Spurious correlation #4,447 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Annual US household spending on vehicle insurance and the second variable is Votes for the Democratic Presidential candidate in Ohio. The chart goes from 2000 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 Annual US household spending on vehicle insurance and the second variable is Votes for the Democratic Presidential candidate in Ohio. The chart goes from 2000 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As household spending on vehicle insurance went up, people started feeling the "Bern" when it came to their finances, leading to more votes for the Democrat Presidential candidate in Ohio. It seems like when it comes to car insurance and politics, there's always a lot at stake!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image in the style of cutsy art illustration depicting families gathered around a table in a cozy living room in Ohio, examining and comparing car insurance bills. The scene should display a warm and inviting atmosphere as individuals engage in discussions about the upcoming election, displaying eagerness to find financial relief and political change. Additionally, include visual cues that indicate the growing support for the Democrat Presidential candidate with subtle symbols or imagery to represent this sentiment. 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: Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. 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: Cutsy art illustration Cyberpunk Charcoal Symbolism Cutsy art illustration
ChatGPT: Cutsy art illustration

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 Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio, in the following situation: As household spending on vehicle insurance went up, people started feeling the "Bern" when it came to their finances, leading to more votes for the Democrat Presidential candidate in Ohio. It seems like when it comes to car insurance and politics, there's always a lot at stake!. Keep the description simple.
ChatGPT: In Ohio, more and more households were seen carefully examining their car insurance bills. As the numbers crept higher, the support for the Democrat Presidential candidate also seemed to be on the rise. The scene showed families comparing insurance plans while engaging in discussions about the upcoming election, with a shared eagerness to find financial relief and political change.

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: [ Cutsy art illustration ] and depicting this scene: In Ohio, more and more households were seen carefully examining their car insurance bills. As the numbers crept higher, the support for the Democrat Presidential candidate also seemed to be on the rise. The scene showed families comparing insurance plans while engaging in discussions about the upcoming election, with a shared eagerness to find financial relief and political change. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image in the style of cutsy art illustration depicting families gathered around a table in a cozy living room in Ohio, examining and comparing car insurance bills. The scene should display a warm and inviting atmosphere as individuals engage in discussions about the upcoming election, displaying eagerness to find financial relief and political change. Additionally, include visual cues that indicate the growing support for the Democrat Presidential candidate with subtle symbols or imagery to represent this sentiment.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in Annual US household spending on vehicle insurance positively influenced Votes for the Democrat Presidential candidate in Ohio. Include one good pun.

AI academic paper

(Because p < 0.01)

The Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

The Journal of Quirky Quantitative Analysis

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 Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. 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 some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

[[ABSTRACT]]
The importance of vehicle insurance and political preferences may seem like comparing apples to oranges, but our research team has dived headfirst into the depths of these seemingly unrelated topics to unearth potential connections. Utilizing a savory mix of data from the Bureau of Labor Statistics, MIT Election Data and Science Lab, and the Harvard Dataverse, we embarked on a journey to uncover the tantalizing tango of transportation trends.

Our findings revealed a correlation coefficient of 0.9893045 and a statistically significant p-value less than 0.01 for the period spanning from 2000 to 2020. This suggests that there may indeed be a strong relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the heartland state of Ohio.

While some may find this correlation perplexing, and perhaps drive them around the bend, our research indicates that there might be underlying factors at play. Perhaps individuals who spend more on vehicle insurance are angling for a political candidate who promises better road infrastructure or has a penchant for environmental policies that could affect the automotive industry. Then again, this correlation may simply be a fortunate fluke – after all, correlation does not necessarily imply causation.

In conclusion, our research sheds light on an unexpected intersection of consumer spending and political leanings. The results, though eyebrow-raising, reiterate the importance of exploring uncharted territories in research, because sometimes the most "outlandish" correlations can lead to new perspectives and insights.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
In the tumultuous tango of political preferences and economic behaviors, unlikely connections often emerge, beckoning researchers to waltz into unexplored territory. The allure of unveiling hidden patterns and uncovering unsuspected relationships fuels the fervor of academic inquiry. Our research delves into the curious correlation between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the quintessential battleground state of Ohio.
The capricious nature of human decision-making is a compelling subject of study, and the interplay between consumer expenditures and political affiliations offers a tantalizing puzzle to solve. While on the surface, the connection between purchasing vehicle insurance and casting a vote may appear as incongruous as a cow attempting a ballet, delve deeper, and unsuspected alignments may come to light.
Our investigation is not merely a whimsical pursuit of linking disparate domains, but an earnest endeavor to scrutinize the potential interplay between financial security in vehicular matters and political inclinations in the realm of governance. As researchers, we are acutely aware of the need to approach our subject matter with the utmost diligence, although the occasional detour into levity cannot be discounted.
The purpose of this research is twofold: first, to illuminate a potential relationship between annual household spending on vehicle insurance and voting behavior in a crucial political context; and second, to underscore the value of exploring unconventional associations, even if they may initially appear as improbable as a penguin performing the cha-cha.
In light of our findings, we invite the academic community to accompany us on this whimsical expedition as we unravel the mysterious dance between transportation trends, consumer choices, and political allegiances. While the idea of connecting vehicle insurance spending with voting preferences may raise a few eyebrows, our exploration may just yield fruitful insights that make the connection clearer than a freshly polished windshield.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find a positive correlation between Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. The study presents compelling statistical evidence and provides a solid foundation for our research. Building on this, "Doe et al." delve into the potential drivers of this correlation, suggesting that political inclinations may impact individuals' perceptions of necessary expenditures, such as vehicle insurance.

However, as we continue down this rabbit hole of vehicular expenditures and political predilections, it's important to consider the broader landscape of consumer behavior and its intersection with political choices. "Jones et al." emphasize the complex interplay of economic factors and political leanings, hinting at the possibility that discretionary spending may align with ideological preferences.

But let's face it, studying vehicle insurance spending and voting behavior is a wild ride in and of itself - a rollercoaster of statistical analysis and political musings. As we navigate this academic theme park, we must not overlook the relevance of books that could be related to our enigmatic exploration. "The Art of Racing in the Rain" by Garth Stein may not directly address the correlation between insurance spending and political votes, but it certainly highlights the profound bond between humans and their four-wheeled companions.

On a lighter note, fiction can sometimes hold a mirror to reality in the most unexpected ways. "Drive" by James Sallis combines the thrill of a high-octane getaway with a compelling narrative, reminding us that the road to uncovering hidden connections may be paved with unexpected twists and turns.

In the realm of childhood whimsy, the imagery of vehicular mayhem often takes center stage. "Wacky Races," a beloved cartoon featuring eccentric characters racing in outlandish vehicles, serves as a reminder that the world of transportation is rife with unpredictability and, dare I say, a dash of hilarity. Similarly, "Arthur," the endearing aardvark known for his everyday escapades, occasionally dips into transportation mishaps, providing a lighthearted perspective on the vehicular follies we encounter in both fiction and reality.

As we weave through this colorful tapestry of literature and childhood nostalgia, it becomes clear that the intersection of transportation, spending, and politics is not merely a dry subject for academic discourse. Rather, it is a playground of unexpected correlations and lighthearted parallels, beckoning researchers to embrace the joyful absurdity of academic discovery. So, as we buckle up for the next phase of our inquiry, let's remember to view this unusual connection through the lens of both scholarly rigor and unbridled whimsy. After all, who said academic research can't have a sense of humor?

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 Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and MIT Election Data and Science Lab, Harvard Dataverse .

For the time period 2000 to 2020, you found a correlation 0.9893045, r-squared of 0.9787234, 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 Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

[[ABSTRACT]]
The importance of vehicle insurance and political preferences may seem like comparing apples to oranges, but our research team has dived headfirst into the depths of these seemingly unrelated topics to unearth potential connections. Utilizing a savory mix of data from the Bureau of Labor Statistics, MIT Election Data and Science Lab, and the Harvard Dataverse, we embarked on a journey to uncover the tantalizing tango of transportation trends.

Our findings revealed a correlation coefficient of 0.9893045 and a statistically significant p-value less than 0.01 for the period spanning from 2000 to 2020. This suggests that there may indeed be a strong relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the heartland state of Ohio.

While some may find this correlation perplexing, and perhaps drive them around the bend, our research indicates that there might be underlying factors at play. Perhaps individuals who spend more on vehicle insurance are angling for a political candidate who promises better road infrastructure or has a penchant for environmental policies that could affect the automotive industry. Then again, this correlation may simply be a fortunate fluke – after all, correlation does not necessarily imply causation.

In conclusion, our research sheds light on an unexpected intersection of consumer spending and political leanings. The results, though eyebrow-raising, reiterate the importance of exploring uncharted territories in research, because sometimes the most "outlandish" correlations can lead to new perspectives and insights.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
In the tumultuous tango of political preferences and economic behaviors, unlikely connections often emerge, beckoning researchers to waltz into unexplored territory. The allure of unveiling hidden patterns and uncovering unsuspected relationships fuels the fervor of academic inquiry. Our research delves into the curious correlation between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the quintessential battleground state of Ohio.
The capricious nature of human decision-making is a compelling subject of study, and the interplay between consumer expenditures and political affiliations offers a tantalizing puzzle to solve. While on the surface, the connection between purchasing vehicle insurance and casting a vote may appear as incongruous as a cow attempting a ballet, delve deeper, and unsuspected alignments may come to light.
Our investigation is not merely a whimsical pursuit of linking disparate domains, but an earnest endeavor to scrutinize the potential interplay between financial security in vehicular matters and political inclinations in the realm of governance. As researchers, we are acutely aware of the need to approach our subject matter with the utmost diligence, although the occasional detour into levity cannot be discounted.
The purpose of this research is twofold: first, to illuminate a potential relationship between annual household spending on vehicle insurance and voting behavior in a crucial political context; and second, to underscore the value of exploring unconventional associations, even if they may initially appear as improbable as a penguin performing the cha-cha.
In light of our findings, we invite the academic community to accompany us on this whimsical expedition as we unravel the mysterious dance between transportation trends, consumer choices, and political allegiances. While the idea of connecting vehicle insurance spending with voting preferences may raise a few eyebrows, our exploration may just yield fruitful insights that make the connection clearer than a freshly polished windshield.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis unveiled a striking correlation coefficient of 0.9893045 between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. This eyebrow-raising correlation suggests a remarkably strong relationship between these two seemingly unrelated variables. The r-squared value of 0.9787234 further emphasized the robustness of this association, indicating that approximately 97.87% of the variation in votes for the Democrat presidential candidate in Ohio could be explained by the variation in annual US household spending on vehicle insurance.

Intriguingly, the p-value of less than 0.01 underscores the statistical significance of this relationship. This indicates that the probability of observing such a strong association between household spending on vehicle insurance and voting behavior in Ohio due to chance alone is less than 1%, a probability lower than finding a parking spot at a popular shopping mall during the holiday season.

In Fig. 1, the scatterplot vividly depicts the enthralling relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. The data points align themselves along a distinctive upward trend, resembling the seamless coordination of a well-rehearsed dance routine.

While this correlation may leave some scratching their heads as if tormented by an elusive itch in a hard-to-reach spot, it is crucial to exercise caution in drawing causal inferences from these findings. As the age-old adage goes, "correlation does not imply causation." The strong correlation may be attributed to confounding variables lurking in the shadows, yet to be unveiled.

These findings introduce a curious conundrum that invites speculation and contemplation. One might ponder if individuals with higher expenditures on vehicle insurance are swayed by political candidates who promise smoother roads, metaphorically and literally, or advocate for policies that could impact the automotive industry. Or, this correlation may indeed be a fortuitous fluke, a reminder that in the intricate web of statistical analysis, surprises are as inevitable as potholes on a poorly-maintained road.

In closing, our research has cast a spotlight on an unexpected nexus between consumer spending and political inclinations. While this correlation may initially appear as peculiar as a unicycle in rush-hour traffic, it underscores the value of exploring unconventional relationships in the pursuit of knowledge. This intersection between seemingly disparate realms may offer new perspectives and insights, akin to stumbling upon a hidden treasure trove in the vast terrain of research.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

[[ABSTRACT]]
The importance of vehicle insurance and political preferences may seem like comparing apples to oranges, but our research team has dived headfirst into the depths of these seemingly unrelated topics to unearth potential connections. Utilizing a savory mix of data from the Bureau of Labor Statistics, MIT Election Data and Science Lab, and the Harvard Dataverse, we embarked on a journey to uncover the tantalizing tango of transportation trends.

Our findings revealed a correlation coefficient of 0.9893045 and a statistically significant p-value less than 0.01 for the period spanning from 2000 to 2020. This suggests that there may indeed be a strong relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the heartland state of Ohio.

While some may find this correlation perplexing, and perhaps drive them around the bend, our research indicates that there might be underlying factors at play. Perhaps individuals who spend more on vehicle insurance are angling for a political candidate who promises better road infrastructure or has a penchant for environmental policies that could affect the automotive industry. Then again, this correlation may simply be a fortunate fluke – after all, correlation does not necessarily imply causation.

In conclusion, our research sheds light on an unexpected intersection of consumer spending and political leanings. The results, though eyebrow-raising, reiterate the importance of exploring uncharted territories in research, because sometimes the most "outlandish" correlations can lead to new perspectives and insights.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find a positive correlation between Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. The study presents compelling statistical evidence and provides a solid foundation for our research. Building on this, "Doe et al." delve into the potential drivers of this correlation, suggesting that political inclinations may impact individuals' perceptions of necessary expenditures, such as vehicle insurance.
However, as we continue down this rabbit hole of vehicular expenditures and political predilections, it's important to consider the broader landscape of consumer behavior and its intersection with political choices. "Jones et al." emphasize the complex interplay of economic factors and political leanings, hinting at the possibility that discretionary spending may align with ideological preferences.
But let's face it, studying vehicle insurance spending and voting behavior is a wild ride in and of itself - a rollercoaster of statistical analysis and political musings. As we navigate this academic theme park, we must not overlook the relevance of books that could be related to our enigmatic exploration. "The Art of Racing in the Rain" by Garth Stein may not directly address the correlation between insurance spending and political votes, but it certainly highlights the profound bond between humans and their four-wheeled companions.
On a lighter note, fiction can sometimes hold a mirror to reality in the most unexpected ways. "Drive" by James Sallis combines the thrill of a high-octane getaway with a compelling narrative, reminding us that the road to uncovering hidden connections may be paved with unexpected twists and turns.
In the realm of childhood whimsy, the imagery of vehicular mayhem often takes center stage. "Wacky Races," a beloved cartoon featuring eccentric characters racing in outlandish vehicles, serves as a reminder that the world of transportation is rife with unpredictability and, dare I say, a dash of hilarity. Similarly, "Arthur," the endearing aardvark known for his everyday escapades, occasionally dips into transportation mishaps, providing a lighthearted perspective on the vehicular follies we encounter in both fiction and reality.
As we weave through this colorful tapestry of literature and childhood nostalgia, it becomes clear that the intersection of transportation, spending, and politics is not merely a dry subject for academic discourse. Rather, it is a playground of unexpected correlations and lighthearted parallels, beckoning researchers to embrace the joyful absurdity of academic discovery. So, as we buckle up for the next phase of our inquiry, let's remember to view this unusual connection through the lens of both scholarly rigor and unbridled whimsy. After all, who said academic research can't have a sense of humor?

[[RESULTS]]
The statistical analysis unveiled a striking correlation coefficient of 0.9893045 between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. This eyebrow-raising correlation suggests a remarkably strong relationship between these two seemingly unrelated variables. The r-squared value of 0.9787234 further emphasized the robustness of this association, indicating that approximately 97.87% of the variation in votes for the Democrat presidential candidate in Ohio could be explained by the variation in annual US household spending on vehicle insurance.
Intriguingly, the p-value of less than 0.01 underscores the statistical significance of this relationship. This indicates that the probability of observing such a strong association between household spending on vehicle insurance and voting behavior in Ohio due to chance alone is less than 1%, a probability lower than finding a parking spot at a popular shopping mall during the holiday season.
In Fig. 1, the scatterplot vividly depicts the enthralling relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. The data points align themselves along a distinctive upward trend, resembling the seamless coordination of a well-rehearsed dance routine.
While this correlation may leave some scratching their heads as if tormented by an elusive itch in a hard-to-reach spot, it is crucial to exercise caution in drawing causal inferences from these findings. As the age-old adage goes, "correlation does not imply causation." The strong correlation may be attributed to confounding variables lurking in the shadows, yet to be unveiled.
These findings introduce a curious conundrum that invites speculation and contemplation. One might ponder if individuals with higher expenditures on vehicle insurance are swayed by political candidates who promise smoother roads, metaphorically and literally, or advocate for policies that could impact the automotive industry. Or, this correlation may indeed be a fortuitous fluke, a reminder that in the intricate web of statistical analysis, surprises are as inevitable as potholes on a poorly-maintained road.
In closing, our research has cast a spotlight on an unexpected nexus between consumer spending and political inclinations. While this correlation may initially appear as peculiar as a unicycle in rush-hour traffic, it underscores the value of exploring unconventional relationships in the pursuit of knowledge. This intersection between seemingly disparate realms may offer new perspectives and insights, akin to stumbling upon a hidden treasure trove in the vast terrain of research.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study robustly supported the previous research conducted by Smith et al., which found a positive correlation between annual US household spending on vehicle insurance and votes for the Democrat Presidential candidate in Ohio. While some may find this connection as surprising as unexpectedly finding the perfect parallel parking spot on a bustling city street, our findings reinforce the notion that there may indeed be a compelling relationship between these seemingly disparate variables.

Drawing from the lighthearted insights of the literature review, it's intriguing to consider whether individuals with higher vehicle insurance spending are attracted to political candidates who promise smoother roads, both figuratively and literally. This presents a vivid reminder that in the realm of political influence, the road to winning over voters may indeed be paved with the asphalt of crucial policy agendas. Additionally, the potential impact of environmental policies on the automotive industry could also be a driving force behind this correlation – a reminder that political platforms can fuel the engines of consumer sentiment.

The statistical significance of the correlation coefficient and the p-value, resembling the elusive quest for a prime parking space at a bustling shopping center, highlights that this relationship is not simply a matter of happenstance. It is akin to the perfect alignment of wheels on a well-coordinated vehicle, inviting us to admire the effortless synchrony of these seemingly incongruent variables.

However, as with any thrilling tango, it's essential to tread carefully when inferring causation from correlation. Just like navigating a twisting, turning mountain road, caution is key when attributing the observed correlation to a direct cause-and-effect relationship. The confounding variables lurking in the shadows, akin to twists and turns on a winding road, remind us that surprises are as inevitable as unexpected potholes.

In conclusion, our research has not only reaffirmed the existence of a thought-provoking nexus between consumer spending and political inclinations but has also underscored the value of exploring unconventional relationships in the pursuit of scholarly knowledge. This curious connection serves as a testament to the unexpected intersections that can lead us to elusive insights, much like discovering hidden treasure amidst the labyrinthine terrain of research.

Now, for a bit of whimsy, it's as if our research has stumbled upon a delightfully unexpected correlation, akin to discovering a comical clown car in the midst of a stately parade. This further emphasizes that the pursuit of knowledge is not always a straight and narrow path; sometimes, it's the unexpected detours that lead to the most enlightening destinations. So, as we inch closer to uncovering the most unexpected of scholarly truths, let's not forget to embrace the exhilarating, offbeat journey that is academic research. After all, who said scholarly pursuits couldn't have a touch of humor?

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 Annual US household spending on vehicle insurance and Votes for the Democrat Presidential candidate in Ohio. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

[[ABSTRACT]]
The importance of vehicle insurance and political preferences may seem like comparing apples to oranges, but our research team has dived headfirst into the depths of these seemingly unrelated topics to unearth potential connections. Utilizing a savory mix of data from the Bureau of Labor Statistics, MIT Election Data and Science Lab, and the Harvard Dataverse, we embarked on a journey to uncover the tantalizing tango of transportation trends.

Our findings revealed a correlation coefficient of 0.9893045 and a statistically significant p-value less than 0.01 for the period spanning from 2000 to 2020. This suggests that there may indeed be a strong relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the heartland state of Ohio.

While some may find this correlation perplexing, and perhaps drive them around the bend, our research indicates that there might be underlying factors at play. Perhaps individuals who spend more on vehicle insurance are angling for a political candidate who promises better road infrastructure or has a penchant for environmental policies that could affect the automotive industry. Then again, this correlation may simply be a fortunate fluke – after all, correlation does not necessarily imply causation.

In conclusion, our research sheds light on an unexpected intersection of consumer spending and political leanings. The results, though eyebrow-raising, reiterate the importance of exploring uncharted territories in research, because sometimes the most "outlandish" correlations can lead to new perspectives and insights.

[[INTRDUCTION]]
INTRODUCTION
In the tumultuous tango of political preferences and economic behaviors, unlikely connections often emerge, beckoning researchers to waltz into unexplored territory. The allure of unveiling hidden patterns and uncovering unsuspected relationships fuels the fervor of academic inquiry. Our research delves into the curious correlation between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the quintessential battleground state of Ohio.
The capricious nature of human decision-making is a compelling subject of study, and the interplay between consumer expenditures and political affiliations offers a tantalizing puzzle to solve. While on the surface, the connection between purchasing vehicle insurance and casting a vote may appear as incongruous as a cow attempting a ballet, delve deeper, and unsuspected alignments may come to light.
Our investigation is not merely a whimsical pursuit of linking disparate domains, but an earnest endeavor to scrutinize the potential interplay between financial security in vehicular matters and political inclinations in the realm of governance. As researchers, we are acutely aware of the need to approach our subject matter with the utmost diligence, although the occasional detour into levity cannot be discounted.
The purpose of this research is twofold: first, to illuminate a potential relationship between annual household spending on vehicle insurance and voting behavior in a crucial political context; and second, to underscore the value of exploring unconventional associations, even if they may initially appear as improbable as a penguin performing the cha-cha.
In light of our findings, we invite the academic community to accompany us on this whimsical expedition as we unravel the mysterious dance between transportation trends, consumer choices, and political allegiances. While the idea of connecting vehicle insurance spending with voting preferences may raise a few eyebrows, our exploration may just yield fruitful insights that make the connection clearer than a freshly polished windshield.

[[RESULTS]]
The statistical analysis unveiled a striking correlation coefficient of 0.9893045 between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. This eyebrow-raising correlation suggests a remarkably strong relationship between these two seemingly unrelated variables. The r-squared value of 0.9787234 further emphasized the robustness of this association, indicating that approximately 97.87% of the variation in votes for the Democrat presidential candidate in Ohio could be explained by the variation in annual US household spending on vehicle insurance.
Intriguingly, the p-value of less than 0.01 underscores the statistical significance of this relationship. This indicates that the probability of observing such a strong association between household spending on vehicle insurance and voting behavior in Ohio due to chance alone is less than 1%, a probability lower than finding a parking spot at a popular shopping mall during the holiday season.
In Fig. 1, the scatterplot vividly depicts the enthralling relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio. The data points align themselves along a distinctive upward trend, resembling the seamless coordination of a well-rehearsed dance routine.
While this correlation may leave some scratching their heads as if tormented by an elusive itch in a hard-to-reach spot, it is crucial to exercise caution in drawing causal inferences from these findings. As the age-old adage goes, "correlation does not imply causation." The strong correlation may be attributed to confounding variables lurking in the shadows, yet to be unveiled.
These findings introduce a curious conundrum that invites speculation and contemplation. One might ponder if individuals with higher expenditures on vehicle insurance are swayed by political candidates who promise smoother roads, metaphorically and literally, or advocate for policies that could impact the automotive industry. Or, this correlation may indeed be a fortuitous fluke, a reminder that in the intricate web of statistical analysis, surprises are as inevitable as potholes on a poorly-maintained road.
In closing, our research has cast a spotlight on an unexpected nexus between consumer spending and political inclinations. While this correlation may initially appear as peculiar as a unicycle in rush-hour traffic, it underscores the value of exploring unconventional relationships in the pursuit of knowledge. This intersection between seemingly disparate realms may offer new perspectives and insights, akin to stumbling upon a hidden treasure trove in the vast terrain of research.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In wrapping up this curious crusade through the labyrinth of data, we have peeled back the layers of the tantalizing tango of transportation trends. Our findings illuminated a correlation between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in Ohio that is stronger than Arnold Schwarzenegger's biceps in his prime – statistically speaking, of course.

The significance of this correlation, with a p-value lower than a politician's approval rating during a scandal, cannot be ignored. However, we must tread cautiously on this intriguing path, acknowledging that correlation does not prove causation, just as owning a car does not guarantee driving skills, as anyone who has witnessed a parallel parking attempt can attest.

Our results open doors to a world of pondering and speculation. Does greater insurance spending lead voters to lean left in search of pothole-free promises, or could it be a mere coincidence, much like finding spare change in couch cushions? The mysteries abound, like a car disappearing in a crowded parking lot on a Friday evening.

While the correlation may appear as unexpected as seeing a unicyclist in rush-hour traffic, our expedition has shown the value of probing unconventional connections. This research reminds us that in the wild world of statistical analysis, surprises are as common as road construction during rush hour.

In conclusion, we assert that no further research is needed in this area. After all, sometimes it's best to leave the mysterious dancing between vehicle insurance spending and political leanings to the imagination, just like the secret seasoning of a well-guarded family recipe.

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 Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky Quantitative Analysis

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 Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Whimsical Economic Studies

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 Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio with an abstract of The importance of vehicle insurance and political preferences may seem like comparing apples to oranges, but our research team has dived headfirst into the depths of these seemingly unrelated topics to unearth potential connections. Utilizing a savory mix of data from the Bureau of Labor Statistics, MIT Election Data and Science Lab, and the Harvard Dataverse, we embarked on a journey to uncover the tantalizing tango of transportation trends.
Our findings revealed a correlation coefficient of 0.9893045 and a statistically significant p-value less than 0.01 for the period spanning from 2000 to 2020. This suggests that there may indeed be a strong relationship between annual US household spending on vehicle insurance and votes for the Democrat presidential candidate in the heartland state of Ohio.
While some may find this correlation perplexing, and perhaps drive them around the bend, our research indicates that there might be underlying factors at play. Perhaps individuals who spend more on vehicle insurance are angling for a political candidate who promises better road infrastructure or has a penchant for environmental policies that could affect the automotive industry. Then again, this correlation may simply be a fortunate fluke – after all, correlation does not necessarily imply causation.
In conclusion, our research sheds light on an unexpected intersection of consumer spending and political leanings. The results, though eyebrow-raising, reiterate the importance of exploring uncharted territories in research, because sometimes the most "outlandish" correlations can lead to new perspectives and insights.

ChatGPT:

annual, US, household spending, vehicle insurance, Democrat, presidential candidate, Ohio, correlation, Bureau of Labor Statistics, MIT Election Data and Science Lab, Harvard Dataverse, correlation coefficient, p-value, 2000-2020, road infrastructure, environmental policies, automotive industry, consumer spending, political leanings

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

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Data details

Annual US household spending on vehicle insurance
Detailed data title: Average annual household spend on vehicle insurance
Source: Bureau of Labor Statistics
See what else correlates with Annual US household spending on vehicle insurance

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

Correlation r = 0.9893045 (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 = 0.9787234 (Coefficient of determination)
This means 97.9% of the change in the one variable (i.e., Votes for the Democratic Presidential candidate in Ohio) is predictable based on the change in the other (i.e., Annual US household spending on vehicle insurance) over the 6 years from 2000 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.000171.^Show 0.0001709793877821904000000000
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 0.99 in 0.0171% of random cases. Said differently, if you correlated 5,849 random variables^Note Which I absolutely did.
with the same 5 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.9, 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.

	2000	2004	2008	2012	2016	2020
Annual US household spending on vehicle insurance (Household spend)	778	964	1113	1018	660	758
Votes for the Democratic Presidential candidate in Ohio (Percentage of votes)	46.4404	48.7067	51.5043	50.6667	43.5581	45.2393

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.
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([778,964,1113,1018,660,758,])
array_2 = np.array([46.4404,48.7067,51.5043,50.6667,43.5581,45.2393,])
array_1_name = "Annual US household spending on vehicle insurance"
array_2_name = "Votes for the Democratic Presidential candidate in Ohio"

# 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.

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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 Annual US household spending on vehicle insurance
Line chart for only Votes for the Democratic Presidential candidate in Ohio
AI-generated correlation image
The spurious research paper: The Tantalizing Tango of Transportation Trends: Examining the Relationship Between Annual US Household Spending on Vehicle Insurance and Votes for the Democrat Presidential Candidate in Ohio

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Correlation ID: 4447 · Black Variable ID: 19931 · Red Variable ID: 26151


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