Download png, svg
AI explanation
As more robberies occurred in Montana, the demand for police car chases increased. This led to a spike in the consumption of gasoline. However, due to a series of wacky shipping mishaps, the majority of this extra petroleum somehow ended up in Uzbekistan, where it was used in a comically elaborate scheme to power a giant, robbers-themed amusement park. This, in turn, led to an unexpected surge in petrol consumption in Uzbekistan. It's a classic case of crime in one corner of the world leading to a slippery situation in another!
Model: dalle-3
Prompt: [ A photo-realistic image ] A split screen image. On the left side, depict a quiet town in Montana with a vintage gas station nestled among peaceful surroundings, as a group of masked robbers hold up the station, stealing fuel cans and siphoning gasoline from parked cars. The scene should convey a sense of tension and urgency as the robbers carry out their illicit activities. On the right side, depict a bustling city in Uzbekistan with a line of cars at a busy petrol station, portraying the consumption of large amounts of petroleum as drivers fill up their tanks. The contrast between the two scenes should be evident, capturing the essence of the prompt. 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: Robberies in Montana and Petroluem consumption in Uzbekistan. 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: Cubism Glitch art inspired by bauhaus A photo-realistic image Scalar art
ChatGPT: A photo-realistic image
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 Robberies in Montana and Petroluem consumption in Uzbekistan together. Keep the description simple.
ChatGPT: In a quiet town in Montana, a group of masked robbers holds up a gas station, stealing fuel cans and siphoning gasoline from parked cars. Meanwhile, in a bustling city in Uzbekistan, a line of cars wait at a busy petrol station, consuming large amounts of petroleum as the drivers fill up their tanks.
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 photo-realistic image ] and depicting this scene: In a quiet town in Montana, a group of masked robbers holds up a gas station, stealing fuel cans and siphoning gasoline from parked cars. Meanwhile, in a bustling city in Uzbekistan, a line of cars wait at a busy petrol station, consuming large amounts of petroleum as the drivers fill up their tanks. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] A split screen image. On the left side, depict a quiet town in Montana with a vintage gas station nestled among peaceful surroundings, as a group of masked robbers hold up the station, stealing fuel cans and siphoning gasoline from parked cars. The scene should convey a sense of tension and urgency as the robbers carry out their illicit activities. On the right side, depict a bustling city in Uzbekistan with a line of cars at a busy petrol station, portraying the consumption of large amounts of petroleum as drivers fill up their tanks. The contrast between the two scenes should be evident, capturing the essence of the prompt.
*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.
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 Robberies in Montana positively influenced Petroluem consumption in Uzbekistan.
AI academic paper
 |
Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption Journal of International Criminology and Energy Consumption Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Make the title a clever pun.
Your research team used data from FBI Criminal Justice Information Services and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.7106527 and p < 0.01 for 1992 to 2021.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Here is the title and abstract of the paper:
[[TITLE]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The world of research often leads us down unexpected paths, and in the world of data analysis, the most curious relationships can emerge. Unlikely pairs, such as Montana and Uzbekistan, may seem unrelated at first glance, but our investigation has brought to light an intriguing connection between the robbery rates in the former and petroleum consumption in the latter. As the saying goes, "Where there's smoke, there's fire," and indeed, our data analysis has uncovered some rather incendiary correlations.
It is well known that correlations do not necessarily imply causation, but they sure do make heads turn and raise eyebrows. As researchers, we must not only think outside the box but also at times question the very existence of the box itself. Our findings suggest that while one might not expect a relationship between robberies in the vast plains of Montana and the petroleum consumption in the distant land of Uzbekistan, stranger things have been proven in the world of data analysis. It's a wild, wild world out there, and our research only serves to reinforce the notion that the unexpected can indeed yield valuable insights.
Join us on this intriguing journey as we uncover the mysterious dance between crime in the wild west and oil consumption in distant lands. After all, as we know, "Truth is stranger than fiction," and sometimes, data analysis can be just as bewildering. So buckle up and get ready for a ride as we dive headfirst into this surprising correlation. Who knows, we might just uncover a true "oil heist" in the making!
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.
Here is the title and abstract of the paper:
[[TITLE]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of research often leads us down unexpected paths, and in the world of data analysis, the most curious relationships can emerge. Unlikely pairs, such as Montana and Uzbekistan, may seem unrelated at first glance, but our investigation has brought to light an intriguing connection between the robbery rates in the former and petroleum consumption in the latter. As the saying goes, "Where there's smoke, there's fire," and indeed, our data analysis has uncovered some rather incendiary correlations.
It is well known that correlations do not necessarily imply causation, but they sure do make heads turn and raise eyebrows. As researchers, we must not only think outside the box but also at times question the very existence of the box itself. Our findings suggest that while one might not expect a relationship between robberies in the vast plains of Montana and the petroleum consumption in the distant land of Uzbekistan, stranger things have been proven in the world of data analysis. It's a wild, wild world out there, and our research only serves to reinforce the notion that the unexpected can indeed yield valuable insights.
Join us on this intriguing journey as we uncover the mysterious dance between crime in the wild west and oil consumption in distant lands. After all, as we know, "Truth is stranger than fiction," and sometimes, data analysis can be just as bewildering. So buckle up and get ready for a ride as we dive headfirst into this surprising correlation. Who knows, we might just uncover a true "oil heist" in the making!
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The examination of the relationship between robbery rates in Montana and petroleum consumption in Uzbekistan has garnered considerable interest in academic and research circles. Smith, in "The Economic Impacts of Crime," highlights the pervasive impact of criminal activities on various economic sectors, drawing attention to the potential ripple effects on industries such as energy consumption. Similarly, Doe's analysis in "Criminal Behavior and Its Socioeconomic Consequences" underscores the need for a comprehensive understanding of the socioeconomic factors influencing criminal behavior and its potential impact on global markets, including the petroleum industry.
Furthermore, Jones et al., in "Energy Dynamics in Central Asia," explores the intricate web of geopolitical and economic factors shaping energy consumption patterns among Central Asian countries, shedding light on the nuanced dynamics that may contribute to unexpected correlations with seemingly unrelated phenomena.
Turning to non-fiction literature on related themes, "The Oil Road: Journeys from the Caspian Sea to the City of London" by Makan, offers insightful perspectives on the geopolitical implications of the oil trade, presenting a holistic view of the energy landscape that may indirectly intersect with criminal activities in distant regions.
Delving into the world of fiction, the work of James Ellroy, particularly "American Tabloid," offers a provocative, albeit fictional, account of criminal intrigue and political maneuverings set against the backdrop of historical events, inviting readers to consider the potential interplay between criminal operations and global economic machinations.
In the realm of television, series such as "Breaking Bad" and "Ozark" have captivated audiences with their portrayal of criminal enterprises and the intricate web of relationships that underpin illicit activities, thus offering a fictional lens through which to contemplate the complexities of criminal behavior and unexpected economic connections.
While the literature presents a blend of serious economic analyses and fictional narratives, it is evident that the relationship between Montana robberies and Uzbekistan's petroleum consumption has captured the imagination of authors and creators across diverse genres, reflecting the multidimensional nature of the topic at hand.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from FBI Criminal Justice Information Services and Energy Information Administration . You used data from 1992 to 2021
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
[[INTRODUCTION]]
The world of research often leads us down unexpected paths, and in the world of data analysis, the most curious relationships can emerge. Unlikely pairs, such as Montana and Uzbekistan, may seem unrelated at first glance, but our investigation has brought to light an intriguing connection between the robbery rates in the former and petroleum consumption in the latter. As the saying goes, "Where there's smoke, there's fire," and indeed, our data analysis has uncovered some rather incendiary correlations.
It is well known that correlations do not necessarily imply causation, but they sure do make heads turn and raise eyebrows. As researchers, we must not only think outside the box but also at times question the very existence of the box itself. Our findings suggest that while one might not expect a relationship between robberies in the vast plains of Montana and the petroleum consumption in the distant land of Uzbekistan, stranger things have been proven in the world of data analysis. It's a wild, wild world out there, and our research only serves to reinforce the notion that the unexpected can indeed yield valuable insights.
Join us on this intriguing journey as we uncover the mysterious dance between crime in the wild west and oil consumption in distant lands. After all, as we know, "Truth is stranger than fiction," and sometimes, data analysis can be just as bewildering. So buckle up and get ready for a ride as we dive headfirst into this surprising correlation. Who knows, we might just uncover a true "oil heist" in the making!
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
In order to unravel the enigmatic relationship between Montana robberies and Uzbekistan's petroleum consumption, our research team embarked on a data collection odyssey that took us through the depths of the FBI's Criminal Justice Information Services and the labyrinths of the Energy Information Administration. The years of interest spanned from 1992 to 2021, providing us with a substantial dataset to tease out any potential connections.
The first step of our convoluted journey involved summoning the powers of the internet, where we dived into the digital seas in search of the most reliable and comprehensive sources of data. As we combed through the vast expanse of information, we stumbled across the hallowed halls of the FBI's Criminal Justice Information Services, where crime data from the state of Montana awaited our perusal. The treacherous path to obtaining this data was not without its pitfalls – we braved through websites with designs straight out of the '90s and navigated through the myriad of bureaucratic forms, emerging triumphantly with a trove of robbery statistics in hand.
Meanwhile, in a parallel quest for knowledge, we ventured into the complex realms of the Energy Information Administration, where the secrets of Uzbekistan's petroleum consumption lay buried within tables and spreadsheets. Navigating these data archives was akin to traversing an ever-shifting desert of figures and statistics, where the mirages of incomplete datasets and outliers threatened to lead us astray.
Having assembled these disparate pieces of the puzzle, we sought to wield the mighty sword of statistical analysis to cut through the Gordian knot of correlation. Armed with our trusty software and an unyielding determination, we set about calculating Pearson's correlation coefficient, hoping to unveil the hidden threads binding these seemingly disparate phenomena. Our calculations were met with astonishment as the correlation coefficient of 0.7106527 emerged from the mists of probability, pointing towards a striking association that defied conventional expectations.
The pivotal moment arrived as we confronted the formidable p-value, grappling with its elusive nature and wielding our statistical arsenal to wrestle significance from the jaws of uncertainty. The triumphant revelation of a p-value less than 0.01 sent shockwaves through the research team, leaving us in awe of the unexpected depths of this correlation.
It is worth noting that our analysis was not without its perils, as we encountered the siren call of spurious correlations and the lurking specter of confounding variables. Navigating these treacherous waters required a keen eye and a healthy dose of skepticism to distinguish true signals from mere noise, akin to finding a needle in a haystack filled with red herrings.
In conclusion, our methodology may have been fraught with digital peril and statistical acrobatics, but through our intrepid efforts, we have shed light on the unlikeliest of correlations. It is our hope that future researchers will set sail on their own voyages of discovery, embracing the unexpected and reveling in the bizarre, for as we have learned, the most improbable connections may hold the key to unlocking the mysteries of our world.
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Your research team collected data from all across the internet, but mostly just used information from FBI Criminal Justice Information Services and Energy Information Administration .
For the time period 1992 to 2021, you found a correlation 0.7106527, r-squared of 0.5050273, 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]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The world of research often leads us down unexpected paths, and in the world of data analysis, the most curious relationships can emerge. Unlikely pairs, such as Montana and Uzbekistan, may seem unrelated at first glance, but our investigation has brought to light an intriguing connection between the robbery rates in the former and petroleum consumption in the latter. As the saying goes, "Where there's smoke, there's fire," and indeed, our data analysis has uncovered some rather incendiary correlations.
It is well known that correlations do not necessarily imply causation, but they sure do make heads turn and raise eyebrows. As researchers, we must not only think outside the box but also at times question the very existence of the box itself. Our findings suggest that while one might not expect a relationship between robberies in the vast plains of Montana and the petroleum consumption in the distant land of Uzbekistan, stranger things have been proven in the world of data analysis. It's a wild, wild world out there, and our research only serves to reinforce the notion that the unexpected can indeed yield valuable insights.
Join us on this intriguing journey as we uncover the mysterious dance between crime in the wild west and oil consumption in distant lands. After all, as we know, "Truth is stranger than fiction," and sometimes, data analysis can be just as bewildering. So buckle up and get ready for a ride as we dive headfirst into this surprising correlation. Who knows, we might just uncover a true "oil heist" in the making!
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
We went on a wild ride through the data in our quest to uncover the connection between Montana robberies and Uzbekistan's petroleum consumption. Lo and behold, we stumbled upon some rather unexpected findings! Our analysis revealed a correlation coefficient of 0.7106527, an r-squared of 0.5050273, and a p-value of less than 0.01 for the time period of 1992 to 2021. These results certainly raised more than a few eyebrows and got the research team buzzing with excitement.
The intriguing connections between these seemingly unrelated variables are depicted vividly in the scatterplot in Fig. 1. This visualization perfectly captures the strong correlation we uncovered. It's almost as if the data itself wanted to tell a story, a tale of crime and oil that transcends borders and defies conventional wisdom.
This unexpected correlation underscores the fascinating nature of data analysis. It's like finding a needle in a haystack, but in this case, the needle turned out to be a bandit running away with a barrel of oil. Who would have thought that such mischief and mayhem in Montana could have any bearing on the petroleum consumption in Uzbekistan? It just goes to show that in the wild world of data analysis, truth can indeed be stranger than fiction.
In conclusion, our findings not only highlight this unlikely connection but also emphasize the value of exploring unconventional relationships in the realm of data analysis. As the saying goes, "You never know until you look!" Keep an open mind, and who knows what kind of mysterious connections you might unearth. There's a whole world of surprising correlations out there waiting to be discovered, so keep your eyes peeled and your data sets close at hand!
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
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]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
[[LITERATURE REVIEW]]
The examination of the relationship between robbery rates in Montana and petroleum consumption in Uzbekistan has garnered considerable interest in academic and research circles. Smith, in "The Economic Impacts of Crime," highlights the pervasive impact of criminal activities on various economic sectors, drawing attention to the potential ripple effects on industries such as energy consumption. Similarly, Doe's analysis in "Criminal Behavior and Its Socioeconomic Consequences" underscores the need for a comprehensive understanding of the socioeconomic factors influencing criminal behavior and its potential impact on global markets, including the petroleum industry.
Furthermore, Jones et al., in "Energy Dynamics in Central Asia," explores the intricate web of geopolitical and economic factors shaping energy consumption patterns among Central Asian countries, shedding light on the nuanced dynamics that may contribute to unexpected correlations with seemingly unrelated phenomena.
Turning to non-fiction literature on related themes, "The Oil Road: Journeys from the Caspian Sea to the City of London" by Makan, offers insightful perspectives on the geopolitical implications of the oil trade, presenting a holistic view of the energy landscape that may indirectly intersect with criminal activities in distant regions.
Delving into the world of fiction, the work of James Ellroy, particularly "American Tabloid," offers a provocative, albeit fictional, account of criminal intrigue and political maneuverings set against the backdrop of historical events, inviting readers to consider the potential interplay between criminal operations and global economic machinations.
In the realm of television, series such as "Breaking Bad" and "Ozark" have captivated audiences with their portrayal of criminal enterprises and the intricate web of relationships that underpin illicit activities, thus offering a fictional lens through which to contemplate the complexities of criminal behavior and unexpected economic connections.
While the literature presents a blend of serious economic analyses and fictional narratives, it is evident that the relationship between Montana robberies and Uzbekistan's petroleum consumption has captured the imagination of authors and creators across diverse genres, reflecting the multidimensional nature of the topic at hand.
[[RESULTS]]
We went on a wild ride through the data in our quest to uncover the connection between Montana robberies and Uzbekistan's petroleum consumption. Lo and behold, we stumbled upon some rather unexpected findings! Our analysis revealed a correlation coefficient of 0.7106527, an r-squared of 0.5050273, and a p-value of less than 0.01 for the time period of 1992 to 2021. These results certainly raised more than a few eyebrows and got the research team buzzing with excitement.
The intriguing connections between these seemingly unrelated variables are depicted vividly in the scatterplot in Fig. 1. This visualization perfectly captures the strong correlation we uncovered. It's almost as if the data itself wanted to tell a story, a tale of crime and oil that transcends borders and defies conventional wisdom.
This unexpected correlation underscores the fascinating nature of data analysis. It's like finding a needle in a haystack, but in this case, the needle turned out to be a bandit running away with a barrel of oil. Who would have thought that such mischief and mayhem in Montana could have any bearing on the petroleum consumption in Uzbekistan? It just goes to show that in the wild world of data analysis, truth can indeed be stranger than fiction.
In conclusion, our findings not only highlight this unlikely connection but also emphasize the value of exploring unconventional relationships in the realm of data analysis. As the saying goes, "You never know until you look!" Keep an open mind, and who knows what kind of mysterious connections you might unearth. There's a whole world of surprising correlations out there waiting to be discovered, so keep your eyes peeled and your data sets close at hand!
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our study has unraveled an unexpected association between Montana robberies and Uzbekistan's petroleum consumption, laying bare the intriguing interplay between criminal activity and the global energy market. These findings not only corroborate prior research but also add a new dimension to the understanding of unorthodox correlations in the realm of data analysis.
Delving into the literature review, we come across the captivating and, dare I say, criminal intrigue depicted in James Ellroy's works. While fictional, these narratives offer a thought-provoking lens through which we can contemplate the intricate connections between criminal activities and unexpected economic phenomena. Little did we know that these works would foreshadow the tantalizing connections we would uncover in our own research. Similarly, the television series "Breaking Bad" and "Ozark" may have served as mere entertainment, but it is evident that these narratives resonate with the multifaceted nature of our findings.
The correlations we unveiled align with the economic analyses put forth by Smith and Doe, highlighting the socioeconomic ramifications of criminal activities on industries, including energy consumption. As we whimsically ventured through the data, we couldn't help but recall the insights from Makan's "The Oil Road," which offered perspectives on the geopolitical implications of the oil trade. Little did we expect that our journey through data would lead us to parallel the complexities depicted in this non-fiction work. It seems that truth truly is stranger than fiction.
Our results not only uphold the precedence set by existing research but also underscore the importance of thinking outside the box and considering the unexpected in the world of data analysis. The data truly had its own tale to tell, weaving a narrative of crime and oil that transcends geographical boundaries. The strong correlation coefficient, as depicted in our vibrant scatterplot, serves as a testament to the enthralling nature of these unconventional connections. It's as if the data itself were a master storyteller, unveiling a narrative that captivated our research team and surely turned a few heads in the academic community.
In essence, our findings not only validate the existence of this intriguing correlation but also emphasize the excitement and potential lurking within unconventional data relationships. As researchers, it is imperative to remain open to the unexpected and be prepared to uncover mysteries that challenge our preconceptions. After all, in the world of data analysis, as in life, the most surprising connections can often yield the most profound insights. Keep an open mind, and who knows what kind of mysterious connections you might unearth. There's a whole world of surprising correlations out there waiting to be discovered, so keep your eyes peeled and your data sets close at hand!
Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Robberies in Montana and Petroluem consumption in Uzbekistan. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
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]]
"Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption"
[[ABSTRACT]]
In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
[[INTRDUCTION]]
The world of research often leads us down unexpected paths, and in the world of data analysis, the most curious relationships can emerge. Unlikely pairs, such as Montana and Uzbekistan, may seem unrelated at first glance, but our investigation has brought to light an intriguing connection between the robbery rates in the former and petroleum consumption in the latter. As the saying goes, "Where there's smoke, there's fire," and indeed, our data analysis has uncovered some rather incendiary correlations.
It is well known that correlations do not necessarily imply causation, but they sure do make heads turn and raise eyebrows. As researchers, we must not only think outside the box but also at times question the very existence of the box itself. Our findings suggest that while one might not expect a relationship between robberies in the vast plains of Montana and the petroleum consumption in the distant land of Uzbekistan, stranger things have been proven in the world of data analysis. It's a wild, wild world out there, and our research only serves to reinforce the notion that the unexpected can indeed yield valuable insights.
Join us on this intriguing journey as we uncover the mysterious dance between crime in the wild west and oil consumption in distant lands. After all, as we know, "Truth is stranger than fiction," and sometimes, data analysis can be just as bewildering. So buckle up and get ready for a ride as we dive headfirst into this surprising correlation. Who knows, we might just uncover a true "oil heist" in the making!
[[RESULTS]]
We went on a wild ride through the data in our quest to uncover the connection between Montana robberies and Uzbekistan's petroleum consumption. Lo and behold, we stumbled upon some rather unexpected findings! Our analysis revealed a correlation coefficient of 0.7106527, an r-squared of 0.5050273, and a p-value of less than 0.01 for the time period of 1992 to 2021. These results certainly raised more than a few eyebrows and got the research team buzzing with excitement.
The intriguing connections between these seemingly unrelated variables are depicted vividly in the scatterplot in Fig. 1. This visualization perfectly captures the strong correlation we uncovered. It's almost as if the data itself wanted to tell a story, a tale of crime and oil that transcends borders and defies conventional wisdom.
This unexpected correlation underscores the fascinating nature of data analysis. It's like finding a needle in a haystack, but in this case, the needle turned out to be a bandit running away with a barrel of oil. Who would have thought that such mischief and mayhem in Montana could have any bearing on the petroleum consumption in Uzbekistan? It just goes to show that in the wild world of data analysis, truth can indeed be stranger than fiction.
In conclusion, our findings not only highlight this unlikely connection but also emphasize the value of exploring unconventional relationships in the realm of data analysis. As the saying goes, "You never know until you look!" Keep an open mind, and who knows what kind of mysterious connections you might unearth. There's a whole world of surprising correlations out there waiting to be discovered, so keep your eyes peeled and your data sets close at hand!
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
As we wrap up this wild ride through the world of data analysis, we can't help but marvel at the unexpected correlation we stumbled upon between Montana robberies and Uzbekistan's petroleum consumption. It's like finding a hidden treasure in the most unlikely of places, or perhaps a bandit's stash of stolen oil. Our findings not only shed light on the fascinating and criminal connections between these seemingly disparate entities but also remind us that the realm of data analysis is full of delightful surprises, much like a box of chocolates - you never know what you're gonna get!
Despite the eyebrow-raising nature of our results, we must acknowledge that further research in this area may not be the most fruitful pursuit. After all, how much can we truly expect to glean from the relationship between crime in the wild west and oil consumption in distant lands? It seems that this peculiar correlation stands as a quirky anomaly in the vast landscape of data analysis, like a comet streaking across the statistical sky. As the dust settles on this intriguing journey, it becomes clear that sometimes, in the world of correlations, the most unexpected connections can yield the most entertaining insights.
So, with a nod to the curious case of Montana robberies and Uzbekistan's petroleum consumption, we assert that perhaps this is where the road ends for this particular tale. There are plenty more fish in the sea of data to catch, and who knows what kind of whimsical wonders await the avid explorer. As we bid adieu to this unusual correlation, we turn our gaze to the horizon, eagerly awaiting the next perplexing puzzle that data analysis has in store for us. After all, in the world of data, anything is possible, and the most surprising discoveries may be just around the corner.
In the words of the great data explorers, "Onward and upward, adventuring towards the next statistical frontier!"
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 Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption. Respond only with the name of a fictional academic journal.
ChatGPT:
Journal of International Criminology and Energy Consumption
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 Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption. Respond only with the name of a fictional organization.
ChatGPT:
The Society for Cross-Cultural Criminology and Resource Allocation
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 Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption with an abstract of In this paper, we delve into the unexpected connection between robbery rates in Montana and petroleum consumption in Uzbekistan. Using data from the FBI's Criminal Justice Information Services and the Energy Information Administration, we sought to uncover any potential correlations, and boy did we stumble upon some intriguing findings! Interestingly, we discovered a surprisingly strong correlation coefficient of 0.7106527 and a p-value of less than 0.01 for the years 1992 to 2021. Our research sheds light on the fascinating and perhaps, dare I say, criminal connections between unlikely pairs of entities. While further investigation is warranted, this study underscores the importance of thinking outside the box and considering the unexpected in the world of data analysis. After all, sometimes, the most bizarre correlations can reveal the most captivating insights.
ChatGPT:
robbery rates in Montana, petroleum consumption in Uzbekistan, correlation analysis, FBI Criminal Justice Information Services, Energy Information Administration, data analysis, unexpected correlations, criminal connections, correlation coefficient, p-value, 1992-2021, bizarre correlations, insights from 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
Robberies in MontanaDetailed data title: The robbery rate per 100,000 residents in Montana
Source: FBI Criminal Justice Information Services
See what else correlates with Robberies in Montana
Petroluem consumption in Uzbekistan
Detailed data title: Volume of petroluem consumption consumed in Uzbekistan in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in Uzbekistan
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 = 0.5050273 (Coefficient of determination)
This means 50.5% of the change in the one variable (i.e., Petroluem consumption in Uzbekistan) is predictable based on the change in the other (i.e., Robberies in Montana) over the 30 years from 1992 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.081E-5. 0.0000108073992403144520000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.71 in 0.001081% of random cases. Said differently, if you correlated 92,529 random variables You don't actually need 92 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 29 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 29 because we have two variables measured over a period of 30 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.
[ 0.47, 0.85 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
| 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| Robberies in Montana (Robbery rate) | 26.9 | 32.4 | 32.7 | 29.1 | 29.7 | 20.4 | 26.4 | 27.1 | 22.5 | 25.4 | 31.1 | 32.5 | 25.1 | 18.9 | 24.7 | 24.3 | 21.1 | 23.4 | 15.5 | 17.1 | 20.1 | 21.4 | 19.7 | 20.3 | 25.8 | 22.2 | 25.5 | 19.5 | 25.8 | 26.1 |
| Petroluem consumption in Uzbekistan (Million Barrels/Day) | 191.1 | 177.8 | 173.4 | 183.8 | 143.2 | 141 | 147.4 | 142.2 | 141.176 | 138.241 | 137.035 | 142.44 | 131.742 | 105.444 | 102.848 | 93.0258 | 89.6673 | 92.6514 | 82.2352 | 75.7911 | 68.0119 | 63.5313 | 59.5448 | 86 | 90 | 91 | 95 | 98.1668 | 91.2701 | 91.7934 |
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
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
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. 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([26.9,32.4,32.7,29.1,29.7,20.4,26.4,27.1,22.5,25.4,31.1,32.5,25.1,18.9,24.7,24.3,21.1,23.4,15.5,17.1,20.1,21.4,19.7,20.3,25.8,22.2,25.5,19.5,25.8,26.1,])
array_2 = np.array([191.1,177.8,173.4,183.8,143.2,141,147.4,142.2,141.176,138.241,137.035,142.44,131.742,105.444,102.848,93.0258,89.6673,92.6514,82.2352,75.7911,68.0119,63.5313,59.5448,86,90,91,95,98.1668,91.2701,91.7934,])
array_1_name = "Robberies in Montana"
array_2_name = "Petroluem consumption in Uzbekistan"
# 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. 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.
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Download images for these variables:
- High resolution line chart
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 Robberies in Montana
- Line chart for only Petroluem consumption in Uzbekistan
- AI-generated correlation image
- The spurious research paper: Stealing the Gas: An Exploration of the Relationship between Montana Robberies and Uzbekistan's Petroleum Consumption
You're a rater extraordinaire!
Correlation ID: 1645 · Black Variable ID: 20200 · Red Variable ID: 25060
