Spurious correlation #1,478 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Number of Movies Released Annually and the second variable is US hotel industry's revenue per available room. The chart goes from 2001 to 2014, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Number of Movies Released Annually and the second variable is US hotel industry's revenue per available room. The chart goes from 2001 to 2014, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of movies released annually increased, more people sought out cinematic experiences, leading to a greater demand for hotel accommodations. Coincidentally, these films were so captivating that audiences couldn't help but book rooms to extend their movie-watching vacations. It's like the movies were saying, "Check in for drama, check out with a comedy of errors!"

^{Show image generation prompt} Model: dalle-3
Prompt: An image that looks like it is a scene from A Disney Movie, illustrating a bustling movie premiere event at a grand hotel ballroom. The scene showcases a long red carpet stretching out from the entrance, with movie posters adorning the walls. Inside the ballroom, guests are elegantly dressed, and the atmosphere is filled with excitement and glamour. Meanwhile, in the hotel's lobby, luxurious accommodations are on display with guests enjoying the lavish amenities, symbolizing the thriving revenue per available room in the US hotel industry. 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: Number of Movies Released Annually and US hotel industry's revenue per available room. 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: An image that looks like it is a scene from A Disney Movie Pop art Cutsy art illustration Psychedelic Celtic art
ChatGPT: An image that looks like it is a scene from A Disney Movie

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 Number of Movies Released Annually and US hotel industry's revenue per available room together. Keep the description simple.
ChatGPT: A bustling movie premiere event at a hotel's grand ballroom, with a red carpet stretching out and movie posters lining the walls, showcasing the excitement of the many movies being released annually. At the same time, in the hotel's lobby, guests are seen enjoying luxurious accommodations, symbolizing the thriving revenue per available room in the US hotel industry.

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: [ An image that looks like it is a scene from A Disney Movie ] and depicting this scene: A bustling movie premiere event at a hotel's grand ballroom, with a red carpet stretching out and movie posters lining the walls, showcasing the excitement of the many movies being released annually. At the same time, in the hotel's lobby, guests are seen enjoying luxurious accommodations, symbolizing the thriving revenue per available room in the US hotel industry. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: An image that looks like it is a scene from A Disney Movie, illustrating a bustling movie premiere event at a grand hotel ballroom. The scene showcases a long red carpet stretching out from the entrance, with movie posters adorning the walls. Inside the ballroom, guests are elegantly dressed, and the atmosphere is filled with excitement and glamour. Meanwhile, in the hotel's lobby, luxurious accommodations are on display with guests enjoying the lavish amenities, symbolizing the thriving revenue per available room in the US hotel industry.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Number of Movies Released Annually caused US hotel industry's revenue per available room to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

Journal of Popular Culture and Economic Trends

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Number of Movies Released Annually and US hotel industry's revenue per available room. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

[[ABSTRACT]]
Lights, camera, action! In this study, we delve into the glitzy world of Hollywood and its surprising connection to the seemingly unrelated domain of the US hotel industry. Utilizing data from Box Office Mojo and Statista, we endeavored to uncover whether there exists a relationship between the number of movies released annually and the revenue per available room in US hotels. Contrary to the typical advice to avoid movie sequels, our research discovered a striking correlation coefficient of 0.9716652 and a statistically significant p-value of less than 0.01 for the years 2001 to 2014, indicating a strong positive association between these two seemingly disparate entities. Our findings suggest that just as a blockbuster sequel often outperforms its predecessor, an increase in the number of movies released annually tends to bolster the revenues within the US hotel industry. So, the next time you're booking a hotel room, remember that the number of movies hitting the big screen may have more impact on your bill than you'd expect!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Ah, the world of academic research. Where statistics reign supreme, hypotheses are put to the test, and the occasional pun slips through the cracks like a lab mouse on the lam. Today, dear readers, we embark on a journey through the realm of unlikely correlations and unexpected connections as we explore the relationship between the number of movies released annually and the revenue per available room in the US hotel industry. Get ready for some reel-y fascinating findings!
As we confront the glittering chaos of Hollywood and the orderly domain of hotel management, one might wonder what possible link could exist between these seemingly disparate worlds. After all, the only thing that seems to connect a movie and a hotel stay is the occasional mini bar robbery by a mischievous film character. Yet, in the midst of this seeming madness, we are driven by the pursuit of knowledge, buoyed by the hope of uncovering hidden patterns and uncovering truths stranger than fiction.
Our esteemed colleagues in the fields of Box Office Mojo and Statista have graciously provided us with a wealth of data, allowing us to chase the elusive trail of correlation and causation within this peculiar pair of variables. Armed with statistical tools and a healthy appreciation for cinematic drama, we set out to answer the question: Could the number of movies released annually truly influence the revenue per available room in the US hotel industry, or are we simply fishing for plot twists in an ocean of noise?
As we navigate the labyrinth of data and dive into the world of regression analysis, let us remember that in the world of science, not every correlation is necessarily causation – no matter how compelling it may seem. Nevertheless, armed with our trusty p-values and correlation coefficients, we march forward to unravel the mystery at hand.
So, join us, intrepid readers, in this grand pursuit of knowledge. For in the midst of our numerical odyssey, we may just uncover a truth stranger than fiction, a link more unexpected than a surprise plot twist – a connection between theatrical trends and traveling treasures that has been hiding in plain sight all along. Let the curtains rise, and the journey begin!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The connection between the number of movies released annually and the revenue per available room in the US hotel industry has intrigued researchers and analysts for decades, leading to an eclectic blend of studies and sources that span the spectrum from serious academic inquiries to whimsical flights of imagination.

In their seminal work, Smith and Doe (2010) delved into the economic implications of film industry dynamics on various sectors, including the lodging industry. Their findings suggested a potential link between the volume of cinematic releases and hotel revenues, laying the groundwork for further investigation. However, Jones (2015) presented a counterargument, positing that the influence of movie releases on the hotel industry was negligible at best, prompting a spirited debate among scholars.

Turning to non-fiction literature, "Reel Economics" by Due and Paying (2011) provides a comprehensive overview of the economic forces at play in the film industry and their ripple effects across different sectors, planting the seed of curiosity about the potential impact on hospitality establishments. On a more unconventional note, "Popcorn Economics: How Movies Shape Our Financial World" by Kernel Korn (2013) takes a more whimsical approach, exploring the hidden financial lessons embedded within cinematic narratives.

Shifting into the realm of fiction, the works of Michael Cinema and Lisa Lodging, such as "The Suite Life of Cinematic Revenues" and "The Box Office and the Bedside Table," offer imaginative narratives that blur the lines between the reel and the real, sparking the imagination and raising questions about the interplay between movies and hotel revenues.

But we cannot overlook the impact of childhood influences on our perceptions, can we? Enter "Hotel Transylvania" and "The Princess Diaries," animated and live-action expressions that captivate young hearts and minds, subtly weaving in the notion that a captivating movie can lead to a desire for a luxurious hotel stay – a correlation that transcends generations.

As we weave through this diverse tapestry of literature, it becomes clear that the marriage of movies with the hotel industry is as complex and multifaceted as a Hollywood blockbuster, leaving room for both serious analysis and flights of fancy. So, buckle up, dear readers, as we navigate this unconventional landscape where popcorn dreams meet balance sheet realities, and perhaps discover that the silver screen and the hotel room are more interconnected than meets the eye.

And now, onto the empirical analysis, where we will thrust our statistical swords into the heart of this conundrum and unveil the secrets hidden within the glitz and glamour. Onward, to the next chapter of our scholarly saga!

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Number of Movies Released Annually and US hotel industry's revenue per available room. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Box Office Mojo and Statista . You used data from 2001 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]]
Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

[[ABSTRACT]]
Lights, camera, action! In this study, we delve into the glitzy world of Hollywood and its surprising connection to the seemingly unrelated domain of the US hotel industry. Utilizing data from Box Office Mojo and Statista, we endeavored to uncover whether there exists a relationship between the number of movies released annually and the revenue per available room in US hotels. Contrary to the typical advice to avoid movie sequels, our research discovered a striking correlation coefficient of 0.9716652 and a statistically significant p-value of less than 0.01 for the years 2001 to 2014, indicating a strong positive association between these two seemingly disparate entities. Our findings suggest that just as a blockbuster sequel often outperforms its predecessor, an increase in the number of movies released annually tends to bolster the revenues within the US hotel industry. So, the next time you're booking a hotel room, remember that the number of movies hitting the big screen may have more impact on your bill than you'd expect!

[[INTRODUCTION]]
Ah, the world of academic research. Where statistics reign supreme, hypotheses are put to the test, and the occasional pun slips through the cracks like a lab mouse on the lam. Today, dear readers, we embark on a journey through the realm of unlikely correlations and unexpected connections as we explore the relationship between the number of movies released annually and the revenue per available room in the US hotel industry. Get ready for some reel-y fascinating findings!
As we confront the glittering chaos of Hollywood and the orderly domain of hotel management, one might wonder what possible link could exist between these seemingly disparate worlds. After all, the only thing that seems to connect a movie and a hotel stay is the occasional mini bar robbery by a mischievous film character. Yet, in the midst of this seeming madness, we are driven by the pursuit of knowledge, buoyed by the hope of uncovering hidden patterns and uncovering truths stranger than fiction.
Our esteemed colleagues in the fields of Box Office Mojo and Statista have graciously provided us with a wealth of data, allowing us to chase the elusive trail of correlation and causation within this peculiar pair of variables. Armed with statistical tools and a healthy appreciation for cinematic drama, we set out to answer the question: Could the number of movies released annually truly influence the revenue per available room in the US hotel industry, or are we simply fishing for plot twists in an ocean of noise?
As we navigate the labyrinth of data and dive into the world of regression analysis, let us remember that in the world of science, not every correlation is necessarily causation – no matter how compelling it may seem. Nevertheless, armed with our trusty p-values and correlation coefficients, we march forward to unravel the mystery at hand.
So, join us, intrepid readers, in this grand pursuit of knowledge. For in the midst of our numerical odyssey, we may just uncover a truth stranger than fiction, a link more unexpected than a surprise plot twist – a connection between theatrical trends and traveling treasures that has been hiding in plain sight all along. Let the curtains rise, and the journey begin!

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Aha! Welcome to the inner workings of our grand quest for knowledge – the methodology. Much like a mad scientist cobbling together an experiment from spare parts, we combined sources from Box Office Mojo and Statista to cultivate a fertile ground for our research. Our data collection spanned the years 2001 to 2014, a time when cinematic sagas rose and fell, and hotel industry dramas played out behind closed doors – or rather, within those available rooms.

To kick things off, we aggregated annual data on the number of movies released in the United States. We dove headfirst into the seemingly limitless sea of sequels, prequels, remakes, and reboots, diligently counting each entry as if tallying a legion of mischievous lab mice. Notably, we refrained from including made-for-television movies or straight-to-DVD releases, as we preferred our data to have a bit more box office glamour and pizzazz. Our method may have been unorthodox, but when it comes to counting movies, a little dramatic flair never hurt anyone.

Next up, we turned our attention to the ever-fascinating US hotel industry. We set out to decipher the intricate patterns of revenue per available room, that coveted metric of hotel success. Armed with spreadsheets and perhaps a touch of delirium from the onslaught of movie titles swirling in our minds, we painstakingly gathered the revenue data from various sources, ensuring that each dollar earned was given its due place in the grand theater of statistics.

Now, here comes the twist in our tale – the link between these two seemingly unrelated realms. To entwine the variables of movie releases and hotel revenues, we employed the ancient and mystical art of statistical analysis. With a flourish of our calculators and a sprinkle of regression models, we sought to unravel the knotty web of correlation and causation, much like unraveling a particularly perplexing plotline.

In a moment of statistical revelation, we discovered a correlation coefficient of 0.9716652, sparkling like a precious gem amidst a sea of data points. Like a surprising plot twist in a Sherlock Holmes mystery, the p-value emerged with a significance of less than 0.01, making our correlation statistically robust and as convincing as a blockbuster’s opening weekend.

And there you have it – the unconventional, yet undeniably successful, concoction of methodologies that brought us to our exhilarating conclusion. As we dust off the research debris and bid adieu to our lab coats, we stand in awe of the unexpected alliance between the glitzy world of cinema and the understated domain of hotel revenues. With our findings in tow, let us crack open this treasure trove of knowledge and bask in the glow of our academic achievement – like a film buff reveling in a theater filled with hidden Easter eggs.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Number of Movies Released Annually and US hotel industry's revenue per available room. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

[[ABSTRACT]]
Lights, camera, action! In this study, we delve into the glitzy world of Hollywood and its surprising connection to the seemingly unrelated domain of the US hotel industry. Utilizing data from Box Office Mojo and Statista, we endeavored to uncover whether there exists a relationship between the number of movies released annually and the revenue per available room in US hotels. Contrary to the typical advice to avoid movie sequels, our research discovered a striking correlation coefficient of 0.9716652 and a statistically significant p-value of less than 0.01 for the years 2001 to 2014, indicating a strong positive association between these two seemingly disparate entities. Our findings suggest that just as a blockbuster sequel often outperforms its predecessor, an increase in the number of movies released annually tends to bolster the revenues within the US hotel industry. So, the next time you're booking a hotel room, remember that the number of movies hitting the big screen may have more impact on your bill than you'd expect!

[[LITERATURE REVIEW]]
The connection between the number of movies released annually and the revenue per available room in the US hotel industry has intrigued researchers and analysts for decades, leading to an eclectic blend of studies and sources that span the spectrum from serious academic inquiries to whimsical flights of imagination.
In their seminal work, Smith and Doe (2010) delved into the economic implications of film industry dynamics on various sectors, including the lodging industry. Their findings suggested a potential link between the volume of cinematic releases and hotel revenues, laying the groundwork for further investigation. However, Jones (2015) presented a counterargument, positing that the influence of movie releases on the hotel industry was negligible at best, prompting a spirited debate among scholars.
Turning to non-fiction literature, "Reel Economics" by Due and Paying (2011) provides a comprehensive overview of the economic forces at play in the film industry and their ripple effects across different sectors, planting the seed of curiosity about the potential impact on hospitality establishments. On a more unconventional note, "Popcorn Economics: How Movies Shape Our Financial World" by Kernel Korn (2013) takes a more whimsical approach, exploring the hidden financial lessons embedded within cinematic narratives.
Shifting into the realm of fiction, the works of Michael Cinema and Lisa Lodging, such as "The Suite Life of Cinematic Revenues" and "The Box Office and the Bedside Table," offer imaginative narratives that blur the lines between the reel and the real, sparking the imagination and raising questions about the interplay between movies and hotel revenues.
But we cannot overlook the impact of childhood influences on our perceptions, can we? Enter "Hotel Transylvania" and "The Princess Diaries," animated and live-action expressions that captivate young hearts and minds, subtly weaving in the notion that a captivating movie can lead to a desire for a luxurious hotel stay – a correlation that transcends generations.
As we weave through this diverse tapestry of literature, it becomes clear that the marriage of movies with the hotel industry is as complex and multifaceted as a Hollywood blockbuster, leaving room for both serious analysis and flights of fancy. So, buckle up, dear readers, as we navigate this unconventional landscape where popcorn dreams meet balance sheet realities, and perhaps discover that the silver screen and the hotel room are more interconnected than meets the eye.
And now, onto the empirical analysis, where we will thrust our statistical swords into the heart of this conundrum and unveil the secrets hidden within the glitz and glamour. Onward, to the next chapter of our scholarly saga!

[[RESULTS]]
In analyzing the relationship between the number of movies released annually and the revenue per available room in the US hotel industry, we uncovered some truly fascinating findings. Our statistical analysis revealed a remarkably high correlation coefficient of 0.9716652, indicating a strong positive association between these two variables. This result may surprise those who thought the only thing hotels and movies had in common was the occasional room service cameo in a film.
Additionally, the r-squared value of 0.9441334 suggests that a staggering 94.41% of the variation in hotel revenue per available room can be explained by changes in the number of movie releases annually. This tells us that the connection between cinematic trends and hotel treasures isn't just a fleeting cameo – it's a leading role in the story of revenue generation.
Our p-value of less than 0.01 further solidifies the robustness of the relationship we uncovered. This means that our findings are statistically significant, and we can be more confident in the strength of the association than a movie buff quoting obscure film trivia.
To visually capture the strength of the relationship found in our analysis, we present Figure 1, a scatterplot that showcases the compelling correlation between the number of movies released annually and the revenue per available room in the US hotel industry. Just as a well-directed film captures the imagination of its audience, this figure highlights the captivating connection between theatrical releases and hotel revenue.
The results of our study serve as a reminder that in the world of data analysis, the plot can thicken in ways that defy expectations. Much like the plot twist in a suspenseful thriller, our findings reveal a connection that goes beyond what one might anticipate at first glance. So, the next time you find yourself pondering the economic impact of the latest blockbuster, remember that its influence might extend far beyond the box office – it could be checking into a hotel room right alongside you.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Ah, the plot thickens, much like a well-crafted mystery novel or a bowl of gravy left out for too long. Our findings, much to our delight, have lent support to the prior research that hinted at a curious connection between the frequency of movies and the revenue of hotel rooms. It appears that the reel world and the room world are more entwined than a pair of earbuds at the bottom of a backpack.

Firstly, let's revisit Smith and Doe's (2010) economic exploration, which, much like a Sherlock Holmes tale, uncovered the potential link between the volume of cinematic releases and hotel revenues. Our results align with their findings, almost as if we were two characters in a buddy cop movie, working together to solve a case. Similarly, the whimsical contemplations of Kernel Korn (2013) in "Popcorn Economics" seem to hold unexpected kernels of truth, as our study has unraveled a tangible and statistically significant relationship between silver screen sagas and hotel revenue drama. It's almost as if our findings are the surprise twist that makes you rethink the entire plot of the movie.

On the lighter side, the captivating narratives by Michael Cinema and Lisa Lodging have taken on a new depth of meaning, akin to a Pixar movie that resonates with both kids and adults. It seems that the whimsical notion of a captivating movie leading to a desire for a luxurious hotel stay has morphed from fantasy into empirical reality, like a superhero origin story coming to life.

In essence, our results provide empirical weight to the theories and musings that have permeated the world of cinematic and hospitality discourse. The correlation coefficient of 0.9716652 we uncovered is as strong as Hercules after a power smoothie, indicating a robust positive association. The r-squared value of 0.9441334 further solidifies the interwoven nature of these two variables, much like a perfectly folded fitted sheet – surprisingly snug.

This brings us to the p-value of less than 0.01, which cements the statistical significance of our findings like an Oscar-winning acceptance speech. Just as a box office hit leaves a lasting impression, our study has left an indelible mark on the understanding of the connection between movies and hotel revenues.

In conclusion, our research has not only supported the prior literature but also added a new dimension to the discourse, much like a 3D movie that makes you feel like you're part of the action. So, the next time you check into a hotel and hear the faint echo of a movie soundtrack in the corridors, take a moment to appreciate the unseen threads that weave the worlds of entertainment and hospitality together. And who knows, maybe a rom-com and a room-service dessert might be just the duo to boost both movie and hotel revenues simultaneously.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Number of Movies Released Annually and US hotel industry's revenue per available room. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

[[ABSTRACT]]
Lights, camera, action! In this study, we delve into the glitzy world of Hollywood and its surprising connection to the seemingly unrelated domain of the US hotel industry. Utilizing data from Box Office Mojo and Statista, we endeavored to uncover whether there exists a relationship between the number of movies released annually and the revenue per available room in US hotels. Contrary to the typical advice to avoid movie sequels, our research discovered a striking correlation coefficient of 0.9716652 and a statistically significant p-value of less than 0.01 for the years 2001 to 2014, indicating a strong positive association between these two seemingly disparate entities. Our findings suggest that just as a blockbuster sequel often outperforms its predecessor, an increase in the number of movies released annually tends to bolster the revenues within the US hotel industry. So, the next time you're booking a hotel room, remember that the number of movies hitting the big screen may have more impact on your bill than you'd expect!

[[INTRDUCTION]]
Ah, the world of academic research. Where statistics reign supreme, hypotheses are put to the test, and the occasional pun slips through the cracks like a lab mouse on the lam. Today, dear readers, we embark on a journey through the realm of unlikely correlations and unexpected connections as we explore the relationship between the number of movies released annually and the revenue per available room in the US hotel industry. Get ready for some reel-y fascinating findings!
As we confront the glittering chaos of Hollywood and the orderly domain of hotel management, one might wonder what possible link could exist between these seemingly disparate worlds. After all, the only thing that seems to connect a movie and a hotel stay is the occasional mini bar robbery by a mischievous film character. Yet, in the midst of this seeming madness, we are driven by the pursuit of knowledge, buoyed by the hope of uncovering hidden patterns and uncovering truths stranger than fiction.
Our esteemed colleagues in the fields of Box Office Mojo and Statista have graciously provided us with a wealth of data, allowing us to chase the elusive trail of correlation and causation within this peculiar pair of variables. Armed with statistical tools and a healthy appreciation for cinematic drama, we set out to answer the question: Could the number of movies released annually truly influence the revenue per available room in the US hotel industry, or are we simply fishing for plot twists in an ocean of noise?
As we navigate the labyrinth of data and dive into the world of regression analysis, let us remember that in the world of science, not every correlation is necessarily causation – no matter how compelling it may seem. Nevertheless, armed with our trusty p-values and correlation coefficients, we march forward to unravel the mystery at hand.
So, join us, intrepid readers, in this grand pursuit of knowledge. For in the midst of our numerical odyssey, we may just uncover a truth stranger than fiction, a link more unexpected than a surprise plot twist – a connection between theatrical trends and traveling treasures that has been hiding in plain sight all along. Let the curtains rise, and the journey begin!

[[RESULTS]]
In analyzing the relationship between the number of movies released annually and the revenue per available room in the US hotel industry, we uncovered some truly fascinating findings. Our statistical analysis revealed a remarkably high correlation coefficient of 0.9716652, indicating a strong positive association between these two variables. This result may surprise those who thought the only thing hotels and movies had in common was the occasional room service cameo in a film.
Additionally, the r-squared value of 0.9441334 suggests that a staggering 94.41% of the variation in hotel revenue per available room can be explained by changes in the number of movie releases annually. This tells us that the connection between cinematic trends and hotel treasures isn't just a fleeting cameo – it's a leading role in the story of revenue generation.
Our p-value of less than 0.01 further solidifies the robustness of the relationship we uncovered. This means that our findings are statistically significant, and we can be more confident in the strength of the association than a movie buff quoting obscure film trivia.
To visually capture the strength of the relationship found in our analysis, we present Figure 1, a scatterplot that showcases the compelling correlation between the number of movies released annually and the revenue per available room in the US hotel industry. Just as a well-directed film captures the imagination of its audience, this figure highlights the captivating connection between theatrical releases and hotel revenue.
The results of our study serve as a reminder that in the world of data analysis, the plot can thicken in ways that defy expectations. Much like the plot twist in a suspenseful thriller, our findings reveal a connection that goes beyond what one might anticipate at first glance. So, the next time you find yourself pondering the economic impact of the latest blockbuster, remember that its influence might extend far beyond the box office – it could be checking into a hotel room right alongside you.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our foray into the entangled worlds of movie magic and hotel revenues has left us with a reel-y astonishing discovery. Our findings not only suggest a strong positive association between the number of movies released annually and the revenue per available room in US hotels but also underscore the power of statistics to uncover surprising connections. It's enough to make even the most hardened statistician do a double take and wonder, "What in the world?"

The results of our research drive home the point that while cause and effect may sometimes seem as elusive as a good Wi-Fi signal in an old hotel, careful statistical analysis can reveal correlations that are as clear as day. So, the next time someone tells you that delving into the world of numbers is about as exciting as watching paint dry, you can regale them with the tale of our discovery – a tale in which numbers twirled across the dance floor of statistical significance with all the grace of a Hollywood darling.

As we wrap up this journey through the unexpected symphony of movies and hotel revenue, we must acknowledge that sometimes even the most seemingly unrelated variables can waltz together in a statistically harmonious duet. Our research has laid bare a truth that is more enchanting than a rom-com and more surprising than a plot twist in a mystery thriller.

With these findings in hand, we boldly declare that further investigation into this correlation is simply unnecessary. After all, we've already uncovered a connection more surprising than a plot twist in a telenovela. As our lab coats flutter in the wind of statistical discovery, we bid adieu to this peculiar pair of variables, leaving them to dance in the data fields, knowing that sometimes, the most captivating stories are found where we least expect them.

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 Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Popular Culture and Economic Trends

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 Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Cinematic Commerce and Hospitality Research

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 Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue with an abstract of Lights, camera, action! In this study, we delve into the glitzy world of Hollywood and its surprising connection to the seemingly unrelated domain of the US hotel industry. Utilizing data from Box Office Mojo and Statista, we endeavored to uncover whether there exists a relationship between the number of movies released annually and the revenue per available room in US hotels. Contrary to the typical advice to avoid movie sequels, our research discovered a striking correlation coefficient of 0.9716652 and a statistically significant p-value of less than 0.01 for the years 2001 to 2014, indicating a strong positive association between these two seemingly disparate entities. Our findings suggest that just as a blockbuster sequel often outperforms its predecessor, an increase in the number of movies released annually tends to bolster the revenues within the US hotel industry. So, the next time you're booking a hotel room, remember that the number of movies hitting the big screen may have more impact on your bill than you'd expect!

ChatGPT:

movie releases, US hotel revenue, Hollywood, Box Office Mojo, Statista, correlation coefficient, hotel industry, revenue per available room, blockbuster sequel, movie industry, hospitality industry

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Number of Movies Released Annually
Source: Box Office Mojo
See what else correlates with Number of Movies Released Annually

US hotel industry's revenue per available room
Source: Statista
See what else correlates with US hotel industry's revenue per available room

Correlation r = 0.9716652 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.9441334 (Coefficient of determination)
This means 94.4% of the change in the one variable (i.e., US hotel industry's revenue per available room) is predictable based on the change in the other (i.e., Number of Movies Released Annually) over the 14 years from 2001 through 2014.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.03E-9.^Show 0.0000000070289662796105030000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.97 in 7.03E-7% of random cases. Said differently, if you correlated 142,268,430 random variables^Note You don't actually need 142 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 13 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 13 because we have two variables measured over a period of 14 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.

[ 0.91, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
Number of Movies Released Annually (Number of movies)	482	479	506	551	547	608	631	608	521	536	601	669	686	708
US hotel industry's revenue per available room (USD)	49.91	48.71	48.92	52.8	57.37	61.79	65.57	64.25	53.57	56.48	61.07	65.16	68.69	73.37

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([482,479,506,551,547,608,631,608,521,536,601,669,686,708,])
array_2 = np.array([49.91,48.71,48.92,52.8,57.37,61.79,65.57,64.25,53.57,56.48,61.07,65.16,68.69,73.37,])
array_1_name = "Number of Movies Released Annually"
array_2_name = "US hotel industry's revenue per available room"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Number of Movies Released Annually
Line chart for only US hotel industry's revenue per available room
AI-generated correlation image
The spurious research paper: Theatrical Trends and Traveling Treasures: Exploring the Link Between Movie Releases and US Hotel Revenue

View another random correlation

How fun was this correlation?

Hats off to you for rating!

Correlation ID: 1478 · Black Variable ID: 15 · Red Variable ID: 209


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: