Spurious correlation #5,320 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air quality in Tuscaloosa, Alabama and the second variable is Votes for the Republican Presidential candidate in Alabama. The chart goes from 1980 to 2020, 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 quality in Tuscaloosa, Alabama and the second variable is Votes for the Republican Presidential candidate in Alabama. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The fresher air blew in a strong conservative breeze, allowing for better respiration and clearer right-leaning thoughts. It seems like cleaner air really made the Republicans 'breathe easy' and head to the polls!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image with the style of a meme, depicting a scene in Tuscaloosa, Alabama. Show a bustling city scene with vibrant colors and clear skies. Republican voters are confidently heading to the polls, exuding a sense of determination and purpose. The clean air and serene atmosphere should be evident, conveying a sense of ease and inspiration. 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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: A photo-realistic image Cute 2D animation a meme Charcoal An image that looks like it is a scene from 'Star Wars'
ChatGPT: A meme

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama, in the following situation: The fresher air blew in a strong conservative breeze, allowing for better respiration and clearer right-leaning thoughts. It seems like cleaner air really made the Republicans 'breathe easy' and head to the polls!. Keep the description simple.
ChatGPT: In Tuscaloosa, Alabama, crisp, fresh air sweeps through the city, carrying with it a conservative breeze. The clear sky and low pollution levels seem to inspire a sense of ease, as Republican voters can be seen heading confidently to the polls, taking in deep breaths of the clean air as they make their way to cast their ballots.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A meme ] and depicting this scene: In Tuscaloosa, Alabama, crisp, fresh air sweeps through the city, carrying with it a conservative breeze. The clear sky and low pollution levels seem to inspire a sense of ease, as Republican voters can be seen heading confidently to the polls, taking in deep breaths of the clean air as they make their way to cast their ballots. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image with the style of a meme, depicting a scene in Tuscaloosa, Alabama. Show a bustling city scene with vibrant colors and clear skies. Republican voters are confidently heading to the polls, exuding a sense of determination and purpose. The clean air and serene atmosphere should be evident, conveying a sense of ease and inspiration.

*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 quality in Tuscaloosa, Alabama caused Votes for the Republican Presidential candidate in Alabama to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama

Journal of Environmental Politics and Policy

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama"

[[ABSTRACT]]
In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.

Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.

The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it."

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The field of political science has long been preoccupied with unraveling the intricate web of factors that influence voter behavior. From socioeconomic status to cultural values, researchers have dedicated copious amounts of brainpower to understanding what makes people tick when they step into the voting booth. However, a lesser-explored phenomenon has been the potential influence of environmental factors on political preferences. Enter our study, where we set out to examine the correlation between air quality and Republican votes in the state of Alabama.
Air quality and political leanings may seem like an unlikely pair, but as we dug into this relationship, it became clear that there's more than meets the eye - or more appropriately, the lung. This exploration led us to ask ourselves, "Is there a breath of truth to the idea that the quality of the air we breathe impacts the candidates we choose to support?" And as it turns out, our findings cast quite a bit of light on this hazy subject.
Our approach to this investigation was nothing to sneeze at. We collected air quality data from Tuscaloosa, Alabama, utilizing information from the Environmental Protection Agency. Meanwhile, to obtain comprehensive voting data, we turned to the MIT Election Data and Science Lab, Harvard Dataverse, and other reputable sources. To ensure methodological rigor, we performed extensive statistical analyses that would leave even the most seasoned statistician wheezing with approval.
Upon delving into our data, a remarkable statistical correlation emerged, revealing a strong connection between air quality in Tuscaloosa and votes for the Republican presidential candidate. Our findings painted a picture clearer than a pristine, pollution-free skyline - the correlation coefficient of 0.9761697 left little room for ambiguity. It was almost as if the relationship between clean air and Republican votes had been etched in stone, or perhaps more accurately, etched in the crystal-clear air itself.
Now, at this point, you might be wondering, "What in the world do air quality and political preferences have to do with each other?" Well, the answer seems to be as plain as the nose on your face - or, in this case, as plain as the breath in your lungs. It appears that the residents of Tuscaloosa, and likely by extension the wider population of Alabama, may have been influenced by more than just political rhetoric. Clean air seems to have been a breath of fresh political air, breathing new life into the phrase, "Let's clear the air on political preferences."
This unanticipated connection highlights the multifaceted influences that shape voter behavior, demonstrating the need for continued exploration in this area. After all, as the saying goes, "When it comes to politics, it's not just about hot air - it's about clean air too." And we aim to untangle this knotty issue one statistical analysis at a time.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

As we dive into the existing body of research on the intricate interplay between environmental factors and political preferences, it becomes evident that the influence of air quality on voting behavior has been as elusive as a puff of smoke. Smith (2010) meticulously analyzed the correlation between air pollution levels and political party affiliations, revealing a nuanced relationship that extends beyond mere political rhetoric. However, what Smith's study didn't account for is the breath-taking impact of clean air on Republican votes, quite literally.

Doe (2015) further expanded on this line of inquiry by examining the impact of air quality on voter turnout and party support, shedding light on the multifaceted nature of environmental influences. Yet, the clean sweep of the correlation between pristine air and Republican votes seems to have eluded previous scholarly endeavors, leaving it as fresh as a daisy-scented breeze.

Jones (2018) delved into the economic implications of air quality on political leanings, uncovering the intricate dance between environmental well-being and voting behavior. While Jones' work adds valuable insights into the broader picture, it fails to capture the specific aroma of Republican support that seems to emanate from pollutant-free air.

Now, enough with the serious scholarly business. Let's clear the air and take a breather to appreciate some relevant non-fiction works that could blow you away with their insights. "The Air We Breathe: A Field Guide to the World of Atmospheric Gases" (Brown, 2019) offers a refreshing look at the very air we're discussing, making it a breath of fresh air for our bibliographic pursuits.

Switching gears into the realms of fiction, "The Polluted Politics: A Tale of Smoke and Mirrors" (Green, 2017) takes the reader on an imaginative journey through a world where political preferences are as murky as the smog-laden skies. While purely fictional, it might just open the floodgates of our understanding when it comes to the impact of air quality on voter choices.

And let's not forget the candid voices of social media, where a tweet by @CleanAirVoter passionately declares, "I vote Republican for the clean air hugs!"

But let's not get too carried away in the clouds of amusement. This important line of inquiry holds significant implications for understanding the complex web of influences that shape political preferences. After all, as the old adage goes, "Where there's air, there's a whey... err, there's a way to influence political leanings."

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse . You used data from 1980 to 2020

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]]
"Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama"

[[ABSTRACT]]
In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.

Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.

The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it."

[[INTRODUCTION]]
The field of political science has long been preoccupied with unraveling the intricate web of factors that influence voter behavior. From socioeconomic status to cultural values, researchers have dedicated copious amounts of brainpower to understanding what makes people tick when they step into the voting booth. However, a lesser-explored phenomenon has been the potential influence of environmental factors on political preferences. Enter our study, where we set out to examine the correlation between air quality and Republican votes in the state of Alabama.
Air quality and political leanings may seem like an unlikely pair, but as we dug into this relationship, it became clear that there's more than meets the eye - or more appropriately, the lung. This exploration led us to ask ourselves, "Is there a breath of truth to the idea that the quality of the air we breathe impacts the candidates we choose to support?" And as it turns out, our findings cast quite a bit of light on this hazy subject.
Our approach to this investigation was nothing to sneeze at. We collected air quality data from Tuscaloosa, Alabama, utilizing information from the Environmental Protection Agency. Meanwhile, to obtain comprehensive voting data, we turned to the MIT Election Data and Science Lab, Harvard Dataverse, and other reputable sources. To ensure methodological rigor, we performed extensive statistical analyses that would leave even the most seasoned statistician wheezing with approval.
Upon delving into our data, a remarkable statistical correlation emerged, revealing a strong connection between air quality in Tuscaloosa and votes for the Republican presidential candidate. Our findings painted a picture clearer than a pristine, pollution-free skyline - the correlation coefficient of 0.9761697 left little room for ambiguity. It was almost as if the relationship between clean air and Republican votes had been etched in stone, or perhaps more accurately, etched in the crystal-clear air itself.
Now, at this point, you might be wondering, "What in the world do air quality and political preferences have to do with each other?" Well, the answer seems to be as plain as the nose on your face - or, in this case, as plain as the breath in your lungs. It appears that the residents of Tuscaloosa, and likely by extension the wider population of Alabama, may have been influenced by more than just political rhetoric. Clean air seems to have been a breath of fresh political air, breathing new life into the phrase, "Let's clear the air on political preferences."
This unanticipated connection highlights the multifaceted influences that shape voter behavior, demonstrating the need for continued exploration in this area. After all, as the saying goes, "When it comes to politics, it's not just about hot air - it's about clean air too." And we aim to untangle this knotty issue one statistical analysis at a time.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To carry out this investigation, we employed a mix of data collection methods and analytical techniques that were as methodical as they were meteorological, if you catch my drift. Our study focused on examining the relationship between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate in the state.

First and foremost, we obtained air quality data from the Environmental Protection Agency, which allowed us to assess various air pollutants such as ozone, particulate matter, sulfur dioxide, and nitrogen dioxide. We wanted to ensure that our analysis was as thorough as a gust of wind, covering a comprehensive range of factors that could potentially influence political preferences. We also compiled voting data from the MIT Election Data and Science Lab, Harvard Dataverse, and other reputable sources, ensuring that our investigation was as politically inclusive as it was meticulously statistical.

Given the multi-decade scope of our study, spanning from 1980 to 2020, we faced the daunting task of cleaning and validating the data, a process that some might say was akin to dusting off an old bookshelf and making sure all the books were in order. We meticulously checked for any anomalies in the data, ensuring that our analytical foundation was as sturdy as a reinforced building in a windstorm.

Following the tedious but necessary data preparation phase, our statistical analysis was about as intricate as a spider weaving a web. We calculated correlation coefficients, conducted regression analyses, and employed spatial modeling techniques to map out the geographic distribution of both air quality and Republican votes in Alabama. Our goal was to leave no statistical stone unturned, or as they say in the air quality business, to make sure there were no pollutant particles left floating around.

To assess the robustness of our findings, we performed a battery of statistical tests and employed rigorous hypothesis testing techniques. We aimed to make our conclusions about as airtight as a sealed, filtered ventilation system, leaving little room for doubt about the significance of our results.

In a tongue-and-cheek twist of fate, the data also allowed us to confirm another correlation: the quality of air in Tuscaloosa was not only connected to voting behavior, but also to the likelihood of a "fresh breeze" pun making its way into this research paper. But I digress.

In summary, our methodology combined meticulous data collection, rigorous validation procedures, and advanced statistical analyses, all aimed at unraveling the potential impact of air quality on political preferences. It was a complex endeavor, but we like to think the journey was as refreshing as a breath of clean air on a summer day.

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse .

For the time period 1980 to 2020, you found a correlation 0.9761697, r-squared of 0.9529072, 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]]
"Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama"

[[ABSTRACT]]
In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.

Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.

The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it."

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The field of political science has long been preoccupied with unraveling the intricate web of factors that influence voter behavior. From socioeconomic status to cultural values, researchers have dedicated copious amounts of brainpower to understanding what makes people tick when they step into the voting booth. However, a lesser-explored phenomenon has been the potential influence of environmental factors on political preferences. Enter our study, where we set out to examine the correlation between air quality and Republican votes in the state of Alabama.
Air quality and political leanings may seem like an unlikely pair, but as we dug into this relationship, it became clear that there's more than meets the eye - or more appropriately, the lung. This exploration led us to ask ourselves, "Is there a breath of truth to the idea that the quality of the air we breathe impacts the candidates we choose to support?" And as it turns out, our findings cast quite a bit of light on this hazy subject.
Our approach to this investigation was nothing to sneeze at. We collected air quality data from Tuscaloosa, Alabama, utilizing information from the Environmental Protection Agency. Meanwhile, to obtain comprehensive voting data, we turned to the MIT Election Data and Science Lab, Harvard Dataverse, and other reputable sources. To ensure methodological rigor, we performed extensive statistical analyses that would leave even the most seasoned statistician wheezing with approval.
Upon delving into our data, a remarkable statistical correlation emerged, revealing a strong connection between air quality in Tuscaloosa and votes for the Republican presidential candidate. Our findings painted a picture clearer than a pristine, pollution-free skyline - the correlation coefficient of 0.9761697 left little room for ambiguity. It was almost as if the relationship between clean air and Republican votes had been etched in stone, or perhaps more accurately, etched in the crystal-clear air itself.
Now, at this point, you might be wondering, "What in the world do air quality and political preferences have to do with each other?" Well, the answer seems to be as plain as the nose on your face - or, in this case, as plain as the breath in your lungs. It appears that the residents of Tuscaloosa, and likely by extension the wider population of Alabama, may have been influenced by more than just political rhetoric. Clean air seems to have been a breath of fresh political air, breathing new life into the phrase, "Let's clear the air on political preferences."
This unanticipated connection highlights the multifaceted influences that shape voter behavior, demonstrating the need for continued exploration in this area. After all, as the saying goes, "When it comes to politics, it's not just about hot air - it's about clean air too." And we aim to untangle this knotty issue one statistical analysis at a time.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our investigation unveiled a striking correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate in the state. The correlation coefficient of 0.9761697 illuminated a remarkably robust relationship between these seemingly disparate variables. It seems that the voters in Alabama may have found something in the clear air that spoke to their political preferences - perhaps a breath of fresh political perspective amidst the haze of campaign speeches and party affiliations.

The correlation was further substantiated by an r-squared value of 0.9529072, indicating that a substantial proportion of the variation in Republican votes in Alabama could be explained by fluctuations in air quality. It's almost as if the cleaner the air, the clearer their political leanings. It's as though the voters were echoing, "We like our air and our politics squeaky clean!"

The significance levels, denoted by p < 0.01, firmly bolstered the robustness of the findings, lending credence to the notion that air quality played a notable role in shaping the political preferences of Alabama voters. It's as if the voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

Figure 1 displays a striking scatterplot illustrating the strong positive correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate. The data points align like a well-orchestrated political rally, making it abundantly clear that clean air and Republican votes have been walking hand in hand, or should we say, breathing in sync, over the past several decades.

The implications of these findings are as far-reaching as a gust of wind on election day. It appears that the quality of the air we breathe may have seeped into the very fabric of political decision-making, exerting an influence that extends beyond the realms of traditional predictors of voter behavior. Who would have thought that when it comes to casting ballots, clean air has been just as crucial as campaign promises? It’s as if the voters were saying, “We might not have been environmentalists, but we sure liked the air clean when we made our political choice!”

In conclusion, our study has shed light on an unexpected yet impactful dimension of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political preferences. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it." And it seems that in the case of Alabama, cleaner air has been a breath of fresh political appeal.

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama"

[[ABSTRACT]]
In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.

Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.

The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it."

[[LITERATURE REVIEW]]
As we dive into the existing body of research on the intricate interplay between environmental factors and political preferences, it becomes evident that the influence of air quality on voting behavior has been as elusive as a puff of smoke. Smith (2010) meticulously analyzed the correlation between air pollution levels and political party affiliations, revealing a nuanced relationship that extends beyond mere political rhetoric. However, what Smith's study didn't account for is the breath-taking impact of clean air on Republican votes, quite literally.
Doe (2015) further expanded on this line of inquiry by examining the impact of air quality on voter turnout and party support, shedding light on the multifaceted nature of environmental influences. Yet, the clean sweep of the correlation between pristine air and Republican votes seems to have eluded previous scholarly endeavors, leaving it as fresh as a daisy-scented breeze.
Jones (2018) delved into the economic implications of air quality on political leanings, uncovering the intricate dance between environmental well-being and voting behavior. While Jones' work adds valuable insights into the broader picture, it fails to capture the specific aroma of Republican support that seems to emanate from pollutant-free air.
Now, enough with the serious scholarly business. Let's clear the air and take a breather to appreciate some relevant non-fiction works that could blow you away with their insights. "The Air We Breathe: A Field Guide to the World of Atmospheric Gases" (Brown, 2019) offers a refreshing look at the very air we're discussing, making it a breath of fresh air for our bibliographic pursuits.
Switching gears into the realms of fiction, "The Polluted Politics: A Tale of Smoke and Mirrors" (Green, 2017) takes the reader on an imaginative journey through a world where political preferences are as murky as the smog-laden skies. While purely fictional, it might just open the floodgates of our understanding when it comes to the impact of air quality on voter choices.
And let's not forget the candid voices of social media, where a tweet by @CleanAirVoter passionately declares, "I vote Republican for the clean air hugs!"
But let's not get too carried away in the clouds of amusement. This important line of inquiry holds significant implications for understanding the complex web of influences that shape political preferences. After all, as the old adage goes, "Where there's air, there's a whey... err, there's a way to influence political leanings."

[[RESULTS]]
The results of our investigation unveiled a striking correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate in the state. The correlation coefficient of 0.9761697 illuminated a remarkably robust relationship between these seemingly disparate variables. It seems that the voters in Alabama may have found something in the clear air that spoke to their political preferences - perhaps a breath of fresh political perspective amidst the haze of campaign speeches and party affiliations.
The correlation was further substantiated by an r-squared value of 0.9529072, indicating that a substantial proportion of the variation in Republican votes in Alabama could be explained by fluctuations in air quality. It's almost as if the cleaner the air, the clearer their political leanings. It's as though the voters were echoing, "We like our air and our politics squeaky clean!"
The significance levels, denoted by p < 0.01, firmly bolstered the robustness of the findings, lending credence to the notion that air quality played a notable role in shaping the political preferences of Alabama voters. It's as if the voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"
Figure 1 displays a striking scatterplot illustrating the strong positive correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate. The data points align like a well-orchestrated political rally, making it abundantly clear that clean air and Republican votes have been walking hand in hand, or should we say, breathing in sync, over the past several decades.
The implications of these findings are as far-reaching as a gust of wind on election day. It appears that the quality of the air we breathe may have seeped into the very fabric of political decision-making, exerting an influence that extends beyond the realms of traditional predictors of voter behavior. Who would have thought that when it comes to casting ballots, clean air has been just as crucial as campaign promises? It’s as if the voters were saying, “We might not have been environmentalists, but we sure liked the air clean when we made our political choice!”
In conclusion, our study has shed light on an unexpected yet impactful dimension of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political preferences. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it." And it seems that in the case of Alabama, cleaner air has been a breath of fresh political appeal.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings have unearthed a notable connection between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate, akin to the pleasant surprise of finding fresh air in a stuffy room. The strength of the correlation uncovered in our study amplifies the existing body of research regarding the influential role of environmental factors in shaping political preferences. It's as if our findings are declaring, "Clean air isn’t just for breathing – it’s for voting, too!"

Drawing upon the threads of prior research woven through the fabric of our investigation, we find that the correlation coefficient of 0.9761697 echoes the whispers of previous studies, emphasizing the undeniable influence of air quality on the political inclinations of the residents of Tuscaloosa. It's as if the research gods were saying, "Let there be clean air, and let there be Republican votes."

The r-squared of 0.9529072 further solidifies the significance of our results, serving as a reminder that the cleaner the air, the clearer their political leanings. The notion that air quality may extend beyond the realms of environmental well-being to sway the political compass of voters rings as true as a fresh bell of clean air. It's almost as if the air quality was whispering to the voters, "Psst...vote Republican."

The robustness of our findings, denoted by p < 0.01, stands as a testament to the resounding impact of clean air on political choices, akin to the resolute stance of a committed voter on Election Day. It's as if the statistical significance were spelling out, "We may not have seen much pollution, but we still breathed in deeply for our political decision-making."

Our results align harmoniously with prior scholarly endeavors, breathing new life into the discussion of environmental influences on political preferences. By uncovering the resounding influence of clean air on Republican votes, our study not only adds depth to the existing literature but also breathes vigor into the understanding of the unanticipated pathways through which environmental factors intersect with political decisions. It's as if the academic community were collectively saying, "Just like air, the influence of environmental factors on voter behavior can't be overlooked."

In essence, our study serves as a breath of fresh air in the realm of political research, propelling us to consider the far-reaching implications of clean air on the democratic process. It prompts us to contemplate the extent to which the very air we breathe may subtly, yet significantly, shape the tapestry of political preferences. It’s as if the research were saying, "Clean air isn’t just for the lungs, it’s for the ballots, too!"

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 quality in Tuscaloosa, Alabama and Votes for the Republican Presidential candidate in Alabama. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama"

[[ABSTRACT]]
In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.

Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.

The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"

This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it."

[[INTRDUCTION]]
The field of political science has long been preoccupied with unraveling the intricate web of factors that influence voter behavior. From socioeconomic status to cultural values, researchers have dedicated copious amounts of brainpower to understanding what makes people tick when they step into the voting booth. However, a lesser-explored phenomenon has been the potential influence of environmental factors on political preferences. Enter our study, where we set out to examine the correlation between air quality and Republican votes in the state of Alabama.
Air quality and political leanings may seem like an unlikely pair, but as we dug into this relationship, it became clear that there's more than meets the eye - or more appropriately, the lung. This exploration led us to ask ourselves, "Is there a breath of truth to the idea that the quality of the air we breathe impacts the candidates we choose to support?" And as it turns out, our findings cast quite a bit of light on this hazy subject.
Our approach to this investigation was nothing to sneeze at. We collected air quality data from Tuscaloosa, Alabama, utilizing information from the Environmental Protection Agency. Meanwhile, to obtain comprehensive voting data, we turned to the MIT Election Data and Science Lab, Harvard Dataverse, and other reputable sources. To ensure methodological rigor, we performed extensive statistical analyses that would leave even the most seasoned statistician wheezing with approval.
Upon delving into our data, a remarkable statistical correlation emerged, revealing a strong connection between air quality in Tuscaloosa and votes for the Republican presidential candidate. Our findings painted a picture clearer than a pristine, pollution-free skyline - the correlation coefficient of 0.9761697 left little room for ambiguity. It was almost as if the relationship between clean air and Republican votes had been etched in stone, or perhaps more accurately, etched in the crystal-clear air itself.
Now, at this point, you might be wondering, "What in the world do air quality and political preferences have to do with each other?" Well, the answer seems to be as plain as the nose on your face - or, in this case, as plain as the breath in your lungs. It appears that the residents of Tuscaloosa, and likely by extension the wider population of Alabama, may have been influenced by more than just political rhetoric. Clean air seems to have been a breath of fresh political air, breathing new life into the phrase, "Let's clear the air on political preferences."
This unanticipated connection highlights the multifaceted influences that shape voter behavior, demonstrating the need for continued exploration in this area. After all, as the saying goes, "When it comes to politics, it's not just about hot air - it's about clean air too." And we aim to untangle this knotty issue one statistical analysis at a time.

[[RESULTS]]
The results of our investigation unveiled a striking correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate in the state. The correlation coefficient of 0.9761697 illuminated a remarkably robust relationship between these seemingly disparate variables. It seems that the voters in Alabama may have found something in the clear air that spoke to their political preferences - perhaps a breath of fresh political perspective amidst the haze of campaign speeches and party affiliations.
The correlation was further substantiated by an r-squared value of 0.9529072, indicating that a substantial proportion of the variation in Republican votes in Alabama could be explained by fluctuations in air quality. It's almost as if the cleaner the air, the clearer their political leanings. It's as though the voters were echoing, "We like our air and our politics squeaky clean!"
The significance levels, denoted by p < 0.01, firmly bolstered the robustness of the findings, lending credence to the notion that air quality played a notable role in shaping the political preferences of Alabama voters. It's as if the voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"
Figure 1 displays a striking scatterplot illustrating the strong positive correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate. The data points align like a well-orchestrated political rally, making it abundantly clear that clean air and Republican votes have been walking hand in hand, or should we say, breathing in sync, over the past several decades.
The implications of these findings are as far-reaching as a gust of wind on election day. It appears that the quality of the air we breathe may have seeped into the very fabric of political decision-making, exerting an influence that extends beyond the realms of traditional predictors of voter behavior. Who would have thought that when it comes to casting ballots, clean air has been just as crucial as campaign promises? It’s as if the voters were saying, “We might not have been environmentalists, but we sure liked the air clean when we made our political choice!”
In conclusion, our study has shed light on an unexpected yet impactful dimension of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political preferences. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it." And it seems that in the case of Alabama, cleaner air has been a breath of fresh political appeal.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has uncovered a compelling correlation between air quality in Tuscaloosa, Alabama, and votes for the Republican presidential candidate, with a correlation coefficient so strong, it's almost like a political alliance formed in the stratosphere. These findings highlight the need to not only consider traditional predictors of voter behavior but also to take a deep breath and acknowledge the influence of environmental factors. It's as if voters were saying, "We may not have been environmentalists, but we sure liked the air clean when we made our political choice!"

Now, as we wrap up our discussion, let's clear the air with a classic dad joke: Why did the Republican voter bring a ladder to the polling station? Because they heard the air up there was cleaner!

As for the future of this line of research, it seems that no more research is needed in this area. We've truly exhausted the topic, leaving it as fresh and clean as the Alabama air itself!

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 Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Politics and Policy

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 Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Environmental and Political Analysis (SEPA)

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 Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama with an abstract of In this research study, we delved into the curious connection between air quality and political preferences, particularly focusing on the correlation between air quality in Tuscaloosa, Alabama, and votes cast for the Republican presidential candidate. Examining the relationship between breathable air and political leanings may seem airy-fairy, but our findings revealed sturdy statistical insights that deserve to be given the clean air treatment.
Utilizing data from the Environmental Protection Agency and the MIT Election Data and Science Lab, Harvard Dataverse, we undertook a comprehensive analysis spanning the presidential elections from 1980 to 2020. Our research team performed rigorous statistical analyses and uncovered a correlation coefficient of 0.9761697, signaling a remarkably robust relationship between air quality and Republican votes in Alabama. To put it more plainly, the connection between breathing clean air and favoring the Republican candidate has been consistently as clear as, well, clean air.
The significance levels, denoted by p < 0.01, underscore the robustness of the findings, leaving little room for doubt that clean air has played a significant role in shaping political preferences in the state. It seems that when it comes to casting their ballot, the residents of Alabama might have been influenced not only by campaign promises but also by the promise of fresh, unpolluted air. It's as if voters were saying, "We may not have seen much smog on election day, but we still made our decision based on the haze we've been breathing in!"
This research sheds light on an unexpected yet impactful aspect of voter behavior, emphasizing the need for further exploration and understanding of the multifaceted influences that shape political choices. As the old saying goes, "Politics is like the air we breathe - it’s often foul, but we can't do without it.

ChatGPT:

air quality, political preferences, Republican votes, Alabama, correlation, environmental protection agency data, MIT election data and science lab, Harvard Dataverse, statistical analysis, presidential elections, clean air, significance levels, voter behavior, political choices

*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 quality in Tuscaloosa, Alabama
Detailed data title: Percentage of days with good or moderate air quality in Tuscaloosa, AL
Source: Environmental Protection Agency
See what else correlates with Air quality in Tuscaloosa, Alabama

Votes for the Republican Presidential candidate in Alabama
Detailed data title: Percentage of all votes cast for the Republican Presidential candidate in Alabama
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Alabama

Correlation r = 0.9761697 (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 = 0.9529072 (Coefficient of determination)
This means 95.3% of the change in the one variable (i.e., Votes for the Republican Presidential candidate in Alabama) is predictable based on the change in the other (i.e., Air quality in Tuscaloosa, Alabama) over the 7 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00017.^Show 0.0001662313155986080700000000
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 0.98 in 0.017% of random cases. Said differently, if you correlated 6,016 random variables^Note Which I absolutely did.
with the same 6 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 6 because we have two variables measured over a period of 7 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.

[ 0.84, 1 ] 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.

	1980	2000	2004	2008	2012	2016	2020
Air quality in Tuscaloosa, Alabama (Good air quality)	92.4242	96.2617	99.2727	99.6255	100	100	100
Votes for the Republican Presidential candidate in Alabama (Percentage of votes)	48.7501	56.4838	62.4607	60.3169	60.5458	62.0831	62.0316

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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.
Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.

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([92.4242,96.2617,99.2727,99.6255,100,100,100,])
array_2 = np.array([48.7501,56.4838,62.4607,60.3169,60.5458,62.0831,62.0316,])
array_1_name = "Air quality in Tuscaloosa, Alabama"
array_2_name = "Votes for the Republican Presidential candidate in Alabama"

# 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 quality in Tuscaloosa, Alabama
Line chart for only Votes for the Republican Presidential candidate in Alabama
AI-generated correlation image
The spurious research paper: Air-ing Out Political Preferences: The Influence of Air Quality on Republican Votes in Alabama

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Correlation ID: 5320 · Black Variable ID: 21368 · Red Variable ID: 26051


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