Spurious correlation #2,984 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of animal control workers in Nebraska and the second variable is Jet fuel used in Zambia. The chart goes from 2003 to 2018, 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 The number of animal control workers in Nebraska and the second variable is Jet fuel used in Zambia. The chart goes from 2003 to 2018, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As more animal control workers in Nebraska wrangled up stray critters, there was a surplus of buffalo chips. These dried dung discs were then exported to Zambia, where they were used as an alternative to traditional jet fuel, leading to a rise in overall jet fuel usage. It's a real gas how a little Midwestern moo-ving and shaking could propel the aviation industry in Zambia!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a comical meme showing a map with a small town in Nebraska on one side and Zambia on the other. In the Nebraska area, show a comically exaggerated number of squirrels overtaking the town, with acorns scattered everywhere. Meanwhile, on the Zambian side, depict vehicles with jet fuel tanks, with a visual representation of global acorn shortage causing this switch. The image should emphasize the contrasting situations in these two locations due to the unexpected relationship between the local squirrel population and jet fuel consumption. 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: The number of animal control workers in Nebraska and Jet fuel used in Zambia. 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: Pop art 3D Pixar Style a Polaroid photo Surrealism a meme
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 The number of animal control workers in Nebraska and Jet fuel used in Zambia, in the following situation: Because as the number of animal control workers in Nebraska decreased, the local squirrel population multiplied exponentially. Squirrels, known for their love of acorns, started hoarding them en masse, causing a shortage in the region. This shortage led to a worldwide acorn crisis, forcing Zambia to turn to using jet fuel as an alternative energy source for their vehicles, resulting in a surge in jet fuel consumption. It's a classic case of "acorn-y" causation!. Keep the description simple.
ChatGPT: In a small Nebraska town, the number of animal control workers dwindled, causing a surge in the local squirrel population. The squirrels multiplied exponentially, leading to a shortage of acorns. Meanwhile, halfway across the world in Zambia, the surge in jet fuel consumption became noticeable as vehicles there switched to using it due to a worldwide acorn crisis.

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 a small Nebraska town, the number of animal control workers dwindled, causing a surge in the local squirrel population. The squirrels multiplied exponentially, leading to a shortage of acorns. Meanwhile, halfway across the world in Zambia, the surge in jet fuel consumption became noticeable as vehicles there switched to using it due to a worldwide acorn crisis. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a comical meme showing a map with a small town in Nebraska on one side and Zambia on the other. In the Nebraska area, show a comically exaggerated number of squirrels overtaking the town, with acorns scattered everywhere. Meanwhile, on the Zambian side, depict vehicles with jet fuel tanks, with a visual representation of global acorn shortage causing this switch. The image should emphasize the contrasting situations in these two locations due to the unexpected relationship between the local squirrel population and jet fuel consumption.

*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 The number of animal control workers in Nebraska positively influenced Jet fuel used in Zambia. Include one good pun.

AI academic paper

(Because p < 0.01)

Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

Journal of Animal Behavior and Environmental Impacts

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of animal control workers in Nebraska and Jet fuel used in Zambia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

[[ABSTRACT]]
This research paper explores the seemingly unrelated spheres of animal control workers in Nebraska and jet fuel consumption in Zambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration covering the period from 2003 to 2018, a surprising correlation between the two variables has emerged. The correlation coefficient of 0.6486427 with a p-value less than 0.01 has raised some eyebrows in the academic community, leading to a thorough investigation of this unexpected relationship.

This study provides a howlingly entertaining analysis of the connection between these two seemingly disparate factors. The findings leave us wondering whether the number of animal control workers in Nebraska could be a key indicator of jet fuel demand in the distant land of Zambia. It's almost as if these variables are barking up the same tree!

While the causality remains to be determined, this paper sheds a comical light on the unexpected ways in which seemingly unrelated factors may intersect. This research challenges conventional assumptions and stimulates further inquiry, proving that even the most unexpected links can unearth valuable insights. It's as if the world of animal control and the world of jet fuel are not so "fur" apart after all!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The field of statistics often uncovers surprising correlations, leading researchers to exclaim, "What in carnation?!" In the tradition of unearthing unexpected connections, this study delves into the unlikely relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. Have you heard about the scientist who tried to cross a lion with a parrot? He ended up with a talking lion, and it really gave him paws for thought!
As researchers, we are accustomed to dealing with variables that seem to have as much in common as a hedgehog and a hairdryer. However, the correlation coefficient of 0.6486427 with a p-value less than 0.01 has led us down a particularly wild rabbit hole. If trends continue the way they have been, it's almost as if we're witnessing a case of "pawsitively" predictive analytics, or should we say, "pawsitively preposterous"!
Amidst the jest, this study is grounded in a thorough analysis of data sourced from the Bureau of Labor Statistics and the Energy Information Administration's comprehensive records spanning from 2003 to 2018. The data crunching process revealed a statistically significant association that left us scratching our heads as we pondered the possibility of animal control workers in Nebraska exerting an unforeseen influence on the demand for jet fuel in Zambia. It's almost as if these variables are in "fur-midable" cahoots!
While the precise cause-and-effect dynamics are yet to be deciphered, this study showcases the potential for unexpected intersections between seemingly unrelated variables. With a touch of humor and a dash of statistical rigor, we aim to demonstrate that even the most surprising connections can yield valuable insights. After all, in the world of research, truth is often stranger than fiction, and sometimes it's just as "pawsome" as a good dad joke!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Impact of Animal Control Policies on Community Health" by Smith et al., the authors examine the role of animal control workers in managing public health risks associated with stray animals. Their study emphasizes the importance of efficient animal control practices in preventing the spread of diseases and ensuring the well-being of both humans and animals. Similarly, in "A Comprehensive Analysis of Global Jet Fuel Consumption" by Doe and Jones, the authors delve into the intricacies of jet fuel demand across various regions, highlighting the factors that drive consumption patterns.

These serious studies set the stage for our unexpectedly delightful investigation into the correlation between the number of animal control workers in Nebraska and jet fuel usage in Zambia. It's as if the worlds of animal control and jet fuel are colliding in a parallel universe where airborne canines fuel their jetpacks with, you guessed it, jet fuel! Talk about "pawsitively" peculiar circumstances!

Drawing from the literature on animal welfare and resource consumption, "Animal Liberation" by Peter Singer and "The Omnivore's Dilemma" by Michael Pollan offer insights into the ethical and environmental considerations related to animal care and resource utilization. On the lighter side, fictional works such as "The Art of Racing in the Rain" by Garth Stein and "Marley & Me" by John Grogan infuse the theme of animal companionship with heartwarming tales and, dare we say, a generous sprinkle of "pawsitivity."

Moreover, popular movies such as "Bolt" and "The Secret Life of Pets" provide an entertaining lens through which to contemplate the interconnectedness of the animal world and human activities. After all, who wouldn't want to watch a movie where a jet-setting hamster saves the day by fueling up aircraft with acorn-infused jet fuel? Sometimes truth is indeed stranger than fiction, unless we're discussing the plot of an animated animal extravaganza!

In conclusion, the existing literature sets the context for our whimsically intriguing study, unraveling a web of connections that goes beyond the boundaries of rationality and ventures into the realm of sheer, unadulterated "pawsomeness." With a nod to the absurd and a wink to the unexpected, our research confronts the entangled mysteries of animal control and jet fuel consumption, leaving readers howling with laughter and pondering the potential marvels of statistical serendipity.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of animal control workers in Nebraska and Jet fuel used in Zambia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration . You used data from 2003 to 2018

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]]
Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

[[ABSTRACT]]
This research paper explores the seemingly unrelated spheres of animal control workers in Nebraska and jet fuel consumption in Zambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration covering the period from 2003 to 2018, a surprising correlation between the two variables has emerged. The correlation coefficient of 0.6486427 with a p-value less than 0.01 has raised some eyebrows in the academic community, leading to a thorough investigation of this unexpected relationship.

This study provides a howlingly entertaining analysis of the connection between these two seemingly disparate factors. The findings leave us wondering whether the number of animal control workers in Nebraska could be a key indicator of jet fuel demand in the distant land of Zambia. It's almost as if these variables are barking up the same tree!

While the causality remains to be determined, this paper sheds a comical light on the unexpected ways in which seemingly unrelated factors may intersect. This research challenges conventional assumptions and stimulates further inquiry, proving that even the most unexpected links can unearth valuable insights. It's as if the world of animal control and the world of jet fuel are not so "fur" apart after all!

[[INTRODUCTION]]
The field of statistics often uncovers surprising correlations, leading researchers to exclaim, "What in carnation?!" In the tradition of unearthing unexpected connections, this study delves into the unlikely relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. Have you heard about the scientist who tried to cross a lion with a parrot? He ended up with a talking lion, and it really gave him paws for thought!
As researchers, we are accustomed to dealing with variables that seem to have as much in common as a hedgehog and a hairdryer. However, the correlation coefficient of 0.6486427 with a p-value less than 0.01 has led us down a particularly wild rabbit hole. If trends continue the way they have been, it's almost as if we're witnessing a case of "pawsitively" predictive analytics, or should we say, "pawsitively preposterous"!
Amidst the jest, this study is grounded in a thorough analysis of data sourced from the Bureau of Labor Statistics and the Energy Information Administration's comprehensive records spanning from 2003 to 2018. The data crunching process revealed a statistically significant association that left us scratching our heads as we pondered the possibility of animal control workers in Nebraska exerting an unforeseen influence on the demand for jet fuel in Zambia. It's almost as if these variables are in "fur-midable" cahoots!
While the precise cause-and-effect dynamics are yet to be deciphered, this study showcases the potential for unexpected intersections between seemingly unrelated variables. With a touch of humor and a dash of statistical rigor, we aim to demonstrate that even the most surprising connections can yield valuable insights. After all, in the world of research, truth is often stranger than fiction, and sometimes it's just as "pawsome" as a good dad joke!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved a painstaking examination of data gathered from the Bureau of Labor Statistics and the Energy Information Administration for the years 2003 to 2018. The first step in the process was to collect information on the number of animal control workers in Nebraska and the amount of jet fuel consumed in Zambia during the specified period. It's as if we were wading through a jungle of data, hoping to emerge with a "roar"-missible connection!

The data were then subjected to a series of rigorous statistical analyses, including correlation and regression analyses, to determine the nature and strength of the relationship between the two variables. These analyses provided us with a "paw-sitively" enlightening insight into the potential connection between seemingly unrelated phenomena. It's almost as if we were sifting through data like an archaeologist in search of ancient "cata-bone-lisms"!

Furthermore, to ensure the robustness of our findings, sensitivity analyses were conducted to assess the impact of potential outliers and alternative model specifications. These analyses helped us to "unleash" the true essence of the relationship between animal control workers in Nebraska and jet fuel consumption in Zambia. It's almost as if we were navigating through statistical wilderness, hoping to catch a glimpse of a mythical "correlation-a-saurus"!

The final phase of the methodology involved cross-validation procedures and Monte Carlo simulations to test the stability and reliability of the observed relationship. These procedures allowed us to "fetch" the most accurate and "pawsome" estimates of the connection between the two variables. It's almost as if we were embarking on a statistical safari, armed with nothing but our calculators and a "roar"-ing sense of curiosity about this unexpected connection!

In conclusion, the methodology employed in this research was as thorough as it was amusing, offering a blend of statistical rigor and whimsical humor to unravel the enigmatic relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. It's almost as if we were chasing after the elusive "hypothesis-a-saurus," eager to bring it back to the scientific kingdom and proclaim, "Look what we've "dog"gedly uncovered!"

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of animal control workers in Nebraska and Jet fuel used in Zambia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

[[ABSTRACT]]
This research paper explores the seemingly unrelated spheres of animal control workers in Nebraska and jet fuel consumption in Zambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration covering the period from 2003 to 2018, a surprising correlation between the two variables has emerged. The correlation coefficient of 0.6486427 with a p-value less than 0.01 has raised some eyebrows in the academic community, leading to a thorough investigation of this unexpected relationship.

This study provides a howlingly entertaining analysis of the connection between these two seemingly disparate factors. The findings leave us wondering whether the number of animal control workers in Nebraska could be a key indicator of jet fuel demand in the distant land of Zambia. It's almost as if these variables are barking up the same tree!

While the causality remains to be determined, this paper sheds a comical light on the unexpected ways in which seemingly unrelated factors may intersect. This research challenges conventional assumptions and stimulates further inquiry, proving that even the most unexpected links can unearth valuable insights. It's as if the world of animal control and the world of jet fuel are not so "fur" apart after all!

[[LITERATURE REVIEW]]
In "The Impact of Animal Control Policies on Community Health" by Smith et al., the authors examine the role of animal control workers in managing public health risks associated with stray animals. Their study emphasizes the importance of efficient animal control practices in preventing the spread of diseases and ensuring the well-being of both humans and animals. Similarly, in "A Comprehensive Analysis of Global Jet Fuel Consumption" by Doe and Jones, the authors delve into the intricacies of jet fuel demand across various regions, highlighting the factors that drive consumption patterns.
These serious studies set the stage for our unexpectedly delightful investigation into the correlation between the number of animal control workers in Nebraska and jet fuel usage in Zambia. It's as if the worlds of animal control and jet fuel are colliding in a parallel universe where airborne canines fuel their jetpacks with, you guessed it, jet fuel! Talk about "pawsitively" peculiar circumstances!
Drawing from the literature on animal welfare and resource consumption, "Animal Liberation" by Peter Singer and "The Omnivore's Dilemma" by Michael Pollan offer insights into the ethical and environmental considerations related to animal care and resource utilization. On the lighter side, fictional works such as "The Art of Racing in the Rain" by Garth Stein and "Marley & Me" by John Grogan infuse the theme of animal companionship with heartwarming tales and, dare we say, a generous sprinkle of "pawsitivity."
Moreover, popular movies such as "Bolt" and "The Secret Life of Pets" provide an entertaining lens through which to contemplate the interconnectedness of the animal world and human activities. After all, who wouldn't want to watch a movie where a jet-setting hamster saves the day by fueling up aircraft with acorn-infused jet fuel? Sometimes truth is indeed stranger than fiction, unless we're discussing the plot of an animated animal extravaganza!
In conclusion, the existing literature sets the context for our whimsically intriguing study, unraveling a web of connections that goes beyond the boundaries of rationality and ventures into the realm of sheer, unadulterated "pawsomeness." With a nod to the absurd and a wink to the unexpected, our research confronts the entangled mysteries of animal control and jet fuel consumption, leaving readers howling with laughter and pondering the potential marvels of statistical serendipity.

[[RESULTS]]
The analysis of the data revealed a surprisingly strong positive correlation between the number of animal control workers in Nebraska and the consumption of jet fuel in Zambia over the period of 2003 to 2018. The correlation coefficient of 0.6486427 indicates a moderately strong relationship between these seemingly unrelated variables. It's almost as if they are saying, "It's not impawsible for us to be linked!"
Further statistical scrutiny yielded an r-squared value of 0.4207374, suggesting that approximately 42.07% of the variation in jet fuel consumption in Zambia can be explained by the number of animal control workers in Nebraska. As we dove deeper into the analysis, we couldn't help but wonder if the animal control workers were using jet fuel to chase after particularly speedy critters!
The p-value of less than 0.01 implies that the observed correlation is statistically significant, leaving our research team pondering the profound questions of causality and unanticipated linkages. As we tried to wrap our heads around these findings, we couldn't help but think, "What do you call a bear without ears? B!" It's clear that this unexpected correlation is no teddy bear picnic, but rather a thought-provoking enigma that requires further investigation.
The scatterplot (Fig. 1) visually represents the robust positive relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. This visual evidence further reinforces the unexpected nature of this correlation, reminding us that truth can indeed by stranger than fiction, and sometimes just as surprising as a well-timed dad joke!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The correlation between the number of animal control workers in Nebraska and the consumption of jet fuel in Zambia has raised some tail-wagging inquiries in the academic community. Our findings echo the sentiments expressed in prior research such as Smith et al.'s investigation into animal control policies and Doe and Jones' analysis of global jet fuel consumption. It's as if these serious studies have set the stage for our unexpectedly delightful investigation. Let's dig our claws into these results!

The statistically significant correlation coefficient found between the number of animal control workers in Nebraska and jet fuel consumption in Zambia supports the notion that these seemingly independent variables may indeed be related, albeit through indirect and unforeseen mechanisms. It's almost as if these variables are unleashing a pawsome synergy no one anticipated! Utilizing data spanning over a decade, our findings bark with confidence that a relationship exists. "What do you get if you cross a snake and a pie? A python!" - a relationship as unexpected as a reptilian dessert!

The r-squared value, suggesting that over 42% of the variation in jet fuel consumption in Zambia can be explained by the number of animal control workers in Nebraska, adds a purrfectly bewitching layer to this enigmatic correlation. The plot thickens as we contemplate the potential implications of this seemingly inconceivable link. "How does a scientist freshen their breath? With experi-mints!" We are faced with a conundrum that requires further exploration and a dash of laughter.

The visual representation of the relationship between these unlikely variables in the scatterplot further bolsters the validity of our findings. As we gaze upon this visually compelling evidence, we can't help but think of the unforeseen connections that make life as intriguing as a double-helix dad joke. "I told my wife she should embrace her mistakes. She gave me a hug!" - just as unexpected as a hug from an alpaca in the lab!

This study underscores the importance of approaching research with open-mindedness and a willingness to embrace the unexpected. The incredible link between the number of animal control workers in Nebraska and jet fuel consumption in Zambia challenges our understanding of causal relationships and statistical predictability, defying expectations with a "pawsitively" delightful twist. It's as if the statistical gods have decided to tickle our funny bones with this inexplicable correlation, leaving us in a state of bemused wonder and eager anticipation of future research endeavors.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of animal control workers in Nebraska and Jet fuel used in Zambia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

[[ABSTRACT]]
This research paper explores the seemingly unrelated spheres of animal control workers in Nebraska and jet fuel consumption in Zambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration covering the period from 2003 to 2018, a surprising correlation between the two variables has emerged. The correlation coefficient of 0.6486427 with a p-value less than 0.01 has raised some eyebrows in the academic community, leading to a thorough investigation of this unexpected relationship.

This study provides a howlingly entertaining analysis of the connection between these two seemingly disparate factors. The findings leave us wondering whether the number of animal control workers in Nebraska could be a key indicator of jet fuel demand in the distant land of Zambia. It's almost as if these variables are barking up the same tree!

While the causality remains to be determined, this paper sheds a comical light on the unexpected ways in which seemingly unrelated factors may intersect. This research challenges conventional assumptions and stimulates further inquiry, proving that even the most unexpected links can unearth valuable insights. It's as if the world of animal control and the world of jet fuel are not so "fur" apart after all!

[[INTRDUCTION]]
The field of statistics often uncovers surprising correlations, leading researchers to exclaim, "What in carnation?!" In the tradition of unearthing unexpected connections, this study delves into the unlikely relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. Have you heard about the scientist who tried to cross a lion with a parrot? He ended up with a talking lion, and it really gave him paws for thought!
As researchers, we are accustomed to dealing with variables that seem to have as much in common as a hedgehog and a hairdryer. However, the correlation coefficient of 0.6486427 with a p-value less than 0.01 has led us down a particularly wild rabbit hole. If trends continue the way they have been, it's almost as if we're witnessing a case of "pawsitively" predictive analytics, or should we say, "pawsitively preposterous"!
Amidst the jest, this study is grounded in a thorough analysis of data sourced from the Bureau of Labor Statistics and the Energy Information Administration's comprehensive records spanning from 2003 to 2018. The data crunching process revealed a statistically significant association that left us scratching our heads as we pondered the possibility of animal control workers in Nebraska exerting an unforeseen influence on the demand for jet fuel in Zambia. It's almost as if these variables are in "fur-midable" cahoots!
While the precise cause-and-effect dynamics are yet to be deciphered, this study showcases the potential for unexpected intersections between seemingly unrelated variables. With a touch of humor and a dash of statistical rigor, we aim to demonstrate that even the most surprising connections can yield valuable insights. After all, in the world of research, truth is often stranger than fiction, and sometimes it's just as "pawsome" as a good dad joke!

[[RESULTS]]
The analysis of the data revealed a surprisingly strong positive correlation between the number of animal control workers in Nebraska and the consumption of jet fuel in Zambia over the period of 2003 to 2018. The correlation coefficient of 0.6486427 indicates a moderately strong relationship between these seemingly unrelated variables. It's almost as if they are saying, "It's not impawsible for us to be linked!"
Further statistical scrutiny yielded an r-squared value of 0.4207374, suggesting that approximately 42.07% of the variation in jet fuel consumption in Zambia can be explained by the number of animal control workers in Nebraska. As we dove deeper into the analysis, we couldn't help but wonder if the animal control workers were using jet fuel to chase after particularly speedy critters!
The p-value of less than 0.01 implies that the observed correlation is statistically significant, leaving our research team pondering the profound questions of causality and unanticipated linkages. As we tried to wrap our heads around these findings, we couldn't help but think, "What do you call a bear without ears? B!" It's clear that this unexpected correlation is no teddy bear picnic, but rather a thought-provoking enigma that requires further investigation.
The scatterplot (Fig. 1) visually represents the robust positive relationship between the number of animal control workers in Nebraska and jet fuel consumption in Zambia. This visual evidence further reinforces the unexpected nature of this correlation, reminding us that truth can indeed by stranger than fiction, and sometimes just as surprising as a well-timed dad joke!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, this research has illuminated a rather unexpected correlation between the number of animal control workers in Nebraska and the consumption of jet fuel in Zambia. It's almost as if the animal control workers are fueling an entirely different kind of chase across continents! This correlation coefficient of 0.6486427 with a p-value less than 0.01 has left us with more questions than answers. It's as if we're caught in an elaborate game of "cat and mouse," or should we say, "control worker and jet."

However, this study has certainly highlighted the need to approach data analysis with an open mind and perhaps a sense of humor. Who knew that the world of animal control and the world of jet fuel could be so closely entwined? This research suggests that sometimes, the most unexpected connections can lead to intriguing discoveries. It's almost as if statistics has a sense of humor, playing practical jokes on unsuspecting researchers!

Yet, despite the compelling findings of this study, it may be time to leave this unlikely duo to their own devices. It's clear that further research in this area is not warranted. After all, when it comes to the interplay between animal control workers in Nebraska and jet fuel consumption in Zambia, it's probably best to leave well enough alone. There's only so much correlation one can "bear"!

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 Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Animal Behavior and Environmental Impacts

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 Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia. Respond only with the name of a fictional organization.

ChatGPT:

Society for International Comparative Zoological 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 Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia with an abstract of This research paper explores the seemingly unrelated spheres of animal control workers in Nebraska and jet fuel consumption in Zambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration covering the period from 2003 to 2018, a surprising correlation between the two variables has emerged. The correlation coefficient of 0.6486427 with a p-value less than 0.01 has raised some eyebrows in the academic community, leading to a thorough investigation of this unexpected relationship.
This study provides a howlingly entertaining analysis of the connection between these two seemingly disparate factors. The findings leave us wondering whether the number of animal control workers in Nebraska could be a key indicator of jet fuel demand in the distant land of Zambia. It's almost as if these variables are barking up the same tree!
While the causality remains to be determined, this paper sheds a comical light on the unexpected ways in which seemingly unrelated factors may intersect. This research challenges conventional assumptions and stimulates further inquiry, proving that even the most unexpected links can unearth valuable insights. It's as if the world of animal control and the world of jet fuel are not so "fur" apart after all!

ChatGPT:

pawsing for jet fuel, animal control workers, Nebraska, jet fuel consumption, Zambia, correlation coefficient, causality, Bureau of Labor Statistics, Energy Information Administration, unexpected relationship, comical light, surprising correlation, data analysis

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

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Data details

The number of animal control workers in Nebraska
Detailed data title: BLS estimate of animal control workers in Nebraska
Source: Bureau of Larbor Statistics
See what else correlates with The number of animal control workers in Nebraska

Jet fuel used in Zambia
Detailed data title: Volume of jet fuel used consumed in Zambia in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Jet fuel used in Zambia

Correlation r = 0.6486427 (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 = 0.4207374 (Coefficient of determination)
This means 42.1% of the change in the one variable (i.e., Jet fuel used in Zambia) is predictable based on the change in the other (i.e., The number of animal control workers in Nebraska) over the 16 years from 2003 through 2018.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0066.^Show 0.0065647142390038710000000000
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 0.65 in 0.66% of random cases. Said differently, if you correlated 152 random variables^Note Which I absolutely did.
with the same 15 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 15 because we have two variables measured over a period of 16 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.

[ 0.23, 0.87 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
The number of animal control workers in Nebraska (Animal Control Workers)	60	80	70	110	60	60	50	50	90	90	80	50	60	60	30	70
Jet fuel used in Zambia (Million Barrels/Day)	0.955945	0.996667	1.04285	1.10803	0.651781	0.823333	0.608329	0.695233	0.716959	1.04285	1.04285	0.825589	1	0.583279	0.514946	0.632936

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([60,80,70,110,60,60,50,50,90,90,80,50,60,60,30,70,])
array_2 = np.array([0.955945,0.996667,1.04285,1.10803,0.651781,0.823333,0.608329,0.695233,0.716959,1.04285,1.04285,0.825589,1,0.583279,0.514946,0.632936,])
array_1_name = "The number of animal control workers in Nebraska"
array_2_name = "Jet fuel used in Zambia"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
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Line chart for only The number of animal control workers in Nebraska
Line chart for only Jet fuel used in Zambia
AI-generated correlation image
The spurious research paper: Pawsing for Jet Fuel: An Unlikely Link Between Animal Control Workers in Nebraska and Jet Fuel Consumption in Zambia

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Correlation ID: 2984 · Black Variable ID: 10961 · Red Variable ID: 25091


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