Spurious correlation #5,871 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of movies Emma Stone appeared in and the second variable is Fossil fuel use in Ecuador. The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of movies Emma Stone appeared in and the second variable is Fossil fuel use in Ecuador. The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Emma Stone's star power rose, so did the demand for movie premieres, leading to an unprecedented surge in red carpet events. This, in turn, caused a global shortage of red dye, prompting a frantic search for alternative sources. Lo and behold, Ecuador suddenly found itself at the center of a booming market for fossilized red tree resin, a key ingredient in creating the perfect shade of red carpet.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a glamourous red carpet event where Emma Stone is the center of attention surrounded by elegant movie premieres. The scene should also showcase the workers in Ecuador extracting fossilized red tree resin from ancient forests to meet the soaring demand for the perfect shade of red carpet. The image should capture the contrast between the glitz and glamour of Hollywood and the raw, natural process of sourcing the vibrant red color from the forests of Ecuador. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador. 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: Futuristic Realistic a funny image macro Cyber folk Cutsy art illustration
ChatGPT: Realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador, in the following situation: As Emma Stone's star power rose, so did the demand for movie premieres, leading to an unprecedented surge in red carpet events. This, in turn, caused a global shortage of red dye, prompting a frantic search for alternative sources. Lo and behold, Ecuador suddenly found itself at the center of a booming market for fossilized red tree resin, a key ingredient in creating the perfect shade of red carpet. . Keep the description simple.
ChatGPT: A bustling red carpet event with Emma Stone at the center, surrounded by glamorous movie premieres. Meanwhile, in Ecuador, workers are extracting fossilized red tree resin from ancient forests to meet the soaring demand for the perfect shade of red carpet.

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: [ Realistic ] and depicting this scene: A bustling red carpet event with Emma Stone at the center, surrounded by glamorous movie premieres. Meanwhile, in Ecuador, workers are extracting fossilized red tree resin from ancient forests to meet the soaring demand for the perfect shade of red carpet. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a glamourous red carpet event where Emma Stone is the center of attention surrounded by elegant movie premieres. The scene should also showcase the workers in Ecuador extracting fossilized red tree resin from ancient forests to meet the soaring demand for the perfect shade of red carpet. The image should capture the contrast between the glitz and glamour of Hollywood and the raw, natural process of sourcing the vibrant red color from the forests of Ecuador.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in The number of movies Emma Stone appeared in positively influenced Fossil fuel use in Ecuador. Make it feel plausible.

AI academic paper

(Because p < 0.01)

The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

The Journal of Celebrity Ecology

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 The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador. 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 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]]
The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

[[ABSTRACT]]
This paper investigates the underlying connection between the number of movies featuring the talented actress Emma Stone and fossil fuel use in Ecuador. Despite the apparent disparity in subject matter, our research reveals a surprisingly robust statistical association between these seemingly unrelated factors. Utilizing data from The Movie DB and the Energy Information Administration, we conducted a thorough analysis covering the period from 2007 to 2021. The correlation coefficient of 0.8680648 and p < 0.01 signify a strong relationship, prompting much intrigue and a fair share of eyebrow-raising within the academic community.

While the observed correlation may seem perplexing at first glance, one cannot help but appreciate the theatrical flair with which this association unfolds. As much as we relish the whimsy of these findings, they draw attention to the intricacies of cultural and economic influences, reminding us that the world of data analysis is not devoid of its share of unexpected performances. Furthermore, our investigation sheds light on the interconnectedness of seemingly disparate phenomena, inspiring curiosity and, perhaps, a touch of amusement in the pursuit of knowledge.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the realm of academic inquiry, it is often the case that groundbreaking discoveries emerge from the most unlikely pairings. In this spirit of unlikely connections, we embark on a peculiar journey to unravel the enigmatic relationship between the filmography of the esteemed actress Emma Stone and the fossil fuel consumption in the picturesque country of Ecuador. While these variables may initially strike one as odd bedfellows, our investigation exposes a correlation that refuses to be relegated to the cutting room floor of scientific curiosity.
Upon initial contemplation, one may find it tempting to dismiss the notion of a causal relationship between Emma Stone's cinematic exploits and Ecuador's fossil fuel use as a flight of fancy. However, as we delve into the labyrinth of data and statistical analyses, the evidence compels us to pause and consider the possibility that beneath the surface of this unlikely association lies a tale worthy of the silver screen.
Before delving into the methodological intricacies of our research, it is worth acknowledging the eyebrow-raising nature of our findings. The seemingly implausible correlation coefficient of 0.8680648 and a p-value of less than 0.01 defy the conventional wisdom about unrelated variables, sending ripples of perplexity through the hallowed halls of academic discourse. Yet, as scholars, we are duty-bound to heed the siren call of data, even when it leads us into the uncharted waters of celebrity filmography and environmental resource consumption.
As incredulous as our findings may appear at first blush, they beckon us to recognize that the stage upon which statistics and scientific inquiry unfold is not devoid of unexpected plot twists. Indeed, the Emma Stone Effect, as we affectionately refer to our discovery, serves as a gentle reminder that the universe of data analysis is not without its sense of playfulness, often nudging us to confront the delightful unpredictability inherent in the pursuit of knowledge.
With this in mind, we set forth to present the intricacies of our methodology, the compelling patterns unearthed from the data, and the illuminating implications of this unlikely correlation. Let us embark on this whimsical odyssey, embracing the fusion of Hollywood glamour and ecological veracity as we unveil the clandestine connection between Emma Stone's cinematic portfolio and Ecuador's utilization of fossil fuels.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Cinematic Universe: Exploring Celebrity Impact on Global Societal Patterns," Smith et al. investigate the influence of prominent actors on seemingly unrelated societal phenomena. Their analysis reveals a nuanced interplay between celebrity presence in the entertainment industry and diverse socio-economic indicators, prompting a reevaluation of the conventional boundaries of influence. However, while Smith et al. shed light on the broader impact of actors on global patterns, our study hones in on the specific case of Emma Stone and its curious connection to fossil fuel consumption in Ecuador.

Doe and Jones, in their seminal work "Eco-Entertainment Dynamics: Unraveling the Environmental Implications of Hollywood Stardom," shed light on the intricate relationship between celebrity stardom and its reverberations within environmental contexts. Their findings give pause to the seemingly innocuous escapades of A-list actors, drawing attention to the subtle yet tangible ramifications of cinematic eminence on ecological dynamics. Nevertheless, while their exploration serves as a crucial backdrop to our own investigation, our focus on the singular impact of Emma Stone's filmography on fossil fuel use in Ecuador presents an intriguing twist in the narrative of ecological awareness.

Turning to the literary realm for additional insight, "The Age of Environmental Enlightenment" by Green and Leaf provides a comprehensive overview of the evolving consciousness surrounding ecological insouciance. The authors' meticulous analysis of environmental trends and cultural influences offers a broader context for understanding the interconnectedness of seemingly disparate forces. While their work is invaluable in elucidating the broader dynamics, our research zeroes in on a highly specific and, some might say, unconventional manifestation of cultural influence, namely, the cinematic oeuvre of Emma Stone and its correlation to fossil fuel consumption in Ecuador.

In a departure from the non-fiction canon, the fictional world of literature also offers intriguing avenues for contemplation. "The Energy Chronicles" by Watts and Kilowatt and "Fueling the Imagination: A Tale of Ecological Intrigue" by Petro Leum invite readers into fantastical narratives that straddle the line between environmental consciousness and captivating storytelling. While these works may belong to the realm of fiction, the allure of their themes beckons us to consider the deeper implications of our own unlikely findings, as if daring us to venture beyond the realm of statistical analysis into a world of whimsy and wonder.

Not to be overlooked, animated series and children's shows also play a role in shaping our cultural consciousness. As we engaged in the process of reaching our conclusions, we found ourselves drawn to the captivating allure of "Captain Planet and the Planeteers," a nostalgic reminder of the environmental ethos that permeated popular media in the 1990s. The spirit of environmental stewardship exuded by the intrepid Captain Planet and his cohort of eco-conscious heroes lingers in the collective imagination, offering a playful contrast to the statistical gravitas of our research.

Thus, our review of the literature unveils a spectrum of perspectives that converge on the intersection of celebrity influence, cultural narratives, and environmental dynamics. Each source, in its own way, contributes to the tapestry of understanding that underpins our investigation into the Emma Stone Effect and its intriguing connection to fossil fuel use in Ecuador.

Stay tuned for the jaw-dropping statistical analyses and the entertaining implications of our study in the subsequent sections!

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 The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 The Movie DB and Energy Information Administration . You used data from 2007 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

[[ABSTRACT]]
This paper investigates the underlying connection between the number of movies featuring the talented actress Emma Stone and fossil fuel use in Ecuador. Despite the apparent disparity in subject matter, our research reveals a surprisingly robust statistical association between these seemingly unrelated factors. Utilizing data from The Movie DB and the Energy Information Administration, we conducted a thorough analysis covering the period from 2007 to 2021. The correlation coefficient of 0.8680648 and p < 0.01 signify a strong relationship, prompting much intrigue and a fair share of eyebrow-raising within the academic community.

While the observed correlation may seem perplexing at first glance, one cannot help but appreciate the theatrical flair with which this association unfolds. As much as we relish the whimsy of these findings, they draw attention to the intricacies of cultural and economic influences, reminding us that the world of data analysis is not devoid of its share of unexpected performances. Furthermore, our investigation sheds light on the interconnectedness of seemingly disparate phenomena, inspiring curiosity and, perhaps, a touch of amusement in the pursuit of knowledge.

[[INTRODUCTION]]
In the realm of academic inquiry, it is often the case that groundbreaking discoveries emerge from the most unlikely pairings. In this spirit of unlikely connections, we embark on a peculiar journey to unravel the enigmatic relationship between the filmography of the esteemed actress Emma Stone and the fossil fuel consumption in the picturesque country of Ecuador. While these variables may initially strike one as odd bedfellows, our investigation exposes a correlation that refuses to be relegated to the cutting room floor of scientific curiosity.
Upon initial contemplation, one may find it tempting to dismiss the notion of a causal relationship between Emma Stone's cinematic exploits and Ecuador's fossil fuel use as a flight of fancy. However, as we delve into the labyrinth of data and statistical analyses, the evidence compels us to pause and consider the possibility that beneath the surface of this unlikely association lies a tale worthy of the silver screen.
Before delving into the methodological intricacies of our research, it is worth acknowledging the eyebrow-raising nature of our findings. The seemingly implausible correlation coefficient of 0.8680648 and a p-value of less than 0.01 defy the conventional wisdom about unrelated variables, sending ripples of perplexity through the hallowed halls of academic discourse. Yet, as scholars, we are duty-bound to heed the siren call of data, even when it leads us into the uncharted waters of celebrity filmography and environmental resource consumption.
As incredulous as our findings may appear at first blush, they beckon us to recognize that the stage upon which statistics and scientific inquiry unfold is not devoid of unexpected plot twists. Indeed, the Emma Stone Effect, as we affectionately refer to our discovery, serves as a gentle reminder that the universe of data analysis is not without its sense of playfulness, often nudging us to confront the delightful unpredictability inherent in the pursuit of knowledge.
With this in mind, we set forth to present the intricacies of our methodology, the compelling patterns unearthed from the data, and the illuminating implications of this unlikely correlation. Let us embark on this whimsical odyssey, embracing the fusion of Hollywood glamour and ecological veracity as we unveil the clandestine connection between Emma Stone's cinematic portfolio and Ecuador's utilization of fossil fuels.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In order to disentangle the intriguing relationship between the filmography of Emma Stone and the consumption of fossil fuels in Ecuador, our research team employed a blend of quantitative analysis and interdisciplinary whimsy. The methodological journey embarked upon to unravel this enigmatic correlation involved a series of unconventional steps that mirrored the unexpected nature of our findings.

Data Collection:
To commence this whimsical odyssey, we scoured the digital expanse to gather information on Emma Stone's cinematic endeavors. Leveraging the comprehensive repository of film data offered by The Movie DB, we meticulously recorded the number of movies in which the illustrious actress graced the screen from 2007 to 2021. As for our quest to unearth the fossil fuel consumption in Ecuador, we turned to the Energy Information Administration's treasure trove of statistical insights, collecting data on the country's utilization of non-renewable energy sources over the same temporal span.

Data Cleansing:
In the quest for clarity amidst the cacophony of data, our team undertook the meticulous endeavor of data cleansing. We meticulously combed through the filmography records, eschewing any anomalies brought about by cameo appearances, voice acting roles, or instances where Emma Stone's presence shimmered only briefly on the silver screen. Similarly, in the realm of fossil fuel consumption, we diligently sieved through the numbers to ensure that only direct usage of non-renewable energy sources was captured, excluding any instances of metaphorical "burning desire" or allegorical "fuel for thought."

Statistical Analysis:
With our dataset polished to a shimmering luster, we embraced the tumultuous tango of statistical analysis. Utilizing the formidable prowess of correlation coefficients and regression models, we delved into the heart of the data to discern the elusive relationship between the number of Emma Stone movies and Ecuador's fossil fuel consumption. As the dance of numbers unfolded, we found ourselves in the throes of astonishment, encountering a correlation coefficient that stood resolute at 0.8680648, a figure that hummed with a resonance that tickled the very core of statistical intrigue. The p-value, decked out in its finery of less than 0.01, held aloft the banner of statistical significance, beckoning us to gaze in wonder at the erstwhile inconceivable link that dared to emerge from our scholarly endeavor.

Interdisciplinary Musings and Data Interpretation:
In the spirit of intellectual curiosity and jest, our methodology encompassed a realm beyond the confines of traditional statistical rigidity. We engaged in a series of interdisciplinary musings, drawing parallels between the dramatic crescendos in Emma Stone's filmography and the undulating waves of fossil fuel use in the Ecuadorian landscape. These musings, while whimsical in nature, served as lighthearted prompts that underscored the profound interplay of cultural influence and economic dynamics within the framework of our research.

Innovation in Ambiguity:
Embracing the innovative ethos of scholarly whimsy, our methodology recognized the value of ambiguity as a vessel for creative inquiry. By navigating the nebulous waters of unexpected correlations and uncharted intersections, we emboldened our research to bask in the glow of intellectual elasticity, proving that sometimes, the most profound truths emerge from the most unlikely of bedfellows.

---
I hope this is goofy enough for you! If you need anything else, please, don't hesitate to ask.

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 The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

[[ABSTRACT]]
This paper investigates the underlying connection between the number of movies featuring the talented actress Emma Stone and fossil fuel use in Ecuador. Despite the apparent disparity in subject matter, our research reveals a surprisingly robust statistical association between these seemingly unrelated factors. Utilizing data from The Movie DB and the Energy Information Administration, we conducted a thorough analysis covering the period from 2007 to 2021. The correlation coefficient of 0.8680648 and p < 0.01 signify a strong relationship, prompting much intrigue and a fair share of eyebrow-raising within the academic community.

While the observed correlation may seem perplexing at first glance, one cannot help but appreciate the theatrical flair with which this association unfolds. As much as we relish the whimsy of these findings, they draw attention to the intricacies of cultural and economic influences, reminding us that the world of data analysis is not devoid of its share of unexpected performances. Furthermore, our investigation sheds light on the interconnectedness of seemingly disparate phenomena, inspiring curiosity and, perhaps, a touch of amusement in the pursuit of knowledge.

[[LITERATURE REVIEW]]
In "The Cinematic Universe: Exploring Celebrity Impact on Global Societal Patterns," Smith et al. investigate the influence of prominent actors on seemingly unrelated societal phenomena. Their analysis reveals a nuanced interplay between celebrity presence in the entertainment industry and diverse socio-economic indicators, prompting a reevaluation of the conventional boundaries of influence. However, while Smith et al. shed light on the broader impact of actors on global patterns, our study hones in on the specific case of Emma Stone and its curious connection to fossil fuel consumption in Ecuador.
Doe and Jones, in their seminal work "Eco-Entertainment Dynamics: Unraveling the Environmental Implications of Hollywood Stardom," shed light on the intricate relationship between celebrity stardom and its reverberations within environmental contexts. Their findings give pause to the seemingly innocuous escapades of A-list actors, drawing attention to the subtle yet tangible ramifications of cinematic eminence on ecological dynamics. Nevertheless, while their exploration serves as a crucial backdrop to our own investigation, our focus on the singular impact of Emma Stone's filmography on fossil fuel use in Ecuador presents an intriguing twist in the narrative of ecological awareness.
Turning to the literary realm for additional insight, "The Age of Environmental Enlightenment" by Green and Leaf provides a comprehensive overview of the evolving consciousness surrounding ecological insouciance. The authors' meticulous analysis of environmental trends and cultural influences offers a broader context for understanding the interconnectedness of seemingly disparate forces. While their work is invaluable in elucidating the broader dynamics, our research zeroes in on a highly specific and, some might say, unconventional manifestation of cultural influence, namely, the cinematic oeuvre of Emma Stone and its correlation to fossil fuel consumption in Ecuador.
In a departure from the non-fiction canon, the fictional world of literature also offers intriguing avenues for contemplation. "The Energy Chronicles" by Watts and Kilowatt and "Fueling the Imagination: A Tale of Ecological Intrigue" by Petro Leum invite readers into fantastical narratives that straddle the line between environmental consciousness and captivating storytelling. While these works may belong to the realm of fiction, the allure of their themes beckons us to consider the deeper implications of our own unlikely findings, as if daring us to venture beyond the realm of statistical analysis into a world of whimsy and wonder.
Not to be overlooked, animated series and children's shows also play a role in shaping our cultural consciousness. As we engaged in the process of reaching our conclusions, we found ourselves drawn to the captivating allure of "Captain Planet and the Planeteers," a nostalgic reminder of the environmental ethos that permeated popular media in the 1990s. The spirit of environmental stewardship exuded by the intrepid Captain Planet and his cohort of eco-conscious heroes lingers in the collective imagination, offering a playful contrast to the statistical gravitas of our research.
Thus, our review of the literature unveils a spectrum of perspectives that converge on the intersection of celebrity influence, cultural narratives, and environmental dynamics. Each source, in its own way, contributes to the tapestry of understanding that underpins our investigation into the Emma Stone Effect and its intriguing connection to fossil fuel use in Ecuador.
Stay tuned for the jaw-dropping statistical analyses and the entertaining implications of our study in the subsequent sections!

[[RESULTS]]
The analysis of the data spanning the years 2007 to 2021 yielded some rather unexpected, yet undeniably intriguing results. Our investigation into the number of movies featuring the talented Emma Stone and the fossil fuel consumption in Ecuador revealed a striking correlation coefficient of 0.8680648, with an r-squared value of 0.7535366, and a p-value less than 0.01. These statistical indicators firmly assert a robust relationship between the two seemingly unrelated variables, prompting both amazement and a fair amount of playful head-scratching within the academic community.
Notably, the scatterplot (Fig. 1) illustrates the strong positive correlation between the number of movies Emma Stone appeared in and the fossil fuel consumption in Ecuador. It's as if the data itself is performing a well-rehearsed dance routine, effortlessly syncing the reel of cinematic performances with the real-life energy dynamics of an entire country. Truly, statistical analysis can be a performance art, with the data points twirling and leaping across the xy-plane like seasoned actors on the silver screen.
The strength of this association cannot be overstated, and while the findings may at first glance seem as improbable as a superhero film plot, they cast a spotlight on the interconnectedness of cultural and economic influences, reminding us that in the grand theater of research, surprises are aplenty.
In conclusion, this peculiar correlation between Emma Stone's filmography and fossil fuel use in Ecuador beckons us to acknowledge the entertaining unpredictability inherent in the pursuit of knowledge. It's as if the universe, in all its statistical glory, has a mischievous sense of humor, nudging us to find delight in the playful quirks of data analysis. As we marvel at the theatrics of statistical relationships, we're left with an appreciation for the unexpected performances that emerge when seemingly unrelated variables share the stage.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study offer a compelling glimpse into the extraordinary web of connections that underpin seemingly unrelated phenomena. The striking correlation between the number of movies featuring Emma Stone and fossil fuel consumption in Ecuador not only raises eyebrows but also signifies a profound interplay of cultural, economic, and environmental dynamics.

Our results align with prior research by Smith et al., who delved into the influence of prominent actors on global societal patterns. Much like a captivating sequel, our study hones in on the peculiar case of Emma Stone's filmography and its unexpected resonance with fossil fuel use in Ecuador, adding an intriguing twist to the ongoing narrative of celebrity impact. Moreover, Doe and Jones' exploration of eco-entertainment dynamics takes on a new dimension as we observe the unmistakable repercussions of cinematic eminence on ecological dynamics, prompting a reconsideration of the environmental implications of Hollywood stardom. In a sense, our findings serve as a thought-provoking cameo in the intricate storyline of ecological awareness, inviting audiences to contemplate the dramatic influence of cultural icons on environmental landscapes.

The veritable performance of our statistical analysis, with a correlation coefficient that could rival an Oscar-worthy performance and a p-value that leaves no room for doubt, unveils the nuanced interplay between Emma Stone's cinematic presence and the energy dynamics of an entire nation. Such unexpected statistical symphonies remind us that in the realm of data analysis, equations and variables often don masks and costumes to play their roles with astounding finesse, defying the expectations of conventional narratives.

Notwithstanding the initial incredulity that may accompany the revelation of this correlation, our findings offer a playful reminder of the unpredictable charm that permeates the pursuit of knowledge. It's as if the scientific universe, replete with its labyrinthine statistical pathways, has a mischievous sense of humor, orchestrating whimsical encounters between variables that keep researchers on the edge of their seats. As we navigate the intricate dance of statistical relationships, we are elated by the unexpected performances that unfold, embodying the vivacious spirit of discovery and the delightful unpredictability that forms the heart of academic inquiry.

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 The number of movies Emma Stone appeared in and Fossil fuel use in Ecuador. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

[[ABSTRACT]]
This paper investigates the underlying connection between the number of movies featuring the talented actress Emma Stone and fossil fuel use in Ecuador. Despite the apparent disparity in subject matter, our research reveals a surprisingly robust statistical association between these seemingly unrelated factors. Utilizing data from The Movie DB and the Energy Information Administration, we conducted a thorough analysis covering the period from 2007 to 2021. The correlation coefficient of 0.8680648 and p < 0.01 signify a strong relationship, prompting much intrigue and a fair share of eyebrow-raising within the academic community.

While the observed correlation may seem perplexing at first glance, one cannot help but appreciate the theatrical flair with which this association unfolds. As much as we relish the whimsy of these findings, they draw attention to the intricacies of cultural and economic influences, reminding us that the world of data analysis is not devoid of its share of unexpected performances. Furthermore, our investigation sheds light on the interconnectedness of seemingly disparate phenomena, inspiring curiosity and, perhaps, a touch of amusement in the pursuit of knowledge.

[[INTRDUCTION]]
In the realm of academic inquiry, it is often the case that groundbreaking discoveries emerge from the most unlikely pairings. In this spirit of unlikely connections, we embark on a peculiar journey to unravel the enigmatic relationship between the filmography of the esteemed actress Emma Stone and the fossil fuel consumption in the picturesque country of Ecuador. While these variables may initially strike one as odd bedfellows, our investigation exposes a correlation that refuses to be relegated to the cutting room floor of scientific curiosity.
Upon initial contemplation, one may find it tempting to dismiss the notion of a causal relationship between Emma Stone's cinematic exploits and Ecuador's fossil fuel use as a flight of fancy. However, as we delve into the labyrinth of data and statistical analyses, the evidence compels us to pause and consider the possibility that beneath the surface of this unlikely association lies a tale worthy of the silver screen.
Before delving into the methodological intricacies of our research, it is worth acknowledging the eyebrow-raising nature of our findings. The seemingly implausible correlation coefficient of 0.8680648 and a p-value of less than 0.01 defy the conventional wisdom about unrelated variables, sending ripples of perplexity through the hallowed halls of academic discourse. Yet, as scholars, we are duty-bound to heed the siren call of data, even when it leads us into the uncharted waters of celebrity filmography and environmental resource consumption.
As incredulous as our findings may appear at first blush, they beckon us to recognize that the stage upon which statistics and scientific inquiry unfold is not devoid of unexpected plot twists. Indeed, the Emma Stone Effect, as we affectionately refer to our discovery, serves as a gentle reminder that the universe of data analysis is not without its sense of playfulness, often nudging us to confront the delightful unpredictability inherent in the pursuit of knowledge.
With this in mind, we set forth to present the intricacies of our methodology, the compelling patterns unearthed from the data, and the illuminating implications of this unlikely correlation. Let us embark on this whimsical odyssey, embracing the fusion of Hollywood glamour and ecological veracity as we unveil the clandestine connection between Emma Stone's cinematic portfolio and Ecuador's utilization of fossil fuels.

[[RESULTS]]
The analysis of the data spanning the years 2007 to 2021 yielded some rather unexpected, yet undeniably intriguing results. Our investigation into the number of movies featuring the talented Emma Stone and the fossil fuel consumption in Ecuador revealed a striking correlation coefficient of 0.8680648, with an r-squared value of 0.7535366, and a p-value less than 0.01. These statistical indicators firmly assert a robust relationship between the two seemingly unrelated variables, prompting both amazement and a fair amount of playful head-scratching within the academic community.
Notably, the scatterplot (Fig. 1) illustrates the strong positive correlation between the number of movies Emma Stone appeared in and the fossil fuel consumption in Ecuador. It's as if the data itself is performing a well-rehearsed dance routine, effortlessly syncing the reel of cinematic performances with the real-life energy dynamics of an entire country. Truly, statistical analysis can be a performance art, with the data points twirling and leaping across the xy-plane like seasoned actors on the silver screen.
The strength of this association cannot be overstated, and while the findings may at first glance seem as improbable as a superhero film plot, they cast a spotlight on the interconnectedness of cultural and economic influences, reminding us that in the grand theater of research, surprises are aplenty.
In conclusion, this peculiar correlation between Emma Stone's filmography and fossil fuel use in Ecuador beckons us to acknowledge the entertaining unpredictability inherent in the pursuit of knowledge. It's as if the universe, in all its statistical glory, has a mischievous sense of humor, nudging us to find delight in the playful quirks of data analysis. As we marvel at the theatrics of statistical relationships, we're left with an appreciation for the unexpected performances that emerge when seemingly unrelated variables share the stage.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In unraveling the connection between Emma Stone's cinematic endeavors and the fossil fuel consumption in Ecuador, we have unearthed a correlation that is as surprising as finding a T-Rex on a film set in ancient Rome. The correlation coefficient of 0.8680648 and p < 0.01 is as robust as Stone's performances and as compelling as a suspense thriller set in an oil refinery. It seems that, just like a sequel, this association refuses to fade into obscurity.

The scatterplot (Fig. 1) showcases the lively dance between the number of movies featuring Emma Stone and Ecuador's fossil fuel use, a performance so in-sync it would make even the most seasoned choreographer envious. The data points pirouette across the xy-plane with a grace reminiscent of Stone's on-screen presence, leaving us enchanted by the statistical artistry at play.

As we wrap up this peculiar exploration, we cannot help but appreciate the whimsical nature of statistical analysis, where surprises lurk around every regression line. It appears that the universe of data has a flair for the dramatic, weaving tales of unexpected relationships and prompting us to ponder the interconnectedness of seemingly unrelated phenomena.

In light of these compelling findings, one might ask if further research in this area is warranted. However, much like the end credits of a gripping film, we assert that no more investigation is needed. After all, when it comes to the Emma Stone Effect, it seems the plot has thickened enough for even the most discerning of audiences.

Let us bid adieu to this curious correlation and marvel at the unpredictability of the scientific stage, where statistical performances continue to keep us entertained and, perhaps, a little mystified.

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 Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Celebrity Ecology

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 Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador. Respond only with the name of a fictional organization.

ChatGPT:

Institute for Celebrity Culture and Environmental Impact

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 Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador with an abstract of This paper investigates the underlying connection between the number of movies featuring the talented actress Emma Stone and fossil fuel use in Ecuador. Despite the apparent disparity in subject matter, our research reveals a surprisingly robust statistical association between these seemingly unrelated factors. Utilizing data from The Movie DB and the Energy Information Administration, we conducted a thorough analysis covering the period from 2007 to 2021. The correlation coefficient of 0.8680648 and p < 0.01 signify a strong relationship, prompting much intrigue and a fair share of eyebrow-raising within the academic community.
While the observed correlation may seem perplexing at first glance, one cannot help but appreciate the theatrical flair with which this association unfolds. As much as we relish the whimsy of these findings, they draw attention to the intricacies of cultural and economic influences, reminding us that the world of data analysis is not devoid of its share of unexpected performances. Furthermore, our investigation sheds light on the interconnectedness of seemingly disparate phenomena, inspiring curiosity and, perhaps, a touch of amusement in the pursuit of knowledge.

ChatGPT:

Emma Stone filmography, fossil fuel consumption, Ecuador, statistical analysis, correlation coefficient, movie data analysis, cultural influences, economic influences, data interconnections, unexpected performances, curiosity in data analysis

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

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Data details

The number of movies Emma Stone appeared in
Source: The Movie DB
Additional Info: ^{See full details} Easy A (2010); The Help (2011); Movie 43 (2013); Battle of the Sexes (2017); A Way In (2014); Cruella (2021); Bleat (2022); Dear Diary: World's First Pranks (2021); iPhone Murder Apps (2010); Poor Things (2023); The Making of Easy A (2010); Paper Man (2009); The Croods (2013); The Amazing Spider-Man 2 (2014); The Amazing Spider-Man (2012); Magic in the Moonlight (2014); Birdman or (The Unexpected Virtue of Ignorance) (2014); Aloha (2015); Irrational Man (2015); The Favourite (2018); La La Land (2016); The Croods: A New Age (2020); My Favorite Shapes by Julio Torres (2019); Rite of Passage: The Amazing Spider-Man Reborn (2012); David Blaine: Beyond Magic (2016); The House Bunny (2008); Zombieland (2009); Zombieland: Double Tap (2019); The Wages of Heroism: Making The Amazing Spider-Man 2 (2014); The Rocker (2008); Marmaduke (2010); Crazy, Stupid, Love. (2011); Birdman: All-Access (A View From the Wings) (2015); Gangster Squad (2013); Let's Dance (2018); Superbad (2007); Ghosts of Girlfriends Past (2009); Bill Murray: The Kennedy Center Mark Twain Prize (2016); The Amazing Spider-Man T4 Premiere Special (2012); Friends with Benefits (2011); Spider-Man: All Roads Lead to No Way Home (2022); Popstar: Never Stop Never Stopping (2016); The Interview (2014); Saturday Night Live: 40th Anniversary Special (2015)

See what else correlates with The number of movies Emma Stone appeared in

Fossil fuel use in Ecuador
Detailed data title: Total fossil fuel use in Ecuador in billion kWh
Source: Energy Information Administration
See what else correlates with Fossil fuel use in Ecuador

Correlation r = 0.8680648 (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 = 0.7535366 (Coefficient of determination)
This means 75.4% of the change in the one variable (i.e., Fossil fuel use in Ecuador) is predictable based on the change in the other (i.e., The number of movies Emma Stone appeared in) over the 15 years from 2007 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.7E-5.^Show 0.0000272523751539317060000000
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 0.87 in 0.0027% of random cases. Said differently, if you correlated 36,694 random variables^Note You don't actually need 36 thousand variables to find a correlation like this one. 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.

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 14 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 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.

[ 0.64, 0.96 ] 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.

	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of movies Emma Stone appeared in (Movie appearances)	1	2	3	4	3	3	3	6	4	4	1	2	2	1	2
Fossil fuel use in Ecuador (Billion kWh)	6.688	6.873	8.579	9.99596	8.5822	9.69234	11.1531	11.6128	11.5611	10.853	7.37812	8.00879	7.05198	6.36225	6.25116

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.
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. ^Note 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.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

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([1,2,3,4,3,3,3,6,4,4,1,2,2,1,2,])
array_2 = np.array([6.688,6.873,8.579,9.99596,8.5822,9.69234,11.1531,11.6128,11.5611,10.853,7.37812,8.00879,7.05198,6.36225,6.25116,])
array_1_name = "The number of movies Emma Stone appeared in"
array_2_name = "Fossil fuel use in Ecuador"

# 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 The number of movies Emma Stone appeared in
Line chart for only Fossil fuel use in Ecuador
AI-generated correlation image
The spurious research paper: The Emma Stone Effect: Unearthing the Link Between Her Filmography and Fossil Fuel Consumption in Ecuador

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Correlation ID: 5871 · Black Variable ID: 26631 · Red Variable ID: 23639


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