Spurious correlation #2,494 · 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 Salinas, California and the second variable is Customer satisfaction with American Airlines. The chart goes from 1994 to 2021, 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 Salinas, California and the second variable is Customer satisfaction with American Airlines. The chart goes from 1994 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As air pollution in Salinas, California increased, it led to a rise in atmospheric turbulence. This inadvertently provided American Airlines passengers with an unexpected rollercoaster-like experience, leaving them pleasantly surprised and more satisfied with their flights. You could say the smog in the air really lifted their moods! This is one instance where pollution oddly cleared the way for happier travelers.

^{Show image generation prompt} Model: dalle-3
Prompt: "Generate an image of a cyberpunk-inspired airport check-in scene in Salinas, California. The thick smog creates a hazy illusion of efficiency and prompt service at American Airlines check-in counters. Passengers navigate through the obscured lines with unexpected ease, leading to some accidentally boarding the wrong flights. Show surprising upgrades and empty middle seats in an oddly positive experience with the airline." 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 Salinas, California and Customer satisfaction with American Airlines. 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: Celtic art Magic realism Oragami Data cyber art Psychedelic
ChatGPT: Data cyber art

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 Salinas, California and Customer satisfaction with American Airlines, in the following situation: The smog actually obscured the view of the long lines at the airport, creating the illusion of efficiency and prompt service. As a result, passengers were pleasantly surprised by the perceived improvement in American Airlines' performance. The decreased visibility also caused passengers to accidentally board the wrong flights, resulting in unexpected upgrades and empty middle seats, leading to a bizarrely positive experience with the airline.. Keep the description simple.
ChatGPT: In Salinas, California, thick smog blankets the airport, creating a hazy illusion of efficiency and prompt service at American Airlines check-in counters. Passengers, pleasantly surprised by the perceived improvement, navigate through the obscured lines with unexpected ease. The reduced visibility causes some to accidentally board the wrong flights, resulting in surprising upgrades and empty middle seats, leading to an oddly positive experience with the airline.

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: [ Data cyber art ] and depicting this scene: In Salinas, California, thick smog blankets the airport, creating a hazy illusion of efficiency and prompt service at American Airlines check-in counters. Passengers, pleasantly surprised by the perceived improvement, navigate through the obscured lines with unexpected ease. The reduced visibility causes some to accidentally board the wrong flights, resulting in surprising upgrades and empty middle seats, leading to an oddly positive experience with the airline. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: "Generate an image of a cyberpunk-inspired airport check-in scene in Salinas, California. The thick smog creates a hazy illusion of efficiency and prompt service at American Airlines check-in counters. Passengers navigate through the obscured lines with unexpected ease, leading to some accidentally boarding the wrong flights. Show surprising upgrades and empty middle seats in an oddly positive experience with the airline."

*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 an increase in Air pollution in Salinas, California caused Customer satisfaction with American Airlines to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

The Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines

Journal of Airborne Customer Experience Research

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 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 Air pollution in Salinas, California and Customer satisfaction with American Airlines. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"The Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines"

[[ABSTRACT]]
This research paper examines the curious correlation between air pollution levels in Salinas, California and customer satisfaction with American Airlines. Employing data from the Environmental Protection Agency and the American Customer Satisfaction Index from 1994 to 2021, our study delves into the complex interaction between environmental factors and consumer perceptions. The findings revealed a correlation coefficient of 0.6575377 and a p-value less than 0.01, suggesting a robust statistical relationship. This unexpected connection between the air quality in Salinas and customer opinions about American Airlines highlights the intricate web of influences that shape consumer attitudes. As we unravel this intriguing relationship, we offer fresh insight into the interplay of environmental conditions and customer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As we soar into the realms of research and statistics, we are often reminded that the world is full of unexpected connections and correlations. One might not expect a link between the air pollution levels in Salinas, California, and the satisfaction levels of customers flying with American Airlines. However, as we embark on this scientific journey, we will unravel the smoggy connection that ties these seemingly disparate variables together.
In the world of research, one must be ready to navigate through the fog of data, sift through the haze of statistical noise, and emerge with clarity and insight. Our endeavor into understanding the relationship between air quality and customer satisfaction is no different. We have meticulously collected and analyzed data from the Environmental Protection Agency and the American Customer Satisfaction Index, spanning nearly three decades. Through our rigorous examination, we have uncovered a correlation coefficient of 0.6575377 and a p-value of less than 0.01, indicating a robust and significant statistical relationship.
The smoggy tales of Salinas and the skies of American Airlines may seem like an odd pairing, like a mismatched pair of socks. Nonetheless, our findings point to a symbiotic association between the environment and consumer perceptions. These unexpected twists in the realm of air travel and environmental quality beckon for closer scrutiny, inviting us to explore the unforeseen interplay of factors that shape customer experiences. As we embark on this investigation, we invite you to join us in uncovering the peculiar ties between air pollution and the customer journey with a touch of dry humor and a dash of scientific whimsy.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that air pollution can have detrimental effects on respiratory health and overall well-being. Similarly, "Doe and Jones" suggest that exposure to high levels of air pollutants may lead to increased rates of asthma and other respiratory illnesses among the local population. These serious and scholarly studies lay a strong foundation for understanding the potential health implications of air pollution in urban areas such as Salinas, California.

However, as we dig deeper into the literature, we encounter some unexpected and lighthearted connections. In "How to Travel the World on $50 a Day" by Matt Kepnes, the author shares anecdotes of navigating through smog-filled cities and the surprising impact of air quality on his travel experiences. While not a traditional academic source, Kepnes' travel insights provide a fresh perspective on the intersection of air pollution and travel satisfaction. Additionally, in "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, the author humorously mulls over the idea of cosmic smog and its potential influence on intergalactic travel - a whimsical take on the broader concept of atmospheric conditions and transportation experiences.

Furthermore, social media platforms offer glimpses of public perceptions related to air travel and air quality. An Instagram post by @WanderlustAdventurer muses on the smoggy landscapes encountered during a layover in Salinas, juxtaposing the beauty of the region with concerns about air pollution. Meanwhile, a tweet from @JetsetterJane humorously laments the idea of "navigating through smog like a detective in a noir film" while waiting for a delayed American Airlines flight. Though not traditional academic sources, these social media snippets provide intriguing glimpses into the public discourse surrounding air travel and environmental conditions.

As we progress through our exploration of the air pollution-airline satisfaction nexus, these unexpected and humorous connections prompt us to approach our research with a blend of scholarly rigor and a lighthearted spirit.

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 Air pollution in Salinas, California and Customer satisfaction with American Airlines. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 2021

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines"

[[ABSTRACT]]
This research paper examines the curious correlation between air pollution levels in Salinas, California and customer satisfaction with American Airlines. Employing data from the Environmental Protection Agency and the American Customer Satisfaction Index from 1994 to 2021, our study delves into the complex interaction between environmental factors and consumer perceptions. The findings revealed a correlation coefficient of 0.6575377 and a p-value less than 0.01, suggesting a robust statistical relationship. This unexpected connection between the air quality in Salinas and customer opinions about American Airlines highlights the intricate web of influences that shape consumer attitudes. As we unravel this intriguing relationship, we offer fresh insight into the interplay of environmental conditions and customer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

[[INTRODUCTION]]
As we soar into the realms of research and statistics, we are often reminded that the world is full of unexpected connections and correlations. One might not expect a link between the air pollution levels in Salinas, California, and the satisfaction levels of customers flying with American Airlines. However, as we embark on this scientific journey, we will unravel the smoggy connection that ties these seemingly disparate variables together.
In the world of research, one must be ready to navigate through the fog of data, sift through the haze of statistical noise, and emerge with clarity and insight. Our endeavor into understanding the relationship between air quality and customer satisfaction is no different. We have meticulously collected and analyzed data from the Environmental Protection Agency and the American Customer Satisfaction Index, spanning nearly three decades. Through our rigorous examination, we have uncovered a correlation coefficient of 0.6575377 and a p-value of less than 0.01, indicating a robust and significant statistical relationship.
The smoggy tales of Salinas and the skies of American Airlines may seem like an odd pairing, like a mismatched pair of socks. Nonetheless, our findings point to a symbiotic association between the environment and consumer perceptions. These unexpected twists in the realm of air travel and environmental quality beckon for closer scrutiny, inviting us to explore the unforeseen interplay of factors that shape customer experiences. As we embark on this investigation, we invite you to join us in uncovering the peculiar ties between air pollution and the customer journey with a touch of dry humor and a dash of scientific whimsy.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved the collection and analysis of data from the Environmental Protection Agency (EPA) and the American Customer Satisfaction Index (ACSI) to investigate the connection between air pollution in Salinas, California, and customer satisfaction with American Airlines. The data utilized encompassed the period from 1994 to 2021, offering a comprehensive overview of nearly three decades of environmental and consumer trends.

To capture the atmospheric conditions in Salinas, various air quality parameters, including particulate matter, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide, were extracted from the EPA's air quality monitoring stations across the region. These data were then subjected to stringent quality control measures to ensure their accuracy and reliability, akin to separating the wheat from the chaff of environmental data.

Simultaneously, customer satisfaction ratings pertaining to American Airlines were obtained from the ACSI, covering a broad spectrum of dimensions such as booking process, in-flight service, seat comfort, and overall experience. The ratings were carefully scrutinized to discern any patterns or trends that might be associated with the local air quality in Salinas, the gateway to this meta-analysis of smog and satisfaction.

The collection of such diverse and extensive datasets required a multifaceted approach to data management – akin to balancing beakers and test tubes in a scientific laboratory. These datasets were then subjected to rigorous statistical analyses, including correlation and regression techniques, to identify any potential relationships and quantify the strength of the association between air pollution levels and customer satisfaction – in essence, refining the raw elements of data into the gold of scientific insight.

The statistical analyses were further complemented by advanced econometric modeling, utilizing time series and panel data methods to account for the temporal and spatial dimensions inherent to both air quality and customer satisfaction measures. These modeling techniques provided a deeper understanding of the dynamic interplay between the two variables, akin to unraveling the intricate dance of air currents and consumer preferences.

Furthermore, to capture the nuanced effects of air pollution on customer satisfaction, various sub-analyses were conducted, stratifying the data based on different seasons, flight routes, and customer demographics – akin to dissecting the layers of an onion to reveal the hidden flavors within.

Finally, the study incorporated robust sensitivity analyses to assess the stability of the findings and identify potential sources of bias or confounding factors. These sensitivity analyses served as a reliable litmus test for the validity and robustness of the observed connection between air pollution in Salinas and customer satisfaction with American Airlines, akin to stress-testing a scientific hypothesis under various conditions.

In essence, the methodology adopted in this study combined the rigor of statistical analysis with the art of data interpretation, offering a comprehensive and nuanced exploration of the smoggy connection between environmental quality and consumer perceptions.

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 Air pollution in Salinas, California and Customer satisfaction with American Airlines. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines"

[[ABSTRACT]]
This research paper examines the curious correlation between air pollution levels in Salinas, California and customer satisfaction with American Airlines. Employing data from the Environmental Protection Agency and the American Customer Satisfaction Index from 1994 to 2021, our study delves into the complex interaction between environmental factors and consumer perceptions. The findings revealed a correlation coefficient of 0.6575377 and a p-value less than 0.01, suggesting a robust statistical relationship. This unexpected connection between the air quality in Salinas and customer opinions about American Airlines highlights the intricate web of influences that shape consumer attitudes. As we unravel this intriguing relationship, we offer fresh insight into the interplay of environmental conditions and customer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that air pollution can have detrimental effects on respiratory health and overall well-being. Similarly, "Doe and Jones" suggest that exposure to high levels of air pollutants may lead to increased rates of asthma and other respiratory illnesses among the local population. These serious and scholarly studies lay a strong foundation for understanding the potential health implications of air pollution in urban areas such as Salinas, California.
However, as we dig deeper into the literature, we encounter some unexpected and lighthearted connections. In "How to Travel the World on $50 a Day" by Matt Kepnes, the author shares anecdotes of navigating through smog-filled cities and the surprising impact of air quality on his travel experiences. While not a traditional academic source, Kepnes' travel insights provide a fresh perspective on the intersection of air pollution and travel satisfaction. Additionally, in "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, the author humorously mulls over the idea of cosmic smog and its potential influence on intergalactic travel - a whimsical take on the broader concept of atmospheric conditions and transportation experiences.
Furthermore, social media platforms offer glimpses of public perceptions related to air travel and air quality. An Instagram post by @WanderlustAdventurer muses on the smoggy landscapes encountered during a layover in Salinas, juxtaposing the beauty of the region with concerns about air pollution. Meanwhile, a tweet from @JetsetterJane humorously laments the idea of "navigating through smog like a detective in a noir film" while waiting for a delayed American Airlines flight. Though not traditional academic sources, these social media snippets provide intriguing glimpses into the public discourse surrounding air travel and environmental conditions.
As we progress through our exploration of the air pollution-airline satisfaction nexus, these unexpected and humorous connections prompt us to approach our research with a blend of scholarly rigor and a lighthearted spirit.

[[RESULTS]]
Upon careful analysis of the data collected from the Environmental Protection Agency and the American Customer Satisfaction Index, a rather unexpected and remarkable correlation emerged. The correlation coefficient between air pollution levels in Salinas, California, and customer satisfaction with American Airlines was found to be 0.6575377. This finding suggests a moderately strong positive relationship between these two variables, demonstrating that as air pollution levels increased, so did the customer satisfaction with American Airlines.
Furthermore, the r-squared value of 0.4323559 indicates that approximately 43.24% of the variability in customer satisfaction with American Airlines can be explained by the variations in air pollution levels in Salinas, California. In other words, the smoggy conditions seem to play a substantial role in shaping the perceptions of airline travelers.
The p-value of less than 0.01 highlights the statistical significance of this relationship, indicating that the likelihood of observing such a strong association between air pollution and customer satisfaction purely by chance is minimal. This compelling statistical evidence further underscores the intriguing nature of the smoggy connection we have unraveled.
To visually depict this unexpected correlation, a scatterplot (Fig. 1) has been included, showcasing the strong relationship between air pollution levels in Salinas, California, and customer satisfaction with American Airlines. The figure vividly captures the remarkable intertwining of these two seemingly disparate variables, reminding us of the whimsical and wondrous twists that can be uncovered in the realm of research and statistics.
In summary, our findings illuminate the peculiar relationship between environmental conditions and customer perceptions in the context of air travel. The unexpected twists and turns of this investigation serve as a reminder of the captivating complexities waiting to be unveiled in the world of scientific inquiry and statistical analysis.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study offer compelling support for the idea that air pollution levels in Salinas, California have an intriguing and significant connection to customer satisfaction with American Airlines. This unexpected link between smoggy air and travel contentment serves as a vivid reminder of the delightful and whimsical nature of research inquiries. Our findings, with a correlation coefficient of 0.6575377 and a p-value of less than 0.01, provide a robust basis for further exploring the intersection of environmental factors and consumer experiences.

Building upon the existing literature, including the scholarly works by "Smith et al." and "Doe and Jones" that highlight the detrimental effects of air pollution on respiratory health, our study adds a lighthearted twist to the discussion. Drawing on unexpected sources such as "How to Travel the World on $50 a Day" by Matt Kepnes and "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, we expand the scholarly discourse to include the humorous and whimsical aspects of air quality and its influence on travel satisfaction. This broader perspective encourages us to approach our research with a blend of scholarly rigor and a lighthearted spirit, allowing for a more comprehensive understanding of the complexities involved.

As previously suggested in the literature review, the unexpected and humorous connections unearthed in our study prompt us to consider the broader implications of our findings with a sense of curiosity and wonder. The correlation coefficient, r-squared value, and p-value all point to a significant and meaningful relationship between air pollution in Salinas and the perceptions of American Airlines customers. This compelling statistical evidence illustrates the captivating complexities waiting to be unveiled in the world of scientific inquiry and statistical analysis, reminding us of the intriguing and often whimsical twists that can emerge from rigorous research.

In conclusion, the unexpected connection between air pollution in Salinas, California and customer satisfaction with American Airlines reinforces the intricate web of influences that shape consumer attitudes. As we continue to unravel the smoggy connection between environmental conditions and travel contentment, we are reminded of the wondrous twists that can be uncovered in the realm of research and statistics. This study stands as a testament to the delightful and often whimsical nature of scientific inquiry, offering fresh insight into the interplay of environmental factors and consumer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

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 Air pollution in Salinas, California and Customer satisfaction with American Airlines. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines"

[[ABSTRACT]]
This research paper examines the curious correlation between air pollution levels in Salinas, California and customer satisfaction with American Airlines. Employing data from the Environmental Protection Agency and the American Customer Satisfaction Index from 1994 to 2021, our study delves into the complex interaction between environmental factors and consumer perceptions. The findings revealed a correlation coefficient of 0.6575377 and a p-value less than 0.01, suggesting a robust statistical relationship. This unexpected connection between the air quality in Salinas and customer opinions about American Airlines highlights the intricate web of influences that shape consumer attitudes. As we unravel this intriguing relationship, we offer fresh insight into the interplay of environmental conditions and customer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

[[INTRDUCTION]]
As we soar into the realms of research and statistics, we are often reminded that the world is full of unexpected connections and correlations. One might not expect a link between the air pollution levels in Salinas, California, and the satisfaction levels of customers flying with American Airlines. However, as we embark on this scientific journey, we will unravel the smoggy connection that ties these seemingly disparate variables together.
In the world of research, one must be ready to navigate through the fog of data, sift through the haze of statistical noise, and emerge with clarity and insight. Our endeavor into understanding the relationship between air quality and customer satisfaction is no different. We have meticulously collected and analyzed data from the Environmental Protection Agency and the American Customer Satisfaction Index, spanning nearly three decades. Through our rigorous examination, we have uncovered a correlation coefficient of 0.6575377 and a p-value of less than 0.01, indicating a robust and significant statistical relationship.
The smoggy tales of Salinas and the skies of American Airlines may seem like an odd pairing, like a mismatched pair of socks. Nonetheless, our findings point to a symbiotic association between the environment and consumer perceptions. These unexpected twists in the realm of air travel and environmental quality beckon for closer scrutiny, inviting us to explore the unforeseen interplay of factors that shape customer experiences. As we embark on this investigation, we invite you to join us in uncovering the peculiar ties between air pollution and the customer journey with a touch of dry humor and a dash of scientific whimsy.

[[RESULTS]]
Upon careful analysis of the data collected from the Environmental Protection Agency and the American Customer Satisfaction Index, a rather unexpected and remarkable correlation emerged. The correlation coefficient between air pollution levels in Salinas, California, and customer satisfaction with American Airlines was found to be 0.6575377. This finding suggests a moderately strong positive relationship between these two variables, demonstrating that as air pollution levels increased, so did the customer satisfaction with American Airlines.
Furthermore, the r-squared value of 0.4323559 indicates that approximately 43.24% of the variability in customer satisfaction with American Airlines can be explained by the variations in air pollution levels in Salinas, California. In other words, the smoggy conditions seem to play a substantial role in shaping the perceptions of airline travelers.
The p-value of less than 0.01 highlights the statistical significance of this relationship, indicating that the likelihood of observing such a strong association between air pollution and customer satisfaction purely by chance is minimal. This compelling statistical evidence further underscores the intriguing nature of the smoggy connection we have unraveled.
To visually depict this unexpected correlation, a scatterplot (Fig. 1) has been included, showcasing the strong relationship between air pollution levels in Salinas, California, and customer satisfaction with American Airlines. The figure vividly captures the remarkable intertwining of these two seemingly disparate variables, reminding us of the whimsical and wondrous twists that can be uncovered in the realm of research and statistics.
In summary, our findings illuminate the peculiar relationship between environmental conditions and customer perceptions in the context of air travel. The unexpected twists and turns of this investigation serve as a reminder of the captivating complexities waiting to be unveiled in the world of scientific inquiry and statistical analysis.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the smoggy connection between air pollution in Salinas, California, and customer satisfaction with American Airlines has unraveled an unexpected and intriguing relationship. The statistically significant correlation coefficient of 0.6575377 showcases the remarkable intertwining of these variables, much like the unexpected fusion of peanut butter and jelly. Our findings suggest that approximately 43.24% of the variability in customer satisfaction with American Airlines can be explained by the variations in air pollution levels in Salinas, reminding us that sometimes, things are not just up in the air.

The p-value of less than 0.01 further solidifies the robustness of this association, indicating that the likelihood of observing such a strong relationship purely by chance is as rare as finding a statistical unicorn. Our scatterplot vividly captures the whimsical dance of air pollution and customer satisfaction, painting a picture worth a thousand p-values.

Through this research, we have illuminated the curious interplay of environmental conditions and consumer perceptions, inviting us to ponder the smoggy tales that whisper through the winds of statistical significance. As we bask in the glow of these unexpected findings, we are reminded that in the world of research, just like in a foggy morning, clarity can emerge from the most unexpected places.

With this, we boldly assert that no additional research is needed to further explore the smoggy connection between air pollution in Salinas, California, and customer satisfaction with American Airlines. We leave this field of study with a satisfied chuckle and a statistical wink, knowing that even the most unexpected pairings can reveal insightful relationships in the realm of research and statistics.

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Airborne Customer Experience Research

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Air Quality and Consumer Experience Research

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 Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines with an abstract of This research paper examines the curious correlation between air pollution levels in Salinas, California and customer satisfaction with American Airlines. Employing data from the Environmental Protection Agency and the American Customer Satisfaction Index from 1994 to 2021, our study delves into the complex interaction between environmental factors and consumer perceptions. The findings revealed a correlation coefficient of 0.6575377 and a p-value less than 0.01, suggesting a robust statistical relationship. This unexpected connection between the air quality in Salinas and customer opinions about American Airlines highlights the intricate web of influences that shape consumer attitudes. As we unravel this intriguing relationship, we offer fresh insight into the interplay of environmental conditions and customer experiences, while also exploring the unexpected twists of air travels and smoggy tales.

ChatGPT:

Air pollution, Salinas California, customer satisfaction, American Airlines, correlation, environmental factors, consumer perceptions, EPA data, American Customer Satisfaction Index, statistical relationship, consumer attitudes, air quality, customer opinions, influences, interplay, environmental conditions, customer experiences, air travel, smoggy tales

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

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Data details

Air pollution in Salinas, California
Detailed data title: Percentage of days with moderate or worse air quality in Salinas, CA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Salinas, California

Customer satisfaction with American Airlines
Source: American Customer Satisfaction Index
See what else correlates with Customer satisfaction with American Airlines

Correlation r = 0.6575377 (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 = 0.4323559 (Coefficient of determination)
This means 43.2% of the change in the one variable (i.e., Customer satisfaction with American Airlines) is predictable based on the change in the other (i.e., Air pollution in Salinas, California) over the 28 years from 1994 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00014.^Show 0.0001434936023806970000000000
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 0.66 in 0.014% of random cases. Said differently, if you correlated 6,969 random variables^Note Which I absolutely did.
with the same 27 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 27 because we have two variables measured over a period of 28 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.

[ 0.38, 0.83 ] 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	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Salinas, California (Bad air quality days)	3.83562	7.12329	14.4809	4.65753	6.30137	13.9726	7.37705	6.0274	10.137	8.76712	6.55738	3.83562	5.47945	4.10959	7.37705	2.73973	7.94521	8.21918	9.28962	12.6027	7.67123	9.31507	11.2022	16.7123	12.6027	9.58904	18.306	12.0548
Customer satisfaction with American Airlines (Consumer Satisfaction)	70	71	71	62	67	64	63	62	63	67	66	64	62	60	62	60	63	63	64	65	66	66	72	76	74	73	74	75

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([3.83562,7.12329,14.4809,4.65753,6.30137,13.9726,7.37705,6.0274,10.137,8.76712,6.55738,3.83562,5.47945,4.10959,7.37705,2.73973,7.94521,8.21918,9.28962,12.6027,7.67123,9.31507,11.2022,16.7123,12.6027,9.58904,18.306,12.0548,])
array_2 = np.array([70,71,71,62,67,64,63,62,63,67,66,64,62,60,62,60,63,63,64,65,66,66,72,76,74,73,74,75,])
array_1_name = "Air pollution in Salinas, California"
array_2_name = "Customer satisfaction with American Airlines"

# 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 Salinas, California
Line chart for only Customer satisfaction with American Airlines
AI-generated correlation image
The spurious research paper: The Smoggy Connection: Unraveling the Relationship Between Air Pollution in Salinas, California and Customer Satisfaction with American Airlines

View another random correlation

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Correlation ID: 2494 · Black Variable ID: 21190 · Red Variable ID: 19834


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