Download png, svg
AI explanation
It turns out the robbers were the ones causing all the high-speed chases! Without them around, the roads are a lot safer. Plus, with fewer robberies, there's less need for getaway cars, so it's really a win-win situation for everyone - except the robbers, of course. It's like they say, "Steer clear of crime and you'll brake for fewer accidents!"
Model: dalle-3
Prompt: Prompt: Create a dynamic pop art style image of a group of robbers fleeing a bank in New Mexico. Show them jumping into a vibrant, retro-styled getaway car, with exaggerated angles and bold colors. As the car speeds away, depict a chaotic chain reaction of car crashes on the busy US interstate, using explosive graphic elements and high contrast color blocking to capture the energy and urgency of the scene. 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 New Mexico and Car crashes in the US. 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: Woodblock painting 3D Pixar Style Pop art Parallax art Cute 2D animation
ChatGPT: Pop art
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 New Mexico and Car crashes in the US, in the following situation: The increase in robberies in New Mexico led to a surge in car crashes in the US as getaway drivers panicked, leading to reckless driving and collisions on the roads.. Keep the description simple.
ChatGPT: A group of robbers hastily exits a bank in New Mexico, jumping into a waiting getaway car. As they peel away onto the highway, the panicked driver speeds recklessly, causing a chain reaction of car crashes on the busy US interstate.
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: [ Pop art ] and depicting this scene: A group of robbers hastily exits a bank in New Mexico, jumping into a waiting getaway car. As they peel away onto the highway, the panicked driver speeds recklessly, causing a chain reaction of car crashes on the busy US interstate. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Create a dynamic pop art style image of a group of robbers fleeing a bank in New Mexico. Show them jumping into a vibrant, retro-styled getaway car, with exaggerated angles and bold colors. As the car speeds away, depict a chaotic chain reaction of car crashes on the busy US interstate, using explosive graphic elements and high contrast color blocking to capture the energy and urgency of the scene.
*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 a decrease in Robberies in New Mexico caused Car crashes in the US to decrease. Include one good pun.
AI academic paper
 |
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US The Journal of Transportation and Criminology Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 New Mexico and Car crashes in the US. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from FBI Criminal Justice Information Services and Statista to assess this nagging question. You found a correlation coefficient of 0.8346049 and p < 0.01 for 1991 to 2014.
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]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 New Mexico and Car crashes in the US. 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]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The intersection of criminal behavior and traffic safety has long been a topic of interest, yet the specific linkage between robberies in New Mexico and car crashes in the broader United States has been a less traversed terrain in academic research. While the disparate nature of these occurrences might seem as incongruous as a tumbleweed rolling through rush-hour traffic, our investigation sets out to unravel the peculiar relationship between the two.
As researchers, we are accustomed to turning over every stone in our pursuit of understanding, but even we were caught off guard by the unexpected alignment of these seemingly unrelated events. Our findings have unveiled a correlation so striking, it's as if Bonnie and Clyde themselves were steering the wheel of statistical destiny. Pardon our penchant for dramatization, but the strength of the association we've uncovered between highway robberies in the Land of Enchantment and vehicular mishaps nationwide is nothing short of a statistical hold-up.
With furrowed brows and a mounting sense of curiosity, we embarked on this expedition into uncharted statistical territory. The data we sought lay buried in the annals of FBI Criminal Justice Information Services and Statista, waiting to be unearthed like buried treasure from a treasure map made of Excel spreadsheets.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Robberies in New Mexico and Car crashes in the US. 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.
Here is the title and abstract of the paper:
[[TITLE]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of criminal behavior and traffic safety has long been a topic of interest, yet the specific linkage between robberies in New Mexico and car crashes in the broader United States has been a less traversed terrain in academic research. While the disparate nature of these occurrences might seem as incongruous as a tumbleweed rolling through rush-hour traffic, our investigation sets out to unravel the peculiar relationship between the two.
As researchers, we are accustomed to turning over every stone in our pursuit of understanding, but even we were caught off guard by the unexpected alignment of these seemingly unrelated events. Our findings have unveiled a correlation so striking, it's as if Bonnie and Clyde themselves were steering the wheel of statistical destiny. Pardon our penchant for dramatization, but the strength of the association we've uncovered between highway robberies in the Land of Enchantment and vehicular mishaps nationwide is nothing short of a statistical hold-up.
With furrowed brows and a mounting sense of curiosity, we embarked on this expedition into uncharted statistical territory. The data we sought lay buried in the annals of FBI Criminal Justice Information Services and Statista, waiting to be unearthed like buried treasure from a treasure map made of Excel spreadsheets.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
Smith (2010) deduced that the prevalence of robberies in New Mexico exhibits fluctuations over time, with certain periods resembling a rollercoaster ride of criminal activity. This rollercoaster, much like the emotional journey of watching a telenovela, takes unsuspecting viewers through the twists and turns of criminal behavior. We must acknowledge, however, that car crashes in the U.S. have been a topic of much interest in their own right, with Doe (2015) illustrating the myriad factors that contribute to these vehicular mishaps, much like a complicated recipe for disaster.
In "Highway Economics" by Anderson (2018), a thorough inquiry into the economic implications of highway transportation reveals that the cost of road accidents extends far beyond the physical damage, much like the aftermath of a bank heist. Conversely, in "Car Trouble" by Brown (2013), an exploration of the various mechanical failures contributing to car crashes paints a picture of vehicular turmoil reminiscent of a particularly chaotic action movie car chase scene.
Turning to the realm of fiction, "The Great Gatsby" by Fitzgerald (1925) may at first seem unrelated to our topic, but consider the wild parties, extravagant lifestyles, and sense of recklessness evoked in the novel - it's not hard to draw a tenuous parallel with the goings-on of highway robberies and car crashes. Additionally, the aptly titled "Wheels of Fortune" by Speedster (1999) immerses readers in a world of high-speed chases and daring escapes, mirroring the adrenaline-fueled nature of both criminal activities and traffic incidents.
Further regarding our methodology, in "Family Dollar: A Comprehensive Study of In-store Receipts and Random Ramblings" by Anonymous (2017), we uncovered crucial insights by meticulously scouring plunging paper rolls for any stray correlation, whether it involved potato chips and soda or an unexpected connection between robberies and car crashes. While we don't recommend this approach for academic pursuits, the results proved to be surprisingly enlightening.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Robberies in New Mexico and Car crashes in the US. 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 Statista . You used data from 1991 to 2014
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
[[INTRODUCTION]]
The intersection of criminal behavior and traffic safety has long been a topic of interest, yet the specific linkage between robberies in New Mexico and car crashes in the broader United States has been a less traversed terrain in academic research. While the disparate nature of these occurrences might seem as incongruous as a tumbleweed rolling through rush-hour traffic, our investigation sets out to unravel the peculiar relationship between the two.
As researchers, we are accustomed to turning over every stone in our pursuit of understanding, but even we were caught off guard by the unexpected alignment of these seemingly unrelated events. Our findings have unveiled a correlation so striking, it's as if Bonnie and Clyde themselves were steering the wheel of statistical destiny. Pardon our penchant for dramatization, but the strength of the association we've uncovered between highway robberies in the Land of Enchantment and vehicular mishaps nationwide is nothing short of a statistical hold-up.
With furrowed brows and a mounting sense of curiosity, we embarked on this expedition into uncharted statistical territory. The data we sought lay buried in the annals of FBI Criminal Justice Information Services and Statista, waiting to be unearthed like buried treasure from a treasure map made of Excel spreadsheets.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
Our research methodology employed a multi-faceted approach that aimed to lasso together the disparate datasets and corral them into a coherent analysis. We began by harnessing data from the FBI Criminal Justice Information Services and Statista, which served as the watering holes from which we quenched our thirst for statistical insight. The periods between 1991 and 2014 were our chosen trail to follow, as we sought to apprehend the temporal nuances of these elusive phenomena and, if you'll excuse the pun, drive through the winding roads of correlation.
To map the string of robberies in New Mexico, our research posse used the FBI Crime Data Explorer, carefully navigating through the thorny underbrush of criminal data to pinpoint the instances of highway banditry. As for the car crash data, we rode shotgun with the National Highway Traffic Safety Administration (NHTSA), corralling information from their Fatality Analysis Reporting System (FARS) and General Estimates System (GES). With the finesse of a rodeo cowboy, we tamed the unwieldy datasets into submission, ready to saddle up for our statistical journey.
Next, we deployed the trusty lasso of statistical analysis to capture the fleeting patterns of association. We employed Pearson correlation coefficients, leveraging its seasoned expertise in wrangling quantitative relationships. This stalwart statistical steed allowed us to quantify the degree of connection between the incursions of highway bandits and the calamities on the open road, while avoiding the statistical tumbleweeds that often impede rigorous research.
With the data wrangled, corralled, and accounted for, we subjected our findings to rigorous scrutiny, mindful not to let any wild outliers bolt through our analytical fences. The statistical hounds were released to sniff out any lurking confounders, lest they seek refuge in the dusty corners of our data saloon. Our methodology aimed to leave no stone unturned, keeping an eagle-eyed lookout for any potential statistical rustlers trying to hoodwink our findings.
In summary, our methodology combined the unyielding precision of statistical analysis with the frontier spirit of exploratory research, ultimately rounding up a bounty of evidence that sheds light on the unexpected connection between robberies in New Mexico and car crashes across the United States.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.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 New Mexico and Car crashes in the US. 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 Statista .
For the time period 1991 to 2014, you found a correlation 0.8346049, r-squared of 0.6965654, and p < 0.01.
One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.
Here is the title and abstract of the paper:
[[TITLE]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of criminal behavior and traffic safety has long been a topic of interest, yet the specific linkage between robberies in New Mexico and car crashes in the broader United States has been a less traversed terrain in academic research. While the disparate nature of these occurrences might seem as incongruous as a tumbleweed rolling through rush-hour traffic, our investigation sets out to unravel the peculiar relationship between the two.
As researchers, we are accustomed to turning over every stone in our pursuit of understanding, but even we were caught off guard by the unexpected alignment of these seemingly unrelated events. Our findings have unveiled a correlation so striking, it's as if Bonnie and Clyde themselves were steering the wheel of statistical destiny. Pardon our penchant for dramatization, but the strength of the association we've uncovered between highway robberies in the Land of Enchantment and vehicular mishaps nationwide is nothing short of a statistical hold-up.
With furrowed brows and a mounting sense of curiosity, we embarked on this expedition into uncharted statistical territory. The data we sought lay buried in the annals of FBI Criminal Justice Information Services and Statista, waiting to be unearthed like buried treasure from a treasure map made of Excel spreadsheets.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The analysis revealed a notable correlation coefficient of 0.8346049 between robberies in New Mexico and car crashes in the United States, indicating a strong positive relationship between these seemingly unrelated phenomena. The r-squared value of 0.6965654 suggests that approximately 70% of the variation in car crashes can be explained by the variation in robberies in New Mexico.
The p-value of less than 0.01 indicates that the correlation is statistically significant, affirming that this connection is not just a chance encounter. This finding suggests that as the number of robberies in New Mexico increases, there is a corresponding increase in car crashes across the United States. One might say this correlation is as clear as the painted lines on a freshly paved road.
In Figure 1, the scatterplot visually encapsulates the compelling association between these two variables, resembling a cross-country road trip with stops at every crime scene and crash site along the way. The strength of this relationship is striking, much like stumbling upon a desert oasis after a long and arduous statistical journey.
This unsuspected convergence of criminal activity and vehicular mayhem prompts reflection on the broader implications of these findings. The interplay between law enforcement and transportation safety, it seems, is not just a matter of policing the streets but also safeguarding the nation's highways and byways. This research valiantly ventures into uncharted statistical terrains, shedding light on a connection that, much like a coyote in the night, has long lurked in the shadows of data analysis.
This unexpected correlation between robberies in New Mexico and car crashes in the US invites further inquiry and contemplation, encapsulating the essence of a statistically wild ride down the interstate of academic investigation.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Robberies in New Mexico and Car crashes in the US. 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]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
[[LITERATURE REVIEW]]
Smith (2010) deduced that the prevalence of robberies in New Mexico exhibits fluctuations over time, with certain periods resembling a rollercoaster ride of criminal activity. This rollercoaster, much like the emotional journey of watching a telenovela, takes unsuspecting viewers through the twists and turns of criminal behavior. We must acknowledge, however, that car crashes in the U.S. have been a topic of much interest in their own right, with Doe (2015) illustrating the myriad factors that contribute to these vehicular mishaps, much like a complicated recipe for disaster.
In "Highway Economics" by Anderson (2018), a thorough inquiry into the economic implications of highway transportation reveals that the cost of road accidents extends far beyond the physical damage, much like the aftermath of a bank heist. Conversely, in "Car Trouble" by Brown (2013), an exploration of the various mechanical failures contributing to car crashes paints a picture of vehicular turmoil reminiscent of a particularly chaotic action movie car chase scene.
Turning to the realm of fiction, "The Great Gatsby" by Fitzgerald (1925) may at first seem unrelated to our topic, but consider the wild parties, extravagant lifestyles, and sense of recklessness evoked in the novel - it's not hard to draw a tenuous parallel with the goings-on of highway robberies and car crashes. Additionally, the aptly titled "Wheels of Fortune" by Speedster (1999) immerses readers in a world of high-speed chases and daring escapes, mirroring the adrenaline-fueled nature of both criminal activities and traffic incidents.
Further regarding our methodology, in "Family Dollar: A Comprehensive Study of In-store Receipts and Random Ramblings" by Anonymous (2017), we uncovered crucial insights by meticulously scouring plunging paper rolls for any stray correlation, whether it involved potato chips and soda or an unexpected connection between robberies and car crashes. While we don't recommend this approach for academic pursuits, the results proved to be surprisingly enlightening.
[[RESULTS]]
The analysis revealed a notable correlation coefficient of 0.8346049 between robberies in New Mexico and car crashes in the United States, indicating a strong positive relationship between these seemingly unrelated phenomena. The r-squared value of 0.6965654 suggests that approximately 70% of the variation in car crashes can be explained by the variation in robberies in New Mexico.
The p-value of less than 0.01 indicates that the correlation is statistically significant, affirming that this connection is not just a chance encounter. This finding suggests that as the number of robberies in New Mexico increases, there is a corresponding increase in car crashes across the United States. One might say this correlation is as clear as the painted lines on a freshly paved road.
In Figure 1, the scatterplot visually encapsulates the compelling association between these two variables, resembling a cross-country road trip with stops at every crime scene and crash site along the way. The strength of this relationship is striking, much like stumbling upon a desert oasis after a long and arduous statistical journey.
This unsuspected convergence of criminal activity and vehicular mayhem prompts reflection on the broader implications of these findings. The interplay between law enforcement and transportation safety, it seems, is not just a matter of policing the streets but also safeguarding the nation's highways and byways. This research valiantly ventures into uncharted statistical terrains, shedding light on a connection that, much like a coyote in the night, has long lurked in the shadows of data analysis.
This unexpected correlation between robberies in New Mexico and car crashes in the US invites further inquiry and contemplation, encapsulating the essence of a statistically wild ride down the interstate of academic investigation.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The results of our analysis uncover a striking relationship between robberies in New Mexico and car crashes in the United States, providing empirical support for the curious linkage hinted at in earlier literature. The notable correlation coefficient of 0.8346049 aligns with the findings of Smith (2010), who described the rollercoaster-like fluctuations in robbery prevalence. Just as riding a rollercoaster elicits a mix of fear and exhilaration, our findings evoke a similar blend of amazement and intrigue at the intertwined nature of criminal activities and road safety.
In this statistical rollercoaster, the p-value of less than 0.01 serves as the exhilarating loop-de-loop, confirming the robustness of the relationship. Much like a specially designed recipe, the findings provide substantial evidence that as the occurrences of robberies in New Mexico fluctuate, there is a corresponding oscillation in car crashes across the nation. This robust statistical recipe, with its strong p-value flavor, leaves little room for doubt and hints at a novel way to play with crime and crash data.
Our results also align with the multifaceted analysis presented by Anderson (2018), showcasing the intricate economic implications of highway transportation. The variation in car crashes explained by the variation in robberies could be likened to the costs of road accidents extending beyond just the physical damage – the ripple effects of criminal activities, much like a highway hold-up, reach far and wide. This undeniable correlation has now become the unexpected, yet intriguing centerpiece of the highway economics tableau, demonstrating that there may indeed be some "high stakes" involved in the interconnected dynamics of crime and crashes.
Building on the unexpected connections drawn from fiction, our findings reflect the pulsating energy and risky maneuvers commonly associated with high-speed chases and daring escapades, mirroring the adrenaline-fueled nature of both criminal activities and traffic incidents. The visualization of the scatterplot, depicting the compelling association between robberies in New Mexico and car crashes across the U.S., brings to mind an enthralling cross-country road trip with stops at each crime scene and crash site. This vivid imagery serves as a testament to the robustness and strength of this connection, akin to encountering a desert oasis after embarking on a statistical journey through uncharted terrains.
In summary, our results offer compelling evidence of the unexpected and compelling link between robberies in New Mexico and car crashes in the U.S., defying conventional expectations and urging further exploration. These findings beckon us to delve deeper into the statistical wilderness, where intriguing patterns may well emerge from the shadows of data analysis, much like a coyote emerging from the darkness of the night.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Robberies in New Mexico and Car crashes in the US. 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]]
The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
[[ABSTRACT]]
This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
[[INTRDUCTION]]
The intersection of criminal behavior and traffic safety has long been a topic of interest, yet the specific linkage between robberies in New Mexico and car crashes in the broader United States has been a less traversed terrain in academic research. While the disparate nature of these occurrences might seem as incongruous as a tumbleweed rolling through rush-hour traffic, our investigation sets out to unravel the peculiar relationship between the two.
As researchers, we are accustomed to turning over every stone in our pursuit of understanding, but even we were caught off guard by the unexpected alignment of these seemingly unrelated events. Our findings have unveiled a correlation so striking, it's as if Bonnie and Clyde themselves were steering the wheel of statistical destiny. Pardon our penchant for dramatization, but the strength of the association we've uncovered between highway robberies in the Land of Enchantment and vehicular mishaps nationwide is nothing short of a statistical hold-up.
With furrowed brows and a mounting sense of curiosity, we embarked on this expedition into uncharted statistical territory. The data we sought lay buried in the annals of FBI Criminal Justice Information Services and Statista, waiting to be unearthed like buried treasure from a treasure map made of Excel spreadsheets.
[[RESULTS]]
The analysis revealed a notable correlation coefficient of 0.8346049 between robberies in New Mexico and car crashes in the United States, indicating a strong positive relationship between these seemingly unrelated phenomena. The r-squared value of 0.6965654 suggests that approximately 70% of the variation in car crashes can be explained by the variation in robberies in New Mexico.
The p-value of less than 0.01 indicates that the correlation is statistically significant, affirming that this connection is not just a chance encounter. This finding suggests that as the number of robberies in New Mexico increases, there is a corresponding increase in car crashes across the United States. One might say this correlation is as clear as the painted lines on a freshly paved road.
In Figure 1, the scatterplot visually encapsulates the compelling association between these two variables, resembling a cross-country road trip with stops at every crime scene and crash site along the way. The strength of this relationship is striking, much like stumbling upon a desert oasis after a long and arduous statistical journey.
This unsuspected convergence of criminal activity and vehicular mayhem prompts reflection on the broader implications of these findings. The interplay between law enforcement and transportation safety, it seems, is not just a matter of policing the streets but also safeguarding the nation's highways and byways. This research valiantly ventures into uncharted statistical terrains, shedding light on a connection that, much like a coyote in the night, has long lurked in the shadows of data analysis.
This unexpected correlation between robberies in New Mexico and car crashes in the US invites further inquiry and contemplation, encapsulating the essence of a statistically wild ride down the interstate of academic investigation.
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Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our study has unveiled a compelling correlation between robberies in the enchanting state of New Mexico and car crashes across the sprawling expanse of the United States. The strength and statistical significance of this association suggest a deeper entanglement between criminal activities and road safety, akin to a tumbleweed having a secret rendezvous with a roadrunner. Our findings not only raise eyebrows but also hint at a potential avenue for further exploration, much like stumbling upon a mysterious detour during a road trip.
The robust correlation coefficient and visually captivating scatterplot paint a clear picture of the interconnectedness of these seemingly disparate phenomena, much like finding hidden treasures on a convoluted statistical treasure map. The implications of this unexpected convergence extend beyond the realm of academic curiosity, offering a fresh perspective that is as illuminating as a beacon on a dark desert highway.
However, as much as we relish the thrill of unraveling statistical mysteries, we must acknowledge that every statistical road has its end. In the spirit of closure, we assert that no more research is needed in this area, for we have confidently navigated the statistical expanse and arrived at a noteworthy destination, much like voyagers who have triumphantly reached the end of a particularly intriguing road trip.
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Transportation and Criminology
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US. Respond only with the name of a fictional organization.
ChatGPT:
The Society for Interdisciplinary Research on Unlikely Connections (SIRUC)
Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US with an abstract of This study delves into the intriguing yet understudied connection between robberies in New Mexico and car crashes across the United States. Utilizing data from the FBI Criminal Justice Information Services and Statista, we analyzed the temporal dynamics from 1991 to 2014 to uncover a surprising correlation. Our findings reveal a robust correlation coefficient of 0.8346049 with a statistically significant p-value of less than 0.01, indicating a strong association between the two seemingly disparate phenomena. The implications of this relationship extend beyond mere coincidence, inviting further investigation into the intertwined nature of criminal activities and road safety. This research sheds light on the intricate interplay between law enforcement and transportation, offering a fresh perspective that is both thought-provoking and, dare we say, highway robbery in its own right.
ChatGPT:
robberies, New Mexico, car crashes, United States, FBI Criminal Justice Information Services, Statista, correlation, temporal dynamics, criminal activities, road safety, law enforcement, transportation, highway robbery
*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 New MexicoDetailed data title: The robbery rate per 100,000 residents in New Mexico
Source: FBI Criminal Justice Information Services
See what else correlates with Robberies in New Mexico
Car crashes in the US
Detailed data title: Total annual vehicle crash count in the US
Source: Statista
See what else correlates with Car crashes in the US
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 = 0.6965654 (Coefficient of determination)
This means 69.7% of the change in the one variable (i.e., Car crashes in the US) is predictable based on the change in the other (i.e., Robberies in New Mexico) over the 24 years from 1991 through 2014.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.0E-7. 0.0000003976036622747268000000
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 0.83 in 4.0E-5% of random cases. Said differently, if you correlated 2,515,067 random variables You don't actually need 2 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 23 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 23 because we have two variables measured over a period of 24 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.
[ 0.65, 0.93 ] 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.
| 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | |
| Robberies in New Mexico (Robbery rate) | 120.3 | 139.3 | 138.4 | 140.8 | 154.5 | 162.4 | 171.4 | 163.4 | 148.2 | 137.4 | 147.2 | 119.1 | 103.1 | 108.4 | 98.3 | 107.2 | 117.9 | 108.4 | 96.1 | 78.2 | 82.7 | 88.6 | 87.8 | 100 |
| Car crashes in the US (Car crashes) | 6117 | 6000 | 6106 | 6496 | 6699 | 6770 | 6624 | 6335 | 6279 | 6394 | 6323 | 6316 | 6328 | 6181 | 6159 | 5973 | 6024 | 5811 | 5505 | 5419 | 5338 | 5615 | 5687 | 6064 |
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. - Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves.
Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
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([120.3,139.3,138.4,140.8,154.5,162.4,171.4,163.4,148.2,137.4,147.2,119.1,103.1,108.4,98.3,107.2,117.9,108.4,96.1,78.2,82.7,88.6,87.8,100,])
array_2 = np.array([6117,6000,6106,6496,6699,6770,6624,6335,6279,6394,6323,6316,6328,6181,6159,5973,6024,5811,5505,5419,5338,5615,5687,6064,])
array_1_name = "Robberies in New Mexico"
array_2_name = "Car crashes in the US"
# 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 New Mexico
- Line chart for only Car crashes in the US
- AI-generated correlation image
- The spurious research paper: The Highwaymen: Exploring the Hold-Up between Robberies in New Mexico and Car Crashes in the US
I'm genuinely thankful for your rating!
Correlation ID: 3981 · Black Variable ID: 20206 · Red Variable ID: 229
