction scores were intertwined, we couldn't help but let out an exasperated "air-yah kidding me?" And while we approached our analysis with the utmost scientific rigor, we also couldn't resist the occasional "hot air" pun to lighten the mood. It's important to "vent" a little, even in the midst of scholarly pursuits.
Our study, while grounded in empirical evidence and statistical analysis, also opened a window of opportunity for whimsical contemplation. We pondered whether the shoppers, while perusing the latest fashions, were unwittingly taking in more than just the scent of new fabric. Perhaps they were unknowingly inhaling the essence of Iron Mountain's airborne particles, leaving them yearning for oxygen amidst the perfume section. It's enough to make one quip, "Looks like someone left an 'iron' in the air."
As we embarked on this scholarly expedition, we couldn't help but be swept up in the paradox of it all – the irony, if you will. Who would have thought that the intangible tendrils of pollution could intertwine with the tangible fabric of retail experiences? It's as perplexing as finding a pair of mismatched socks in the laundry – a twist of fate that demands careful examination.
In the pursuit of understanding, we must be willing to explore the uncharted territories of knowledge, even if it means navigating through the unlikeliest of landscapes. With our findings, we hope to shed light on the interplay between environmental factors and consumer satisfaction, offering a breath of fresh air in the realm of retail research. And who knows, perhaps our study will pave the way for a new era of pun-infused academic inquiry – a field ripe for exploration and dad jokes galore.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis revealed a robust negative correlation between air pollution levels in Iron Mountain, Michigan, and customer satisfaction with Dillard's. The correlation coefficient of -0.7076608 indicated a strong inverse relationship between these two variables, showcasing a surprising link between environmental quality and retail patron contentment. It's almost as if the smog was casting a shadow on the shoppers' bliss – a cloud of dissatisfaction, if you will.

The r-squared value of 0.5007838 further corroborated the strength of this relationship, suggesting that a whopping 50% of the variation in customer satisfaction scores could be attributed to changes in air pollution levels. It's a statistic that speaks volumes – even above the hum of industrial machinery.

Our p-value of less than 0.01 underscored the significance of these findings, indicating that the observed correlation was not merely a fluke but a substantial and noteworthy phenomenon. It seems that the impact of air pollution on consumer satisfaction was a breath of fresh air in the world of retail research – a discovery that left us gasping for more insights.

Furthermore, the scatterplot depicted in Fig. 1 offers a visual representation of the startling correlation between air pollution and customer satisfaction. The data points align in a manner that brings to mind the sweeping gusts of air pollution exerting an unseen force on the shoppers' delight – a gust of discouragement in an otherwise breezy shopping experience.

As we unpacked these results, we couldn't help but marvel at the unexpected confluence of distant variables, which offered a fascinating portrayal of the interplay between atmospheric conditions and consumer contentment. It's a tale of environmental influence that breathes new life into the study of retail dynamics – a narrative where the air pollution becomes as much a part of the shopping experience as the latest fashion trends.

In essence, our findings underscore the importance of considering external environmental factors in understanding customer satisfaction, emphasizing the need to cultivate not only a welcoming shopping environment but also a breathable one. After all, in the realm of retail, good air quality may just be the unsung hero of customer contentment – a breath of fresh air, if you will.

And remember, when it comes to the connection between air pollution and customer satisfaction, the findings might leave you asthmatic for more.

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 Iron Mountain, Michigan and Customer satisfaction with Dillard's. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

[[ABSTRACT]]
In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.

Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.

Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.

In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

[[LITERATURE REVIEW]]
As we venture into the scholarly expanse of literature, we encounter a myriad of studies exploring the multifaceted relationship between environmental factors and consumer behavior. Smith et al. conducted a comprehensive analysis of air pollution's impact on retail experiences in "Green Skies, Happy Buys," uncovering a notable association between smog levels and customer satisfaction. Meanwhile, Doe's research in "Eco-Commerce Chronicles" further emphasizes the need to consider atmospheric nuances in understanding consumer preferences.
Now, before we delve any further, let's take a moment to appreciate the irony of our situation. The connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's might seem like a breath of fresh air in the realm of retail research, but the implications are nothing to sneeze at. It's almost as unexpected as finding a vacuum cleaner at a summer picnic – certainly not your typical pairing, but we're here to explore the unexpected, after all.
Turning our attention to non-fiction works of relevance, we find "Atmospheric Anomalies" by Wendy Jones, delving into the intricate interplay between atmospheric conditions and human experiences. Additionally, "The Retail Odyssey" by John Smith offers valuable insights into the nuanced factors shaping consumer satisfaction, including the impact of environmental influences. These scholarly contributions provide a solid foundation for our inquiry, but we cannot ignore the tantalizing allure of fiction in this exploration.
In the realm of fiction, the works of Agatha Christie provide an unexpected but pertinent parallel. In "Mist over the Mall," Christie weaves a tale of mystery set in a department store, where the hidden truths in the foggy air mirror the complexities of our own investigation. Similarly, the magical realism of Haruki Murakami's "The Wind-Up Bird Chronicle" draws attention to the untold stories swirling amidst the atmospheric currents, offering a whimsical yet contemplative backdrop to our scholarly pursuit.
Now, who could forget the cinematic contributions to our intellectual quest? Movies like "Up in the Air" and "The Secret Life of Walter Mitty" may seem tangentially related at first glance, but their exploration of personal journeys tied to environmental elements speaks to the underlying theme of our investigation. As we navigate through this labyrinth of scholarly and imaginative endeavors, we're reminded that sometimes the most unexpected connections yield the most fascinating insights.
In the pursuit of knowledge, we must be willing to tread the winding paths of inquiry, armed with data and a healthy sense of humor. After all, when grappling with the unexpected correlation between air pollution and customer satisfaction, a good dad joke might just be the breath of fresh air we need. So, let us press on with scholarly rigor and a touch of whimsy, for the depths of academic exploration are truly inexhaustible – much like a well-ventilated room with a dad joke echoing through the corridors.

[[RESULTS]]
The results of our analysis revealed a robust negative correlation between air pollution levels in Iron Mountain, Michigan, and customer satisfaction with Dillard's. The correlation coefficient of -0.7076608 indicated a strong inverse relationship between these two variables, showcasing a surprising link between environmental quality and retail patron contentment. It's almost as if the smog was casting a shadow on the shoppers' bliss – a cloud of dissatisfaction, if you will.
The r-squared value of 0.5007838 further corroborated the strength of this relationship, suggesting that a whopping 50% of the variation in customer satisfaction scores could be attributed to changes in air pollution levels. It's a statistic that speaks volumes – even above the hum of industrial machinery.
Our p-value of less than 0.01 underscored the significance of these findings, indicating that the observed correlation was not merely a fluke but a substantial and noteworthy phenomenon. It seems that the impact of air pollution on consumer satisfaction was a breath of fresh air in the world of retail research – a discovery that left us gasping for more insights.
Furthermore, the scatterplot depicted in Fig. 1 offers a visual representation of the startling correlation between air pollution and customer satisfaction. The data points align in a manner that brings to mind the sweeping gusts of air pollution exerting an unseen force on the shoppers' delight – a gust of discouragement in an otherwise breezy shopping experience.
As we unpacked these results, we couldn't help but marvel at the unexpected confluence of distant variables, which offered a fascinating portrayal of the interplay between atmospheric conditions and consumer contentment. It's a tale of environmental influence that breathes new life into the study of retail dynamics – a narrative where the air pollution becomes as much a part of the shopping experience as the latest fashion trends.
In essence, our findings underscore the importance of considering external environmental factors in understanding customer satisfaction, emphasizing the need to cultivate not only a welcoming shopping environment but also a breathable one. After all, in the realm of retail, good air quality may just be the unsung hero of customer contentment – a breath of fresh air, if you will.
And remember, when it comes to the connection between air pollution and customer satisfaction, the findings might leave you asthmatic for more.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our study illuminated an intriguing connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's, shedding light on the unexpected influence of environmental quality on retail experiences. The robust negative correlation we discovered reiterates the importance of atmospheric conditions in shaping consumer contentment, reminding us that sometimes the most surprising connections produce the most illuminating insights. It's like finding a hidden clearance section during a rainy day – unexpected, but ultimately rewarding!

Our findings align with prior research by Smith et al. and Doe, underscoring the significant impact of air pollution on retail experiences. The negative correlation coefficient we observed is as clear as the air on a crisp winter morning, affirming the relevance of atmospheric nuances in understanding consumer preferences. It's almost as if the smog is fogging up our preconceived notions about retail dynamics – a cloud of revelation, if you will.

The underlying relationship between air pollution and customer satisfaction, as depicted in our results, serves as a powerful testament to the diverse factors shaping retail dynamics. It's as if the winds of inquiry brought the hidden influence of air pollution into clear view, offering a breath of insight into the intricate tapestry of consumer behavior. Much like a well-timed dad joke, the unexpected correlation between environmental quality and retail patron contentment adds a touch of lighthearted surprise to the scholarly conversation.

Moreover, our study contributes to the expanding narrative surrounding the impact of environmental factors on consumer experiences, emphasizing the need to consider the atmospheric 'flavors' that permeate retail environments. It's almost as if we've uncovered a hidden layer of complexity, akin to finding an unexpected fashion accessory in the depths of a department store. Our research calls attention to the oft-overlooked significance of air quality in the realm of customer satisfaction, underscoring the need to breathe new life into the study of retail dynamics.

In essence, this investigation opens the door to a realm of inquiry that's as fresh and invigorating as a gust of clean air on a hot summer's day. After all, the connection between air pollution and customer satisfaction is no small matter – it's an atmospheric interplay that shapes our everyday interactions in surprising ways. And just like a well-executed dad joke, it leaves us smiling with its unexpected relevance, reminding us that even the most unconventional connections can yield valuable insights.

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 Iron Mountain, Michigan and Customer satisfaction with Dillard's. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

[[ABSTRACT]]
In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.

Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.

Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.

In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

[[INTRDUCTION]]
The pursuit of knowledge often leads researchers down unexpected and unconventional paths. As curious minds, we have a knack for uncovering correlations in the most unlikely of places. In this pursuit of intellectual enlightenment, we found ourselves delving into the captivating realm of atmospheric intricacies and retail satisfaction. If knowledge is power, then we are determined to wield the mighty sword of insight, even if it leads us through the smog.
It is often said that truth is stranger than fiction, and our investigation into the connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with Dillard's certainly attests to that maxim. Armed with data and a healthy dose of skepticism, we embarked on this inquiry with equal parts enthusiasm and trepidation. After all, who would have thought that the fumes of industrial activity could have a measurable impact on the delight of Dillard's patrons? It's almost as surprising as finding a sale on a sunny day – a rare occurrence indeed!
As researchers, we are no strangers to combing through volumes of data in pursuit of meaningful relationships. So, when we stumbled upon evidence that air pollution levels and customer satisfaction scores were intertwined, we couldn't help but let out an exasperated "air-yah kidding me?" And while we approached our analysis with the utmost scientific rigor, we also couldn't resist the occasional "hot air" pun to lighten the mood. It's important to "vent" a little, even in the midst of scholarly pursuits.
Our study, while grounded in empirical evidence and statistical analysis, also opened a window of opportunity for whimsical contemplation. We pondered whether the shoppers, while perusing the latest fashions, were unwittingly taking in more than just the scent of new fabric. Perhaps they were unknowingly inhaling the essence of Iron Mountain's airborne particles, leaving them yearning for oxygen amidst the perfume section. It's enough to make one quip, "Looks like someone left an 'iron' in the air."
As we embarked on this scholarly expedition, we couldn't help but be swept up in the paradox of it all – the irony, if you will. Who would have thought that the intangible tendrils of pollution could intertwine with the tangible fabric of retail experiences? It's as perplexing as finding a pair of mismatched socks in the laundry – a twist of fate that demands careful examination.
In the pursuit of understanding, we must be willing to explore the uncharted territories of knowledge, even if it means navigating through the unlikeliest of landscapes. With our findings, we hope to shed light on the interplay between environmental factors and consumer satisfaction, offering a breath of fresh air in the realm of retail research. And who knows, perhaps our study will pave the way for a new era of pun-infused academic inquiry – a field ripe for exploration and dad jokes galore.

[[RESULTS]]
The results of our analysis revealed a robust negative correlation between air pollution levels in Iron Mountain, Michigan, and customer satisfaction with Dillard's. The correlation coefficient of -0.7076608 indicated a strong inverse relationship between these two variables, showcasing a surprising link between environmental quality and retail patron contentment. It's almost as if the smog was casting a shadow on the shoppers' bliss – a cloud of dissatisfaction, if you will.
The r-squared value of 0.5007838 further corroborated the strength of this relationship, suggesting that a whopping 50% of the variation in customer satisfaction scores could be attributed to changes in air pollution levels. It's a statistic that speaks volumes – even above the hum of industrial machinery.
Our p-value of less than 0.01 underscored the significance of these findings, indicating that the observed correlation was not merely a fluke but a substantial and noteworthy phenomenon. It seems that the impact of air pollution on consumer satisfaction was a breath of fresh air in the world of retail research – a discovery that left us gasping for more insights.
Furthermore, the scatterplot depicted in Fig. 1 offers a visual representation of the startling correlation between air pollution and customer satisfaction. The data points align in a manner that brings to mind the sweeping gusts of air pollution exerting an unseen force on the shoppers' delight – a gust of discouragement in an otherwise breezy shopping experience.
As we unpacked these results, we couldn't help but marvel at the unexpected confluence of distant variables, which offered a fascinating portrayal of the interplay between atmospheric conditions and consumer contentment. It's a tale of environmental influence that breathes new life into the study of retail dynamics – a narrative where the air pollution becomes as much a part of the shopping experience as the latest fashion trends.
In essence, our findings underscore the importance of considering external environmental factors in understanding customer satisfaction, emphasizing the need to cultivate not only a welcoming shopping environment but also a breathable one. After all, in the realm of retail, good air quality may just be the unsung hero of customer contentment – a breath of fresh air, if you will.
And remember, when it comes to the connection between air pollution and customer satisfaction, the findings might leave you asthmatic for more.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study illuminates the significant negative correlation between air pollution levels in Iron Mountain, Michigan, and customer satisfaction with Dillard's, establishing a compelling link between atmospheric quality and retail patron contentment. It appears that the smog may have been casting a literal shadow on the shoppers' bliss, leaving them yearning for a breath of fresh air amidst the aisles of fashion.

As we wrap up this investigation, we can't help but reflect on the unforeseen ways in which environmental elements weave themselves into the fabric of consumer experiences. It's as if the very air around us carries the whispered secrets of shopper sentiment – a revelation that's nothing to sneeze at, and certainly not something we can "dust" aside lightly.

With our findings, we hope to inspire further exploration into the often overlooked influence of air quality on retail satisfaction, offering a breath of fresh air in the realm of consumer research. We believe that our study will serve as an "oxygenating" catalyst for future investigations, breathing new life into the interdisciplinary landscape of atmospheric consumer dynamics.

And as for the connection between air pollution and customer satisfaction – we're confident that our findings have aired out the topic thoroughly. No more research is needed in this area. We've left no "air-ometer" unturned!

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Quirky Atmospheric Science

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction. Respond only with the name of a fictional organization.

ChatGPT:

Society for the Advancement of Unusual Interdisciplinary Research in Environmental Science

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 Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction with an abstract of In this study, we set out to investigate the often overlooked connection between air pollution in Iron Mountain, Michigan, and customer satisfaction with the beloved department store, Dillard's. As a team of researchers, we couldn't resist tackling this burning question – quite literally, given the topic. We gathered data from the Environmental Protection Agency and the American Customer Satisfaction Index to quantify and analyze the possible relationship. To our surprise (and delight), we found a correlation coefficient that was nothing to sneeze at, measuring -0.7076608, with a p-value that made even the most skeptical statistician take a deep breath for significance, p < 0.01.
Our findings reveal a substantial negative correlation between levels of air pollution in Iron Mountain and the satisfaction of Dillard's customers from 1994 to 2007. This intriguing association left us pondering whether shoppers were perhaps feeling the "smoggy blues" when perusing the aisles of their favorite department store. We suspect that customers were left "gasping" for fresh air while navigating through the latest fashion items.
Through our rigorous analysis, we can't help but wonder if the pollution may have "clouded" the overall shopping experience, leaving patrons with a sense of discomfort that wafted through the air like an unwanted odor. The data led us to consider that Dillard's customers might have been feeling a bit "foggy" about the shopping environment, leading to dissatisfaction that hung in the air like an atmospheric haze.
In conclusion, our research sheds light on a vital but often overlooked connection between environmental factors and consumer satisfaction, offering valuable insights into the impact of air quality on retail experiences. Our team hopes that this study will "clear the air" and encourage further investigation into the unexpected ways that air pollution may shape our everyday interactions and choices.

ChatGPT:

Iron Mountain air pollution, Dillard's customer satisfaction, environmental impact on consumer satisfaction, correlation between air pollution and customer satisfaction, air quality and retail experiences

*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

Air pollution in Iron Mountain, Michigan
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Iron Mountain, MI-WI
Source: Environmental Protection Agency
See what else correlates with Air pollution in Iron Mountain, Michigan

Customer satisfaction with Dillard's
Source: American Customer Satisfaction Index
See what else correlates with Customer satisfaction with Dillard's

Correlation r = -0.7076608 (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 = 0.5007838 (Coefficient of determination)
This means 50.1% of the change in the one variable (i.e., Customer satisfaction with Dillard's) is predictable based on the change in the other (i.e., Air pollution in Iron Mountain, Michigan) over the 14 years from 1994 through 2007.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0046.^Show 0.0046349230318199430000000000
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 -0.71 in 0.46% of random cases. Said differently, if you correlated 216 random variables^Note Which I absolutely did.
with the same 13 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 13 because we have two variables measured over a period of 14 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.

[ -0.9, -0.28 ] 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.

	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007
Air pollution in Iron Mountain, Michigan (Bad air quality days)	0.00485437	0.0110497	0.0171429	0.0124224	0.0326087	0.0549451	0.0115607	0.0391061	0.010989	0.00546448	0.00549451	0.0153846	0.00403226	0.0144231
Customer satisfaction with Dillard's (Consumer Satisfaction)	75	74	74	73	71	68	72	75	75	75	77	76	75	76

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.
Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis.

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([0.00485437,0.0110497,0.0171429,0.0124224,0.0326087,0.0549451,0.0115607,0.0391061,0.010989,0.00546448,0.00549451,0.0153846,0.00403226,0.0144231,])
array_2 = np.array([75,74,74,73,71,68,72,75,75,75,77,76,75,76,])
array_1_name = "Air pollution in Iron Mountain, Michigan"
array_2_name = "Customer satisfaction with Dillard's"

# 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 Iron Mountain, Michigan
Line chart for only Customer satisfaction with Dillard's
AI-generated correlation image
The spurious research paper: The Iron-y of Air Pollution: An Examination of Its Link with Dillard's Customer Satisfaction

View another random correlation

How fun was this correlation?

Your correlation rating is out of this world!

Correlation ID: 3120 · Black Variable ID: 21741 · Red Variable ID: 19845


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