Spurious correlation #4,940 · 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 Visalia, California and the second variable is The number of telemarketers in California. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

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

Download png, svg

AI explanation

As air pollution decreased in Visalia, California, the local plant life flourished. This led to an overabundance of wild shrubbery and foliage, which, coincidentally, happened to be the preferred habitat of the elusive telemarketerus annoyus species. The flourishing greenery attracted these telemarketers away from their phones and into the lush, natural call centers, where they could be heard in the distance discussing the latest in leafy opportunities. It seems that for these telemarketers, the grass was literally greener on the other side of the line. As a result, their numbers dwindled in the man-made urban jungle of California's telemarketing industry.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a hyper-realistic scene depicting the thriving foliage and wild shrubbery in Visalia, California. Show the telemarketerus annoyus species mingling amongst the lush greenery, abandoning their phones. Let the abundance of nature and life in the scene contrast with the diminished presence of telemarketers in the urban jungle. Emphasize the vibrant and flourishing plant life, and the camaraderie of the telemarketerus annoyus species in their natural habitat. 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 Visalia, California and The number of telemarketers in California. 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: Futuristic Hyper-realistic Minimalism Concept art An image that looks like it is a scene from a Pixar movie
ChatGPT: Hyper-realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Visalia, California and The number of telemarketers in California, in the following situation: As air pollution decreased in Visalia, California, the local plant life flourished. This led to an overabundance of wild shrubbery and foliage, which, coincidentally, happened to be the preferred habitat of the elusive telemarketerus annoyus species. The flourishing greenery attracted these telemarketers away from their phones and into the lush, natural call centers, where they could be heard in the distance discussing the latest in leafy opportunities. It seems that for these telemarketers, the grass was literally greener on the other side of the line. As a result, their numbers dwindled in the man-made urban jungle of California's telemarketing industry.. Keep the description simple.
ChatGPT: The air in Visalia, California cleared, allowing plants to thrive. As a result, wild shrubbery and foliage flourished, attracting the telemarketerus annoyus species. They abandoned their phones, gathering in the lush greenery, discussing leafy opportunities. Meanwhile, the number of telemarketers dwindled in California's urban jungle.

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: [ Hyper-realistic ] and depicting this scene: The air in Visalia, California cleared, allowing plants to thrive. As a result, wild shrubbery and foliage flourished, attracting the telemarketerus annoyus species. They abandoned their phones, gathering in the lush greenery, discussing leafy opportunities. Meanwhile, the number of telemarketers dwindled in California's urban jungle. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a hyper-realistic scene depicting the thriving foliage and wild shrubbery in Visalia, California. Show the telemarketerus annoyus species mingling amongst the lush greenery, abandoning their phones. Let the abundance of nature and life in the scene contrast with the diminished presence of telemarketers in the urban jungle. Emphasize the vibrant and flourishing plant life, and the camaraderie of the telemarketerus annoyus species in their natural habitat.

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

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

AI academic paper

(Because p < 0.01)

The Visalia, California Air May Be a Telemarketer's Fare

The Journal of Quirky Phenomena

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 Visalia, California and The number of telemarketers in California. 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 name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
The Visalia, California Air May Be a Telemarketer's Fare

[[ABSTRACT]]
It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.

Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.

As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the whimsical world of research, where correlations often seem as elusive as Bigfoot sightings, we embark on an expedition into the unexpected realm of air pollution and telemarketers. As the saying goes, "When life gives you lemons, make lemonade," and in our case, when life gives you air pollution, make a correlation with telemarketers.
It's a well-documented fact that telemarketers have the uncanny ability to pop up at the most inconvenient times, like dandelions in an otherwise well-kept lawn. Meanwhile, air pollution has long been known to cloud our skies and befoul our senses. What could possibly be the link between these two seemingly unrelated phenomena, you ask? Well, hold onto your hats (or headsets), because we're about to dive into a world where statistical analyses and uncanny connections collide.
We chose the delightful city of Visalia, California, as our focal point for air pollution, a place where the air quality doesn't always make you want to skip down the street singing like a scene from a musical. Meanwhile, we set our sights on the telemarketing employment figures in the Golden State, where individuals are often left wondering if their phone is ringing or if it's just their imagination.
Armed with data from the Environmental Protection Agency and the Bureau of Labor Statistics, we undertook a journey into the world of correlation coefficients and p-values, hoping to shed light on the mysterious dance between air pollution and telemarketing employment. It's a bit like conducting a scientific séance, summoning the spirits of statistical significance to guide us through the murky waters of correlation.
As we unveil our findings, we invite you to join us on this quirky odyssey, where the unexpected blend of air pollution and telemarketers paints a picture more colorful than a rainbow on a Monet canvas. So, buckle up and prepare to be tantalized by our surprising results, because in the world of research, sometimes the most unlikely connections turn out to be the most captivating.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In their comprehensive study, Smith and Doe (2015) uncover the complex interplay between air pollution and employment trends, providing a solid foundation for understanding our current research endeavor. Their rigorous analysis highlights the nuanced dynamics that underlie environmental factors and labor market outcomes, setting the stage for our exploration of the correlation between air pollution in Visalia, California, and the number of telemarketers statewide.

Jones et al. (2017) further contribute to our understanding by delving into the regional impacts of air quality on various industries. Their research elucidates the potential ramifications of environmental conditions on employment opportunities, offering valuable insights into the broader implications of our investigation.

Turning to non-fiction literature relevant to our inquiry, "Air Pollution and Its Economic Impact" by Greenberg (2019) presents a compelling overview of the economic consequences of air pollution, shedding light on the potential influences it may exert on employment patterns. Additionally, "The Art of Telemarketing: Strategies for Success" by Salesman (2018) offers a glimpse into the world of telemarketing, providing a backdrop for understanding the intricacies of this industry.

On the more whimsical side, fictional works such as "The Air Pollution Mysteries" by Cleanwell (2016) and "The Telemarketer's Dilemma" by Dialtone (2014) offer playful narratives that, while not rooted in empirical research, capture the imagination and underscore the cultural significance of our study's themes.

In the realm of television, shows like "Breaking Bad Air" and "The Telemarketing Dead" pique our interest with their dramatic portrayals of air pollution and telemarketing, offering a unique blend of entertainment and potential inspiration for our research. Additionally, "The Office" provides a lighthearted yet insightful portrayal of office dynamics, including the occasional intrusion of telemarketing calls, serving as a reflective backdrop for our exploration of employment trends.

With this eclectic array of literature and media as our backdrop, we embark on our own scholarly escapade, aiming to unearth the unexpected connections between air pollution in Visalia, California, and the proliferation of telemarketers statewide. Brace yourselves for a journey that promises to be as unpredictable as a telemarketer's sales pitch and as enlightening as a clear, pollution-free sky.

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 Visalia, California and The number of telemarketers in California. 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 Bureau of Larbor Statistics . You used data from 2003 to 2022

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Visalia, California Air May Be a Telemarketer's Fare

[[ABSTRACT]]
It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.

Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.

As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

[[INTRODUCTION]]
In the whimsical world of research, where correlations often seem as elusive as Bigfoot sightings, we embark on an expedition into the unexpected realm of air pollution and telemarketers. As the saying goes, "When life gives you lemons, make lemonade," and in our case, when life gives you air pollution, make a correlation with telemarketers.
It's a well-documented fact that telemarketers have the uncanny ability to pop up at the most inconvenient times, like dandelions in an otherwise well-kept lawn. Meanwhile, air pollution has long been known to cloud our skies and befoul our senses. What could possibly be the link between these two seemingly unrelated phenomena, you ask? Well, hold onto your hats (or headsets), because we're about to dive into a world where statistical analyses and uncanny connections collide.
We chose the delightful city of Visalia, California, as our focal point for air pollution, a place where the air quality doesn't always make you want to skip down the street singing like a scene from a musical. Meanwhile, we set our sights on the telemarketing employment figures in the Golden State, where individuals are often left wondering if their phone is ringing or if it's just their imagination.
Armed with data from the Environmental Protection Agency and the Bureau of Labor Statistics, we undertook a journey into the world of correlation coefficients and p-values, hoping to shed light on the mysterious dance between air pollution and telemarketing employment. It's a bit like conducting a scientific séance, summoning the spirits of statistical significance to guide us through the murky waters of correlation.
As we unveil our findings, we invite you to join us on this quirky odyssey, where the unexpected blend of air pollution and telemarketers paints a picture more colorful than a rainbow on a Monet canvas. So, buckle up and prepare to be tantalized by our surprising results, because in the world of research, sometimes the most unlikely connections turn out to be the most captivating.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Now, let's peel back the curtain and take a look at the inner workings of our research methodology, where the scientific rigor meets the whimsical quirkiness of our investigation.

Data Collection: To kick off our grand adventure, we scoured the vast expanse of the internet, navigating through the digital wilderness like intrepid explorers in search of the elusive correlation between air pollution and telemarketers. Our primary sources of data included the Environmental Protection Agency (EPA) for air quality measurements in Visalia, California, and the Bureau of Labor Statistics for telemarketing employment figures across the state. We combed through data spanning from 2003 to 2022, capturing a snapshot of trends over the years like enthusiastic photographers chasing the perfect shot.

Air Pollution Assessment: Armed with the EPA's treasure trove of air quality data, we delved into the atmospheric labyrinth of Visalia, California. We left no particulate matter unturned, meticulously analyzing the levels of pollutants with the precision of a seasoned chef measuring ingredients for a gourmet dish. Our quest for data was akin to a hunt for hidden treasure, only instead of gold doubloons, we were in pursuit of PM2.5 concentrations and ozone levels.

Telemarketing Employment Figures: Venturing into the realm of telemarketing, we turned to the Bureau of Labor Statistics for a glimpse into the world of unsolicited phone calls. We sifted through employment figures with the fervor of archeologists uncovering ancient artifacts, seeking to unravel the mystery of telemarketing trends amidst the ebb and flow of air pollution in Visalia.

Statistical Analysis: With our data hoard in hand, we huddled around our statistical cauldron, conjuring up correlation coefficients and p-values like modern-day alchemists seeking the philosopher's stone of significance. We employed sophisticated analytical tools to unravel the enigmatic relationship between air pollution in Visalia and the prevalence of telemarketers in California, aiming to unveil the hidden threads connecting these seemingly disparate phenomena.

The Intersection of Science and Serendipity: As we navigated the labyrinthine corridors of data, we approached our analysis with a dash of whimsy and a sprinkle of scientific curiosity. Much like intrepid explorers charting unknown territories, we embraced uncertainty with the fervent hope of uncovering unexpected connections that transcend the boundaries of conventional wisdom.

In conclusion, our methodology blends the serious rigor of scientific inquiry with the fanciful spirit of exploration, yielding results that are as captivating as they are statistically sound. So, grab your lab coat and embark on this delightful journey of discovery, where the mundane meets the marvelous in the most unexpected of ways.

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 Visalia, California and The number of telemarketers in California. 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 Bureau of Larbor Statistics .

For the time period 2003 to 2022, you found a correlation 0.8932588, r-squared of 0.7979113, 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 Visalia, California Air May Be a Telemarketer's Fare

[[ABSTRACT]]
It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.

Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.

As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the whimsical world of research, where correlations often seem as elusive as Bigfoot sightings, we embark on an expedition into the unexpected realm of air pollution and telemarketers. As the saying goes, "When life gives you lemons, make lemonade," and in our case, when life gives you air pollution, make a correlation with telemarketers.
It's a well-documented fact that telemarketers have the uncanny ability to pop up at the most inconvenient times, like dandelions in an otherwise well-kept lawn. Meanwhile, air pollution has long been known to cloud our skies and befoul our senses. What could possibly be the link between these two seemingly unrelated phenomena, you ask? Well, hold onto your hats (or headsets), because we're about to dive into a world where statistical analyses and uncanny connections collide.
We chose the delightful city of Visalia, California, as our focal point for air pollution, a place where the air quality doesn't always make you want to skip down the street singing like a scene from a musical. Meanwhile, we set our sights on the telemarketing employment figures in the Golden State, where individuals are often left wondering if their phone is ringing or if it's just their imagination.
Armed with data from the Environmental Protection Agency and the Bureau of Labor Statistics, we undertook a journey into the world of correlation coefficients and p-values, hoping to shed light on the mysterious dance between air pollution and telemarketing employment. It's a bit like conducting a scientific séance, summoning the spirits of statistical significance to guide us through the murky waters of correlation.
As we unveil our findings, we invite you to join us on this quirky odyssey, where the unexpected blend of air pollution and telemarketers paints a picture more colorful than a rainbow on a Monet canvas. So, buckle up and prepare to be tantalized by our surprising results, because in the world of research, sometimes the most unlikely connections turn out to be the most captivating.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis unveiled a surprising correlation between air pollution in Visalia, California, and the number of telemarketers in California. Our statistical analysis yielded a correlation coefficient of 0.8932588, indicating a strong positive relationship between these seemingly unrelated variables. In other words, as the air pollution in Visalia increased, so did the number of telemarketers in the state.

The r-squared value of 0.7979113 further illuminated the robustness of this connection, highlighting that a substantial proportion of the variation in telemarketing employment can be explained by changes in air pollution levels. It's like finding the missing piece of a puzzle—suddenly, the seemingly disparate elements come together to form a coherent picture of environmental influence on workforce trends.

The p-value of less than 0.01 added an exclamation point to our findings, affirming that the observed relationship between air pollution and telemarketing employment is not just a fluke. If statistics were desserts, this p-value would be the cherry on top of a scientifically scrumptious cake, signaling the significance of our results.

To visually convey the strength of the relationship, we present Fig. 1, a scatterplot that showcases the striking pattern of increasing telemarketers as air pollution levels rise. It's as clear as day—well, perhaps a slightly smoggy day in Visalia—how these variables intertwine in a dance of statistical significance.

Our findings open the door to a world of questions and possibilities, inviting further exploration into the intricate web of environmental factors and labor market dynamics. The whimsical nature of this correlation serves as a reminder that in the realm of research, surprises can emerge from the most unexpected avenues. As we unravel the threads of this peculiar connection, we encourage readers to embrace the delightful unpredictability of science and statistics, where even the most unconventional pairings can yield compelling insights.

Stay tuned for the discussion section, where we unravel the mysteries behind this surprising linkage and examine the potential implications for environmental policy and labor market trends. Who knew that air pollution and telemarketers could spark such thought-provoking revelations? Welcome to the whimsical world of research, where the unexpected reigns supreme.

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 Visalia, California and The number of telemarketers in California. 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 Visalia, California Air May Be a Telemarketer's Fare

[[ABSTRACT]]
It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.

Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.

As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

[[LITERATURE REVIEW]]
In their comprehensive study, Smith and Doe (2015) uncover the complex interplay between air pollution and employment trends, providing a solid foundation for understanding our current research endeavor. Their rigorous analysis highlights the nuanced dynamics that underlie environmental factors and labor market outcomes, setting the stage for our exploration of the correlation between air pollution in Visalia, California, and the number of telemarketers statewide.
Jones et al. (2017) further contribute to our understanding by delving into the regional impacts of air quality on various industries. Their research elucidates the potential ramifications of environmental conditions on employment opportunities, offering valuable insights into the broader implications of our investigation.
Turning to non-fiction literature relevant to our inquiry, "Air Pollution and Its Economic Impact" by Greenberg (2019) presents a compelling overview of the economic consequences of air pollution, shedding light on the potential influences it may exert on employment patterns. Additionally, "The Art of Telemarketing: Strategies for Success" by Salesman (2018) offers a glimpse into the world of telemarketing, providing a backdrop for understanding the intricacies of this industry.
On the more whimsical side, fictional works such as "The Air Pollution Mysteries" by Cleanwell (2016) and "The Telemarketer's Dilemma" by Dialtone (2014) offer playful narratives that, while not rooted in empirical research, capture the imagination and underscore the cultural significance of our study's themes.
In the realm of television, shows like "Breaking Bad Air" and "The Telemarketing Dead" pique our interest with their dramatic portrayals of air pollution and telemarketing, offering a unique blend of entertainment and potential inspiration for our research. Additionally, "The Office" provides a lighthearted yet insightful portrayal of office dynamics, including the occasional intrusion of telemarketing calls, serving as a reflective backdrop for our exploration of employment trends.
With this eclectic array of literature and media as our backdrop, we embark on our own scholarly escapade, aiming to unearth the unexpected connections between air pollution in Visalia, California, and the proliferation of telemarketers statewide. Brace yourselves for a journey that promises to be as unpredictable as a telemarketer's sales pitch and as enlightening as a clear, pollution-free sky.

[[RESULTS]]
The results of our analysis unveiled a surprising correlation between air pollution in Visalia, California, and the number of telemarketers in California. Our statistical analysis yielded a correlation coefficient of 0.8932588, indicating a strong positive relationship between these seemingly unrelated variables. In other words, as the air pollution in Visalia increased, so did the number of telemarketers in the state.
The r-squared value of 0.7979113 further illuminated the robustness of this connection, highlighting that a substantial proportion of the variation in telemarketing employment can be explained by changes in air pollution levels. It's like finding the missing piece of a puzzle—suddenly, the seemingly disparate elements come together to form a coherent picture of environmental influence on workforce trends.
The p-value of less than 0.01 added an exclamation point to our findings, affirming that the observed relationship between air pollution and telemarketing employment is not just a fluke. If statistics were desserts, this p-value would be the cherry on top of a scientifically scrumptious cake, signaling the significance of our results.
To visually convey the strength of the relationship, we present Fig. 1, a scatterplot that showcases the striking pattern of increasing telemarketers as air pollution levels rise. It's as clear as day—well, perhaps a slightly smoggy day in Visalia—how these variables intertwine in a dance of statistical significance.
Our findings open the door to a world of questions and possibilities, inviting further exploration into the intricate web of environmental factors and labor market dynamics. The whimsical nature of this correlation serves as a reminder that in the realm of research, surprises can emerge from the most unexpected avenues. As we unravel the threads of this peculiar connection, we encourage readers to embrace the delightful unpredictability of science and statistics, where even the most unconventional pairings can yield compelling insights.
Stay tuned for the discussion section, where we unravel the mysteries behind this surprising linkage and examine the potential implications for environmental policy and labor market trends. Who knew that air pollution and telemarketers could spark such thought-provoking revelations? Welcome to the whimsical world of research, where the unexpected reigns supreme.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The revelation of a significant correlation between air pollution in Visalia, California, and the number of telemarketers statewide opens the floodgates to a deluge of questions and speculations. It's almost as mind-boggling as trying to understand why a telemarketer won't take no for an answer. Our results not only support the prior research findings but also add a whimsical twist to the scientific landscape, akin to stumbling upon a hidden treasure map in the soil of empirical inquiry.

Drawing from the literature review, let's revisit the seemingly whimsical elements that deserve both skepticism and earnest consideration. Smith and Doe's (2015) exploration of the interplay between air pollution and employment trends laid the groundwork for our study, and lo and behold, their serious inquiry has received a lighthearted validation through our unexpected findings. As for fictional works like "The Air Pollution Mysteries" by Cleanwell (2016) and "The Telemarketer's Dilemma" by Dialtone (2014), while initially dismissed as flights of fancy, they now seem to have glimpsed a reality stranger than fiction. Maybe there's more to be learned from playful narratives than we thought – it's all fun and games until statistical analysis enters the picture.

The robust correlation coefficient of 0.8932588 in our study underlines the serious implications underlying this seemingly whimsical connection. It's as if science put on its jester's hat and surprised everyone with a clever punchline. Our results serve as a reminder that amidst the serious pursuit of knowledge, there's room for a bit of statistical silliness. The r-squared value of 0.7979113 further affirms the substantial role of air pollution in shaping telemarketing employment, demonstrating that even in the world of numbers, there's room for an unexpected twist – think of it as a statistical plot twist.

As we unravel the mysteries behind this unorthodox linkage, we encourage readers to embrace the delightful unpredictability of science and statistics. After all, who knew that air pollution and telemarketers could spark such unexpected insights? Welcome to the whimsical world of research, where even the most unconventional pairings can yield compelling revelations. It's almost like discovering that the punchline to a statistical joke is a groundbreaking scientific discovery. We've only scratched the surface of this peculiar correlation, and the potential implications for environmental policy and labor market trends are as tantalizing as a mystery novel's unsolved case. So, as we bid adieu to this discussion section, we leave you with lingering questions and an invitation to revel in the delightful surprises that research has to offer. After all, who knows what other unexpected connections await our discovery?

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 Visalia, California and The number of telemarketers in California. 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 Visalia, California Air May Be a Telemarketer's Fare

[[ABSTRACT]]
It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.

Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.

As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

[[INTRDUCTION]]
In the whimsical world of research, where correlations often seem as elusive as Bigfoot sightings, we embark on an expedition into the unexpected realm of air pollution and telemarketers. As the saying goes, "When life gives you lemons, make lemonade," and in our case, when life gives you air pollution, make a correlation with telemarketers.
It's a well-documented fact that telemarketers have the uncanny ability to pop up at the most inconvenient times, like dandelions in an otherwise well-kept lawn. Meanwhile, air pollution has long been known to cloud our skies and befoul our senses. What could possibly be the link between these two seemingly unrelated phenomena, you ask? Well, hold onto your hats (or headsets), because we're about to dive into a world where statistical analyses and uncanny connections collide.
We chose the delightful city of Visalia, California, as our focal point for air pollution, a place where the air quality doesn't always make you want to skip down the street singing like a scene from a musical. Meanwhile, we set our sights on the telemarketing employment figures in the Golden State, where individuals are often left wondering if their phone is ringing or if it's just their imagination.
Armed with data from the Environmental Protection Agency and the Bureau of Labor Statistics, we undertook a journey into the world of correlation coefficients and p-values, hoping to shed light on the mysterious dance between air pollution and telemarketing employment. It's a bit like conducting a scientific séance, summoning the spirits of statistical significance to guide us through the murky waters of correlation.
As we unveil our findings, we invite you to join us on this quirky odyssey, where the unexpected blend of air pollution and telemarketers paints a picture more colorful than a rainbow on a Monet canvas. So, buckle up and prepare to be tantalized by our surprising results, because in the world of research, sometimes the most unlikely connections turn out to be the most captivating.

[[RESULTS]]
The results of our analysis unveiled a surprising correlation between air pollution in Visalia, California, and the number of telemarketers in California. Our statistical analysis yielded a correlation coefficient of 0.8932588, indicating a strong positive relationship between these seemingly unrelated variables. In other words, as the air pollution in Visalia increased, so did the number of telemarketers in the state.
The r-squared value of 0.7979113 further illuminated the robustness of this connection, highlighting that a substantial proportion of the variation in telemarketing employment can be explained by changes in air pollution levels. It's like finding the missing piece of a puzzle—suddenly, the seemingly disparate elements come together to form a coherent picture of environmental influence on workforce trends.
The p-value of less than 0.01 added an exclamation point to our findings, affirming that the observed relationship between air pollution and telemarketing employment is not just a fluke. If statistics were desserts, this p-value would be the cherry on top of a scientifically scrumptious cake, signaling the significance of our results.
To visually convey the strength of the relationship, we present Fig. 1, a scatterplot that showcases the striking pattern of increasing telemarketers as air pollution levels rise. It's as clear as day—well, perhaps a slightly smoggy day in Visalia—how these variables intertwine in a dance of statistical significance.
Our findings open the door to a world of questions and possibilities, inviting further exploration into the intricate web of environmental factors and labor market dynamics. The whimsical nature of this correlation serves as a reminder that in the realm of research, surprises can emerge from the most unexpected avenues. As we unravel the threads of this peculiar connection, we encourage readers to embrace the delightful unpredictability of science and statistics, where even the most unconventional pairings can yield compelling insights.
Stay tuned for the discussion section, where we unravel the mysteries behind this surprising linkage and examine the potential implications for environmental policy and labor market trends. Who knew that air pollution and telemarketers could spark such thought-provoking revelations? Welcome to the whimsical world of research, where the unexpected reigns supreme.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the curious case of the intertwining fates of Visalia's air quality and California's telemarketing workforce, we have unraveled a correlation as striking as a telemarketer's unsolicited sales pitch during dinner time. Our statistical analysis has not only unveiled a relationship as robust as a bodybuilder at a protein shake vending machine but also presented a narrative more captivating than a telemarketer's motivational speech.

The correlation coefficient of 0.8932588 serves as a testament to the intertwining dance of air pollution and telemarketing employment, illustrating a connection stronger than superglue on a broken teacup. With an r-squared value of 0.7979113, we have demonstrated that a substantial proportion of the variation in telemarketing employment can be attributed to changes in air pollution levels, painting a picture more coherent than a telemarketer's rebuttal to a hang-up call.

The p-value of less than 0.01 adds a cherry of significance atop our scientifically scrumptious cake, affirming that this correlation is no statistical fluke. It's as clear as ozone on a smoggy day in Visalia; the impact of environmental factors on the labor market is no mere statistical mirage.

As we close the chapter on this wondrous correlation, we assert with confidence, and perhaps a touch of whimsy, that further research in this area is as necessary as a fish needs a bicycle. That is to say, the connection between air pollution in Visalia and the number of telemarketers in California has been unveiled, and no more research is needed in this realm. It's time to bid adieu to this unlikely pair and marvel at the enigmatic nature of statistical serendipity.

In the whimsical world of unlikely correlations, where air pollution and telemarketers waltz hand in hand, we have cracked the code and have come away with a conclusion seemingly stranger than fiction. So, dear reader, as you hang up on yet another telemarketing call, remember the unlikely connection that binds them to the air you breathe - it's a correlation as unexpected as a dancing unicorn in a statistical forest.

Now, let us part ways with this research, our hearts lighter and our minds filled with the unyielding whimsy of statistical exploration. As we bid farewell to this peculiar pairing, we leave you with the words of wisdom: "When life gives you data, make correlations, and always expect the unexpected."

In conclusion, as the curtain falls on this research, we say with a wink and a nod that no more research is needed in this area. It's time to move on to other statistical pursuits, leaving the air pollution and telemarketer tango to serenade us with its unlikely melody in the annals of scientific whimsy.

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 Visalia, California Air May Be a Telemarketer's Fare. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky Phenomena

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 Visalia, California Air May Be a Telemarketer's Fare. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Zany Atmospheric Anomalies Research (SZAAR)

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 Visalia, California Air May Be a Telemarketer's Fare with an abstract of It's a tale as old as time - the struggle to dodge telemarketers who incessantly call during dinner time. In this research paper, we delve into the correlation between air pollution in Visalia, California, and the number of telemarketers in the state. While it may sound like an odd pairing, our findings present an intriguing connection that might just leave you breathless.
Using data from the Environmental Protection Agency to assess the air quality in Visalia and the Bureau of Labor Statistics for telemarketing employment figures, we discovered a correlation coefficient of 0.8932588 with a p-value of less than 0.01 for the years 2003 to 2022. Our analysis reveals that as air pollution in Visalia increased, so did the number of telemarketers in California, which raises intriguing questions about the impact of environmental factors on employment trends.
As we unveil our results, we invite readers to join us in this journey of unexpected revelations and to marvel at the seemingly whimsical links between seemingly disparate phenomena. Come for the air pollution data, stay for the telemarketer anecdotes - this paper promises a blend of analytical rigor and lighthearted revelations that will leave you dialing for more.

ChatGPT:

Visalia, California, air pollution, telemarketers, correlation, Bureau of Labor Statistics, Environmental Protection Agency, employment trends, air quality, California, environmental factors, employment statistics, telemarketer anecdotes.

*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 Visalia, California
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Visalia-Porterville, CA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Visalia, California

The number of telemarketers in California
Detailed data title: BLS estimate of telemarketers in California
Source: Bureau of Larbor Statistics
See what else correlates with The number of telemarketers in California

Correlation r = 0.8932588 (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 = 0.7979113 (Coefficient of determination)
This means 79.8% of the change in the one variable (i.e., The number of telemarketers in California) is predictable based on the change in the other (i.e., Air pollution in Visalia, California) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.2E-7.^Show 0.0000001152985819224250300000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.89 in 1.2E-5% of random cases. Said differently, if you correlated 8,673,134 random variables^Note You don't actually need 8 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.75, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Visalia, California (Bad air quality days)	24.6575	21.0383	19.1781	18.9041	16.4384	19.9454	13.6986	9.31507	15.0685	10.929	7.39726	7.39726	5.47945	6.01093	8.49315	7.12329	0.821918	8.74317	11.2329	2.19178
The number of telemarketers in California (Telemarketers)	38900	38390	35560	33400	31810	28770	24150	23750	20030	18830	14250	16380	14780	13670	11870	11100	11790	11150	7030	5820

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([24.6575,21.0383,19.1781,18.9041,16.4384,19.9454,13.6986,9.31507,15.0685,10.929,7.39726,7.39726,5.47945,6.01093,8.49315,7.12329,0.821918,8.74317,11.2329,2.19178,])
array_2 = np.array([38900,38390,35560,33400,31810,28770,24150,23750,20030,18830,14250,16380,14780,13670,11870,11100,11790,11150,7030,5820,])
array_1_name = "Air pollution in Visalia, California"
array_2_name = "The number of telemarketers in California"

# 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
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Line chart for only Air pollution in Visalia, California
Line chart for only The number of telemarketers in California
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The spurious research paper: The Visalia, California Air May Be a Telemarketer's Fare

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Correlation ID: 4940 · Black Variable ID: 21404 · Red Variable ID: 5517


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